When launching a new product, we have to overcome two barriers. First, we have to make people know that the problem or the solution exists and why it's relevant to them. We’ll call this “category awareness”. In pharmaceutical marketing, for example, this is critical. Recently, a drug was created to address “restless leg syndrome.” The problem the drug companies had was that no one knew that restless leg syndrome even existed. Before selling the drug, the firm had to make people aware of the problem. Then, we have the traditional problem of getting people to choose our product specifically over competitive products. Of course, if what we're talking about is truly new and innovative, we'll have the market to ourselves for at least a few months.
New product marketing is a very common problem for technology marketers. When new applications or solutions are created, it’s often as difficult to make people understand the solution—and the problem it addresses—as it is to make people aware of specific brands offering the solution. New product launches are typically ten or hundred million dollar affairs for large firms like Microsoft, relying on expensive media buys to achieve 10 or 20 point jumps in category or product awareness.
Increasingly, though, category awareness is happening virally online. When Apple introduces a new product, such as the iPad last week, Steve Jobs has a press conference. He is a very good presenter, so a lot of press and industry insiders attend. Articles are written about the new product, and on each of these articles, hundreds of comments are posted. These articles are turned into short “bit.ly” links and tweeted around the Internet. People post the articles are their opinions about the technology onto their Facebook pages. In a period of a couple of days, Apple has used social media to ramp category awareness from 0 to 100, at least among early adopters. And, they did this by not spending much money at all, at least for the free press and the resulting social communication.
Apple is really successful doing this, but some other companies are not. Why is this? I'd argue that the key is making sure that all the most important nodes are plugged in to the initial launch. Of course, this is where Apple has made the huge investment. Over the course of the last 20 years, Apple has made sure that its brand is seen as cutting edge and exciting, ensuring that the most influential media and individuals wait with baited breath for new product announcements. This 20-year capital investment leads to free new product marketing today. Of course, Steve Jobs knows that two or three lousy products or boring presentations in a row will lead to huge damage in his new product marketing asset—which is why he is so, so careful about design, and about that critical initial press conference.
In their book “Will and Vision”, Peter Golder and Gerard Tellis trace almost a hundred years of innovative new products, in an effort to prove or disprove “first mover advantage” in a category. They’ve found that first movers tend to not dominate their markets—rather, second or third movers that are effective new product marketers come in and steal their lunch. In essence, marketing and distribution trumps innovation where really new products are concerned. This book was written in 2002, and I'd argue that we need a sequel, because I'm not so sure the thesis is holding anymore.
In the midst of our current marketing "innovation curve" (2008-2011?) it is much, much easier to create a viral groundswell around a new category instantly than it was in 1970, 1990, or even in 2000. Apple’s iPad, while not a totally new concept (tablet PCs have been tried before) really is a new category. It’s clear that Apple has already dominated it, in a few days, thanks to the increasingly velocity, breadth, and connectivity of communication. Could they lose it? Sure. But it's their's to lose.
For companies much smaller than Apple, which is clearly the strongest example I could have chosen, the lesson, I’d argue, is this: Make sure your new product marketing strategy is as good as you can make it, leveraging the “new communication” that Web 2.0 has given us, before you tell anyone anything about your idea. It might just be possible for small companies to now become huge literally overnight, with enough careful planning. Understand who the early adopters are likely to be. Get them in your crosshairs. Make sure you know how Digg and FB and Twitter and Technorati and all the other communities work together—and try to get a massive bang in your first week in the market. It's totally possible, if your idea is good enough and your marketing savvy is high. This is something that we couldn't do in 2000, and I'd argue innovation is going to be all the better for it.
Tuesday, February 02, 2010
Monday, January 18, 2010
A novel approach to solving healthcare?
In honor of MLK day, I'm not writing about my livelihood, marketing, but rather about a slightly larger and more relevant topic, health care. Don't worry, faithful readers, you'll get more abstruse marketing ideas soon.
I started thinking about this problem in detail because I've recently switched employers, which has meant switching health insurance companies. This has led to the typical bewildering array of changes. Some Doctors take one insurance plan (the old one) but not the new one. Others take plan 517b but not 517c. Other Doctors, such as our internist, don't take any insurance. I don't blame her. In short, it's frustrating for me, but it's far worse for many Americans who have no insurance at all.
At the start of the health care debate, I, like most Americans, couldn't begin to think about how to solve the problem. I only knew that there was a problem. The problem, as most Americans know, is threefold:
In attempting to fix this problem, the Congress has proposed a set of laws which will only make the problem worse. Some of the symptoms of the supply-demand imbalance have been insurance companies tightening up their underwriting policies to prevent enrolling those with preexisting conditions. So, Congress has outlawed these practices. This will simply lead these companies to either (1) exit the market or (2) raise prices again, for everyone. Another approach is outlawing "lifetime limits" on health care. Think of this as an insurance-imposed constraint on potential demand for health care. Once again, it is being done away with. More insurance companies will exit the market or raise prices. This does nothing to help anyone.
At the same time, Congress is covering more Americans with subsidies. This will increase supply, but it won't increase it enough. There is just no possible way to increase supply enough to moderate prices in this system, because we're talking about life and death issues--people will always want infinitely more health care, and I don't blame them. I do too. Without some form of rationing in place, things will only get worse, and the new bill does nothing to ration health care. It will also ensure that a larger and larger proportion of our GDP goes toward health care, which means higher taxes, more borrowing, or both. I'd argue that in the long run, this will wreck the country.
Speaking of rationing, any good or service can be rationed in one of two ways. The most common is market rationing, where a supply and demand naturally balance each other out. A more discredited method is government intervention. This means the government decides what the supply is, and who gets it. When you think about it, the reason health care prices are out of control in this country is that we have reverse-govenment rationing combined with a free market. Many are mandated by the government to receive coverage, while few can pay; at the same time, we have an innovation-driven supply side. The new laws just make this worse and more aggregious than it already is. I would have had more respect for those in Congress had they said the following:
"There are two choices. We can ration by the free market, or we can ration centrally. Free market rationing means none of you who are poor will get health care. Rationing centrally means that those of you are too sick to save or near the end of your lives will be denied care. We need to understand that these are the two options. Let's pick one, or the other, or something in between. Any reverse rationing has to be balanced by real rationing--taking health care away from someone else--or by raising taxes."
At this point in this imaginary debate, I would have presented my approach, which would have been immediately shouting down by Democrats and Republicans alike. The approach has four basic components. First, mandatory health care savings accounts for all Americans. Second, mandatory catastrophic care insurance for all Americans. Third, outlaw Doctor-specific or hospial-specific rates for insurance companies. Doctors and hospitals and pharma companies charge the same price to all buyers. Fourth, a clear income floor beneath which health care savings accounts and catastrophic care accounts are subsidized progressively by the federal government.
Mandatory HSAs would work just like 401(k)s and IRAs. Americans could contribute to them tax-free. By making these "real money", Americans would pick their Doctors and hospitals on the basis of who gave them the best deal. Prices would moderate. Ineffective treatments or dubious drugs would shrivel up and die. There would be no insurance companies. You could go to any Doctor you chose, based on their price and their effectiveness. You would pick a $30 blood pressure drug over a therapeutically identical $300 drug. The maker of the more expensive drug would lower prices or go out of business. Would this "stifle innovation?" I don't think so. I think it would bring innovation back into the realm of the free market.
At first, Americans would revolt at such a system. It sounds too much like "work." We've been accustomed to thinking of health care as a public good we don't have to worry about. But, as Americans got used to it, they'd see prices immediately moderate. They'd also see that health care quickly got much less confusing. Finally, they'd see their accounts grow in value, ensuring that they could cover health care costs as they got older, and even, in some cases, pass saved money they didn't use on to their children for college.
Of course, people also get very sick from time to time. These sicknesses can eat up even a $100,000 balance HSA balance in months. For cases like this, a government catastrophic pool would kick in. This would be funded by a tax, like Medicare. The pool would be accessible by patients with certain conditions, like metastatic cancer or a massive car accident, that would be pre-defined by statute. Every three months, a certain amount would be deposited into the patient's HSA, and once again, the patient (or the patient's family) would be responsible for allocating the funds. The inflow to the HSA account would be a fixed number set by condition. For example, $25,000 a month for colon cancer treatment. At the end of the condition, any unused money would go back to the government.
Of course, this is the element most like the current system, and most susceptible to abuse. However, this is where element three would come in. Because hospitals and Doctors couldn't charge different rates to different people (or firms), the government would know what it truly cost to provide care for a condition, and people would know they could pay for their catastrophic treatment out of the government lifeline. There is still potential for abuse here--no doubt about it--so this would need to be monitored closely.
Element four makes sure all Americans are covered. If you made, say, $60,000 a year as a household, you might qualify for a $4,000 tax credit towards your HSA, ensuring your family could pay for preventative health care. Medicare and Medicaid would be gone. Seniors might also get an additional tax credit to account for increasing health care costs as people reach their 60s and 70s. One other legislative element could be a five- or ten-year phase-in period during which catastrophic funds could be used for preventative care as HSA balances increased over time. The total pool would be mandated as a fixed percentage of GDP--say 2%--and would be gathered through income tax. Yes, it's tax, but remember, Medicare goes away.
So, this system, when you think about it, is novel for one reason. It does away with insurance companies. Insurance companies are great in theory, but in our case, they've distorted the market to the point where prices bear no resemblance to the underlying value of services provided, with governmeny playing key enabler. Furthermore, they've created a bizarre landscape that ties Americans to their jobs and stifles innovation. While I'm sure this proposal needs tuning, it seems to accomplish two goals: keeping prices in check while ensuring that all Americans receive a morally acceptable level of health care. Of course, none of this will ever happen, but one can dream.
