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TIBCO Spotfire's Business Intelligence Blog


Using Predictive Analytics To Become a Real-Time Social Retailer

Consumers rely heavily on their social media connections to drive their purchasing decisions, from the brand and product reviews they glean from complete strangers to feedback that’s shared by their friends and families.

shutterstock 110754098 300x225 Using Predictive Analytics To Become a Real Time Social Retailer Indeed, 72% of consumers trust online reviews as much as personal recommendations, according to Search Engine Land.

Meanwhile, the messaging that companies generate in social media channels about their products also carries a high degree of influence.

Some 78% of consumers say that posts made by companies in social channels influence their buying decisions, according to a study conducted by the CMO Council and Lithium.

Because consumers use a variety of channels to conduct research about products and services before making purchases, retailers can use predictive analytics along with the sentiments consumers share on social media and other channels to guide real-time decision making.

This includes providing offers and messaging to consumers based on what’s known about them (e.g., purchase histories, product interests, most recent web sites and product pages visited, customer lifetime value, product interests shared in social networks, key influencers in their social networks, etc.).

For instance, many customers who enter a retail outlet to scope out products will use their smartphones to compare prices for the same products offered by online retailers through the practice known as showrooming. Showrooming has become so commonplace that the practice has jumped 156% in popularity over the past year, according to a study conducted by marketing firm Vibes.

While customers are showrooming, many will also check social channels to determine whether other consumers have had positive or negative experiences with the products (quality, longevity, etc.). Forward-thinking retailers can mine this type of information in real time and meld these insights with other information that’s known about particular consumers to present them with immediate offers or personalized messaging.

For example, predictive analytics can be applied against individual consumers to determine the attributes in the product selection cycle that matter most to them (price, quality, etc.).

If a particular consumer is deemed to be price sensitive, the retailer can use these insights to create an offer at a price that provides a high statistical probability that he will accept it based on the acceptance rates of comparable consumers and other factors.

Retailers can also glean insights from larger groups of consumers and the information that they share in social media to help guide decisions about merchandising and inventory.

For instance, a growing number of retailers will begin stocking what consumers are tweeting about or pinning on their Pinterest accounts, according to Bernard Luthi, chief marketing officer and chief operating officer at e-commerce company Rakuten.

“Savvy retailers will use social shopping communities as a temperature check for popular product trends and use this insight to inform and refine stocking decisions accordingly,” says Luthi.

This way, executives for an upscale men’s fashion retailer can use predictive analytics to determine the types of blazers, ties, slacks, dress shirts and other garments that customers and prospects in their target audience are buzzing about in social media and act accordingly on these insights.

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