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Maximize Customer Lifetime Value Using Historical Data to Predict Future Buying Behavior

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Acquiring new customers is expensive and it is generally more cost-effective to sell to existing ones. The customer-lifetime-value concept is straightforward, but many companies struggle with determining how to put it into practice.

Do you actually understand your customers’ buying behavior and motivations and what you can do to influence them? Is this information being channeled into your marketing and product development efforts to help you design and position your products and service that will best serve customers’ needs?

Turn your data into actionable insights

By measuring and maximizing current and forecasted customer value across products, segments and time periods, you will be better able to design new programs that accentuate your best customers and provide you with a distinct business advantage.

With Actian Vector, you can connect all your data, from account histories and demographics to mobile and social media interactions, and merge these disparate sources with speed and accuracy. You already have a wealth of data that can reveal insights into your customers’ motivations, all you need to do is put the puzzle together.

Use this information to uncover key purchase drivers and understand why someone purchases or rejects your products. Assign customer value scores by correlating which characteristics and behaviors lead to value at various points of time during the future. Optimize outbound marketing to give prominence to your high-value customers.

Customize inbound customer touch points by arming call centers with highly personalized customer data. This will all lead you to increase customer lifetime value, improving both customer loyalty and profitability.

Predicting future buying behavior

Historical data won’t give you a crystal ball to peer into the future. There are many factors that can influence individual customer’s buying behavior and it is impossible to capture and analyze all of them. That doesn’t mean each transaction or customer interaction is unique and independent. Individual customers have preferences, buying behavior and social influences and use various types of environmental cues to determine what they will (and won’t) purchase.

Because humans are creatures of habit, past actions (often visible in historical data) are strong indicators of how customers will behave during the future. Similarly, different customers often demonstrate common behaviors and buying patterns when influenced by similar forces.

By analyzing the historical data of both individual customers and groups of similar customers, you can develop more accurate customer profiles, conduct micro-segmentation, identify sources of influence and model the actions your company can take not only to understand, but also to change customers’ buying behavior.

Obtain better insights with more diverse data

One of the biggest challenges companies face when analyzing customer data is leveraging data from diverse data sources. Using only one or a few data sources may provide you with multiple points-of-view, but it won’t reveal the holistic perspective of your customers you need to be actually successful.

Integrating more (and diverse) data into your analysis will help you eliminate blind spots, improve the accuracy of your findings and increase the end-results of your marketing efforts. Actian DataConnect is a platform that can help you gather your data-sources where the powerful analytics engine of Actian Vector can then distill them into actionable insights. To learn more, visit

About Pradeep Bhanot

Product Marketing professional, author, father and photographer. Born in Kenya. Lived in England through disco, punk and new romance eras. Moved to California just in time for grunge. Worked with Oracle databases at Oracle Corporation for 13 years. Database Administration for mainframe IBM DB2 and its predecessor SQL/DS at British Telecom and Watson Wyatt. Worked with IBM VSAM at CA Technologies and Serena Software. Microsoft SQL Server powered solutions from 1E and BDNA.

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