I started thinking about this problem in detail because I've recently switched employers, which has meant switching health insurance companies. This has led to the typical bewildering array of changes. Some Doctors take one insurance plan (the old one) but not the new one. Others take plan 517b but not 517c. Other Doctors, such as our internist, don't take any insurance. I don't blame her. In short, it's frustrating for me, but it's far worse for many Americans who have no insurance at all.
At the start of the health care debate, I, like most Americans, couldn't begin to think about how to solve the problem. I only knew that there was a problem. The problem, as most Americans know, is threefold:
- Healthcare costs are soaring at double-digit rates
- Too many Americans are uninsured
- The landscape of insurance companies is bewildering and makes it very difficult for people to move jobs
In attempting to fix this problem, the Congress has proposed a set of laws which will only make the problem worse. Some of the symptoms of the supply-demand imbalance have been insurance companies tightening up their underwriting policies to prevent enrolling those with preexisting conditions. So, Congress has outlawed these practices. This will simply lead these companies to either (1) exit the market or (2) raise prices again, for everyone. Another approach is outlawing "lifetime limits" on health care. Think of this as an insurance-imposed constraint on potential demand for health care. Once again, it is being done away with. More insurance companies will exit the market or raise prices. This does nothing to help anyone.
At the same time, Congress is covering more Americans with subsidies. This will increase supply, but it won't increase it enough. There is just no possible way to increase supply enough to moderate prices in this system, because we're talking about life and death issues--people will always want infinitely more health care, and I don't blame them. I do too. Without some form of rationing in place, things will only get worse, and the new bill does nothing to ration health care. It will also ensure that a larger and larger proportion of our GDP goes toward health care, which means higher taxes, more borrowing, or both. I'd argue that in the long run, this will wreck the country.
Speaking of rationing, any good or service can be rationed in one of two ways. The most common is market rationing, where a supply and demand naturally balance each other out. A more discredited method is government intervention. This means the government decides what the supply is, and who gets it. When you think about it, the reason health care prices are out of control in this country is that we have reverse-govenment rationing combined with a free market. Many are mandated by the government to receive coverage, while few can pay; at the same time, we have an innovation-driven supply side. The new laws just make this worse and more aggregious than it already is. I would have had more respect for those in Congress had they said the following:
"There are two choices. We can ration by the free market, or we can ration centrally. Free market rationing means none of you who are poor will get health care. Rationing centrally means that those of you are too sick to save or near the end of your lives will be denied care. We need to understand that these are the two options. Let's pick one, or the other, or something in between. Any reverse rationing has to be balanced by real rationing--taking health care away from someone else--or by raising taxes."
At this point in this imaginary debate, I would have presented my approach, which would have been immediately shouting down by Democrats and Republicans alike. The approach has four basic components. First, mandatory health care savings accounts for all Americans. Second, mandatory catastrophic care insurance for all Americans. Third, outlaw Doctor-specific or hospial-specific rates for insurance companies. Doctors and hospitals and pharma companies charge the same price to all buyers. Fourth, a clear income floor beneath which health care savings accounts and catastrophic care accounts are subsidized progressively by the federal government.
Mandatory HSAs would work just like 401(k)s and IRAs. Americans could contribute to them tax-free. By making these "real money", Americans would pick their Doctors and hospitals on the basis of who gave them the best deal. Prices would moderate. Ineffective treatments or dubious drugs would shrivel up and die. There would be no insurance companies. You could go to any Doctor you chose, based on their price and their effectiveness. You would pick a $30 blood pressure drug over a therapeutically identical $300 drug. The maker of the more expensive drug would lower prices or go out of business. Would this "stifle innovation?" I don't think so. I think it would bring innovation back into the realm of the free market.
At first, Americans would revolt at such a system. It sounds too much like "work." We've been accustomed to thinking of health care as a public good we don't have to worry about. But, as Americans got used to it, they'd see prices immediately moderate. They'd also see that health care quickly got much less confusing. Finally, they'd see their accounts grow in value, ensuring that they could cover health care costs as they got older, and even, in some cases, pass saved money they didn't use on to their children for college.
Of course, people also get very sick from time to time. These sicknesses can eat up even a $100,000 balance HSA balance in months. For cases like this, a government catastrophic pool would kick in. This would be funded by a tax, like Medicare. The pool would be accessible by patients with certain conditions, like metastatic cancer or a massive car accident, that would be pre-defined by statute. Every three months, a certain amount would be deposited into the patient's HSA, and once again, the patient (or the patient's family) would be responsible for allocating the funds. The inflow to the HSA account would be a fixed number set by condition. For example, $25,000 a month for colon cancer treatment. At the end of the condition, any unused money would go back to the government.
Of course, this is the element most like the current system, and most susceptible to abuse. However, this is where element three would come in. Because hospitals and Doctors couldn't charge different rates to different people (or firms), the government would know what it truly cost to provide care for a condition, and people would know they could pay for their catastrophic treatment out of the government lifeline. There is still potential for abuse here--no doubt about it--so this would need to be monitored closely.
Element four makes sure all Americans are covered. If you made, say, $60,000 a year as a household, you might qualify for a $4,000 tax credit towards your HSA, ensuring your family could pay for preventative health care. Medicare and Medicaid would be gone. Seniors might also get an additional tax credit to account for increasing health care costs as people reach their 60s and 70s. One other legislative element could be a five- or ten-year phase-in period during which catastrophic funds could be used for preventative care as HSA balances increased over time. The total pool would be mandated as a fixed percentage of GDP--say 2%--and would be gathered through income tax. Yes, it's tax, but remember, Medicare goes away.
So, this system, when you think about it, is novel for one reason. It does away with insurance companies. Insurance companies are great in theory, but in our case, they've distorted the market to the point where prices bear no resemblance to the underlying value of services provided, with governmeny playing key enabler. Furthermore, they've created a bizarre landscape that ties Americans to their jobs and stifles innovation. While I'm sure this proposal needs tuning, it seems to accomplish two goals: keeping prices in check while ensuring that all Americans receive a morally acceptable level of health care. Of course, none of this will ever happen, but one can dream.
Tuesday, January 12, 2010
Boiling the Leno Thing Down
Every once in a while, you see a picture that takes a news story and basically explains it better than any interview or analysis can. As my friend John Shomaker said as a preface to his email,
"Money chart. Ridiculous discounts to other shows. Did you ever see it - horrible. Not funny at all, and yet Leno thought it was a different format. Same show, just earlier, but with horrible writers."
I guess that about covers it for Jay Leno. Sorry man. You're just not that funny.
"Money chart. Ridiculous discounts to other shows. Did you ever see it - horrible. Not funny at all, and yet Leno thought it was a different format. Same show, just earlier, but with horrible writers."
I guess that about covers it for Jay Leno. Sorry man. You're just not that funny.
Monday, January 11, 2010
Affordable Transaction Economics
I'm always looking for more scientific ways to spend money on marketing, as people who consistently read this blog know. I spend a lot of time looking at Return on Marketing Investment (ROMI), for example. Lately, I've been dealing a lot with pipeline marketing strategy, and I stumbled upon something that is probably fairly obvious, but I thought I'd outline it formally.
My friend and mentor Tim Furey wrote a book in 1999 called The Channel Advantage. It was a great book, and its simple premise was "match the transaction to the channel that can afford to handle it." There were other key ideas, but this was the one that stuck. In other words, rebuy channels go through e- and tele-channels, big new servers through face-to-face. The reason, simply, is that it costs $10 to take an inbound phone order, and $250 (at least) to drive out to someone's office.
I wanted to take this concept and apply it to a more dynamic problem, the pipeline, specifically nurturing leads. When you think of a lead, it has a "born on date" and then you continue to do "stuff" to it that costs money. Every time you do "stuff" the acquisition cost has increased. So, a key metric for lead nurturing has to be "cumulative marketing dollars spent." That's the basis for affordable transaction economics (ATE) applied to lead nurturing.
In other words, affordable transaction economics (ATE) is simply a way to optimize pipeline marketing activity at every stage of the relationship. The one premise is “don’t spend more cumulatively on a lead than we forecast the lead to contribute to our operating profit.” ATE depends heavily on a lead scoring model, ideally one that can forecast the total value that will result in a lead. Say at day 1, my forecasted lead value is $10. That means that I should have spent no more than $10 on acquiring and nurturing that lead. But, on day 2, we get a lot more information from that lead, and the forecasted lead value goes up to $200. Now, I can afford a telephone call and more. This continues on and on. A case example of how ATE can be used to guide spending on lead nurturing is outlined in the table below.
The constraint of spending no more cumulatively than the forecasted operating profit is an outer limit, by the way. There should be some percentage of revenue that marketing targets for acquisition cost—say, 10%. This target will usually be lower than operating margin.
What does this mean operationally? For marketers who own the upstream end of the pipeline, it means creating two new metrics, cumulative spend per lead (CSPL) and forecasted affordability per lead (FAPL). When CSPL > FAPL, this metric should turn red. These KPIs are nice because they can flow from the individual lead level all the way up to a line of business. To do this, we need to think about what affordability is (how much should I spend per dollar on acquisition?) and we need a good lead scoring model that look at eventual lead value.
ATE can also be used very effectively as a planning framework for designing lead nurturing campaigns. Essentially, the marketer can now plan the mix for a $1 lead, a $10 lead, a $100 lead, and a $1000 lead. This simplifies thinking considerably around what can be very hairy process flows.
My friend and mentor Tim Furey wrote a book in 1999 called The Channel Advantage. It was a great book, and its simple premise was "match the transaction to the channel that can afford to handle it." There were other key ideas, but this was the one that stuck. In other words, rebuy channels go through e- and tele-channels, big new servers through face-to-face. The reason, simply, is that it costs $10 to take an inbound phone order, and $250 (at least) to drive out to someone's office.
