Finding Ready-to-Buy Customers Through Buyer Intent Prediction and Customer Behavior Forecasting

The winning recipe involves big data and sophisticated digital analytics tools, with a dash of old-school predictive techniques thrown in.

Behavioral forecasting or behavioral biometrics is increasingly being used to find businesses ready-to-buy customers and provide info as to what, when and how they’re likely to make a purchase. A study last year found 78 percent of marketers believe use of such “intent data” leads to better ad relevancy, while 67 percent believe it provides a competitive edge.

How does it work?

Much of intent data is automatically gathered via web tools that store info each time an online visitor types something into a search engine, clicks on a link, uses an online shopping cart, makes a purchase or otherwise takes action online. The info is then injected into predictive models that incorporate data sets from other strategic sources including CRMs, marketing automation platforms, social media, financial or customer support systems, industry trade publications, blogs and/or online forums and communities. The resulting intelligence allows for relatively accurate conclusions about pending customer behavior, and it’s considered particularly valuable when it’s “live” and based on immediate need. Behavioral forecasting is a relatively effective way of identifying new potential customers and customizing nurturing strategy to their characteristics. It also allows for highly fruitful ad targeting.

Some examples of companies succeeding at behavior forecasting:

  • AAA regularly analyzes more than 2,500 customer attributes to understand which members might buy its products and services.
  • Mercedes-AMG uses predictive analytics as part of a real-time quality-assurance platform that optimizes engine-testing processes during manufacturing.
  • Mobilink analyzes customer data involving demographics, subscriptions, billing, usage and social network activity, efforts leading to up to 380 percent higher campaign response rates, higher customer retention and wider adoption of its new products and services.
  • Working with Google Adwords, placed targeted online ads that resulted in 30 percent lower cost per click, 37 percent decrease in cost per acquisition and 26 percent higher click-through rate compared to a previous campaign average.

In sales, “Instead of relying on an individual salesperson’s opinion about where the customer is on an important deal, a sales manager can use the platform to view buyer actions, looking for the telltale burst of activity that typically precedes a purchasing decision,” explains Dustin Grosse on “Managers can use these insights to provide support on must-close transactions, allocating resources on the basis of actual data rather than guesswork. And sales reps can use the data … to view customer status from anywhere on any device, anticipate customer needs, and provide the right follow-up pitch to transform a looker into a buyer, tailoring the message to the customer’s current placement within the sales funnel.”

Challenges in behavior forecasting

  • Data from online customer behavior must be combined with other kinds of data to be most meaningful.
  • Customizing your predictive model to have the most meaning for your business or industry.
  • Gathering intent data at scale can be difficult when web users frequently remove cookies and/or use multiple devices to browse the web.
  • Some customers feel stalked by targeted ads and are concerned about related privacy issues.
  • In the aforementioned study, marketers mentioned being inhibited by inaccurate data (57 percent), an inability to combine data sources (49 percent) and an inability to feed intent data into targeting technology (54 percent).

The study concludes, “Basic shortcomings, such as lack of proper technologies and limited human resources, indicate marketers may not be fully equipped to benefit from intent-based targeting just yet.” Clearly, however, some marketers are not only equipped, but also moving at full throttle. Overall, the global behavioral biometric market encompassing hardware, software and integrated solutions (and related services) is slated to grow at a CAGR of 17.34 percent from 2016 to 2020.

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