Know what customers want before they do

To best service their customers, businesses should be meeting their customers' needs before they even realise they have those needs. Here’s how to get proactive with your offering.

In the current information age, businesses are providing online alternatives to in-store shopping, but not all business owners are getting the most out of the data they collect.

Speaking on the My Business Podcast, Mybottleshop founder Steve Rider explains how he incentivises his customers to spend money with him by offering them product suggestions.

“It's about making sure that the customer gets the right offering,” he says.

At the moment, these offerings consist of recommended products, but there are plans to offer free gifts in the form of store credit or products for the customer’s birthday.

A man looks up at many signs, giving him the direction he needsIn order to do this, Mybottleshop uses an online storefront only and this gives Steve the perfect opportunity to collect all the data on their customers they could possibly need, as data is required when purchases are made.

“We know their age, we can track their purchasing behaviour, we can see what they buy, how much they spend, where they’re located, what's the average spend on that suburb,” he explains

“We can do stuff like that that a normal retailer who's got bricks and mortar [can’t]. We get it organically as part of the process; we don't even go out because the customer's interacting with us.”

Steve says that this data is then used to determine suggestions of products that customers would probably want.

“What we can do is based on the purchasing behaviour of the customer, we can then give them more tailored offerings so we're not wasting their time, because it takes a lot of effort and cost to get people to sign up and engage with you,” says Steve.

“We've got all those statistics, a lot of people stay engaged, a bit chunk of people drop off. We want to maximise that engagement. If we give them the wrong offering, then they'll disengage from us.”

In collecting these statistics, not only the customers, and then the business by extension, benefit from providing suggested products based on purchase history; by having the purchasing history of customers on hand, Steve can then go to suppliers with this data in order to try and secure new products.

“As time goes by we'll be able to go to our suppliers and say, ‘Look, you've got a Smirnoff, you've got a peppermint candy flavoured vodka, well we've got 4,000 people who have purchased a very similar product in the last 12 months.’ We'll pitch that offering directly to them,” he says.

A man watches a TVUtilising suggestions for a customer base online has been used by businesses for years. Streaming service and company Netflix operates in a similar manner with suggestions.

Justin Basilico, research/engineering manager – machine learning and recommendation systems explains in a post on Netflix’s tech blog that Netflix members are more likely to resume watching a show if they are in the middle of a binge (watching through a large amount of a TV series and haven’t reached the end), if they have watched part of a movie, or often watches shows at the same approximate time of day repeatedly on the same device.

This data is then used for the Netflix platform’s Continue Watching (CW) model, which lists shows and movies that have not been watched all the way through.

The main metrics collected for information regarding Netflix’s section the time shows are watched, the series of shows watched, and devices watched on; the latter is an example that Justin goes into more detail.

“[For] example, [a] profile has just watched a few minutes of the show Sid the Science Kid on an iPhone and the show Narcos on the Netflix website,” he says.

“In response, the CW model immediately ranks Sid the Science Kid at the top position of the CW row on the iPhone, and puts Narcos at the first position on the website.”

The two different shows being suggested on the two different devices implies that while the same account is being used, two different people are watching on the different accounts. By adapting to the data provided, Netflix can then meet the needs of these two individual people that may be interested continuing through the series of the two different TV shows.

By making the suggestions for the CW model different for each device, Netflix can adapt to the different purposes for providing customers the content they want before they necessarily realise they want to access the content.

Hear more insights from Steve on the My Business Podcast now!

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