The development of multilingual hubs for an improved customer service
Outsourcing has changed dramatically in recent years. A decade ago most companies exploring an outsourcing strategy were looking to reduce their...
Personalisation of the retail experience is an area of focus right now. Retail companies have long struggled to make the online experience match what customers find in-store.
Customers used to a highly-personalised online shopping experience feel anonymous when browsing in stores and seeing that every customer is offered the same discount at the same time.
Improving and personalising the in-store experience relies on data and analysis. Retailers need to find how they can get both online and in-store shoppers to engage in a way that allows their purchases and preferences to be tracked.
Online this is as simple as customers needing to log in to a website or app using an account. In-store can be trickier, although smart retailers are finding that if they release apps combining payment with loyalty systems that improve the in-store experience, customers will use them.
There is one further step that is worth considering when planning a data analysis and personalisation strategy: is it possible to predict what your customer wants or needs before they give any indication directly?
Think about that for a moment. If you could move beyond regular data analysis and apply artificial intelligence and machine learning to all that you know about your customers, then could you predict what they want before they ask for it?
Some companies are already exploring these ideas. The railway system in Stockholm, Sweden, uses AI to analyse how the trains are running. It learns how delays in one part of the system affect others and applies this knowledge to send out warnings to customers before delays actually happen. It is looking at events right now and predicting what will happen in future, and then telling customers what will happen so they get an early warning.
The Clever Sense system learns what you and your friends like to eat and the recommendations you make, allowing it to make excellent restaurant recommendations to you when you are in an unfamiliar town. The Emotient facial recognition system knows how you feel about a product just from the way your face looks when you see it – imagine the possibilities for retailers with this kind of technology?
Basic data analysis already allows some predictions to take place. A customer that starts buying small baby nappies will probably increase the size they purchase in a few weeks, so it’s possible to offer deals for brand switching, but AI allows the brand to predict how a customer might behave before there is any history available to analyse.
I think this is going to be an extremely important area that takes omni-channel, Big Data, and analytics strategies into a completely new area: knowing what the customer wants before they even ask for it. What do you think the future holds for retail and AI? Feel free to get in touch with me on LinkedIn and let me know.
Author: David Turner