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.

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.