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.

• 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:

1. Understand our audiences
2. Map their typical B2B Internet behavior and map their pre-buying cues
3. Build analytical models to tag them
4. Target them via display advertising before they ever come to our site
5. 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.

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.

LinkedIn as a Data Source

AdAge had a good article about a simple segmentation of LinkedIn's user base. I've been thinking lately that LinkedIn could become the best source of B2B Marketing data in the world. Why?

• It's international. People sign up all over Western Europe and North America and increasingly in Asian hubs like Singapore and Shanghai.
• People add all kinds of information on themselves that can be increasingly accessed by text mining tools like SPSS Clementine (which is what they used for the AdAge piece.)
• LinkedIn is increasingly adding meta data to profiles making the datawarehouse much more useful for modeling. The best example of this is the voluntary "alumni" and "company" networks which are self-policing. On the basis of personal experience, the self policing works at least somewhat well.

I can easily see a LinkedIn being purchased by a D&B (or, if they wait long enough, for the opposite to happen) because it will very quickly outstrip D&B as a data source. The biggest hurdle I see is the legal one--I'm not sure how far LinkedIn can go as a data provider without changing their privacy contracts. However, they could do this with a little effort.

I also see LinkedIn as a much more powerful tool in the long run over a FaceBook. This might sound crazy, but it's because I'm not talking about display advertising. It's using the network as a data source. Companies would pay tons of money for an accurate SMB / Enterprise data source that included actual personal relationships inside the company and relationships with people outside the company. Talk about CRM--this takes the B2B CRM approach I spoke about yesterday and really operationalizes it. Combine it with a D&B or InfoUSA and you've got a relationship marketing machine category killer.

The segments that the AdAge work uncovered were interesting:

• First over 30 million people are signed up. Just doing some simple math, if there are 150 M in the "professional class" worldwide, that's 20% penetration. Pretty good.
• First segment they ID'd was Senior Executives at 28% of population. Average income was $104,000--the highest.
• Late Adopters, at 22% of the population, have low network power and were basically asked by colleagues to join. They don't actively tend their networks.
• Savvy Networkers, at 30% of the population, actively tend their networks and have high network influence. They tend to be actively out there sourcing business, recruiting, looking for jobs, etc. They are dynamic and are the biggest "users" of the network. I expect there are a lot of entrepreneurs in here.
• Exploring Options, at 20% of the population, are sort of the middle-of-the-pack folks who are generally happily employed but looking around.

This is a pretty simple segmentation and not all that useful but it gives you some insight into what someone could do with these data. The text mining was especially interesting. They were able to identify and quantify decision makers, decision makers' budgets, salaries at different title levels, etc. They also noted that 60% of users said they'd take a survey in their area of expertise. Think about being able to identify the entire decision making unit (DMU) inside of an SMB and multiply that be 1000. It's mind boggling. I'm waiting for the partnerships on LinkedIn and the real monetization to start.

Also, think about linking this into a collaborative partner marketing system. Microsoft provides LinkedIn matching data to its resellers in exchange for end user data... Yikes. Probably merits a whole other post or an article.

B2B CRM / Branding Model

I was doing some more thinking about a specific B2B CRM and branding model. This relates to a previous post on CRM. I was thinking about how you build an interaction model between the firm and its customers if those customers are businesses. I was also thinking about the function of "branding"--creating consistent communications across all customer touchpoints. A more holistic definition of branding might include all customer interactions, period, including services, products, even non-company-sponsored interactions such as through "key influencers."

The concept, while not earth-shattering, does provide some clarity to the role of branding and CRM in a B2B organization. It also leverages some of the concepts that I've been thinking more about lately, namely the core function of inbound marketing or listening and the imperative of thinking of the company-customer as a decision making unit and not just as a monolith.

Basically the framework divides up the CRM value chain into four parts. Let's stat with Marketing Management. This is the company function, and it is the marketing nerve center where all core decisions are made about the customers and the brand. The functions of marketing management involve product features, packaging, and value proposition; service elements, including all of the myriad post-sale touchpoints with customers; the marketing mix; and of course, segmentation, targeting and positioning.

