The consequences of good intentions

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

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.