The proliferation of different technology stacks and marketing platforms have made retail channels increasingly complex, according to Heath Barlow, market lead of Australasia for cloud-based marketing firm, Emarsys.
Considering that in 2011, there were approximately 150 different marketing and tech companies plying their trade, this number has jumped to around 3500 businesses championing their platform as beneficial to retailers success.
“In the last five years we have had an exponential increase in the amount of businesses providing tech to help marketers, so with the variety of choice, companies have gone all out for each best element of marketing, which has complexities in creating that single view,” he told Inside Retail.
A typical retailer runs around 50 pieces of JavaScript on its website, all coming from different sources leading to the challenge of ‘how do we actually get a single view of the customer’ when different platforms are saying different things.
In striving to increase the personalised aspect of customer experience, retailers have several factors to consider, with 40 per cent of loyalty marketers asserting they struggle with personalisation according to Forrester.
“If you look at it from a purely mathematical perspective, a retailer may have a quarter of a million customers on a database they want to communicate with and a product catalogue of 20,000 SKUS,” said Barlow. “When looking to do personalisation on a product level and you take a quarter of a million times that by 20,000, the possibilities around being able to personalise on a one to one basis at scale become impossible.”
This is where machines can be useful, in deriving trends, patterns and behaviours to actually do personalisation at scale, said Barlow.
Emarsys has recently launched an artificial intelligence marketing tool to bridge the nefarious gap between complex data science and executing personal omnichannel campaigns at scale.
Barlow said that one of its key features is the ability to send out timed content – specific to customers preferences. “A retailer wants to send an email out to their database about a new product release and we can actually send that on a one to one basis when the consumer is most likely to engage with that piece of content,” he said.
This differs to the usual ‘ball and all’ approach of an email campaign going out universally at 9am.
“This ensures that we have a high probability of the person opening that content and engaging, which we ultimately hope will then lead to conversion and buying one of the products on the site.”
“Once we’ve sent the right incentive at the right time…how do we ensure that the content within that email on a product level is applicable to them?”
Barlow said with chief marketing officers ultuimately busy crunching the numbers, AI technology can help empower businesses overall marketing propensity at all levels.
“It’s making it accessible so that rather than sitting in the IT department or with business analysts its sitting in the hands of the marketing department where the data is becoming actionable.”
Svetlana Novichkova, head of e-commerce for food retailer and wholesaler, Japan Centre said adding artificial intelligence to its marketing platform had elevated its personalisation with customers.
“We never knew if the discounts we sent were appropriate or not; were they too big, were they too small, were they even necessary?” she said.
“Managing discounts on an individual level was too time-consuming, and we couldn’t tell who should get what – we were wasting discounts on our regular buyers, but we needed an effective win-back strategy. There was no way for us to know where to target the incentives.”
“With the flick of a switch we can turn every email into a win-back campaign. Incentives are only included when the returns will justify them, otherwise they won’t be used at all.”
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