More and more traditional retailers are moving to unlock business potential from data as cost of storage comes down drastically by the day. World over the adoption of robust data strategies by brick and mortar retailers is also being boosted by open source technologies that help avoid lock-in into propriety software stack. In an age when online retailers are grappling with the lower attention spans (eight seconds now), offline retailers need to move beyond historical data analysis that is done at the data centerandfocus on capturing data at the point of origin outside of the data center as well.
Data in motion – how it enables retailers
Data in motion refers to a stream of data moving through any kind of network and as opposed to data at rest. Consider this, today data can now be generated from a variety of sources at a physical store – from sensors, beacons, weighted shelves, smart hangers, and racks. Imagine if this data could be leveraged to influence a buying decision before a customer walks out of a store. For example, a customer tries a dress at the store and leaves without buying, an incentive in the form of coupon or a spot discount can spur the customer to buy that dress. Now, if this real-time data can also be combined with historical data stored in the data center. For example, if that customer is a regular, the store manager could also decide to delight him or her further through a voucher they could use next time or a freebie to reward their loyalty.
Retailers can also push real-time notifications to all shoppers in stores right before they make their purchases powered by data analytics. Such revenue opportunities did not exist before the advent of data-in-motion.Data gleaned from the sensors and beacons can also provide insights that will help retailers to improve store layouts, use of space and managing inventory. To sum up, the data strategies of retailers should benefit from a combination of historical and real-time data analytics. Either of them in isolation will just not serve the purpose.In fact, data strategies based on real-time and historical data analysis and emerging technologies like AI, ML also holds true for online retailers.
AI, ML role in fostering real-time customer experience
Technologies like Artificial Intelligence (AI) and Machine Learning (ML) will add more impetus to data science tools as they are all about predicting the future. Globally, retailers like Amazon and Walmart have begun leveraging AI and ML. Amazon launched its Amazon Go service in the US based on walk out technology wherein customers can simply use their smartphones to pay through the app up on checkout. Items added to the physical cart are updated on the app. Walmart also introduced a ‘scan and go’ feature in the US that also enables payment via their smartphone after they have scanned the barcodes on their phone. Indian retailers too are gearing up to deploy not just sensors but also AI, ML to enhance sales and customer experiences and we could see some pilots in this direction soon.
However, the key to data-in-motion is building a near real-time streaming application that is scalable, reusable and enables operational agility. Thus, brick and mortar retailers should a take a re-look at their current data strategies to build in a modern data architecture.
To conclude, there needs to a shift from a post-transactional world to a pre-transactional one by re-imagining and re-evaluating existing data strategies. Most retailers do realize that data is the most tangible asset they have and that managing, storing and analyzing data is competitively and fundamentally important. What they need to do, is embrace a real-time framework to not just enhance sales but also considerably elevate customer experience. After all, every shopper wants to walk out of a store,feeling elevated.
The article has been penned down by Kamal Brar, VP and GM of APAC