The Evolving behaviour set of #Customers in the world of #MultiChannelRetailing, is well documented and not always easy to follow…It’s easy to conflate the rapidly changing technology with analysis of those behaviours – while they are linked, they can and should also be thought of as apart…Let’s focus on the behavioural side…
How are these behavioural changes manifesting themselves? It makes sense to think back (briefly) to a shopping trip in the pre-internet age…#Shoppers, would take a predictable and linear path to purchase : making a list, looking for (or seeing) offers, going to a store, picking off the shelf, paying at checkout, going home and then consuming the products they had purchased…and then repeating..
In the multi-channel world, TWO Key Dynamics have changed : The path is non-linear…and different steps can and do occur on different channels…!!
If we consider a shopping trip today – a customer might log on to an account with a grocery retailer and add some items to a basket. Then, a couple of days later, receive an email with a set of offers which they activate on their half-built online basket. They might go in to store to have a look at some new products but decide not to buy them…and then buy them online…They may have an offer shared with them on social media….which they then use in a store (perhaps having found the nearest branch to them using their smartphone)…
Where to spend the Money ?
These non-linear paths and the increase in channels used by customers to shop, has commensurately increased both complexity and opportunity for both brands and retailers to divert and influence customers on those paths. The challenge for #Retailers, is that creating seamlessness in those paths costs money…The challenge for brands is that investment in media to influence those paths costs money…What both retailers and brands want to know is where to best spend that money…
More than ever before, the answer lies with an obsessive focus on the customer and the data their behaviours are generating. Retailers and brands are collecting and looking at data – lots of it – but often those data are used for specific ends. For instance, plenty of retailers will use the data for reviews to look at which products are winning, which products are in decline, but few retailers will capture and present this data to understand customers. Knowing, at the level of an individual, a pattern of review posting will be part of a set of indicators which will allow the retailer and brand to understand that customer’s propensity to purchase again and, maybe, when they would do so..
For instance, if we knew that a customer had posted two consecutive bad reviews of a product from a certain brand, could we use that to understand their likelihood to repurchase a product of that brand? And therefore, perhaps the generosity of the offer or the need to present NPD to that customer. And what could a retailer glean from a customer who had steadily posted a review every two months but was not doing so anymore ?
Retail Data :
Similarly, and specifically in the world of grocery and FMCG, retailers will capture the levels of substitutions and rejections in baskets composed of dozens of SKUs (stock keeping units) when they are delivered or collected. This will be done to optimise the supply chain and picking process. Again, how many retailers are looking at the data through the customer lens? Would a customer who had received 20% of their basket as substitutes three times in a row (and rejected half of those on each occasion) as against a customer who had received three consecutive perfect orders, be more or less likely to lapse ?
The challenge for retailers and brands is technical and commercial. Technically, linking their data assets to understand the multi-channel path to purchase is difficult – legacy systems built for specific ends and ambiguous data ownership structures are significant barriers to overcome. Moreover, in many cases the capture of the data at customer level may not even yet exist. Retailers and brands will need to ensure that their technology plans seek to plug these gaps in understanding..
A Greater Challenge :
Despite appearing to be easier, it may yet be that commercially, the challenge will ultimately be greater: customers will be more aware of the value of the data they are generating and expect to be compensated for it. For that compensation to be sensible, attribution of investment (always a challenge in marketing) will need to be tighter than ever. Was it the coupon on Facebook or the free sample at the Click and Collect point or the interrupt media on the app which activated that customer ? The data will exist but how will we cut it to understand the key to orientating the path to purchase ?
The next few years are likely to continue to present opportunity: technological innovation will lead to more data and more opportunities for customers to exhibit non-linear behaviours – some of which may yet prove even more disruptive in some sectors (such as peer-to-peer selling)..
Getting on top of the current behaviours and using existing data assets to do so will be one of the key differentiators which will define the retailers which win in this turbulent period.