With new technologies emerging each day, retailers have had to rapidly evolve, as well as shift and reinvent their marketing and sales strategies.
E-commerce and mobile platforms have had a profound impact on the industry, buying behaviour and consumers’ everyday lives. Empowered to shop across a plethora of channels and devices with a simple flick of their fingers, customers now demand a seamless shopping experience that satisfies their need for speed, efficiency and personalisation.
It’s not surprising that many retailers are struggling to keep consumers engaged on multiple channels and marketplaces. When the customers are a tap, swipe or click away from abandoning your e-commerce site or from completing an order elsewhere, how do you gain loyalty and retain that customer versus having them defect to a competitor?
The answer is simple: provide them personalised content at the right time and on the right channel. While the answer might be simple, when retailers are spread across so many communication channels and countries – it is by no means an easy task.
To create personalised, engaging content, you need the right data. Data needs to be collected from the customer to understand what they buy, how frequently they buy, how much they spend and how they purchase (PC, smartphone). Once this data is gathered – retailers can then be triggered to communicate back with personalised automated messages encouraging shoppers to take action – for example, reminding them of an abandoned product in their cart, product recommendations or offering discounts.
Luckily in the past decade, many retailers have amassed mountains of data that map consumer buying behaviour within today’s multichannel, multi-device landscape. Where they struggle is having the right tools or knowledge to unlock the data’s hidden value.
Marketers, however, are embracing a more progressive perspective on customer intelligence. After all, analytics have enabled marketers to significantly improve how they design, measure and manage campaigns.
Machine learning solutions are the next evolution. They have gained a lot of traction and an increasing number of retailers are now implementing solutions to store, log and analyse individual shopping behaviours in combination with machine learning.
But what exactly is machine learning and the algorithms that underpin customer retention? Machine learning is the art of teaching an analytics solution to become more intelligent, both through advanced mathematics and feeding human intuition into a solution.
Within the context of marketing, this means having the ability to process diverse behavioural data to understand trends. Data science takes the burden away from having to manage needle-in-a-haystack scenarios. Instead, customer intelligence-driven campaigns will reach your customers with the right offer, wherever they are currently located, and on the right device.
This includes analysis derived from page views, check-outs, add-to-cart events and search queries on a website. Customer interactions with thousands of products are constantly processed — giving instant, individual recommendations with every page refresh. The platform is, as the name suggests, learns and evolves throughout the customer lifecycle and with each and every transaction and move made by the customer. It gets more intelligent by designing specific recommendation models for each stage of the buying process (research and discovery, cart, purchase, post-purchase).
The main benefit of this improved customer intelligence is that it enables the marketer to make more informed decisions about campaigns. It enables the full automation of marketing tasks currently required to engage customers and inspire an order. Marketing messages can move beyond the traditional concepts of a segmentation strategy and automating messages along the customer lifecycle to truly speak to the individual.
After all, shoppers are overwhelmed with marketing messages, but very seldom do these messages engage the customer in a meaningful, relevant way. With machine learning platforms, retailers can communicate on a truly individual basis.
Machine learning may still be relatively new, but it will likely be the next-level of marketing automation and it’s time retailers caught up with the consumer.
By Steven Ledgerwood, UK managing director at Emarsys
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