I wanted to take this concept and apply it to a more dynamic problem, the pipeline, specifically nurturing leads. When you think of a lead, it has a "born on date" and then you continue to do "stuff" to it that costs money. Every time you do "stuff" the acquisition cost has increased. So, a key metric for lead nurturing has to be "cumulative marketing dollars spent." That's the basis for affordable transaction economics (ATE) applied to lead nurturing.
In other words, affordable transaction economics (ATE) is simply a way to optimize pipeline marketing activity at every stage of the relationship. The one premise is “don’t spend more cumulatively on a lead than we forecast the lead to contribute to our operating profit.” ATE depends heavily on a lead scoring model, ideally one that can forecast the total value that will result in a lead. Say at day 1, my forecasted lead value is $10. That means that I should have spent no more than $10 on acquiring and nurturing that lead. But, on day 2, we get a lot more information from that lead, and the forecasted lead value goes up to $200. Now, I can afford a telephone call and more. This continues on and on. A case example of how ATE can be used to guide spending on lead nurturing is outlined in the table below.
The constraint of spending no more cumulatively than the forecasted operating profit is an outer limit, by the way. There should be some percentage of revenue that marketing targets for acquisition cost—say, 10%. This target will usually be lower than operating margin.
What does this mean operationally? For marketers who own the upstream end of the pipeline, it means creating two new metrics, cumulative spend per lead (CSPL) and forecasted affordability per lead (FAPL). When CSPL > FAPL, this metric should turn red. These KPIs are nice because they can flow from the individual lead level all the way up to a line of business. To do this, we need to think about what affordability is (how much should I spend per dollar on acquisition?) and we need a good lead scoring model that look at eventual lead value.
ATE can also be used very effectively as a planning framework for designing lead nurturing campaigns. Essentially, the marketer can now plan the mix for a $1 lead, a $10 lead, a $100 lead, and a $1000 lead. This simplifies thinking considerably around what can be very hairy process flows.
Monday, January 04, 2010
Are Marketing Data Expanding Faster than the Universe?
I'll start by making a statement that I think I can back up: the amount of data available to marketers is growing geometrically, not linearly. Linear growth of marketing data would mean something like the following: An individual starts a new company on January 1, 2010. He buys a list of 1000 names to market to. A year later, he has added 100 names to this list, for 10% growth. A year later, he buys 110 names to market to, for a further 10% growth, and so on, and so on.
Exponential growth is different. When things grow exponentially, they grow as a square of the original. For example, instead of a marketer having 100 names and then 110 names, he’d have something like 100^2 or 10,000 names after a year. This can’t keep up, because we’d simply run out people in the world after two or three years.
The issue is more complicated than this, though, because we’re really talking about not just the number of names growing exponentially, but what we know about these names growing, too. And, to make it even more complicated, we now know more about how these names interact with one another—the network—every year (the gift of Web 2.0). So to recap, we have three sources of information growth for marketers:
1. Increased size of the known universe of names, companies, etc.
2. Increase in what we know about these names
3. Increase in connections between these names, companies, etc.
As far as (2) goes, this is where most of the action is today, and that’s not because people are doing more things that are relevant today that they were 20 years ago, it’s because they’re doing them in a browser, or on a mobile device, or on a game console, and they’ve been cookied or their IP address has been matched in the back end or… you get the picture. And this digitization of behavior is only going to get more extreme, barring a zombie attack or a second Luddite revolution.
The question is, what is the doubling time for marketing (or social science) data today? For microprocessors, according to Moore’s law, speed doubles roughly every 18 months keeping price constant. This has held up fairly consistently over the past 20 years and in my mind is a great empirical proof that de Chardin’s theories on the Omega Point might be true. It would be a great academic study to look at the doubling time for marketing data to add to the table in this amazingly cool article. If you have doubt of the amount of information available about people growing exponentially, take a look at a Facebook event stream. Your life, time stamped.
There are two constraining factors here worth noting, though. The first is information (not data) capacity: A company cannot possibly afford to keep up with exponential doubling of marketing data, whether the "double life" is 18 months or 36 months. It’s not a question of storage cost, it’s a question of ability to deal with the data from a logical perspective. The marketing talent at a company, no matter how big, simply cannot deal with a doubling of information every 18 or 36 months. It's kind of like central planning-- the data has to be federated and put into a competitive marketplace to reach its full potential. So, there has to be some kind of consortium to deal with this complexity, or intermediary vendors distilling the stuff into bite-sized chunks for industries, roles, etc. And, I'd argue, this is exactly what we've seen happen over the past twenty years, starting with retail scanner data in the early 1980s.
The second constraining factor is the question of information ownership and “walls”. What do you think the amount of proprietary information—defined as that owned by a company and no one else—that made up “all you could know” about a customer? I’d guess in 1975 it was 75%. I wonder what it is now? 20%? And what is the final resting point? It’s lower than what it is now. The point is simple: what a company can know about a customer is more than ever sitting out in the public domain, but the challenge is, what do you do with it? This thought experiment, in my view, makes a strong argument for moving towards cloud computing when it comes to marketing applications.
I’m not sure there’s a conclusion here, but I do think it’s worth noting for all of marketers that we’re in the middle of our own Moore’s law moment, and we better keep thinking about how we capitalize on it.
Exponential growth is different. When things grow exponentially, they grow as a square of the original. For example, instead of a marketer having 100 names and then 110 names, he’d have something like 100^2 or 10,000 names after a year. This can’t keep up, because we’d simply run out people in the world after two or three years.
The issue is more complicated than this, though, because we’re really talking about not just the number of names growing exponentially, but what we know about these names growing, too. And, to make it even more complicated, we now know more about how these names interact with one another—the network—every year (the gift of Web 2.0). So to recap, we have three sources of information growth for marketers:
1. Increased size of the known universe of names, companies, etc.
2. Increase in what we know about these names
3. Increase in connections between these names, companies, etc.
As far as (2) goes, this is where most of the action is today, and that’s not because people are doing more things that are relevant today that they were 20 years ago, it’s because they’re doing them in a browser, or on a mobile device, or on a game console, and they’ve been cookied or their IP address has been matched in the back end or… you get the picture. And this digitization of behavior is only going to get more extreme, barring a zombie attack or a second Luddite revolution.
The question is, what is the doubling time for marketing (or social science) data today? For microprocessors, according to Moore’s law, speed doubles roughly every 18 months keeping price constant. This has held up fairly consistently over the past 20 years and in my mind is a great empirical proof that de Chardin’s theories on the Omega Point might be true. It would be a great academic study to look at the doubling time for marketing data to add to the table in this amazingly cool article. If you have doubt of the amount of information available about people growing exponentially, take a look at a Facebook event stream. Your life, time stamped.
There are two constraining factors here worth noting, though. The first is information (not data) capacity: A company cannot possibly afford to keep up with exponential doubling of marketing data, whether the "double life" is 18 months or 36 months. It’s not a question of storage cost, it’s a question of ability to deal with the data from a logical perspective. The marketing talent at a company, no matter how big, simply cannot deal with a doubling of information every 18 or 36 months. It's kind of like central planning-- the data has to be federated and put into a competitive marketplace to reach its full potential. So, there has to be some kind of consortium to deal with this complexity, or intermediary vendors distilling the stuff into bite-sized chunks for industries, roles, etc. And, I'd argue, this is exactly what we've seen happen over the past twenty years, starting with retail scanner data in the early 1980s.
The second constraining factor is the question of information ownership and “walls”. What do you think the amount of proprietary information—defined as that owned by a company and no one else—that made up “all you could know” about a customer? I’d guess in 1975 it was 75%. I wonder what it is now? 20%? And what is the final resting point? It’s lower than what it is now. The point is simple: what a company can know about a customer is more than ever sitting out in the public domain, but the challenge is, what do you do with it? This thought experiment, in my view, makes a strong argument for moving towards cloud computing when it comes to marketing applications.
I’m not sure there’s a conclusion here, but I do think it’s worth noting for all of marketers that we’re in the middle of our own Moore’s law moment, and we better keep thinking about how we capitalize on it.
Monday, December 21, 2009
Part 2 of Latent Pipeline: Digital Topography
The latent pipeline concept that I layed out last week is one that sounds good on its surface, but as a German colleague of mine would say, "where is the detail?" The key to operationalizing latent pipeline is having a really good grasp of marketing from the customer's perspective, that is, online. What I call this (and this is my term) is online topography. I call it this because it reminds me a lot of a topographical map. You have features on the map (mountains, streams, trails) and you also have altitude, a scalar field that permeates the whole two-dimensional plane.
This is similar to what a fully fleshed out online topography looks like. To make one, you need three things:
This is similar to what a fully fleshed out online topography looks like. To make one, you need three things:
- A use case, for example, moms searching for nursery schools in Washington, DC
- A pipeline, from the customer's perspective
- A map of all the places customers go to achieve this use case
Then, you sum all of the customers up (i.e. integrate the function) and you have your topography. There's a lot of math that can go into this, and there's a lot of research that must, but at its simplest, we're still just talking about a graphical representation.
I've included a B2B-relevant use case below, in this case "IT Professionals Purchasing Blade Servers." I totally made this up, and it's based on 30 minutes of research, but it gets the point across. Notice, it uses the same type of graphic as we used to describe latent pipeline:
On the left-most side of the pipeline are "company sites". This would include all of the places customers go on specific companies' sites. I also threw "tele" and "field sales" in there. Notice this is a small piece of the pie. Then there's "relevant public universe" which is where all the pretty icons are. These are the topic-specific sites that customers use to research, price, discuss, and decide through the buying process. These are important for two reasons: (1) you can get on these properties, either through PR or straight advertising, and, (2) you can cookie users on these sites and retarget them on the third slice, "other public behavior." This is the long tail that I discussed in detail in this post.