The marketing management function's decisions are manifested in the outbound marketing function. Using a person as an analogy, this function amounts to "talking." The outbound function should entail communications (TV, radio, email, sales force, partners, etc.); experiences (product experiences; service experiences) and influentials (all of the sources that people listen to--the media, influential bloggers, influential user groups.)

The target of the outbound marketing are the business customers. These must be deconstructed from the firm to the decision making unit inside the firm to the individuals inside the decision making unit. Furthermore, individuals must be understood at the level of both behavior--what they do and attitudes and perceptions about the company and its products--which is of course THE BRAND. I can't emphasize enough how important it is to think of the B2B brand as the collective set of attitudes and perceptions latent in the individuals inside the decision making units at your firm's customers.

Finally, the fourth box is the inbound "listening" activity where your customers talk back to you. There are five primary elements of listening: sampling (tracking studies); chatter (web or otherwise); engagement (two-way conversations); purchase (buying); and loyalty (staying a customer). Generally, these five inbound vehicles paint a holistic view of the health of your company within a company, a DMU and key individuals inside the company.

I like this model because it brings together the core functions of both branding and CRM in a B2B context and provides a framework upon which to hang more complex ideas. It doesn't deal at all with technology (which is key to enabling both the outbound and inbound functions). It doesn't deal with any of the "technical" aspects of marketing, like PR, media mix optimization, etc., but it does allow slots for all of these things. Partner marketing could easily slot in to outbound and inbound, or you could actually build two business customer boxes for partners and end customers. In short, it's just a very versatile framework.

The Disconnect Between CRM Definitions

There are two definitions of CRM. The one used by 95% of practioners goes something like this:

"CRM is the systems and tools to integrate customer and marketing stimulus data together to provide useable information for marketers and managers."

The academic version is a lot more complicated. It includes the above technology, but CRM in academia is a whole branch of research. It seems a bit like an unholy alliance between the people doing the analytical marketing and the people thinking about it. Speaking of unholy alliances, there is an academic department dedicated to CRM at Duke University--The Teradata Center for Customer Relationship Management at Duke University (Fuqua). Three years ago, this group put out a special addendum in the Journal of Marketing on CRM. I read it when it came out, but I just re-read the introduction. In this 20 page pre-read to a bunch of CRM research, which mainly focuses on ROI studies around CRM, they acknowledge the definitional difficulties around CRM. They acknowledge that the concept of CRM has lost some of its meaning:

"...on the basis of our preceding discussion, it could be argued that CRM is the relabeling of a mixture of different marketing ideas in the extant marketing literature..."
(Boulding, Staelin, Ehret and Johnston, "A Customer Relationship Management Roadmap: What is Known, Potential Pitfalls, and Where to Go., Journal of Marketing, October 2005)


The preceding discussion they refer to outlines a bunch of really important topics in marketing that CRM supposedly encompasses:

• Value maximization--firms and customers maximize utility


• Need fulfillment (Levitt 1960)--marketers need to sell needs not products


• Augmented product (Levitt 1969)--customers buy products as solutions / buying experience


• Relationship marketing (Berry 1983)--the ongoing relationship, not just the transaction, is the critical marketing focus


• Market orientation; market focus; market-based learning--information about the customer / market is the key to successful marketing


• One-to-one marketing (Peppers and Rogers 1993)


• Mass customization (Pine 1993)

Where they end up is defining CRM as something building on the above seven concepts and incorporating technology, but definitely not as just "Siebel / Oracle" or "Salesforce.com" Instead they define a bunch propositions, acknowledging that "The field of CRM has begun to converge on a common definition." I have separated these into four "definitional" propositions and another six "success factor" propositions. They didn't do this, but I think it's much clearer this way:

Definitional Propositions

• CRM is the outcome of the continuing evolution and integration of marketing ideas and newly available data, technologies and organizational forms


• CRM enhances firm performance


• Effective CRM implementation does not necessarily require sophisticated analysis, concepts, or technology


• The core of CRM is the concept of dual creation of value

The success factors basically list what it takes for CRM to work. I think this is a pretty good list by itself:

Success Factors or Prerequisites

• CRM effectiveness depends on how it is integrated into the firm's existing processes and capabilities


• The successful implementation of CRM requires that firms carefully consider issues of customer trust and privacy


• The successful implementation of CRM requires that firms carefully consider issues of consumer fairness


