When it comes to bigdata, retail is an industry which is quite mature compared to the ones mentioned in my previous articles, especially if you start looking at companies like Wallmart. The subject of bigdata is already well covered but I hope I can give an additional twist on the current developments by taking M2M into the picture.
Wallmart has leveraged bigdata and analytics for a number of years now. The amount of data they collect is already at the level of petabytes and they save information about all of their customer transactions, more than 1 million per hour, and it is mainly related to receipts and purchases. Even with a single source of data, it already provides a lot of insights when properly stored and analyzed. Today, the average retailer in the US has about 700 Terabytes of stored data. This is huge already. Processing this data requires often big investments especially if you want to process the data in real time. But bigdata is not only for big enterprises it can also be offered as a service and a number of service providers are moving into analytics as a service, this will revolutionize the way retailers do business.
Connecting the right type of data sources could provide value even for the smallest companies. Most retailers own information collected at the point of sale in terms of receipts, purchases done, but that is not the only valuable source. Using video surveillance cameras to analyze customer behavior or combining information from mobile devices or social media could be other valuable sources. Much of this information comes today from mobile and M2M devices, but that data is often stored in silos and it can be difficult to get actionable insights from this data. I think the key for smaller sized retailers will be to expose their own data assets, get that data enriched and get analytics as a service from specialized service providers. The question is now, how can these insights be used and monetized?
Productivity improvements and cost savings
A first application of properly processing retail data is to achieve cost savings. Knowing which items are in demand ahead of time and knowing what will sell can help to optimize inventory and supply chains. Utilizing big data could increase retailers’ operating margins by up to 60%, this could include savings in marketing, merchandising as well as supply chain.
Looking ahead, I could easily imagine in a not so far future, that bigdata and analytics could also become a means to improve in store customer service. Imagine a clerk in a fashion store that would know as you enter his store what you like in terms of clothes, what you sizes are, what colors you prefer and understands your purchasing behavior, he could serve you faster and better. This would result in more customers served and a better sales ratio.
Generate revenues from customer Insights, sentiment and behavior analysis
Another application of bigdata is to improve customer loyalty and reduce churn. To continue with the Wallmart reference, they have developed a product that allows them to reach customers, or friends of customers, who have mentioned something online to inform them about that exact product and include a discount. Using social media and combining that with purchasing data and contact information it allows them to implement pro-active measures and hence avoid churn or even attract new customers. This creates loyalty and strenghtens the brand image.
Retaining customers has also to do with the experience they get when interacting with the retailer, this is in part achieved by delivering relevant ads, offers, and promotions. Personalizing offers means making offers more revelant to a customer by having specific knowledge of that customer’s profile.
Securing increased sales is also related to the ability to rapidly adjust to the competitive environment. Most of us are sensitve to pricing and customers will often look for peer reviews to find the best place and time to buy an item. Being able to quickly adjust to this competitive environment will be key for increasing sales and sometimes even for survival.
M2M and mobile devices will play an increasing role in customer profiling.
There are today many data sources that are already in digital format and could be used in a big data context. From a retailer context we have customer information, this is often tied to the purchases done in that store through an opt-in loyalty program. Then there are the product catalogues and the actual status of the inventory.
The next step in personalization is coming from Mobile Devices, through applications which will let customers specify preferences, provide navigation capabilities to be guided to the right store, get relevant advertising and also get recommendations from other customers on the items they are interested in.
I think though that the biggest disruption might come from a smart usage of video cameras (e.g. surveillance camera) and other M2M sensors. Images from cameras in a store could help identifying who you are and support retailers to pul up a full profile of your customer preferences. Most of us tend to trade in privacy for convenience, so I have not doubt that every individual will sooner or later have pictures of himself tagged somewhere on the net and that could be the starting point to pull more information about you as a customer. But there are other mechanisms to identify you as a person as for instance foursquare check-ins or the social media profile you are using when logging to wifi.
Location information is going to be quite important to target shoppers. This could be obtained from mobile systems, wifi or tracking from video cameras. Video cameras could also be used to analyze how customers are focusing on different items in the store. This could help understand interest for certain items and maybe also decide when and what to discount. Another application of video camera is for instance to get the right sizes of the customers when entering a fashion store.
Digital signage in shopping malls and retail store can also be used to provide tailored messages and personalized ads to shoppers in or outside the store. A few seconds is probably enough to reach out to a shopper with the right message and the impact of the advertisement can probably be measured if the sale is done within the next hour. Digital signage could also be used with cameras to analyze shopping carts and make a last second recommendation on something you might have forgotten to buy.
Other smart devices collecting data about users could be smart mirrors, that record reactions when trying out clothes or screens that provide a personalized greeting when entering a store.
The future of retail – Smart Shopping malls
If I picture the future of retail I would imagine a smart shopping mall where customer experience is at the heart of this business. I would enter the mall and get greeted with a personalized message, the map of the mall would be downloaded to my mobile and I would get a recommendations on a few stores to visit. I would get discounts for the items I been looking for, especially the I have been reading recommendations about on the internet. This would be part of the profile information the mall is pulling up for me. As I walk and watch the digital signs I see more information about the products I am interested in. I enter a shop. The clerk again greets me and guides me directly to the item I am looking for and provides also alternative color options based on my preferences. Not much to ask me since he has all this information on a tablet. As the clerk knows I am price sensitive he gives me an extra discount. If I picture the future of retail I would imagine a smart shopping mall where customer experience is at the heart of this business and where bigdata and analytics provide value at every step .