Catching and re-engaging customers whose shopping habits are slowing down or are at risk of stopping altogether is both easier and more cost-effective than recruiting new ones, argues Peter Ellen, CEO and founder, Big Data for Humans
At the end of April the Big Data for Humans’ team will gather some of London’s best and brightest retailers together for breakfast, to spend a few hours sharing ideas, learning about and discussing customer marketing. This will be the latest event in our sell out Retail Leader Series, which we have been busy planning and hosting in both the UK and in Singapore. The Series has been made all the more interesting and enjoyable due to the diverse mix of retailers we’ve had in attendance, from Debenhams, Harvey Nichols and The Gap to Sephora, Luxasia and Ikea, amongst many others. It has also given everyone at Big Data for Humans, in both our Europe and APAC offices, some really fascinating, grass roots insight into the state of customer marketing today amongst retail businesses of all shapes and sizes. And I can report that, wherever retailers are in the world, and whatever they are selling, their concerns and needs are broadly the same. A need to get closer to customers – those ‘humans’ that are at the heart of all good retail businesses – to better understand them, and to get smarter when it comes to targeting them and building their profits.
Our events take the form of customer marketing workshops to ensure that everyone leaves with an actionable plan to take back to the office, which can be based on one of a number of ‘tracks’ in our customer marketing playbook. There is one issue however that continues to rise to the top of the list of concerns for modern retail marketers – and that is retention. From luxury fashion to furniture to the multiple grocery sector, marketers are grappling with the challenge of how to spot and track loyal customers at risk, how best to target them, and when. It’s not really surprising that this is the case. Current research shows that retailers lose up to half of their active customers every year, whether that’s through a drift to competitors, alternative products or just through inertia. There’s also plenty of compelling evidence to show that catching and re-engaging people whose shopping habits are slowing down or are at risk is a more cost-effective strategy than the far more expensive business of acquisition.
So from hundreds of discussions with retailers around the world, what can I tell you about building a successful retention strategy for your retail business? However creative and well-intentioned, your strategy and plan will be a wasted exercise unless you first of all have a true understanding of your customers, and can pinpoint who are the most valuable and loyal amongst them, and what they are most likely to buy. I believe that, for today’s retail marketers, having this deep and detailed understanding of customers at your fingertips is the single most valuable asset they can possess. I also know that it can only be done through data science. I have heard many tales of retention marketing woe from retailers, frustrated by the lack of impact their campaign has created, the tiny return on quite a large investment, now deemed a waste of precious marketing money. The explanation for this is usually the same each time. Customer segmentation and campaign planning have been based on inaccurate assumptions and generalisations. Most good marketers will not be short of ideas as to how they can bring at risk customers back from the edge, whether that’s through personalisation or a well-timed triggered retention email. But the question I always ask first is: why put your faith and money into guess work and the assumptions of the marketing team, when data science can supercharge your efforts to give each campaign laser-sharp accuracy and up to five times greater return?
I worked in music retail for many years, and it’s here that I developed a fascination with customers. Rather than sit in the back office, my boss always told me to get out onto the shop floor to observe people and to look at who was buying what. It was never a case of simple demographics, and although the marketing budget was tiny and we had no technology at our disposal, we tried to be clever and creative when we grouped customers for our campaigns, based on understanding their taste in music, how they listened to music, what type of films and books this might mean they like, and so on. I set up Big Data for Humans to find a way to super-charge this process using data science and customer insight automation. Imagine having process-driven, dynamic data on customer segmentation and life cycles at your retail fingertips, allowing you to build powerful customer marketing strategies based on accurate insight rather than speculation? What if we could put this data into the hands of all retailers to use quickly and efficiently, rather than waiting for weeks or months for the IT department to get their act together?
For today’s retailers, it has never been easier to bring fast, simple-to-use customer data analysis into the heart of customer marketing. Big Data for Humans pioneering ‘customer graph’ brings a process-driven approach to the task, allowing marketers to pinpoint customer life cycle stages, the recency and frequency of their shopping habits, and ultimately the ‘window of opportunity’ that exists to win them back. There are huge variations between retail sectors, and different product categories. The retention window for a grocery customer and the one for fashion or furniture shoppers are completely different. An up to date dynamic segmentation model is the fastest and most effective way to put this information directly into the hands of marketers, allowing them to clearly see life cycle stages for themselves.
The process needs to continue through to campaign execution. Having accurately found unique customer groups through automated insights, why then revert to guesswork when creating a customer marketing campaign? This is where data science again comes into its own, using logic and accuracy to create powerful, personalised campaigns that have been proven to increase incremental sales by up to five times. Our recent work with a major supermarket proves the point. Having used our Customer Graph to automatically profile customer groups and produce daily lists of ‘at-risk expected purchasers’, their marketing team then used data science to deliver a campaign of retention initiatives with variants for top communities, and discounting and vouchering for high value customers. This delivered a customer retention boost of 3.2%. Another client case study shows how a luxury retailer used personalised retention marketing to deliver over £1million in sales from existing health and beauty customers from one campaign alone. The Customer Graph auto-identified groups of ‘active’ health and beauty buyers, using insight into their favourite brands and products and allowing the marketing team to prepare personalised emails for this group. Their customer marketing plan now includes an ongoing retention marketing track for other key customer groups targeting similar uplifts in sales across the business.
So, if seeing your customers drift to the competition is keeping you awake at night, why not come along to one of our events or give us a call to see what getting to grips with data can do to supercharge your customer marketing?
Big Data for Humans next Customer Marketing Planning Workshop will take place on Tuesday 25 April 2017, 8.30 – 10.30am, St Martins Lane Hotel, London. For further information see http://www.bigdataforhumans.com/information/events or contact our team: email@example.com, telephone: +44 20 3870 1348. @bd4hcorp
(A Retail Times’ sponsored article)