What's not shown in this visual is the importance of each area of the map, the "altitude". For example, I've got Google on here, which would disaggregate into all of the search terms people use for blade servers, which might be the highest mountain on the map by a long shot. I have mixed display and search together here, because I think that's OK. This is not an internet taxonomy, it's a map, which is different. We'll do taxonomies another time.
So, once a marketer has a topography, it becomes possible to start thinking about marketing mix optimization from a whole different perspective, that is, from the customer's perspective and totally digitally. New dimensions of targeting also open up when you think of your site, relevant internet, and audience non-relevant internet together, but linkable via cookieing.
Wednesday, December 16, 2009
Using Audience Targeting to Own the Latent Pipeline
B2B marketers are intimately familiar with the concept of a funnel or a pipeline. Whichever term you use--you might use both--the ideas are the same from company to company. A lead is entered into a system at some point in time. It might be someone inquiring on the web site, or it might even be a name from a list. From this point onwards, that lead can either move forward, do nothing, or drop out of the system. The resulting graphic looks like a funnel.
Having covered that ground, let me state up front that this post is not about pipeline management, acceleration, nurturing, systems, or email marketing. This post is about a fundamental problem with the funnel concept as operationalized at most B2B marketing organizations today, and what I think is a pretty seminal idea on how to fix the problem.
The problem with funnels is that they're missing a lot of the folks that are actually going through the purchase process. This is because we as marketers are dependent on our own internal systems to track these folks. Our own websites; our own emails; our own sales reps; you name it. However, we know there are many individuals with needs that are going down an awareness / consideration / trial / purchase process that we are completely oblivious to. I call this the latent pipeline. For analytics geeks, this should be a comfortable term. It is the implicit pipeline that we don't have information about but that we know exists.
I'd argue that the latent pipeline can be broken into two parts. The first part is the upstream part of the funnel that could be defined as "pre-company web site". This is when latent prospects are starting to think about their needs and what they're going to go do. Today, this is going to be largely addressed via search, assuming that we can intersect people when they type in search terms. There is no question that this is a powerful tool for intercepting prospects, but I'd argue that there's an even more powerful way to target them. More on that later.
The second part are the folks that are going through our stages, as defined via our systems, that we don't know about. So, when we have 10 leads that are "qualified", there are another 50 leads out there that are being qualified by other companies. We don't know about them, so chances are, we'll never get to pitch to them. Ouch! That's pretty harsh.
So, we've defined a pipeline that has an explicit and a latent component, that will look something like this:
This fundamentally changes the concepts of B2B marketing, when you think about it.
Having covered that ground, let me state up front that this post is not about pipeline management, acceleration, nurturing, systems, or email marketing. This post is about a fundamental problem with the funnel concept as operationalized at most B2B marketing organizations today, and what I think is a pretty seminal idea on how to fix the problem.
The problem with funnels is that they're missing a lot of the folks that are actually going through the purchase process. This is because we as marketers are dependent on our own internal systems to track these folks. Our own websites; our own emails; our own sales reps; you name it. However, we know there are many individuals with needs that are going down an awareness / consideration / trial / purchase process that we are completely oblivious to. I call this the latent pipeline. For analytics geeks, this should be a comfortable term. It is the implicit pipeline that we don't have information about but that we know exists.
I'd argue that the latent pipeline can be broken into two parts. The first part is the upstream part of the funnel that could be defined as "pre-company web site". This is when latent prospects are starting to think about their needs and what they're going to go do. Today, this is going to be largely addressed via search, assuming that we can intersect people when they type in search terms. There is no question that this is a powerful tool for intercepting prospects, but I'd argue that there's an even more powerful way to target them. More on that later.
The second part are the folks that are going through our stages, as defined via our systems, that we don't know about. So, when we have 10 leads that are "qualified", there are another 50 leads out there that are being qualified by other companies. We don't know about them, so chances are, we'll never get to pitch to them. Ouch! That's pretty harsh.
So, we've defined a pipeline that has an explicit and a latent component, that will look something like this:
This fundamentally changes the concepts of B2B marketing, when you think about it.
- Acquisition marketing really becomes about understanding the top of the latent funnel
- The scope of CRM can be expanded to include not just leads / opportunities in our CRM system, but to leads / opportunities in other company's CRM systems
I'm not suggesting that we all go do industrial espionage and steal other firm's CRM data. I'm suggesting that through online audience ownership, we can extend the CRM layer from the explicit to the latent, via display advertising. I already posted on this once, so read that one. Basically, I'm arguing that we need to take a few steps to own the latent pipeline:
- Understand our audiences
- Map their typical B2B Internet behavior and map their pre-buying cues
- Build analytical models to tag them
- Target them via display advertising before they ever come to our site
- Keep doing search marketing
In other words, create a rich, targeted online tapestry that is always on, and no longer shackled to company web sites and email. B2C marketers are ahead on this, but B2B has so much more potential.
I know this needs more detail. Next post will be on how one might do the steps above and make it work.
Sunday, December 13, 2009
Monday, October 26, 2009
Segmenting Segmentation
The problem with the word "segmentation" is that it has lost its meaning. Like "analytics", a marketer saying "segmentation" can mean virtually anything. This isn't necessarily a problem, if we are simply using "segmentation" to mean "the parsing of customers in some way", but often people mean something very specific when they say the word, while the people listening interpret something very specific as well, but something specifically totally different.
So, here we go. In my view, any segmentation starts out with business goals. What is the company itself trying to do with its customers? Are we trying to grow profit? Grow revenue? Are we trying to do it in a specific area? Do we have multiple goals? What are their priorities? Ignoring these questions and just going down the path of segmentation without context is a big problem. So start by understanding business goals. The two Bobs would be proud of you, even just doing this.
I've often tried to come up with ways to segment the word segmentation. And yes, I am guilty of the same sin the "Two Bobs" made in Office Space when they wrote "plan to plan" on the whiteboard, but I'm still going down this tautological road.
"He's a real straight shooter, that Andy Hasselwander. He segments his segmentations"
So, here we go. In my view, any segmentation starts out with business goals. What is the company itself trying to do with its customers? Are we trying to grow profit? Grow revenue? Are we trying to do it in a specific area? Do we have multiple goals? What are their priorities? Ignoring these questions and just going down the path of segmentation without context is a big problem. So start by understanding business goals. The two Bobs would be proud of you, even just doing this.
Then, we get to the actual source insight. This is before we do any segmentation. What I mean by insight is pulling all of the stuff we do and can know about customers that would make meeting the business goals defined above easier, and putting them in a giant bin. This can mean old and new market research, qualitative or quantitative, data mining, management hypotheses... it's everything.
Only then do we get to segmentation. Now here I think there's an important thing to consider, which is that "value" segmentation kind of supercedes all the others. The reason is that if we want to do anything to customers, we first need to understand how valuable these customers are. So, I'd argue that segmenting customers first by value--whether customer lifetime value or something less sophisticated--has to happen first before you do anything else. This is probably somewhat controversial, among the 150 people who regularly read this blog. So, I encourage people to engage in a heated, uncivil debate on the topic.
Then, I'll put forth four key "types" of segmentation, in addition to value-based:
- Product. What customers want and need from a product or service. Sometimes called "needs-based segmentation"
- Creative. How customers respond to different visuals, animation, etc. Some of the cool things going on in biometric research are very useful here.
- Promotion. How customers respond to different types of incentives... price, points, free shipping, etc.
- Channel. Which customers use which channels, and which they prefer.
- Targeting / Finding. Where to actually find customers wherever they are
This is represented in my little "segmentation tree" as a continuum, and that's deliberate. This is because the left side of the tree--needs-based segmentation--is really hard to combine with the right side of the tree. Also, making a segmentation scheme do all five things at once is almost impossible. There are two reasons for this:
- A statistical problem called "assignment" which means that you can't make one segmentation based on needs, generated from a survey, fully predict or "find" individual customers
- A scope problem that in any one study, you can't possibly combine all elements of "segmentation" into one uber-model. It's just impossible.
One way around this is with behavioral segmentation, or "deterministic" segmentation. This means you just start out segmenting your customers into groups 100% defined by their behavior, and then define the six attributes in the tree post-facto. This way, you're always certain you can target a customer. It might not be perfectively descriptive, but it's guaranteed to be perfectly actionable.
Later everyone.
Tuesday, October 13, 2009
Verticalizing Internet Marketing
The hottest thing in direct marketing today is targeting individuals online vs. targeting via publishers or content. The idea that we can "know" a user and target them frees up the 90% of online inventory currently referred to as "remnant" space-- e.g. the space left over after GM, Microsoft and Coke buy the top page banners at Washington Post and NY Times. The remnant space can be just as valuable as the prime space if we only knew who the users were. A user interested in gourmet cooking is still a user interested in gourmet cooking when she leaves the NY Times food page and goes to her hotmail account or to her son's preschool website.
Verticalizing the internet is a potential solution. Or rather, verticalizing internet users. The internet is already as verticalized as it's ever going to be. So how would one verticalize internet users? First of all, it's only going to happen on specific networks of sites. You can't, for privacy and chinese wall reasons, put a universal cookie a browser that decodes them perfectly everywhere. I guess you could, and maybe that's an interesting business model. But for now, you need to work within the context of a network or a publisher. Let's use Yahoo! as an example.
So, say Yahoo! decides that they really want to make their inventory appealing for technology marketers, specifically B2B technology marketers. One option would be to put up pages that are really appealing to B2B technology buyers and influencers. These pages, assuming they attract a lot of volume (another key issue for networks / publishers), would get high CPM / CPC from technology marketers. However, once the user leaves this site, they're still valuable, right? So here's the rub, and how you can actually verticalize the internet. You've got the site; you've got the interested users. So you're missing two steps: (1) follow them around, and, (2) identifying the same types of users regardless of where they are on the property. I'd call this "200-level" and "300-level" verticalizing.
