WalmartLabs – taking Big Data into retail
Walmart, the world’s largest retailer, acquired social media firm Kosmix just over a year ago, creating @WalmartLabs, with the intention to use this specialist R&D unit to define the future of commerce by merging social, mobile and retail.
So far WalmartLabs has released two interesting developments using social:
• ShopyCat – the gift recommendation engine
This Facebook application uses your Facebook profile to suggest suitable products for you, based on the interests and hobbies of your friends. An interesting aspect of this approach is that the app will offer links to other retailers if Walmart do not stock a suggested item in their own stores.
The notion that the app may steer customers away from Walmart may seem unusual, but the brand sees more long-term gain in making the service as useful and relevant as possible to its customers.
• Get on the shelf – innovative product pitching
‘Get on the shelf’ was a contest that allowed innovators to pitch their products to Walmart customers, who then voted for the ones they would like to see Walmart stock.
Over a million votes were cast, narrowing the field down to three products that will now be available to purchase in Walmart: a DIY-screw replacement system for glasses; an airtight plate cover for food storage; and the overall winner – a socially conscious bottled water whose company donates its profits to provide clean water supplies.
The next step – Big Data
These examples are innovative approaches to using social media to encourage sales and generation of inventory, but the area that I think will prove the most fascinating is how WalmartLabs will leverage “Big Data” to develop the retailer’s ability to predict market demand and so optimise their supply.
Understanding and fulfilling local demand
This is where the situation becomes truly interesting – stores will be able to optimise their inventory according to their area’s specific tastes and seasonal demands.
One of the examples WalmartLabs’ Venky Harinarayan offers is that of college football. By monitoring social media buzz during college football season, Walmart is able to determine when discussion about college football in a certain locality is beginning to heat up. This lets them know when they should be stocking products that are related to the season and local teams.
Creating demand and making recommendations
As ShopyCat has demonstrated, recommendation engines enable customers to discover new and relevant products, either for themselves or their friends. As I mentioned above, ShopyCat currently directs customers to alternative suppliers, but from understanding customer behaviour and using Big Data, a logical evolution would be for these alternatives to become increasingly niche as Walmart develops supply according to consumer taste.
The ability to bring all of these channels together in-store via mobile will be significant. WalmartLabs are developing in-store navigation using mobile, so I would expect to see apps that offer customers information and the location of recommended items, or prompts for items of interest that are already in close proximity. A reminder of a friend’s upcoming birthday and interest in fishing, while you are passing the sports section, for example, would help you make a relevant purchase while saving time and hassle.
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