Research Paper on Real-time Retail Analytics
Recently I have gone through a white paper which was published on Real-time Analytics. I found the details interesting and just thought of sharing with Retail world. I hope it will be useful for others.
It is always being challenge to be a profitable retailer and remain profitable. Increasing price transparency and online competitors are creating intense margin pressure. The showroom trend where consumers look at products in a physical store still there but buying the product online is also growing. Consumers are becoming more dynamic, increasingly mobile and harder to target and retain.
In response, the world of retail is becoming ever more real-time. Dynamic consumers want product information, order status and even products delivered in real-time. To retain and grow customers, retailers need to make cross-sell and up-sell offers, gather and use feedback in real-time. Even supply chains are moving to real-time as retailers optimize assortment planning, merchandising, pricing and allocation to maximize the return they get on their stores and inventory.
As things happen faster, as more decisions get automated and as the volume of information involved explodes, it becomes impossible to make decisions as rapidly and as accurately as is needed without a fundamental shift in the way decisions are made and the way data is used. If retailers try and apply their historical rules of thumb or use simple after-the-fact weekly or monthly reports they will not be able to make the quality of decisions required for success in the time available.
The use of real-time analytics is becoming central to retail success. Both by focusing on consumers and by driving operational efficiency, real-time analytics are making the difference in retail.
Retail Success by a Real-time Analytics
From segmenting and targeting customers to optimizing prices and markdowns, from targeted offers and customer retention to predicting which sizes will be needed where, analytics makes a rapid, accurate, response to consumer demand possible. Retailers across categories (food, soft, hard, big box) have made a commitment to analytics. Companies like Cabela’s, CVS, FreshDirect, Home Depot, HSN, Hudson’s Bay, Macy’s, Overstock.com, Target and Tesco have invested in analytics to improve business results.
New Approaches for Real-Time Analytical Decisions
Analytics help retailers improve their decisions and thus their key drivers of profitability. Faster decisions are more valuable. A successful retailer must therefore bring analytics to bear even as it makes decisions more rapidly throughout the organization. As their decisions move ever closer to real-time it becomes increasingly difficult to use traditional analytical techniques such as reports or backward-looking analyses run in batch. For truly real-time decisions it also becomes difficult to rely completely on human decision-makers. Real-time decisions put pressure on retailers to use systems not just to store the data but to determine the decision to make and to act on that decision. Retailers need well informed systems that can rapidly make decisions themselves and support more rapid decisions by decision-makers.
These systems require new kinds of analytic infrastructure. To make decisions faster, data must be integrated and analyzed much more rapidly using advances in multi-core and in-memory computing. These technologies allow huge amounts of data to be analyzed quickly enough to be used interactively and to support analysis of up to the second data.
- For human decision makers, advances in visualization and self-service analytics allow even ad-hoc and urgent responses to be based on solid understanding of the data.
- When systems must respond themselves, without relying on a human interlocutor, data mining and predictive analytic models can be used to drive analytic understanding into real-time decisions for straight through processing in operational systems.
Mobile knowledge workers can be empowered to make on the spot decisions in real time using new mobile interfaces. Mobile devices can also deliver fit for
purpose “do this next” systems that analytically determine the right response for front-line staff without the time or skills to use interactive tools.
- Automated systems that embed analytics can make decisions about content delivered to consumers no matter what the channel or circumstance.
- Using these technologies retailers can improve their understanding and make decisions at a more fine grained level. They can deliver these decisions more quickly, act on their understanding more rapidly to increase the value achieved. Such real-time, analytic decision-making can be applied both in consumer-facing decisionsand in operations.
Focus on Consumers with Real-Time Analytics
Consumers increasingly expect loyalty programs, targeted offers that are relevant to them personally, and retailers that act consistently and intelligently cross channels. Some retailers and e-tailers are already delivering excellence in their consumer-facing programs using analytics to ensure finely targeted offers and compelling loyalty programs. These programs raise the expectations of consumers and impact all retailers. Even a retailer whose direct competitors don’t have great capabilities in this area must improve if they are to maintain customer satisfaction and protect their markets from emerging competitors.
The way retailers interact with consumers has to change. They need to move towards markets of 1, offering deep personalization and tightly focusing their loyalty programs to maximize their value. They need to evolve from a campaign focus to a next best offer, or even a next best action, focus while recognizing that consumers are increasingly social, mobile and local. They must make these changes while also responding more quickly to changing conditions. All this requires a focus on analytics and real-time decision making about consumers.
Drive Operational Efficiency with Real-Time Analytics
For most retailers the initial focus of analytics is on improving marketing and other consumer-facing activities. Increasingly, however, retailers are discovering that real-time analytics can also improve operations and that opportunities abound for those that can focus analytics on shrinkage, forecasting, prices, allocation and staffing. The complexity of a modern retailer and its supply chain make the use of analytics increasingly essential while the need to have operations that can respond to rapidly changing market conditions is pushing retailers to focus ever more on real-time decision-making.
Reduce Shrinkage while Improving Service
Shrinkage from fraud and waste is a concern for every retailer but chasing fraud can be frustrating, with only pennies on the dollar being recovered. Fraud prevention programs can easily irritate consumers, by making returns more difficult for instance. Retailers have found that using analytics to identify transactions at high risk of being fraudulent and then preventing those transactions from taking place is dramatically more effective than chasing down fraudsters after the fact. This use of real-time analytics has been proven in financial services and is being increasingly applied in warranty and other areas of retail. Avoiding a one size fits all restriction on returns and using analytics to spot fraud and prevent it while allowing loyal and honest customers to quickly and easily make returns reduces losses while improving customer service.
The real-time analytics is now becoming a must have application for all retails to grow and sustain.
Thanks to the author Mr. James Taylor and Mr. Britt Hackman for sharing such interesting facts on Real-time Analytics.
Thanks and Enjoy reading. – Amit