January 25, 2016 | Written by: mrzimmerman
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On the heels of the National Retail Federation (NRF) annual convention and EXPO, the retail industry is abuzz about what trends will dominate 2016. One of the fastest growing trends in retail today isn’t a new shirt, shoe or accessory, it’s the “data mashup.”
This is the process of combining, correlating and analyzing different internal and external data sources, such as sales data, trend predictions, weather data and social data. Through mashups people can derive powerful insights from the data to improve sales and merchandising, streamline processes, better understand customer behavior, and drive success.
Data mashups gain their name from the music DJ practice of taking two or more songs and merging them to make a new mix. The concept of data mashups is not new, but advances in cognitive analytics and the ability to access multiple data sources in real-time, are putting this practice at the top of the 2016 trends list.
Forward-thinking retailers are beginning to use exogenous data such as weather data and data generated on Facebook or Twitter to create data mashups with internal data such as sales data. In fact, according to Gartner, over the next few years, leading organizations will link analytic initiatives to financial objectives, increase investments in advanced analytics, and incorporate a wider range of exogenous data. Gartner predicts that by 2019, 75% of analytics solutions will incorporate 10 or more exogenous data sources from second-party partners or third-party providers.
Now, because of the ever-present smartphone and social media, shoppers can access real-time weather updates alongside information on sales and new products. Shoppers can make purchases anywhere, anytime on mobile devices, and easily share opinions and reviews on products. As an example, consider how exogenous data such as weather data and social data impacted the 2015 holiday shopping season:
- 51% of holiday shoppers reported plans to get gift ideas from social media, while 50% of shoppers intended to find holiday discounts and sales through social media. (Deloitte University Press, 2015)
- Six of 10 consumers planned to check the weather forecast before holiday shopping. (The Weather Company, 2015)
All across the eastern U.S. holiday shoppers left their down jackets at home during their 2015 holiday shopping, while they experienced one of the warmest combined November and December on record. Eighty-four percent of the US population experiencing temperatures averaging 5.3 degrees warmer than normal.
This surprisingly balmy weather was great for holiday shoppers, but not so great for retailers. December is a make-or-break month in the retail industry, and unseasonably warm temps can torpedo profits and already-thin margins for the whole year. If the weather doesn’t feel seasonal, consumers are not tempted to buy coats or other winter gear.
Putting the Mashup To Work
Here are three examples of how retailers are starting to use data mashups, as well as cognitive and predictive analytics, to outthink mother nature:
- Pricing Models: Using predictive analytics to correlate and analyze weather data with social media conversations, sales and consumer buying patterns, retailers can garner insights to planning pricing models around spikes in product demands. For example, retailers can track weather forecasts for spikes in winter weather, indicating an increased demand for winter boots, while Twitter data can reveal customer sentiment towards a particular boot brand.
- Customer Engagement: Social media platforms such as Twitter have opened a world of opportunity for retailers to directly engage with customers. Moving beyond social listening and push marketing, leading companies are now using social platforms as “early warning systems” to understand how, when and why to engage with customers. For example, retailers can engage consumers with special deals and opportunities just for social media users and monitor and analyze relevant conversations to better understand customer’s likes and dislikes.
- Supply chain efficiency: One global retailer is using the combination of internal and real-time public data, including weather, competitors’ promotions, Twitter feeds, economic data and the news, to identify strong, yet counter intuitive, demand signals. It developed an algorithmic-based situation engine to provide exception forecasts for certain products whose trend and seasonal forecasts do not capture accurate projection. The result: The company fundamentally reoriented its massive supply chain to deliver merchandise based on these real-time forecasts.
The connection between consumers, buying behavior and the weather, coupled with social media, powerful predictive analytics and cognitive computing capabilities, is creating a vast opportunity for businesses to create their own data mashup, understand it and put it to use. The data mashup is and will continue to be a powerful strategy for retailers in an extremely competitive industry—not just during the holiday shopping season, but during every shopping season.