• Inappropriate and incomplete use of CRM metrics can put the firm at risk of developing core rigidities, thus leading to long-term failure


• Successful implementation of CRM requires that firms incorporate knowledge about competition and competitive reaction into CRM processes


• Effective CRM implementation requires coordination of channels, technologies, customers, and employees

Even after going through these, though, I'm struggling for a definition. There's actually one sentence in this paper that I really like that I'm going to quote that is a good definition:

"Indeed, CRM goes beyond a customer focus. Not only does CRM build relationships and use systems to collect and analyze data, but it also includes the integration of all these activities across the firm, linking these activities to both firm and customer value, extending this integration along the value chain, and developing the capability of integrating these activities across the networks of firms that collaborate to generate customer value, while creating shareholder value for the firm."

So there you have it... a good definition for CRM. I'll use it. But of course, most people still just mean Salesforce.com.

Predicting Growth Companies for Sales Coverage

A lot of companies are looking for ways to find the highest potential customers and cover them more aggressively. This is really a key tenant of predictive modeling in the B2B arena--if you can find the customers that will grow, and get them while they're small, you'll ride the curve up with them providing you can keep them loyal (a whole other conversation).

The challenge with this is two-fold. First, you have to find the data to do the modeling. Second, you need to understand the right independent variables that predict future growth. This type of modeling can be done for either Enterprise or SMB, but I think it's got the most potential in SMB.

The best way to do this is to look at year-over-year growth in sales as a dependent variable. The challenge is that year-over-year growth stats are notoriously unreliable from D&B and InfoUSA. One option to get around this is fielding a study with both behavioral questions that are "in the database" (either a purchased list, or the customer database) and the dependent variable, growth in year-over-year sales. We could also ask some hypothesized predictive growth variables.

This study could either be a time series study where we're asking the same companies year after year (most accurate), or we could ask people to "remember" what their revenues were in the past. The trouble is, we'd also need to ask them to "remember" what the predictive variables had been, as well, which is really hard and will probably be biased and wrong. So, I'd definitely recommend implementing a year over year panel. Yes, this screams out for a syndicated data approach.

Once we have our data set, we can now do our modeling. The basic approach is a predictive analysis like multivariate regression, where we use year-over-year revenue growth as the dependent variable, and we use a bunch of other firmographic variables as predictors. The key is finding the right predictors. What are some predictors we've seen work?

• Age of CEO or Principal--the lower the age, the higher the future growth
• Industry micro-vertical--oftentimes, specific micro-verticals outperform peers depending on macroeconomic conditions
• Past revenue growth--while past performance is not necessarily predictive of the future, it usually is
• Recent equity injections or total debt load--companies with more cash tend to grow faster than those that are funding with operating cash
• Geographic area--specifically in certain SMB verticals (retail, services, dining, etc.) taking a look at those geographies that are growing faster than others

There are ton of other ones that could work, but the key is really getting to an "account growth" scoring algorithm that we can then use as one factor for segmenting account coverage. This can also be used to score partners. For some large B2B companies with literally tens of thousands of registered partners, any approach to distinguish the best from the unwashed masses will be very valuable.

Attempt at Categorizing Technology for Managing the B2B Pipeline

NOTE: I’ll keep this post “live” and updated as I get comments and as things change in this space. Latest update: 11-29-06. Updates since original post in red.

I’ve been trying to categorize all the technology that’s available out for B2B marketers into neat buckets. I’m sure Forrester and Gartner have done much better jobs at this than I have, but their reports cost money. For those of you who want a quick-and-dirty categorization of technology with links to company web sites, this post is for you. Please comment to point out companies I’ve missed.

There are a lot of “merging” technologies in this list. What this means is that some technologies started in one place and have spread into others. I’ve tried to note these, but have left the technologies in their “original”—and thus ostensibly focus—space.

Extraction, Transformation, Loading (ETL):

Informatica: PowerCenter http://www.informatica.com/products/powercenter/default.htm

Native Database ETL: SQL Server 2005, Oracle 10g, IBM DB2 all have native ETL technology that is clearly getting better and could be making Informatica obsolete—we’ll see. Informatica is still the Cadillac.

