Every omnichannel retailer is seeking to provide a better shopping experience to influence customers and increase conversions. Personalization and recommendations are just part of this equation. But what sources of data is required in generating these recommendations and creating these unique experiences?
Online retailers are limited to only web-based events like clicks, queries, and cart behavior for modeling customer behavior. But, an omnichannel retailer can combine online data with data from other channels like loyalty programs and in-store interactions resulting in more comprehensive intelligence. Market research has shown that most customers want to be recognized across channels through which they encounter a brand. Customers are also more loyal towards retailers that personalize their experience.
Every action a customer takes is a “signal.” Each signal tells us a little bit more about who they are, what they like, and what they (and users like them) are likely to do next. These signals can include every customer action – from a completed purchase to visiting a retailer website without purchasing anything at all. When a customer comes into a store and picks something off the shelf and doesn’t buy anything, that too is information. These “signals” can be used to make recommendations and influence the customer.
Here are some examples of signals that we should consider capturing:
|starts or opens app||Time, location||App, GPS|
|searches||Time, location, query||App, GPS|
|Taps item||Time, location query, item||App, GPS|
|Adds item to cart||Time, location, item||App, GPS|
|Abandons item in cart||Time, location, item||App, GPS|
|Purchases item||Time, location, item||App, GPS|
|visit||time||Web tag, Appstudio|
|Clicks on item||Time, item||Appstudio|
|Adds an item to cart||Time, item||Appstudio, commerce suite|
|Abandons item in cart||Time, item||Appstudio, commerce suite|
|Purchases item||Time, item||Appstudio, commerce suite|
|Clicks for service||Time, item, reason||Appstudio, CSR|
|Customer calls||Time, Item, reason||CSR|
|Purchase||Time, item, store||Checkout|
|Return||Time, item, store||Checkout|
|interest||Time, item, store, aisle||Mobile, camera|
|Uses coupon||Time, item store, publication||Checkout|
|Employee contact||Time, employee, location, store||Mobile, Camera|
|Opens||Time, topic, item||Lead gen tool|
AI Technology for the Omnichannel Retailer
Even starting with just mere purchase information can begin to reveal insights. But keep in mind that customers are unlikely to use your app while they are in your actual store. Research is showing that instead of using a brand or retailer’s app, people text their friends or talk while shopping. While frustrating if we’re trying to gather and track signals, behaviors something we must embrace. The technology to recognize a customer by appearance is already available and has already been deployed.
Where a customer goes within a store says a lot about them. If a customer spends a lot of time in the sporting goods section they obviously have an interest in sports (or shopping for someone who does). If they pick up everything with Nike stamped on it, then they probably have an affinity for that brand. If they only buy things in the store after talking to an employee, then we may want to make sure an associate always engages them as they enter the store.
Capturing all these signals across the various channels they occur in is one challenge. The next challenge is deploying the right technology on the backend that is critical to any successful retail store of the future. The right technology is needed to receive, process, store, and aggregate customer signals. The right AI technology is needed in the middle to make recommendations and influence customers. Every customer’s visual experience is critical whether it’s merchandising in-store, the layout on our app, or the search on our website.
Learn the most effective way to leverage these AI-powered search techniques at our webinar on August 14th. We’ll explore how to use customer signals to get the right information to the right people at the right time.
And in the web…
The technology to treat every customer like an individual is now available to retailers of all sizes. If you make it easy for online shoppers to search, browse, and buy, and they’ll keep coming back.
Discover how Lucidworks lets you combine human expertise with machine learning to create a shopping experience that is uniquely personal.
- Watch webinar AI and Machine Learning for Omnichannel Retailers
- Check out our recent blog: Increase Retail Sales using Recommendations
- Grab our EBook: How to Create an Amazon-Like Experience with Fusion
- Contact Lucidworks, we’d love to influence you with our recommendations
Contact us today to learn how Lucidworks can help your team create powerful search and discovery applications for your customers and employees.