big data changes shopping journey

When retailers and fashion apparel brands roll out a new campaign, remodel a store or launch a collection, measuring success often depends upon what metrics the company’s leadership feels comfortable using. From in-store traffic and click-throughs to top-line sales and gross margins, the metrics vary and frequently take time to analyze.

But recent technologies have been introduced in the market that allow retailers and brands to crunch reams of data to get an immediate read of a campaign or program’s success. Here, Linda Kirkpatrick, executive vice president of merchants and acceptance at Mastercard, discusses the technology and how it deployed the service provider APT.

WWD: What exactly is APT, and how can retailers and brands use its solutions to make better-informed business decisions?

Linda Kirkpatrick: Applied Predictive Technologies is a leading cloud-based analytics provider empowering organizations to make data-driven decisions through its software. In the spring of 2015, Mastercard acquired APT.

Specifically, APT’s Test & Learn platform helps companies tailor investments and maximize bottom-line impact by harnessing analytics to design, measure and calibrate marketing, merchandising, operations and capital initiatives. Whether it’s a remodel or a new promotional strategy, the Test & Learn software reads a program’s true impact, and recommends the most profitable action to take going forward.

As part of the company’s services portfolio, APT has access to Mastercard’s analytics suite, consulting capabilities, marketing services and global footprint, which have expanded its reach and value since the acquisition.

WWD: How does the APT model of testing work? What are the steps?

L.K.: Per the above, Test & Learn can be used for a wide variety of initiatives, such as remodels, new promotional strategies and more. When a retailer wants to test a new in-store display, it can trial it in some stores and not others, then use APT’s software to conduct tests versus control analysis and determine the true impact of the new display.

Findings from this analysis enable executives to make data-driven decisions (e.g. decide whether to roll out the new display). Further, the software will find out where a program works best and how to roll out for maximum impact (e.g., does the new display work better in bigger stores or smaller stores? In urban stores or rural stores? And, in which stores in the network can we roll it out to get the most impact?). Through APT, users can rapidly analyze massive data sets, drilling down into each metric, category, or location for answers to their most pressing business questions.

WWD: Can you share any case studies or success stories of companies that use it?

L.K.: Before launching its new Smooth Boost plunge bra for its #ImNoAngel campaign, Lane Bryant needed to know how popular the bra might be and how many to produce. The apparel maker turned to statisticians at Applied Predictive Technologies.

The company put Lane Bryant’s bras to the test in a number of representative stores to see how well they sold. Lane Bryant tests hypotheses about regional tastes in a strategic few of its stores, then rolls out products regionally according to consumer preferences.

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