Shopping Cart Analytics Thirty-five years into frequent flyer programs, airlines now calculate that customers who spend more are more valuable than customers who fly more. The economics are straight forward and the meaning of loyalty has forever changed. Selling products at discount to retain ‘loyal’ shoppers is not sustainable.

The nature of loyalty depends on the economics of the business…. Improving the loyalty of bargain hunters rarely delivers sustainable value. — HBR, Mar 2015

IDP delivers data-driven marketing solutions like shopping cart analytics with a focus on ‘invasive’ data management and deep-dive data mining. The economics of loyalty continues to evolve. And marketers depend on shopping cart analytics to monitor shopping behavior, to measure & manage customer loyalty, and to engage loyal customers.

When loyalty involves bribery, it’s bad for business. Confusing loyalty with retention and rewards undermines brand equity more than it creates new opportunities. — HBR, Mar 2015

Shopping Cart Analytics — Benefits

Shopping cart analytics measure, among other things, customer responses to promotions and the value of market baskets. It’s not about what items sell, but what combinations of items and the profit from those combinations. Marketers measure both product and customer profitability, and the value of loyalty.

Ensuring that discounts pay off as investments is crucial to maintaining a fair company profit. Especially true if offering discounts in order to draw customers rather than maintain them — American Marketing Association, Nov 2015

By examining responses to promotions and item affinities – together – marketers move beyond analyzing sales to unlocking marketable insights. Finding a link between products and customers means one targeted promotion can drive sales in other categories, growing basket spend without bribery.

Shopping cart analytics help refine price and placement targets – actions that grow basket spend. Marketers repeat shopping cart analytics at regular intervals to gain even greater value. They validate and anticipate shopping behaviors – unlocking insights before customers reach the check-out.

Shopping Cart Analytics — Challenges

Shopping cart analytics are not rocket science, but require ‘invasive’ data management and deep-dive data mining into transaction detail over a time period long enough (25 months) to consider trends. One of the biggest roadblocks is the maze of data silos, keeping shopping cart analytics out of reach for all reporting tools and most business analysts.

Organizations are tantalizingly close to a vast amount of data that can prove meaningful to business. But it is overwhelming. Somewhere, within that blur is the insight that could change your business. You itch to explore it, but the best your traditional systems can offer are standard, pre-packaged reports. For most of us, the data is unfortunately scattered across dozens of unwieldy and sometimes unreliable spreadsheets. — InsideBigData.com, Sept 2015

The challenge for marketers is using all their collected intelligence to effectively engage customers. Both data volumes and data velocities challenge marketers. Many aren’t sure of the data they have. They don’t believe they have the data they need; and what they have, they question the quality. Or they can’t access. Finally, business analysts, who aren’t trained to manage complex data, create error-filled or incomplete analytics. So marketers fail to get the insights they need, and lose confidence in both reporting and data.

Shopping Cart Analytics — IDP

Shopping Cart AnalyticsEffective use of customer data is a competitive weapon. And the foundation to actionable shopping cart analytics is accurate, reliable supporting data.

At IDP, we help marketers control their data, their analytics and their marketing technology.

We remove the requirement that business analysts manage large and complex data volumes. We bridge the gap between data management and analytics. So analysts focus, not on data, but on making recommendations from analytics. We manage those large data volumes with complex structures. We integrate disparate data sources and build rock-solid analytical data sets.

Finally, we always collaborate with marketing to generate the right analytic methodologies that produce marketable results.

When the supporting data is rock-solid, then shopping cart analytics become much, much easier!