200-level verticalizing. Following them around is pretty easy. This basically means finding the really good vertical targeting-type pages, tagging engaged customers, and making sure we follow them around. This is pure behavioral targeting. We can get quite fancy doing this, by adjusting content on the verticalized page to correlate to segmentation dimensions and then adjusting people's segments based on what they click on. The trick seems to be in the details on this. For as long as I've been a member of Microsoft "Live", for example, I still seem to only get display ads for Hannah Montana movies. I view this as low-hanging fruit.
300-level verticalizing. The real nugget will be identifying all traffic according to vertical. There are two ways to do this. The first way would be doing traditional market research, such as a quantitative instrument, that would be distributed to a broad cross-section of a network or publisher's traffic. All respondents would be pre-linked to as much "targetable" behavioral data as possible. For example, Microsoft could use all the cookie data it aggregates for Windows Live ID users. Then, an analyst does a latent class analysis to derive segments relevant to advertisers; does a multinomial logit model to assign; and hands it off to the ad sales team. A/B testing to determine incrementality, and you're off to the races.
The second way would be to use only behavioral data to create "mock survey data", specifically from the vertical-relevant sites. So, we'd define our orthogonal dimensions that would be relevant to advertisers, such as "self integrators vs. needs help" and "windows vs. open source" etc. for a B2B technology scenario. Then we'd create content on our verticalized site that reflected these dimensions, and start measuring who clicked on and was otherwise engaged with what. We then use the same exact technique I outlined above to define and target segments.
The promise of these approaches is, I think, pretty huge, specifically for B2B where it's much more important to be much more targeted online.
Verticalizing the internet is a potential solution. Or rather, verticalizing internet users. The internet is already as verticalized as it's ever going to be. So how would one verticalize internet users? First of all, it's only going to happen on specific networks of sites. You can't, for privacy and chinese wall reasons, put a universal cookie a browser that decodes them perfectly everywhere. I guess you could, and maybe that's an interesting business model. But for now, you need to work within the context of a network or a publisher. Let's use Yahoo! as an example.
So, say Yahoo! decides that they really want to make their inventory appealing for technology marketers, specifically B2B technology marketers. One option would be to put up pages that are really appealing to B2B technology buyers and influencers. These pages, assuming they attract a lot of volume (another key issue for networks / publishers), would get high CPM / CPC from technology marketers. However, once the user leaves this site, they're still valuable, right? So here's the rub, and how you can actually verticalize the internet. You've got the site; you've got the interested users. So you're missing two steps: (1) follow them around, and, (2) identifying the same types of users regardless of where they are on the property. I'd call this "200-level" and "300-level" verticalizing.
200-level verticalizing. Following them around is pretty easy. This basically means finding the really good vertical targeting-type pages, tagging engaged customers, and making sure we follow them around. This is pure behavioral targeting. We can get quite fancy doing this, by adjusting content on the verticalized page to correlate to segmentation dimensions and then adjusting people's segments based on what they click on. The trick seems to be in the details on this. For as long as I've been a member of Microsoft "Live", for example, I still seem to only get display ads for Hannah Montana movies. I view this as low-hanging fruit.
300-level verticalizing. The real nugget will be identifying all traffic according to vertical. There are two ways to do this. The first way would be doing traditional market research, such as a quantitative instrument, that would be distributed to a broad cross-section of a network or publisher's traffic. All respondents would be pre-linked to as much "targetable" behavioral data as possible. For example, Microsoft could use all the cookie data it aggregates for Windows Live ID users. Then, an analyst does a latent class analysis to derive segments relevant to advertisers; does a multinomial logit model to assign; and hands it off to the ad sales team. A/B testing to determine incrementality, and you're off to the races.
The second way would be to use only behavioral data to create "mock survey data", specifically from the vertical-relevant sites. So, we'd define our orthogonal dimensions that would be relevant to advertisers, such as "self integrators vs. needs help" and "windows vs. open source" etc. for a B2B technology scenario. Then we'd create content on our verticalized site that reflected these dimensions, and start measuring who clicked on and was otherwise engaged with what. We then use the same exact technique I outlined above to define and target segments.
The promise of these approaches is, I think, pretty huge, specifically for B2B where it's much more important to be much more targeted online.
Friday, September 25, 2009
Build vs. Buy to Land Marketing Analytics
I break analytical marketing into two families: performance optimization and performance improvement. Both families are equally popular these days. However, I'm still seeing significant frustration landing "analytical marketing", particularly in large companies. Smaller "born on the web companies" have no problem at all with this. Generally, the founders of these companies built the core strategy around analytics, hired people comfortable with these concepts, and have woven analytics into the core IT systems of the company as it has grown.
However, for larger "digital immigrant" companies, weaving analytics into marketing can be a daunting task. It's no accident that large global consultancies like IBM are investing heavily in analytics consultants. They've realized a simple truth: that no matter how many fancy new airplanes an airline has, they won't get off the ground without skilled pilots and mechanics. So, why not just book a ticket on United?
I can't take credit for the pilot / mechanic / airplane /airline analogy, but it's a good one so I'll use it shamelessly. To be clear:
However, for larger "digital immigrant" companies, weaving analytics into marketing can be a daunting task. It's no accident that large global consultancies like IBM are investing heavily in analytics consultants. They've realized a simple truth: that no matter how many fancy new airplanes an airline has, they won't get off the ground without skilled pilots and mechanics. So, why not just book a ticket on United?
I can't take credit for the pilot / mechanic / airplane /airline analogy, but it's a good one so I'll use it shamelessly. To be clear:
- Pilot = analytics marketing professional. A dork like me. There are different kinds of pilots. There are the ones that can only fly the plane, and then there are the ones that can also probably start their own airline.
- Mechanic = data warehouse, SAS server, web analytics etc. software guru. These are the people that actually understand how the plane is put together and fix it when it breaks.
- Airplane = the data warehouse, SAS, etc. software itself, or "cloud", doesn't matter. These are the $100,000 + CAPEX investments that make analytics possible.
- Airline = IBM Global Services etc.--the firms that will just do this stuff for you. You just get on the airplane and it flies you where you want to go.
Looking at it from the company's perspective that just wants to get better at customer insight, targeting, ROMI, web ad optimization, etc., it's a very difficult strategic question: Where should I start? One could make an argument for starting at any of the four bullet points above, and I can think of case examples of companies who have gone down each path. But, I'll give you what I think the answer is now.
- Don't even bother hiring mechanics if you are less than $500 M in revenue, unless you were born on the web. It's extremely expensive; the people that can truly build great systems are few and far between, and the pain will be enormous.
- Everyone who wants to do analytics needs at least one good "senior pilot". All the other stuff might be outsourced, or you might fly United, but you need one person who really understands the stuff and can direct vendors, etc.
- Whether to buy airplanes or not depends on business context. I'm a huge fan of the cloud and renting airplanes, and this is getting more and more feasible. Companies that provide managed ROMI environment that still house your data like MSights are a powerful option. However, for large companies, buying other components selectively can make sense. And it certainly makes sense if you were born on the web.
- Full-service airlines can also make a lot of sense. Totally outsourcing all analytics to IBM might be the way to go if your culture is simply not built around digital and it's not a core focus. Over time, the company may get enough digital natives on board to make analytics a core competency.
Tuesday, September 15, 2009
"Barrier Removal" Marketing Strategy
"Barrier Removal" is a tactic I've used to great effect many times in strategic situations where customers don't seem to be behaving the way companies want them to. In many cases, to a customer, there are hurdles galore when getting information or making a purchase. To companies, these barriers may not be at all apparent.
Barriers can be physical, rational or emotional. Physical barriers might include:
Barriers can be physical, rational or emotional. Physical barriers might include:
- Long load times on a web page
- Credit disapprovals at a car dealership
- Small type size in an ad
- Long distances to a dealership
Rational barriers might include:
- Higher prices than competitors
- Feature sets mismatched to needs
- Confusing steps to buy
Emotonal barriers might include:
- Fear of the unknown
- The brand doesn't match my identity
- Distrust of the product / brand
When a marketer adds up all the barriers, you get something similar to a Markov chain. Each barrier can be thought of as "erected" or "non-erected." A non-erect barrier means the probability of a customer passing through that barrier is 1--because the barrier doesn't exist. But an erect barrier will cause some fall-out, and will effect all downstream barriers, too.
Doing a buying analysis based on barriers can be a powerful tool. One gets the sense that a company like Apple has done them in their retail stores, but maybe not for their B2B business. Looking at an Apple store, there are very few barriers.
Physical:
- Everything is in stock
- The stores are close to target populations
- There are products available for demo all the time
Rational:
- Prices are clear and intuitive
- Features meet consumers' needs
- Buying is simple--just talk to an associate and you walk out with your mac / ipod.
Emotional:
- The store is appealing and comforting
- The brand is trusted
However, when you look at B2B, there are barriers galore.
Physical:
- Where does one go to get Mac for business? The store? A distributor?
- What if my battery runs out? Why are the batteries internal? (some insulting me emotion here.)
Rational:
- Apple doesn't work seemlessly with Office. Or does it (some confusion mixed in here.)
- Apple doesn't make servers.
- There isn't enough business software available for Apple.
Emotional:
- I've never thought of the Apple brand as being built for businesses.
- Apple spends all its time marketing iPods.
Anyway, it's a simple example for illustrative purposes. But I love doing these barrier / hurdle projects. Maybe I'll do another post on the simple math... which gets to the upside business case of barrier removal.
Thursday, August 27, 2009
Creating a Dynamic View of Customer Status
When trying to manage a population of customers in a high frequency-of-purchase business where unique customer data are obtainable, it can be helpful to look at a grid comparing:
- Frequency of purchase in the last "active" 30 day period, and;
- Last purchase date.
