Big Data

Measure actual customer behavior using big data analytics

Discover how measuring actual customer behavior instead of behavioral intent can dramatically increase your marketing effectiveness.

The consequences of good intentions

If you're trying to understand customers' behaviors, perhaps the last people you should ask are your customers.

CustomerBehavior_thumb_082213.jpg
A traditional marketing approach for predicting consumer behavior is to elicit behavioral intention instead of actual behavior. Frederick F. Reichheld, developer of the very popular Net Promoter Score (NPS), based his loyalty system on a customer survey that asks how likely they are to recommend a company's product or service to their friends.

While this is nice to know, if I'm responsible for my company's marketing strategy, I'm more interested in whether the customer recommended our products to their friends, if their friends bought something from our company as a result, and if their friends had a good experience dealing with our company. In order to get this information, you'll have better luck dealing with your data scientists than your customers. When it comes to understanding and, more importantly, predicting consumer behavior, your big data analytics team will have better answers than your customers.

The feasibility of collecting data on actual consumer behavior

A big data analytics approach that studies actual behavior should supersede the outmoded approach of focusing on behavioral intention. The underpinnings for the behavioral intention approach come from an expectancy-value model known as the Theory of Reasoned Action (TORA). In this model, behavior is a function of behavioral intent, which is in turn affected by attitudes and subjective norms. The idea is to affect behavior by adjusting proposed levers that affect attitudes and/or subjective norms. When skilled marketers tell you that, "Friends don't let friends drive drunk," they're attempting to adjust your normative beliefs through your peers. The classic measurement system for this model is behavioral intention -- not actual behavior -- chiefly because it has traditionally been the easiest information to collect. Collecting data on actual consumer behavior has typically been unfeasible and impractical -- until now.

The emergence of the Internet, e-commerce, and social media has radically altered the landscape of available consumer behavior data. Cash registers and Point-of-Sale (POS) systems are being replaced by e-commerce sites that record every move consumers make -- even when they don't buy something. Casual telephone conversations with girlfriends about recent purchases are being replaced by tweets that can be scanned and analyzed by anyone who follows those Twitter feeds. All of this wonderful data on actual consumer behavior and experiences is there to be measured and analyzed, but there's a catch: You must embrace the new way of thinking that customers can tell you what they think, while data scientists can tell you what those customers actually do.

Big data and social media analytics

The strategy to employ with your big data strategy team is to measure and analyze actual behavior. There's nothing wrong with TORA or measuring behavioral intention for that matter, though the key performance indicator (KPI) centers on actual behavior.

If you sell products online, your big data analytics team should look for digital behaviors that are conducive to your strategic objectives. I specialize in loyalty marketing, so when I'm trying to help a client build a loyal customer base, I look for digital behaviors that indicate a statistically significant level of engagement. Ideally, this is something I can measure directly from the operational data (e.g., web logs). I don't need to ask customers if they love my client's products, because I can see it in their digital behavior.

Big data analytics can also help in understanding the conversation that's happening around your customers' experiences with social media analytics. This is similar to surveying for behavioral intention, but it's more authentic. Although focus groups are designed to elicit open and honest feedback from your customers, it's much better to eavesdrop on a social conversation about your products in real-time.

Coalescing all of this consumer data helps build the bigger consumer experience picture that you should strive to attain. However, none of this is possible without engaging a sharp big data analytics team that can crunch through the complexities of the raw data and translate it into consumer insights.

Conclusion

Today's marketing strategy requires 21st century thinking, which unequivocally involves big data analytics. Behavioral intention is an outdated proxy that's superfluous now that actual behavior can be measured. Measuring actual behavior instead of behavioral intent will dramatically increase your marketing effectiveness.

You should take some time today to see if your big data analytics team can model desired consumer behavior from your operational data. And, abandon the road paved with good consumer intentions -- I think you know where that leads.

About

John Weathington is President and CEO of Excellent Management Systems, Inc., a management consultancy that helps executives turn chaotic information into profitable wisdom.

1 comments
eduj45
eduj45

Good article. However, big data analytics can only be used to understand/predict consumer behviour from past behaviour (only when a purchase has been made) and can hardly understand the underlying motivtion; while TORA and other behavioural intention models can predict even when the buyer has not purchased.

Editor's Picks