Matching in the context of live shopping or livestream shopping is the process of connecting a shopper with the right retail expert to confer with in the live video call.
Given eCommerce websites can have thousands, hundreds of thousands and even millions of customers visiting every week, and far fewer shop assistants and instore experts available at any given time – matching becomes important. The process of matching is therefore a balance of finite resources serving a vast audience.
The clear benefit of getting this right is retail experts’ time is spent well, with the right kind of shopper who needs the help to make the right purchase. Having a positive interaction with the right person at the right time in the consideration process can have far reaching implications for lifetime value and customer satisfaction. Clearly high value customers will want to be attended to by the best people in your organisation too. If matching doesn’t work, you could lose customers.
So, let’s look at the components of a good match.
Elements that make up matching in live shopping
Online dating matching algorithms claim to help you find the right life partner. Many would dispute the validity of these black boxes, and gladly getting someone to talk to about a product you need to buy need not be as complex.
- Interest or needs – on the shopper side, it’s imperative that you know their needs. The immediate page they were on when they made the enquiry, site history as well as any data collected in a prebooked session all form useful starting points.
- Expertise – the other side of the equation of course is the expertise. If the customer is interested in a watch, it will make sense to match them with someone who knows something about watches.
- Location – it often makes sense to have proximity between the shopper and retail expert. If they are local, they have a better chance to have something in common, plus there might be an opportunity for the shopper to visit the store post livestream call.
- Language – seems obvious, but a wasted interaction is going to be when both parties don’t speak the same language. This can be nominated, picked up from the pages they are browsing or even their browser settings
- Previous contact – has someone had a live shopping call before? Is that history logged? Can we learn anything about customer needs from previous calls? The answer here is of course yes. Great matching algorithms will take this into account
- Customer satisfaction – flowing on from previous contact, has a customer given a satisfaction rating such as NPS before? If it was positive, you will likely want to match them to the same or similar retail expert
- Previous confirmed purchases – if you know the last purchase the customer made, this is going to be very helpful in things like personal styling. If the expert knows this, they can anticipate needs and make decisions on complimentary products/additions.
- Customer purchase history – customer purchase history gives the expert a detailed view of customer needs and implied preferences. Have this at your disposal and as part of the matching algorithm, you will be best in class
- Expert skill – logically, you want your best sales people prioritised to the most relevant or best customers. Equally, you might want to put your best people on bringing in new customers. A great matching algorithm will give you that choice
Good and accurate matching in live shopping increases the probability of a positive interaction where customer needs are met. When you are choosing a livestream commerce provider, matching is one of the key success factors for improved average order value and lifetime value.
