• by Tim Altier , December 20, 2011

There’s no shortage of data -- it’s proven data conversion that is in short supply. 

As a data analytics professional, every day I’m faced with finding the ways to make customer data pay dividends with increased sales and improved customer engagement. And I know lots of people who are interested in the same thing but don’t know where to start.

Maybe it’s counterintuitive, but the better the data management, the better the emotional connection with the customer.  A data dive into a customer’s history, purchase habits and channel preferences is like a treasure-trove that can be utilized to enhance the connection with the customer.  And the beauty of that enhancement is that you’re building a foundation for future purchases and even brand advocacy. 

Building a data management system that is streamlined for sales conversion and customer engagement isn’t a simple proposition. Here’s a proven set of guidelines for managing data for maximum conversion:

1.  Smart data collection. Sometimes, we have to really excavate to find all available sources for data.  It’s often hidden or neglected within customer service databases, warranty registrations or sales contact data.  These are frequently rich veins of data ore that can be utilized to support your data program.  In some cases, it’s possible to complement your proprietary data sources with publicly available ones, such as governmental registrations or census data.

2.  Integrate. Data from multiple subsidiaries or software platforms can’t reveal their secrets until they’re blended and streamlined.  Whether you build your own data integration system using off-the-shelf software solutions or outsource the process, it’s a critical piece of the data puzzle.  Skillful integration whacks down your organizational silos and repurposes data from multiple sources into a facile and wonderfully useful data system. 

3.  Mastering the analytics. With the right data management system, data becomes smart.  It can learn in ways that guide your conversion analytics.  You now have the opportunity to record customer interactions into the database, refresh model scores, apply segmentation algorithms and execute decision trees – all designed to deliver the right communication to the customer or prospect at exactly the right time.

An example is our work for a construction equipment company.  We performed an intense analysis of existing and prospective customers. We found one set of customer tendencies and requirements when an owner-operator was the buyer and going to be occupying the seat of his equipment. His interests were comfort, handling, price, reliability and similar measures. But it was another story when the decision-maker was the CFO of multiple construction sites headquartered hundreds of miles from where the equipment was going to be used.  The purchase instead pivoted on factors such as total cost of ownership, throughput capacity and repair and maintenance intervals.  This data  drove the marketing programs to appeal to each audience segment.

4.  Measure everything. With the right system, you have the opportunity to continuously optimize your marketing.  Track every creative tweak.  Measure every campaign against a control group.  Experiment with new channels.  Your interaction with customers will lock into customer needs and expectations like a set of perfectly-meshing gears.

5.  Build a data analysis culture. Data analysis pays for itself, and then pays dividends in your organization because of its measurable ROI. It takes a while to create the culture. Senior leadership commitment to analytics is the first step. Breaking down walls between siloed organizations -- both communication and data-sharing -- will build momentum. A central repository for data will help create a “single source of truth,” so all are singing from the same song sheet. Decision-makers will start to see the benefits and pay-back. From there, it’s easier to build data analytics into every marketing activity and function.

By skillfully managing customer data, marketers can create an analytics infrastructure that feeds intelligent data into marketing programs, helping create an emotional link to the customer. After all, what’s the downside of a marketing program driven by a comprehensive knowledge of the customer?