IBM: Information Integration http://www-306.ibm.com/software/data/integration/
Thanks to Vincent for his post alerting me of this miss. Check out his post for a comparison of Informatica and IBM Information Integration.

Data Quality:

Informatica: Data Quality
http://www.informatica.com/products/data_quality/default.htm

Trillium: Owned now by Harte-Hanks
http://www.trilliumsoftware.com/

Dataflux: Owned by SAS... wait everyone is getting owned in this space...
http://www.dataflux.com/

Data Warehouse (pre-built and otherwise):

Upper Quadrant: UQube
http://www.upperquadrant.com/

Oracle Data Warehouse Builder: http://www.oracle.com/solutions/business_intelligence/warehouse-builder.html

All other relational databases can function as data warehouses. There are very few “pre-built” marketing data warehouses out there. Anyone who knows of any others please comment.

Data Mining:

SAS: Enterprise Miner
http://www.sas.com/technologies/analytics/datamining/miner/

SPSS: Clementine
http://www.spss.com/clementine/

Oracle: Oracle Data Miner
http://www.oracle.com/technology/products/bi/odm/odminer.html


Campaign Management / Marketing Automation / MRM:

Aprimo: Aprimo Enterprise Marketing Management
http://www.aprimo.com/approach/emm.asp

Unica: Unica Affinium Suite / Enterprise Marketing Management
http://www.unica.com/

SAS: SAS Marketing Automation—more focused on analytics than process, but improving
http://www.sas.com/solutions/crm/mktauto/

Siebel: Siebel Enterprise Marketing—suite contains multiple marketing applications but focused on / started with MRM
http://www.oracle.com/applications/crm/siebel/enterprise-marketing/index.html


Lead Management:

Eloqua: Eloqua Conversion Suite—They have a “complete” suite but are strongest in lead management.
http://www.eloqua.com/ps/products_ecs.asp

Marketo: Heard from Jon Miller about his new on-demand marketing automation solution. He also mentions in his post that he thinks Lead Creation and Lead Management shouldn't be separated. I agree that a lot of software does both, but in my experience a lot of companies thinking of them separately, so I'm keeping them discrete. The site currently site says "In Quiet Mode"... hopefully more coming soon.
http://www.marketo.com/index.html

Sales Force Automation:

Siebel: Siebel Sales
http://www.oracle.com/applications/crm/siebel/sales/index.html

Salesforce.com:
http://www.salesforce.com

Microsoft: Dynamics CRM
http://www.microsoft.com/dynamics/crm/default.mspx

SAP: MySAP CRM
http://www.sap.com/solutions/business-suite/crm/index.epx


Web Analytics:

24/7 Real Media
http://www.247realmedia.com/

Elytics, Inc: Elytics Analysis Suite
http://www.elytics.com/products_overview.htm

Google: Google Analytics—free and very good…you have to think they’re going to be continuously improving and eventually offering an “Enterprise” edition
http://www.google.com/analytics/

IBM: IBM SurfAid Analytics Services
http://surfaid.dfw.ibm.com

Omniture: Omniture SiteCatalyst
http://www.omniture.com/products/web_analytics/sitecatalyst

SPSS: SPSS NetGenesis
http://support.spss.com/newSupport/ProductsExt/NetGenesis/ProductMatrix.htm


Reporting and Business Intelligence:

Cognos: Cognos 8 BI / Reporting
http://www.cognos.com/products/cognos8businessintelligence/reporting.html

SAS: SAS Business Intelligence
http://www.sas.com/technologies/bi/

Business Objects: Business Objects XI / Crystal Reports
http://www.businessobjects.com/products/

Microsoft Suite included Business Scorecard Manager, SQL Reporting Services, Office Live, etc.—note: Microsoft’s BI position is still quite confusing, but their overall portfolio of products is compelling.
http://www.microsoft.com/sql/solutions/bi/default.mspx
www.microsoft.com/office/bsm/

Vincent also alerted me to include Hyperion as a BI provider. Hyperion's Essbase and related products are mainly concentrated in the financial space, but they have some interesting unique technology. http://www.hyperion.com/products/
I was intrigued at the "Essbase Analytics" product which seems new (last time I worked in Essbase was four years ago) and seems to allow enterprise-level scenario modeling... could be interesting for mix optimization.