The “Big Data” Customer Journey

Mark Stanley | February 21, 2013

In the article, Advertising Analytics 2.0 that appears in the March 2013 issue of Harvard Business

Review, Wes Nichols makes the case that, "Marketers now have an unprecedented ability to fine-tune

their allocation decisions while making course corrections in real time." The idea in brief, “The days of

correlating sales data with a few dozen discrete advertising variables are over. Many of the world's

biggest companies are now deploying analytics 2.0, a set of capabilities that can chew through terabytes

of data and hundreds of variables in real time to reveal how advertising touch points interact

dynamically. The result: 10% to 30% improvements in marketing performance.”

Nichols lays out three broad activities:

• Attribution quantifies the contribution of each element of advertising.

• Optimization uses predictive analytics tools to run scenarios for business planning.

• Allocation redistributes resources across marketing activities in real time.

All three rely on big data, but we submit that the execution of Allocation activities also depends on two

critical factors:

1. Understanding customer behavior across multiple touch points.

2. Understanding the breadth (amount) and depth (skills) of resources available at any given moment in

time.

If we examine the journey customers take when they go in search of a solution it is quite interesting,

particularly when it comes to making major decisions.

Research Options

Many people will utilize social media to research options and solicit input from friends before making

major purchase decisions. They will utilize online sources to research products, read product reviews

and compare prices well before actually calling or visiting a retail location to make a purchase. In fact,

some estimates suggest that more than half of the buying process happens before the first direct

interaction between buyer and seller.

Linking the Touch Points

The challenge for retailers and enterprises is to identify and link all of the touch points consumers use

prior to making a decision, and more importantly, doing this in real time to engineer a desirable

outcome. At a macro level, customer interaction data can be compiled at various touch points and

compared to historical data models and marketing efforts in play to identify changes in customer behavior

patterns. At a micro level, tools are available to track and reveal the identity of customers at each

interaction point. When enough information has been collected, next-best-offers can be dynamically

served up to the customer to achieve the desired result.

For example, a banking customer logs on to the online banking portal to review information about their

mortgage. While there, they also check to see current rates offered by the bank. Later, that same

customer calls the bank. At this point, the bank already knows the caller’s account status and can infer

the call purpose. Additionally, the routing engine can perform a look-ahead to determine available

resources. So instead of generic options, the bank could greet the caller with, “It looks like you were just

checking mortgage rates online. Would you like to talk to a mortgage specialist about today’s rates?”

The real-time understanding of customer needs and available resources enables dynamic course

correction for marketing initiatives. Any company can begin that journey; businesses that don’t will be

overtaken by those that do.

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