Christmas morning, birthday afternoon and the “go live” date for your customer intelligence platform have something in common: you finally get what you have been waiting for. For the former, it is the toy you have been eyeing at the store for the last few months, and for the latter, it is the integrated view of the customer that will allow you to develop an unprecedented understanding of customer behavior.


An analytical plan is a great way to ensure adoption of the new system, meet ROI expectations and, most importantly, have a good answer when your boss asks, “What did we learn today?” as opposed to being caught off guard.


The analytical plan is a way for your customers, marketing staff, sales personnel and service analysts to think through how they will leverage a new data warehouse, data mart or analytical application. It forces the organization to think about the following: How was the business case originally constructed for customer intelligence? What are the current business challenges that the organization is facing? What have we always wanted to know about our customers? What do we need to know about the performance of our customer programs?



An analytical plan can be broken down into a hierarchy:

  • Mission statement. The mission statement exemplifies what you intend to do with the data assets and application that have been presented to you. This mission statement should not be too aspirational but rather very focused on the next 30 to 60 days. Statements could include, “I want to know the top-performing marketing programs in the organization,” or “I want to know our 100 most profitable customers.”
  • Analysis. For each mission statement, you will have one or many types of analysis you’ll need to perform in order to reach your goals. These analyses could be free-form exploration or reports. Your analysis might include, “Identify the percentage increase in spend year over year” or “Compare responders versus nonresponders for the last 30 days of campaigns.” These analyses turn into miniprojects for your analysts.
  • Data assets. For each piece of analysis, you should identify the types of information you’ll need in order to complete your analysis. For each piece of analysis, you’ll have one or more data categories or subject areas you’ll need to fulfill. For instance, to understand marketing performance, you may need the following subjects: campaign, promotion history and sales. The data assets ensure that your mission and analysis are feasible and can be accomplished within the current scope of your customer intelligence implementation.

This hierarchical pyramid will reflect the types of projects needed to accomplish your goals and how your analysts will carry out those projects.


For many organizations, the integrated view of their customer base is a data playground in the waiting for months or, for some organizations, years. Though everyone would like to have predictive modeling, real-time product recommendations and/or lifetime customer value, there are simpler ways to profile your customer database. Typical starter analysis involves these items:

  • Customer value. Not everybody needs to identify customer profitability down to the penny. A simple proxy for customer value, such as revenue, average order size or average yearly spend, can be used to help companies understand the differential value in their customer base.
  • Brand engagement. Brand or customer engagement can be measured by identifying key customer behaviors, including how many different product categories are purchased, how many different channels are leveraged or how many products within a product category are used. These types of metrics help companies understand the breadth and depth of relationships with their customers.
  • Migration analysis. Once a company understands who the high-value or heavily engaged customers are, the next step is to understand how they got there. With this knowledge, the company can put together targeted marketing programs to create incentives for other customers.

The success of your customer intelligence applications will be determined by the usage levels of your users. With this much information available at their fingertips, it is natural for users to become overwhelmed. To help counteract this pitfall, the analytical plan ensures that users are ready for an all-out assault on the database as soon as it is ready. They know what reports, analysis and data they need in order to provide value within days of the system going live. It doesn’t even really matter if they finish their entire plan. What matters is that they start it.

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