This grid will look like this, when the report is run against the current state of customers:
- Freq of Purchase {0-->∞}
- Last Purchase {0-->∞}
And each customer is plotted along these dimensions.
But, to make things convenient, we cut off "last purchase", at, say, 90 days, and frequency at, say, 4 X for our table. We can then bucketize these scalar dimensions into a neat 4X4 (or nXn) grid like the one above.
A simple customer count in each bucket lets one understand current state. This is a perfect application for an OLAP reporting tool. It's also interesting, however, to understand dynamics: How customer status has changed from previous periods--the real point of this post.
To do this, I use the concept of vectors. A table such as the following can be created:
We can then simply average all of the movements for each unique starting cell location. For example, for the starting cell 1,1, which would correspond to Frequency of Purchase = 1 and Last Purchase <= 30 days, we might get {1.283, 1.321} for movement. These two numbers are the sides of a right triangle, the hypotenuse of which is the vector of customer movement.
There is an even more precise way to do this, which is to measure actual change in frequency of purchase and last purchase for each customer. In this way, we can actually measure customers who are going "beyond the edges" of our table--for example purchasing more than 4 times per 30 day period.
We can then plot our vectors in our table, which might look like this:
Each vector "direction" has a meaning. For example, "North" means more recent; "East" means more frequent / valuable, and so on.
We can also get to the velocity of change--the second derivative--by simply differencing two vectors, say, a month apart. These would tell you how a dynamic trend like the one above is changing. This is a very useful indicator of how marketing programs are impacting customer status.
This approach can also be used with virtually any customer attribute replacing these two dimensions, for example:
- Total value
- Customer Lifetime Value
- Category-Specific Activity
- Etc.
This is a neat approach that can be done with basically any pivot table / OLAP tool and a database of customer purchases.
Thursday, August 20, 2009
Scott Cross (Office Depot) Talks Efficient Customer Response, Customer-Centric Marketing, and ROI
I recently interviewed Scott Cross for an upcoming issue of MarketBridge's client email newsletter (Minds Over Markets). Scott is Director of Strategic Campaigns at Office Depot. Scott's a super smart guy who's tried a lot of innovative things and has a firm grasp on the big picture of B2B Marketing.
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AH: Talk a little about how customer data is being used to bring together vendors, manufacturers, retailers and distributors in a more customer focused strategy, and where that’s headed from a B2B perspective.
SC: The manufacturers don’t really understand who is making the decisions and what information is available within the network of their distributors and retailers. A lot of them aren’t set up to understand the information, and don’t have the capabilities to analyze it. So it’s really a green field, and that offers businesses the greatest upside today in the marketplace.
How can you get that data and marry it with the manufacturer? It’s critical because the manufacturer has the expertise when it comes to their products, customers’ behaviors and reactions to those products, and even customer trends when it comes to the features, benefits and requirements. What distributors and retailers have are customer data -- transactional and market basket data -- that completes the picture. So there is definitely room to grow there. For us, that’s where MarketBridge comes in: bringing the manufacturer together with the retailer.
AH: How has customer insight evolved at Office Depot over the past few years?
SC: Obviously, the retail sector is important for us, but there has been a big insurgence in B2B. But when you look at our customer base, about 80 to 90 percent of our customers are actually businesses now. So we really started trying to understand what drives their buying behavior, and who the decision makers are. In the past we did a lot of qualitative research. Any type of quantitative research was based on surveys, but we didn’t do a lot with customer analytics, database mining, and market basket analysis on the delivery side of the business with our larger contract customers. Most of the market basket analysis was done with the retail customer data.
So we’ve taken it to the next level by looking at the market basket information such as buying trends and behaviors, and looking at high value customers. Now much of our primary research is done with business customers rather than consumers. MarketBridge has played a big role in helping us understand that, and doing a lot of the modeling on our customer data set.
AH: As the manager around customer insight, what are the three most valuable reports that you receive or would like to receive? Which ones help you make better decisions about customers?
SC: I think ideally there are three reports managers want to see. First, a daily overall sales report that shows how you’re doing by product category and by channel is important, because every morning I can see whether we had a good day or a bad day and why, and the most effective channels from a product perspective. We’re also working to understand that at a customer level.
The ideal state is overall sales by product category and channel, and the second layer would be to look at that by customer segment. It’s the ability to drill into your sales vs. plan vs. last year, find out which categories are up and down, and in which customer segments.
The third report would be how we are doing against campaigns: how a campaign is performing by product category, customer segment, and by channel. Then, taking it even further, how a campaign is doing at the sales rep level. It’s really measuring the performance at a high level down to product, channel, customer and sales rep levels.
And the next step would be to get this information in real-time, and I think we’re headed in that direction.
AH: Given that, we often talk about the three dimensions of account structure, product market basket and marketing effectiveness reporting. How much importance do you put on those three when you’re managing your business?
SC: Marketing effectiveness is certainly important. You want to understand what vehicles are working and which customer segments are responding to which vehicles. In today’s economy, knowing which customer segments are doing financially better and have a little more money to invest in our products is important. For example, the public sector is definitely a growth opportunity in the current state of the economy.
Understanding the effectiveness of marketing to those segments is important, but just as critical is the product marketing basket and the account structure. For example, the product marketing basket allows us to understand that customers are buying ink, toner and paper, but they are no longer buying filing products, because budgets have been cut and they can reuse their file folders. So to understand how the product marketing basket changes by segment, by the cyclical nature of the economy and by the overall macroeconomic environment is important.
And the account structure is equally important. Knowing the difference between a small single-office customer down the street and a multinational corporation with small branches all over the country is key. At the same time, how do these different accounts make buying decisions? Is it the owner of a small business vs. the administrator within a large company? When you are able to marry those three together, it’s really powerful information to use in effectively marketing to your customers.
AH: Who understands customers better, the marketing organization or the sales force, and why?
SC: The sales force understands the customer better when it comes to interacting and knowing the way they customers think. When it comes down to one-to-one relationships, the sales force is number one. That said, the marketing organization is more effective in taking that information and using it to segment the customers into similar groupings, so you can have the same type of effect as with the one-to-one sales effort. So in looking at the overall customer base, how to segment them and market to them, and also which product or offering is most relevant to the entire set of customers, marketing is the most effective.
AH: Switching gears, you’ve got a market research person on your team and plenty of analytics people that are dealing with your core systems. How can market researchers and data analytics people better integrate to drive customer insight? How do you take those two separate tools and merge them together?
SC: Our philosophy is that research and analytics should be in the same group, because data is just another view of the customers, so if you can understand the customer through market research, you are getting a better picture of what the customer looks like. So market research and analytics go hand in hand.
AH: With your CPG background, you saw efficient customer response take off with retail scanner data in the supermarket sector, for example. Now it’s finally happening for B2B, and Office Depot is a pioneer in that.
SC: I do think we’re on the cutting edge. You always feel like it can’t go fast enough, and you’re always feeling behind when you look at retailers and all the information they’ve shared for many years. But it makes you realize there’s a lot of opportunity.
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AH: Talk a little about how customer data is being used to bring together vendors, manufacturers, retailers and distributors in a more customer focused strategy, and where that’s headed from a B2B perspective.
SC: The manufacturers don’t really understand who is making the decisions and what information is available within the network of their distributors and retailers. A lot of them aren’t set up to understand the information, and don’t have the capabilities to analyze it. So it’s really a green field, and that offers businesses the greatest upside today in the marketplace.
How can you get that data and marry it with the manufacturer? It’s critical because the manufacturer has the expertise when it comes to their products, customers’ behaviors and reactions to those products, and even customer trends when it comes to the features, benefits and requirements. What distributors and retailers have are customer data -- transactional and market basket data -- that completes the picture. So there is definitely room to grow there. For us, that’s where MarketBridge comes in: bringing the manufacturer together with the retailer.
AH: How has customer insight evolved at Office Depot over the past few years?
SC: Obviously, the retail sector is important for us, but there has been a big insurgence in B2B. But when you look at our customer base, about 80 to 90 percent of our customers are actually businesses now. So we really started trying to understand what drives their buying behavior, and who the decision makers are. In the past we did a lot of qualitative research. Any type of quantitative research was based on surveys, but we didn’t do a lot with customer analytics, database mining, and market basket analysis on the delivery side of the business with our larger contract customers. Most of the market basket analysis was done with the retail customer data.
So we’ve taken it to the next level by looking at the market basket information such as buying trends and behaviors, and looking at high value customers. Now much of our primary research is done with business customers rather than consumers. MarketBridge has played a big role in helping us understand that, and doing a lot of the modeling on our customer data set.
AH: As the manager around customer insight, what are the three most valuable reports that you receive or would like to receive? Which ones help you make better decisions about customers?
SC: I think ideally there are three reports managers want to see. First, a daily overall sales report that shows how you’re doing by product category and by channel is important, because every morning I can see whether we had a good day or a bad day and why, and the most effective channels from a product perspective. We’re also working to understand that at a customer level.
The ideal state is overall sales by product category and channel, and the second layer would be to look at that by customer segment. It’s the ability to drill into your sales vs. plan vs. last year, find out which categories are up and down, and in which customer segments.
The third report would be how we are doing against campaigns: how a campaign is performing by product category, customer segment, and by channel. Then, taking it even further, how a campaign is doing at the sales rep level. It’s really measuring the performance at a high level down to product, channel, customer and sales rep levels.
And the next step would be to get this information in real-time, and I think we’re headed in that direction.
AH: Given that, we often talk about the three dimensions of account structure, product market basket and marketing effectiveness reporting. How much importance do you put on those three when you’re managing your business?
SC: Marketing effectiveness is certainly important. You want to understand what vehicles are working and which customer segments are responding to which vehicles. In today’s economy, knowing which customer segments are doing financially better and have a little more money to invest in our products is important. For example, the public sector is definitely a growth opportunity in the current state of the economy.
