B2B Customer Intelligence - Flexibility

This is the second post in a new Customer Intelligence blog series. Please add comments if you’d like me to expand on any of the points, or have suggestions for other topics. I also welcome any feedback on the content.

Customer Intelligence takes a lot of work. To implement it properly you or your company will need to invest time and resources on technology, data quality and ETL projects. You will also need to hire or contract with analysts who will help to derive the insights needed to properly target your best customers and prospects.

As soon as you’ve invested all of that money, the target will change. Sales goals are redefined yearly at most companies, and even the most stable change companies their approach every couple of years. The Customer Intelligence program you’ve defined needs to flex with your corporate goals to be effective. Rather than build a rigid model that’s engrained into your production systems, a flexible model will allow for longer term success and save money.

Strategic Flexibility

From your first brainstorming session on Customer Intelligence, you need to think about how the program can meet multiple corporate goals. Let’s say that today your company is dedicated to growing it’s customer base through prospect acquisition. If you’re successful at that approach, your company will likely mature into a customer retention focus over time. Seems logical, right? So from the start how do you create prospect scoring approach that will also translate into customer lifetime value models. Which factors are shared in determining prospect conversion and customer longevity? If you were the CEO, which direction would you take the company in next? Now ask others in the brainstorming session what they would do. Set 3 or 4 targets and be sure that your customer intelligence plan can support all of them with little adaptation.

Insourced vs. Outsourced Analysts

One of the first decisions you will need to make is who is going to do the work. I assume if you are reading this that you’ve got a solid background in data quality, project management, and a few other relevant skills that will vary from reader to reader. Are you a PHD statistician? I’m not. Are you willing to spend 80 hours next week writing ETL code? Having done that for a few years (anyone else remember clicking “run” at 2AM on a 4 hour denormalization package, then cuddling up under their desk for a nap?) I’m happy to hand that work to someone else. You will need support, and it will be expensive.

Depending on the size of your Customer Intelligence program and the size of your company, you will need to decide on the best approach. Personally, I’ve used both internally staffed and external analyst firms and I’ve had success and failure with both. I’ve found the key is to have more than one modeler working on the problem to share ideas and help you keep track of the other. There’s no bigger wake-up call than hearing from a statistician that their peer is incapable of doing the work assigned. This stuff is really hard. Not everyone who has a good resume can build a model from scratch that drives your business to the next level.

To stay flexible, you need to be able to grow your team quickly during development and then shed excess analysts while Customer Intelligence programs prove their success. I would recommend a hybrid aprpoach with some full-time employees to maintain models and external consultants available when new models are needed or when your internal folks hit roadblocks.

Developing a Library of Models

Another key to Customer Intelligence flexibility is to build your models on top of a standard data set that can be used over and over again. There are nuances to using corporate data. It’s time consuming to build a dataset of input variables for a statistical model. There are issues based on business definition, geography, time calculations, and data sparsity that all require both business analysis and technical work to get right. By building a single set of denormalized data (think one big table where the transactional keys have been resolved to their text values in the data set) you will save time and money the next time you try to build a new model. Of course you can add variables over time, and even include different versions of the same values. But by building it once and using it repeatedly you will get more results with less effort.

B2B Customer Intelligence Series - Introduction

My job title and primary focus is Data Governance, however the data I spend the most time managing is B2B data. As a former consultant, I constantly find myself suggesting new applications to drive Sales and Marketing programs. Sometimes it’s as basic as dipping into database reserves to find new prospects, but often my extracurricular projects are pure Customer Intelligence, also referred to as CI. Customer Intelligence can cover a lot of ground, so let me define it.

Customer Intelligence is the intersection of Sales, Marketing, and Analytics that helps present the best customers and prospects to inform company strategy and tactical approach.

Customer Intelligence work manifests itself as Analytic projects including predictive modeling, cross-functional leads programs, and Sales and Marketing strategy projects. In most companies I’ve worked with, Customer Intelligence is distributed across several functions and that group collectively defines the companies Customer Relationship approach. In others a central group focuses on Customer Intelligence and coordinates the distribution of related information to drive strategy.

Other companies have no Customer Intelligence. They are Customer Ignorant. In that case, the Sales and Marketing teams approach customers based on generic approaches and anecdotal history of which customers are the “best”. With good products and excellent customer service, this approach can work. However, over time it will open the door for competitors to take over and dominate the market.

Here are a few thoughts for the upcoming series. The plan is for each of these topics to be developed as an entire post over the next few weeks. If you have other suggestions, please comment on the post or tweet a reply to @markgoloboy.

1) Customer Intelligence Flexibility

No matter how successful it is, every sales strategy will be retired someday. It may be next week. It may be next year. But be sure, the target will be set somewhere other than it is today. How do you build a Customer Intelligence strategy that allows your organization to stay agile?

2) Aligning Against the Opportunity

Once properly implemented your Customer Intelligence approach should become an integral part of the sales reps’ day. How do you integrate the information with your existing processes and systems to align against the opportunity?

3) Direct Marketing Impact on Sales

Customer Intelligence can help define a set of high value prospects that deliver return on Marketing investment. Determining which factors influence purchasing requires advanced analytics. What’s the best way to develop analytic models and measure whether they predict a prospect’s likelihood to become a customer?

4) Defining Lifetime Value

Determining Lifetime Value (LTV) of a customer and potential LTV of a prospect can align Sales, Marketing and Product goals with corporate strategy. However, LTV analysis requires that your product and sales data has been collected consistently for your company’s history. How do you develop and use LTV to drive product direction and Sales and Marketing focus?