Leading Retailers Relying On Analytics Tools To Respond To Shifts In Holiday Demand

By John Gaffney
The dynamics of the current retail landscape are fast and furious, with the next few weeks sure to turn up the heat even more as retailers look to make up for lost sales after a slow start to the holiday shopping season. Many experts are predicting customer data analytics, and the optimization processes that result from it, will play an essential role as retailers try to maintain their agility.

Leading online jewelry site Ice.com, which has been in business since 1999, is banking on data to help it get through a “tough” year,” according to Pinny Gniwisch, Vice President of Marketing. Next week the site will add a proprietary customer review application. “The data we collect helps us get to know our customers and helps us interact with them,” he says. “We used to manually search through shopping carts and wish lists to get customer data, and while that can work, it’s not automated. Now it’s automated. All the successful retailers that I track are using data to cut acquisitions costs and drive sales.”

Industry insiders predict data analytics will give leading edge retailers a competitive advantage on many fronts this holiday season. “Very simply data makes retailing more customer-centric,” says Alexi Sarnevitz, senior director of retail strategy for SAS. “That’s important in any environment, but it is essential in a tough economy.”

From a pricing perspective, Sarnevitz believes the stronger retailers will be able to quickly evaluate Thanksgiving weekend sales, and then make adjustments within days. From a marketing perspective, he expects that campaigns will be optimized or pulled based on the data generated by them. Many retailers have begun this process already and are looking at agility as a key to surviving this holiday season.

While SAS has been working closely with leading retailers on predictive modeling and predictive analytics solutions, Sarnevitz points out that past behavior counts too. Retailers who rely closely mine their data will be in better shape from an inventory perspective because their stock levels have been driven by data, not supplier relationships or past year comparisons. An example of how inventory optimization can be seen in recently published case study on Waitrose, a leading UK supermarket chain. Using SAS solutions to achieve accurate demand forecasting, Waitrose was able to realize more efficient stock ordering, delivery and replenishments. Waitrose also reduced its stock holding by 8%, its waste by 4% and was able to improve customer satisfaction significantly by ensuring product availability.

“Data needs to be managed end to end,” says Thomas Redman, author of Data Driven. “Retailers have to put good data in the system, and they will be rewarded with the ability to understand their customers. Those customers will still be here next quarter. All the good things that data brings to a business will be here next quarter too.”