Looks like Magic: The Case for Customer Centricity in Analytics
The following is an excerpt from the new “It Only Looks Like Magic” book from the Merkle team of analytics professionals. Download a preview chapter and learn more about the book at the Merkle consulting website. Congratulations to the authors for a well-done analytics guide.
The Case for Customer Centricity in Analytics
Just having analytics, even good analytics, isn’t enough to take advantage of this new era of customer-centricity. It’s critical to have the right type of analytics. Do you have the analytics required to support customer-centric marketing or just to support campaign-centric or channel-centric marketing? This distinction is a fundamental difference in approach. It impacts everything from the granularity of the data being analyzed to how campaigns are executed. The distinction is most apparent in the analyses that are done to inform digital marketing decisions.
For example, a campaign-centric analysis might identify which email newsletter topics have the highest click-through rates. A customer-centric analysis would identify which topics are most effective in retaining customers. A channel-centric analysis might identify the top viewed pages, but customer-centric analysis would identify which pages have the greatest impact on conversions.
According to Peter Fader of the Wharton School, University of Pennsylvania, being customer-centric means embracing the concept that all customers are not created equal. This may seem like an obvious statement, but most companies do not truly differentiate the customer experience based on customer value, despite the demonstrated fact that a company’s financial success is not equally distributed among its customer.
The challenge starts with analytics. Customer-centric analytics is precursor to customer-centric marketing. Knowing your high-value and low-value customer is a prerequisite to being able to differentiate their customer experience. So why is it so complex and difficult to do customer-centric analytics?
Digital data often disappoints. Although the web has been referred to as the most measured channel, web analytics data is difficult to use in customer-centric analysis. Most web analytics tools are designed to analyze aggregate -level data. Tying website behaviors to individual customers in a way that informs future interactions with those customers usually requires raw weblogs and sophisticated ETL processes to parse the weblogs, match customers and then link to the customer database. Even simple customer-centric analysis often require exporting data and analyzing it in a statistics package such as SAS or R.
The implications multiply as the channels being analyzed multiply. Only a truly customer -centric analytics approach can address multi-channel (online and offline) attribution and experience delivery.