As featured in Practical Ecommerce; JANUARY 2, 2014 • GAGAN MEHRA
The holiday season has ended and the analysis has begun to understand what worked and what did not for ecommerce merchants. Cyber Monday became the biggest online shopping day in history with a 20.6 percent increase in sales over 2012, according to the IBM 2013 Holiday Benchmark Report.
Retailers are increasingly tapping into the avalanche of data from their own sites and from third-party sites to drive sales and better serve their customers. This article will address five key ways Big Data impacted the 2013 holiday shopping season.
1. Contextual Promotions
The use of Big Data has enabled contextual promotions — mostly real-time push notifications based on consumers’ social media activity, tracking their locations, or capturing their interactions on the web and mobile devices. This holiday season contextual promotions were heavily used. IBM’s Cyber Monday Report states, “On average, retailers sent 77 percent more push notifications during the five day holiday shopping period when compared to daily averages over the past two months.” Retailers invested in social media sites like Facebook, Pinterest, and Instagram (among others) during the November and December holiday season. This led to higher referral sales from these sources.
Several physical retailers, including Best Buy and Kohl’s, also deployed location-based promotions to push notifications while the consumer is in or near the store. Some retailers tracked consumers’ locations without their knowledge, raising privacy concerns. Other retailers required an opt in by consumers to receive these promotions.
Additionally, some retailers used mobile apps to send contextual promotions based on tracking shoppers’ activities on the app and their physical locations with using it. Macy’s and J.C. Penney, for example, partnered with Shopkick (a shopping app provider) during this holiday season to reward brick-and-mortar shoppers with discounts or song downloads for trying on clothes, scanning barcodes, or making purchases.
2. Gift Selection
Holidays are all about gift giving. Some retailers used their Big Data recommendation algorithms to make it simpler to select gifts. These retailers built predictive models that process data from multiple sources like social media, wish lists, gift registries, and past purchases to predict the right gift for an individual.
3. Personalized Customer Experience
Retailers have used Big Data to personalize their site content for several years. This was a competitive differentiator during this holiday season, however, as indicated by pre-holiday survey by Baynote, a personalized customer experience solution provider. The survey noted that eighty-one percent of retailers planned to upgrade ecommerce platforms to focus on customer experience, and to increase engagement, revenue, and ultimately lifetime value from improved relationships with shoppers. Retailers can categorize each shopper into a segment of one with its own customized landing pages, product catalog, campaigns, and even content. The result is an enhanced customer experience and an improved conversion rate.
Amazon.com continued to maintain its dominance in this space by using its extremely rich data set to personalize the shopping experience for its millions of shoppers. Another benefit from personalizing the customer experience is increased impulse buys, which become more important during the holidays as shoppers are in the right frame of mind to spend money.
4. Improved Customer Service
The holiday season results in more traffic for ecommerce merchants, which naturally leads to an increase in the volume of customer service issues. To keep customers happy during this time and manage customer service costs, some retailers implemented Big Data solutions to monitor customer activity and proactively respond to negative social media posts or issues. After all, one negative tweet can significantly impact business during this time of the year.
Real-time data feeds inform retailers in advance if customers will experience issues like a slow site, out of stock products, or delayed delivery. Retailers can either proactively correct the issue or notify the customers afterwards. Fab.com, for example, automatically credits a customer the difference if a price of an item drops immediately after purchase. T-Mobile USA has integrated Big Data across multiple IT systems to combine customer transaction and interactions data to better predict customer defections. By monitoring social media interactions with transaction data and billing systems, T-Mobile USA has reportedly reduced customer defections in half in a single quarter. Dell uses Big Data solutions to analyze real-time feeds from weather reports, delivery trucks, and orders to proactively resolve delivery problems before customers are aware of them.
5. Integrated Analytics
Most large retailers serve customers across multiple channels and devices. This makes it critical for those retailers to have a single view of all customer and product activity using data from all sources. Some retailers are already using such solutions and several more deployed such solutions before the holiday season. This one capability is crucial to track other Big Data uses, such as contextual promotions, gift selection, personalized customer experience, and improved customer service.