the
best
customer
guide
Get our free weekly marketing briefings...
"They're always useful, and always brief"   :-)
Enter your email address to get started!
With online conversions in single digits, retailers are increasingly relying on discounts and incentives to turn browsers into customers and boost revenues. But this is a blunt tool that risks disengaging as many customers as it attracts and reduce margins unnecessarily as it fails to take into account a customer's actual propensity to buy. So why offer a discount to a buyer who is already likely to purchase, asks Harry Parkes, Product Director for WHY Analytics from VisualDNA. This article is copyright 2014 The Best Customer Guide.

If a retailer were able to accurately predict a customer's propensity to buy within the first few seconds of them being on the site, then this could fundamentally change that approach. Retailers can exploit personality and behavioural data to tailor the right incentives to the right people at the right time to improve conversions and move away from blanket discount led offers.

The first 30 seconds
Retailers can use behavioural data (clickstream) to predict a visitor's propensity to buy. But this information only offers usable accuracy after a certain length of time the longer the customer has been online the likelihood to buy increases. Offers and discounts at this point may do little more than cannibalise margins.

Retailers clearly need a better way of predicting a customer's buying intent, and quickly - and a way of distinguishing between customers that need an incentive and those that don't. There is no need to offer incentives to a customer with a high purchase intent - and certainly not any money off. But the next tier of customers - those that are almost as likely to buy but may need a little nudge - are a prime target for an incentive, as long as it's the right one.

So how can a retailer identify those customers with a high propensity to buy early enough to make, or withhold a timely offer? VisualDNA have found that combining behavioural data with deep customer personality, demographic and interest data can see up to 95% accuracy in predicting those customers who will buy vs browse. Critically, this prediction can be made within the first minute the customer appears on site.

The right incentive
So how can a brand exploit this first-minute-advantage to maximise the chances of customer purchase? Essentially, with this data a retailer identifies not only the core customers with a high likelihood of purchase, but also the next tiers of customers with a slightly lower buying intent. Rather than blanket offers to every visitor onsite, the retailer can combine this rapid insight into propensity to buy with the understanding of personality type to introduce essential sophistication to the offer model.

The blunt 'x% discount' approach adopted by most retailers is not only unsophisticated it can actively disengage some customers by appearing to devalue the product. Critically, understanding customer personality enables a brand to make a far more relevant offer - and not one that necessarily requires heavy discounting. For example, some extroverts respond well to pressure selling - the idea that 100 people have looked at this offer in the past hour is likely to prompt a positive response. In contrast, individuals with a high agreeableness rating are more likely to respond to social cohesion messages - the fact that 20 people have 'Liked' this product or this product has been 'Favourited' 50 times makes these individuals feel comfortable and reinforces the buying decision.

Retailers can combine this understanding of personality type with the customer's position within the purchase-propensity scale to create relevant offers within the first minute that a customer is online. Critically, using this insight a retailer can move away from cannibalising the revenue and creating a discount led customer base by providing offers and incentives that truly reflect a customer personality.