Understanding the effectiveness of marketing to those segments is important, but just as critical is the product marketing basket and the account structure. For example, the product marketing basket allows us to understand that customers are buying ink, toner and paper, but they are no longer buying filing products, because budgets have been cut and they can reuse their file folders. So to understand how the product marketing basket changes by segment, by the cyclical nature of the economy and by the overall macroeconomic environment is important.
And the account structure is equally important. Knowing the difference between a small single-office customer down the street and a multinational corporation with small branches all over the country is key. At the same time, how do these different accounts make buying decisions? Is it the owner of a small business vs. the administrator within a large company? When you are able to marry those three together, it’s really powerful information to use in effectively marketing to your customers.
AH: Who understands customers better, the marketing organization or the sales force, and why?
SC: The sales force understands the customer better when it comes to interacting and knowing the way they customers think. When it comes down to one-to-one relationships, the sales force is number one. That said, the marketing organization is more effective in taking that information and using it to segment the customers into similar groupings, so you can have the same type of effect as with the one-to-one sales effort. So in looking at the overall customer base, how to segment them and market to them, and also which product or offering is most relevant to the entire set of customers, marketing is the most effective.
AH: Switching gears, you’ve got a market research person on your team and plenty of analytics people that are dealing with your core systems. How can market researchers and data analytics people better integrate to drive customer insight? How do you take those two separate tools and merge them together?
SC: Our philosophy is that research and analytics should be in the same group, because data is just another view of the customers, so if you can understand the customer through market research, you are getting a better picture of what the customer looks like. So market research and analytics go hand in hand.
AH: With your CPG background, you saw efficient customer response take off with retail scanner data in the supermarket sector, for example. Now it’s finally happening for B2B, and Office Depot is a pioneer in that.
SC: I do think we’re on the cutting edge. You always feel like it can’t go fast enough, and you’re always feeling behind when you look at retailers and all the information they’ve shared for many years. But it makes you realize there’s a lot of opportunity.
Monday, August 17, 2009
Sterling Cooper What Happened to You?
As a shameless tag along to last night's Mad Men premiere, I want to develop a hypothesis that I've been kicking around about how agencies, companies, and other elements of the marketing value chain have evolved since 1963.
Back in the 1960s, I've heard from some reputable sources, agencies were much more "do it all marketing companies." The distinctions we make today between a digital agency, an old line agency, an experiential marketing agency, and a marketing strategy firm would be foreign and meaningless back then, because agencies, in many cases, handled all elements of a client's customer facing presence. This was marketing in it purest form. The agency handled research, ideas, creation of message, art, and getting it out to the market. In many cases, agency folks would do things they had no idea how to do, but succeeded anyway. From what I've heard, it was (even removing all the booze, womanizing, and other crap,) a much more exciting time.
I work in a marketing consultancy. We do things that agencies would have done in the 1960s without knowing they were doing them. Did they do those things as well as a specialist would do today? Probably not--but I'm also willing to bet that marketing has lost something to over-specialization and fragmentation. Having all of the people thinking about a brand under one roof has huge benefits. Network effects drove huge creative outbursts and incredible innovation in marketing in the 1960s and 1970s. This is similar to a Google today, at least what I imagine Google to be. You have a company trying to do a whole bunch of cool stuff; specialization has not yet occurred, and innovation happens.
Much more of marketing has been brought in house by companies trying to save money. A lot of this was definitely good, but I think it can also be stagnating. Having people working on your customer facing presence who were working on, say, London Fog last year has benefits. I'm not saying that companies should look to "re outsource" the marketing function, but it's an interesting idea. H1: A company that came along today looking to trade on network effects of multiple creatives handling all elements of a company's customer facing presence... from sales to TV to database to you name it... and really executed on it (and not just said it) might actually be able to make some serious hay.
This is why it would be tough to make a Mad Men today about a company in the "marketing business". If you were going to make a show that good about a company today, it would probably take place at Google. Actually, let's try "company shows" most emblematic of their decade:
Back in the 1960s, I've heard from some reputable sources, agencies were much more "do it all marketing companies." The distinctions we make today between a digital agency, an old line agency, an experiential marketing agency, and a marketing strategy firm would be foreign and meaningless back then, because agencies, in many cases, handled all elements of a client's customer facing presence. This was marketing in it purest form. The agency handled research, ideas, creation of message, art, and getting it out to the market. In many cases, agency folks would do things they had no idea how to do, but succeeded anyway. From what I've heard, it was (even removing all the booze, womanizing, and other crap,) a much more exciting time.
I work in a marketing consultancy. We do things that agencies would have done in the 1960s without knowing they were doing them. Did they do those things as well as a specialist would do today? Probably not--but I'm also willing to bet that marketing has lost something to over-specialization and fragmentation. Having all of the people thinking about a brand under one roof has huge benefits. Network effects drove huge creative outbursts and incredible innovation in marketing in the 1960s and 1970s. This is similar to a Google today, at least what I imagine Google to be. You have a company trying to do a whole bunch of cool stuff; specialization has not yet occurred, and innovation happens.
Much more of marketing has been brought in house by companies trying to save money. A lot of this was definitely good, but I think it can also be stagnating. Having people working on your customer facing presence who were working on, say, London Fog last year has benefits. I'm not saying that companies should look to "re outsource" the marketing function, but it's an interesting idea. H1: A company that came along today looking to trade on network effects of multiple creatives handling all elements of a company's customer facing presence... from sales to TV to database to you name it... and really executed on it (and not just said it) might actually be able to make some serious hay.
This is why it would be tough to make a Mad Men today about a company in the "marketing business". If you were going to make a show that good about a company today, it would probably take place at Google. Actually, let's try "company shows" most emblematic of their decade:
- 1960s: Mad Men (Advertising)
- 1980s: L.A. Law (Legal)
- 1990s: Office Space (IT.. ok, it's a movie, but give me a better one)
- 2000s: Project Runway (Fashion)
Seems like a Google show needs to get made.
P.S. As far as Mad Men last night, while initially underwhelmed, I've been thinking about the episode all morning, which probably means it was actually really good. I didn't like the birth flashback at first, but I now think it was really clever. I loved the London Fog sales call, and I guess the thing that struck me there was that Don was essentially out of ideas and looked pretty impotent. The Sal scene with the bellhop was so awkward but great... and finally Pete's behavior as the junior executive we love to hate reached new levels. Lots of great aesthetic pieces too--the Japanese porn, the Stolichnaya as a cuban cigar equivalent, the use of raincoats as metaphor for (staying in the closet; hiding from the past)... By the way I'm headed to Baltimore tonight. Should I be worried?
Friday, July 24, 2009
Three Ways to Track ROMI
In keeping with this week's "three ways" theme, here's a framework to track ROMI (return on marketing investment) that can pretty much solve any problem. It's high-level, but it's been very helpful for me over the past couple of months in thinking about a very complex, multi-channel marketing measurement problem.
First, Test vs. Control. This means for every direct tactic that you launch, hold out a control group. The control group can be small, but should be selected from the exact same list that you used to generate the campaign. Otherwise, bias can (and will) creep in. Hold out the control list in a "stimulus table" somehwhere in your database. When the tactic has been in market for a long enough time, take a look at the performance of test vs. control customers against a baseline. A good baseline to use is "90 days prior". You can also use "vs. last year" but here you have to watch to make sure that there isn't a disproportionate percentage of customers in either list that weren't active in that time period.
Second, Opt for Customer Lifetime Value or Customer Data When Measuring Return. It's always better to take a look at customer dynamics vs. simply total sales. For example, if I'm doing test-control analysis, I'm much more interested in understand how many customers I acquired, lost, what my impact was on average transaction size, and ultimately what my impact was on CLV than just total sales for the population for that period. Disaggregating gross sales performance into customer-specific data yields huge insights. This is particularly true of subscription or repeat-order businesses.
Third, Econometric Forecasting with a Marketing Component. Putting all of your data into a time series database and understanding the contribution of each marketing tactic to return is the final step. This can work in a direct marketing, advertising, or "mixed" environment. The first step is to leave the marketing stimulus out--build a forecasting model for key independent variables, namely total revenue, new customers, lost customers, and average transaction size (and potentially price as well). Explain as much of the variance as possible using autoregressive terms (seasonality, etc.) and extrinsic data (competitive actions, GDP, business confidence, etc.) When you have a good model, add in marketing stimulus, using appropriate adstocks / decay rates. This should yield a good model for understand what elements of marketing are driving what elements of return.
Taken together, these three marketing measurement techniques can enable a "test and learn" culture at your organization.
Tuesday, July 21, 2009
Customer Insight Three Ways
Customer insight is important to marketers for many reasons. Understanding your customers helps you in formulating market strategy. It helps in campaign design. It helps the sales force understand how to approach acquisition targets or retain existing customers. It is the raw material out of which great marketing happens.
In the B2C space, customer insight is well understood, and whole industries and many great companies have made it their business. Segmentation schemes that provide predictive lift in product design, creative design, and direct marketing campaigns make billions of dollars a year for companies like Axciom and Claritas. However, in the B2B space, customer insight still has a long way to go.
The reason is simple. Companies are a lot more complex than individuals or households. For one thing, companies can be very small--say one worker--or very large. For another, companies have different locations with different functions. Capturing all of this diversity is difficult.
A good way to start simplifying the customer insight question in B2B is to focus on three key dimensions. First, what products are companies buying? Second, how is the company structured from a decision making perspective? Third, what information channels do decision makers and influencers in the company use to make decisions? This simple triad--product / audience / channel--can be very helpful as a "check off" list when thinking about marketing strategy. How would this approach work in a simple marketing campaign to say, sell a new type of tool to construction companies?
