Thanks to all-powerful smartphones and the myriad of other mobile devices available today along with the increasing availability of public Wi-Fi, “showrooming” by retail consumers continues to gain in popularity.

Showrooming – or the use of mobile devices by consumers to compare and contrast products and prices with online retailers during in-store experiences – poses serious challenges to retailers that depend heavily on physical outlets.

However, behavioral data demonstrates that there are also ample opportunities for both retailers and consumer packaged goods (CPG) companies to connect with the mobile in-store consumer.

For instance, while 42 percent of in-store mobile users ultimately made their purchases online, a full 30 percent of consumers made their purchases in-store, according to a study by the Interactive Advertising Bureau in partnership with Ipsos MediaCT.

Moreover, 32 percent of consumers who used their mobile devices while shopping were also more likely to make additional unplanned purchases in the store.

There are several effective ways that CPG and retail leaders can use consumer data and predictive analytics to strengthen in-store purchasing by mobile consumers.

For example, research by Flurry reveals that the amount of time spent by consumers on mobile apps each day has risen from 80 percent last year to 86 percent this year. What this means is that a high percentage of consumers are devoting significant portions of their time on mobile devices to the use of mobile apps.

According to an infographic created by TIBCO Spotfire, retail mobile app purchase revenue is expected to grow 417 percent to $31 billion among global digital consumers by 2016.

CPGs and retailers can draw off these and other insights – including the types of functionality sought in branded mobile apps – to provide customers and prospects with mobile apps that can increase engagement and conversion rates.

In addition, retail and CPG leaders can use behavioral and purchase data with analytics to extend the right in-store offers to the right consumers through the right channels at the right time.

As an example: Let’s say a personal grooming products manufacturer wants to engage female shoppers at a supermarket with an offer to purchase refill cartridges for a ladies’ shaver it makes.

Leveraging statistical algorithms, behavioral data for one set of shoppers indicates that a certain percentage of customers who use the company’s mobile app are most likely to respond to offers provided through the app before they enter the supermarket.

Leveraging statistical algorithms and data visualization tools can also help CPG and retail leaders determine that another subset of target customers is even more likely to respond to offers presented at a certain savings rate when they’re extended in-store via SMS.

Moreover, a high percentage of these same shoppers are also likely to purchase shaving gel as part of their purchases, thus increasing the profitability of those customers who convert.

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Next Steps:

  • Please join us on Wednesday, June 18, 2014 at 11:00 a.m. EDT, for our complimentary Webcast, “Data Discovery and Advanced Analytics for CPG & Retail.” In this webcast, you will learn how TIBCO enables advanced analytics in CPG&R with TIBCO Enterprise Runtime for R (TERR), TIBCO’s enterprise-grade implementation of the popular R language for statistical computing.
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