Personalisation is the use of data to understand what individual customers want, so that you can give it to them before they ask.
A couple of generations ago, the local independent retailer knew all his customers by name and by what they ordered. The customer who called in at 8am every day for a Daily Mail and a packet of mints, the man who popped in at 5.15pm for his packet of cigarettes and a copy of the Mirror, as well as the shoppers who only came in at the weekend to buy sweets for their kids and the Sunday papers.
Relationships were built over regular visits, knowing exactly what the customer wanted before they asked for it and offering extra items at the right time – a bottle of Lucozade, for example, when someone in the family was ill or sourcing the latest sweets that all the kids were talking about.
Today, personalisation still exists but it has grown exponentially. Today, rather than remembering a couple of hundred customers in a corner shop, Amazon remembers the 197 million customers who shop with them each month. Personalisation comes when you know enough about all your customers – online and offline – to deliver content and offers that are highly relevant to them at exactly the right time. Product relevance leads to much higher engagement levels, and sales conversions, than would otherwise be achieved through generic offers.
72% of consumers say they don’t interact with marketing messages that aren’t personalised or tailored to their interests. Personalisation in marketing comms is far more effective because it focuses on precise targeting (like a sniper) rather than mass marketing (scatter gun).
Deep customer knowledge
This can only be achieved with a deep knowledge of your customers. And this is where ‘data’ comes in. Pure Play operators like Amazon have an advantage in that all transactions are done online so they can see all of your transactions, and interactions, easily. But they are also obsessed about learning everything they can about their customers. Every single day, Amazon changes the prices of products on its website 2.5 million times, introducing dynamic pricing techniques on a very large scale. Data drives these changes.
Omni channel operators – or bricks and clicks businesses – have it tougher. A business like John Lewis has to try and match up customers who are shopping in store and online so that they can treat them holistically as one customer not two separate people. This is why so many retailers now have in store loyalty schemes – it is the only way to get a customer to self identify when they spend in store. Identity online is easy – the customer has an account or, at least, a name and delivery address or, even better, they can register their loyalty card online as well. Points don’t only make prizes, they create a single customer entity!
The wholesale advantage
Ironically, this is much easier in the world of wholesale. All customers have to be registered to shop and have to use their account number when they order goods. This has always been the way and this means that wholesalers are sat on years of data about their customers. They know who visits or orders weekly, who comes in three times a week and who only shops seasonally or for specific product categories. While online retailers are building knowledge over time as their customer database is built up, wholesalers already have it to hand in their back office or ERP system.
Enriching the knowledge pool
But additional customer information can be gathered on a continuous basis and added to the knowledge pool to enrich our understanding of customers. Every touch point with customers is an opportunity to learn more. Data can be captured from all types of sources – obviously online and offline as customers are ordering products or on the web as they are searching out promotions, but also via delivery drivers and reception desks, these staff pick up vital knowledge, which can be integrated with other accumulating data sets. Customers can be encouraged to leave reviews on their wholesalers’ feedback pages which provide further data clues (eg. growing resentment).
Data silos must be brought down. Data is power, and even more so when all data sets within an organisation are merged together to produce an outcome greater than the sum of the parts.
Rolling back the years
So, if we compare the average independent wholesaler to our 1970s independent retailer for a moment. Our retailer knew if Mrs Smith from number 27 hadn’t popped in for her copy of Women’s Weekly on a Wednesday and he knew Mr Jones liked a good whisky so could recommend him a new bottle he was stocking.
Turning this to wholesale, it’s really difficult to keep track of customers in the same micro detail without detailed reports about recency and spend. The noisy customer and the ones always trying to get a deal stand out, but what about the quiet customer who visits twice a week, regular as clockwork, and buys the same things every time? These are the customers that sometimes slip under the radar. Amazingly, asking depot managers who their most profitable customer is the question that can be the most challenging – and this is the information that good data can serve up in seconds.
And the sales data you have has to be ‘clean’ and granular. Not easy (and certainly not often the case) in the UK wholesale sector.
Amazon has got this sussed – time for our channel to catch up.