First, the product dimension kicks in. What companies currently have similar tools? What types of industries would need this tool? If the tool is very expensive, is there a lower bound on company size? A marketer could use data on product sales to build a predictive model of the most likely firms to purchase this new tool. It's important to note than in most cases, the product dimension is relevant at the firm and not the contact level.
Then, the audience dimension comes into play. Who is going to make the decision to purchase this tool? Are there key influencers to the purchase, the actual users of the tool? What are the features of the tool that will be most appealing to them? A marketer could create a contact strategy that hits each audience with the selling points that make the most impact--price on the buyer, value on the CFO, features on the users, for example.
Finally, the channel dimension is used to understand how marketing and sales should be executed. Do buyers and influencers read a specific type of publication to understand more about this family of tools? Are there influential communities that they listen to that we need to plug into? Are there key distributors that must carry the product for it to penetrate the market?
Of course, getting customer insight--the raw information--across these three dimensions is difficult. Doing this requires at the very least a qualitative market research study. However, by beginning to think in terms of these three dimensions, marketers will make better decisions and begin to collect information on their B2B customers that they can use over and over again.
Tuesday, March 10, 2009
Best Practices in Pipeline Velocity?
A client of mine is having challenges forecasting and managing the pipeline. They sell servers and storage (typically smaller deals 25K - 100K).
Was curious what others have traditionally seen in terms of deal cycle times (defined as length of time from qualified lead to deal close) and how they are varying in today’s environment. Would also be interested in understanding typical cycle times…
Was curious what others have traditionally seen in terms of deal cycle times (defined as length of time from qualified lead to deal close) and how they are varying in today’s environment. Would also be interested in understanding typical cycle times…
- From when customers respond with an interest until they are connected with the appropriate channel/sales rep
- From sales rep contact to a proposal
- From proposal to deal close
Also, would be great to get any best practices for segmenting, covering, and tracking these types of deals?
Thanks,
Andy Hasselwander
Wednesday, February 25, 2009
Are we in a Depression or a Recession... and what are the Implications for Marketing Strategy?
There are a lot of articles on the value of marketing in a recession. Most of these were written around three recessions: 1982; 1991; and 2001. These recessions ranged from severe (1982) to mild (1991 and 2001). The current "downturn" is definitely a "severe recession"--the question is, is it a depression? I'll set the following rules, somewhat arbitrarily, about separating a recession from a depression. A depression must satisfy 4 of the following 5 conditions:
- Negative GDP growth for at least eight consecutive quarters
- Unemployment > 10%
- Stock market down > 50% from peak
- Real Estate values down > 40% from peak
- Deflation in corp CPI for at least two consecutive quarters
So, using my definition above, which probably passes a sniff test and little else, are we in a depression? We'll have to use probabilities, because these data aren't complete yet.
Negative GDP growth for at least eight consecutive quarters. We're at three now. It'll definitely be five (no one sees growth in the 2nd quarter.) Bernanke says we "might" see growth this year... I'd put the chances at 33%. So let's call this 66%.
Unemployment > 10%. Most are calling 9% this year. I'd argue we could easily see 10% late this year or early next year. I'd put the chances at 50%.
Stock Market Down 50% from Peak. Dow peaked at 14,164 and hit 7,114 yesterday. Good enough for government work. Bingo.
Real Estate Value Down 40% from Peak. Home prices are down 27% since the 2006 peak. Most see 30% as inevitable. I'd put 40% at a 25% chance.
Deflation in Core CPI for at least 2 Quarters. Core CPI slowed to 0.8% in 4th quarter. It'll probably be slow in the current quarter too, but I doubt it'll go negative. So let's call this one 5%.
So I'd put our chances of being in Depression at about 10%. (.66 * .5 * .25). So, one in ten. Unsurprisingly, that's better than Joe Biden's 30% comment, into which probably no thought went. The hair spray probably just got into his brain for a few seconds and he was overwhelmed.
Why all this analysis? It's because if we're in a recession, there's a lot of past data to rely on, but if we're in a depression, we don't have much to go on. So, I'd argue we have to follow recession marketing rules. Here are some snippets gleaned from the press with the help of our friend Dr. Spekman at UVA / Darden about recession marketing rules of thumb / observations:
- An American Business Press / Meldrum and Fewsmith study showed that companies that increase / establish an aggressive market stance coming out of recessions (the 1970 one, in this case) do much better coming out of the slowdown.
- A similar study showed the same thing for the 1974-75 recession.
- A McGraw-Hill study found that 600 B2B companies that increased their spending in 1981-82 grew significantly more than competitors that did not.
- A Cahners and Strategic Planning Institute study of the 1981-82 recession showed that larger companies who spend more on marketing do better in downturns than their smaller competitors. Not surprisingly, this is already being borne out in 2008-09, with Ford and Sears taking share from sick competitors (Ford from GM and Chrysler; Sears from Circuit (bankrupt) and local appliance shops).
- AMA found a similar trend in 1990-1991, with larger / healthier companies spending more and taking share.
- BMW grabbed share after 9/11 in 2001 and credited its success to taking an aggressive market stance with advertising and event marketing. Good quote from BMW: "We change before the market forces us to change."
- After the 2001 recession, B2B Magazine found B2B ad spending recovering and focused on three areas: Supporting established brands, focusing on integrated marketing campaigns (multi-tactic, same creative and targeting) and measuring ROI.
- In 2005, Arvind Rangaswamy and Gary Lilien at Penn State did an analysis of the 2001 recession and found that well positioned companies benefit from increasing spend in downturns. A good analogy was "Athletes often choose times of stress to mount attacks; strong runners and bicycle racers may increase their pace on hills or under other challenging conditions."
The message is both (1) don't cut back on advertising, because that's silly. But it's also that larger, healthier companies with cash get a triple effect in recessions:
- There is less ad spending out there so your ads / DM get more attention.
- Ads are cheaper because media companies are struggling for business.
- You force weaker competitors into an arms race they can't win, forcing them to choose between bankrupting themselves or losing share.
This sounds an awful lot like the arms race in the 1980s. Ronald Reagan's 600 ship navy, high-tech fighters and bombers, and stealthy submarines never fought the Soviets, but because we had more cash, we basically forced them to either (1) lose the arms race or (2) bankrupt themselves. So, if you're strong, now's the time to beat your competitors to a pulp. Not sure I have much insight for the weak in this post. I'll try to think on that problem though.
Sunday, February 08, 2009
Looking Forward to the Snarky Ad Backlash
I have had it with snarky ads.
Snarky: Rudely sarcastic or disrespectful; snide (Dictionary.com)
I'd add this definition: The sarcastic, know-it-all, snotty sense of humor that has become the lingua franca of the late 00's among a certain class of too-cool young adults.
The front-line of the snarky cultural revolution seems to be advertising. It's driving me nuts, and I'm hoping I'm a leading indicator for the rest of the world. The sooner we're rid of the snarky ad revolution, the better. Here are some of my absolute most hated snarky ads. If, after watching these, you can't see this trend, then I must be crazy.
T-Mobile Butt Dialing. Can these two people hate each other any more? The absolute awfulness exhibited by these two idiots turns my stomach. I hope each of them butt-dials 911 and a SWAT team traces the call and accidentally blows them away. This makes me despise T-Mobile and Blackberry.
E-Trade Baby. Hey, it's not cute, it's creepy. However, beyond that, the baby is a snarky jerk. Would anyone want to hang out with this creep, as an adult or a baby? Once again, E-Trade is literally out of my consideration set on the basis of these obnoxious ads alone.
Bud Lite Drawing Guy. The UPS drawing guy isn't snarky, he's just a walking cliche. But the Bud Lite drawing guy is snarky and enjoys making meaningless, ironic comments. I don't like any of the "drawing" ads, but the skier one is particularly bad.
Old Spice Neil Patrick Harris. The snarkiness emanating from Doogie Houser in this one far exceeds any nasty body odor one might have. I guess this has just become the only way to talk to the 20-30 generation (at least that advertisers understand.) Troy McClure was funny--in 1994. It's not funny anymore.
Snarky: Rudely sarcastic or disrespectful; snide (Dictionary.com)
I'd add this definition: The sarcastic, know-it-all, snotty sense of humor that has become the lingua franca of the late 00's among a certain class of too-cool young adults.
The front-line of the snarky cultural revolution seems to be advertising. It's driving me nuts, and I'm hoping I'm a leading indicator for the rest of the world. The sooner we're rid of the snarky ad revolution, the better. Here are some of my absolute most hated snarky ads. If, after watching these, you can't see this trend, then I must be crazy.
T-Mobile Butt Dialing. Can these two people hate each other any more? The absolute awfulness exhibited by these two idiots turns my stomach. I hope each of them butt-dials 911 and a SWAT team traces the call and accidentally blows them away. This makes me despise T-Mobile and Blackberry.
E-Trade Baby. Hey, it's not cute, it's creepy. However, beyond that, the baby is a snarky jerk. Would anyone want to hang out with this creep, as an adult or a baby? Once again, E-Trade is literally out of my consideration set on the basis of these obnoxious ads alone.
Bud Lite Drawing Guy. The UPS drawing guy isn't snarky, he's just a walking cliche. But the Bud Lite drawing guy is snarky and enjoys making meaningless, ironic comments. I don't like any of the "drawing" ads, but the skier one is particularly bad.
Old Spice Neil Patrick Harris. The snarkiness emanating from Doogie Houser in this one far exceeds any nasty body odor one might have. I guess this has just become the only way to talk to the 20-30 generation (at least that advertisers understand.) Troy McClure was funny--in 1994. It's not funny anymore.
I'd love to come up with more, but to do that I'd have to watch more TV. Any comments suggesting other examples of this awful ad genre will be posted. And yes, I'm grumpy.
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