Retailers once had it easy. Little competition, loyal customers and easy sales. Then came an explosion in retail — great for consumers but not so good for retailers.
The days of easy selling were over and retailers had to work hard to build and retain a loyal base.
E-commerce and a connected experience — online and offline - have become a must have to provide consumers with the experience they expected. While getting the omni-channel right is critical for survival, we are now seeing consumers evolving beyond that.
While consumers are able to have a great personalised experience online, physical stores are woefully behind. Retailers have next to no data on what is happening in their stores. Who is that customer walking in? What had the customer browsed through... but not purchased the last time? What does she like?
Having this data is a foundation to provide the personalised experience consumers expect as well as to maximising operational efficiencies in a store. The key to enabling this kind of data augmentation is through the smart use of “natural language processing” and artificial intelligence (AI). With this, we can create the tools to start getting the rich data and personalisation available online into offline stores.
I see this playing out in three stages.
The first stage is to have real-time data of the visitors to your stores and then integrating this with the transaction data to get insights on the store staff’s effectiveness, the conversion rate, campaign effectiveness, etc. I can’t emphasise enough the importance of accuracy.
If the data is not reliable, no action will be taken. Getting this level of accuracy and doing this cost-effectively is possible with the smart use of AI.
Next up is to understand how customers behave in store. Again AI and natural language processing can help you generate heat maps in store and answer questions like:
* Where do the customers tend to spend the most time in a store?
* Which are the most popular sections and products in a store?
* Which sections have poor conversions and how can those be improved by altering the layout?
* What paths do customers take in the store and how does the traffic flow through the various sections?
Analysing customer-staff conversations using NLP to understand which products customers ask for but are unavailable at the store. Or to understand how many customers asked for a discount or didn’t find their fit.
Once these are in place, it opens up doors to truly revolutionary applications of AI. With natural language processing and deep learning, we can now start doing amazing stuff. Imagine these:
* Use AI to identify attributes like age, fit, clothing style and expression to get data on customer behaviour and experiences in store. Do they like the item they browsed at the store? How did they react to the store staff engagement?
* Use natural language processing to identify conversation trends. Are customers asking for black shirts? How many folks wanted a looser fit?
With their permission and tagging customer IDs, you could identify your customers as soon as they walk into the store through facial recognition and have the store associate get instant information on their profile and preferences. And offer clear suggestions on how to personalise the interaction and offerings.
This is the power that retail technology is giving stores today; and it is amazing!
Aneesh Reddy is co-founder and CEO at Capillary Technologies.
