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Tailoring Big Data Initiatives to Predict Customer Behavior

By October 30, 2012Article

As customers continue to seek out individualized purchasing experiences, companies are beginning to realize that their Big Data initiatives must be optimized in order to provide the personalized level of service that customers demand. Though previously available analysis tools were sufficient to deal with the data that companies were collecting, the increase in Big Data is pushing businesses to update those tools in order to deal with the number of transactions occurring today.

Big Data provides information that businesses can analyze to spot real-time shifts in customer behavior and to facilitate stronger connections between customer, products, pricing, promotions and sales. For example, if a promotion does not yield the purchases for which the company was hoping, what can it do to increase sales with a different promotion? To discover meaningful relationships and trends within its data, companies must learn how to best acquire, analyze and act on this new information. The data can assist an organization in decisions to bundle services and solutions and determine pricing and packaging.

In predicting customer behavior, organizations should consider the following tips:

  • Data categories. Organizations typically divide data into two categories: transactional and sub-transactional data. I recommend focusing on sub-transactional data, or clickstream-based data, to identify patterns in user intentions. This data helps your organization make useful predictions about customers’ behaviors. For instance, simply knowing that Joe Smith purchased a jacket from X retailer isn’t as useful as understanding which items Joe looked at beforehand, what time of day or month he purchased it, which device he used to shop, or what promotions he used.  When companies analyze this kind of sub-transactional data, they are able to use this information to predict Joe’s future purchasing behavior.
  • Pricing model. Move away from subscriptions and toward consumptive pricing models. Tie usage to pricing and better match customer behavior to pricing. Using the Big Data solutions, companies can offer services at prices and packages that align with the customer’s actions, while offering the ability to upsell based on those actions as well. Ultimately you’ll be able to increase revenues, enhance growth and reduce churn while better satisfying customer needs.
  • Behavior-based customer offers. Use customers’ predicted behaviors to provide item suggestions and offers. Find an application that allows for the tracking of users’ clickstream behaviors. By including JavaScript code, your organization can create an application to track details about the user and her click-stream behaviors. By working with an application like, your organization can access real-time, in-app messaging or offers to customers based on their specific behavior patterns. This service provides the opportunity to create contextual in-app offers and monetize them immediately to drive more revenue. Think of transportation ticketing; trains, buses and airlines constantly adjust their prices based on the sale trajectory of a specific trip or route over a period of time based on a user’s reaction to the information.  What if you could dynamically address user demand as well?

Preventing customer churn

Improve customer satisfaction by preventing dissatisfaction before it arises. Use real-time data analysis and sub-transactional data to reduce churn. Customers lost to churn need to be replaced. That’s expensive. Keeping customer acquisition costs (CAC) at less than annual recurring revenue is important in today’s online commerce world. Sub-transactional data allows your organization to identify when customers begin to use your solutions less frequently or when they are about to make specific mistakes using your application. Analyze this information and put it to good use by fine-tuning the user experience to lead to more engaged customers.

Today’s cloud and SaaS-based business models often focus on subscriptions. Many industry experts express the mantra that subscription-based recurring revenue is an annuity stream that can drive big valuations for organizations, but merely relying on subscriptions is not sustainable. Subscriptions represent “dumb” transaction data that is inherently limited in providing a holistic understanding of customers. By taking into account a user’s clicks, downloads and views, organizations can adjust pricing in a far more flexible manner.

At MetraTech, we also see far more businesses moving beyond simple subscriptions to consumptive-based business models. Consumptive models are rich with fat, big user-data that can be mined and leveraged for additional revenue.

Acquiring, analyzing and acting on Big Data isn’t as complicated as it sounds. Begin by introducing Big-Data-activity-based capture into your online solutions. Track user behavior and provide offers and options based on those actions. Then introduce a modern commerce and compensation engine to act upon and monetize your insights. Your organization will be able to model and test nearly any pricing model and quickly respond to your users.

The future of commerce belongs to organizations that know their customers not just on a superficial, persona-based level but on a personal one. Winning businesses will leverage how customers speak through their purchases and usage patterns, and those businesses will respond with pricing and offers based on real-time awareness.

Scott Swartz is CEO of MetraTech. Scott founded MetraTech in 1998, after spending time at NetCentric, an early entrant in the business of cloud computing and where he created the industry’s first SGML/XML billing protocol. Prior to NetCentric, Scott was a Director at Cambridge Technology Partners where he led the deployment of complex customer care, billing and logistics solutions for Fortune 100 and 500 companies.

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