Dear Deirdre: All I know about my customers is that they shop with me…

It’s not an uncommon complaint. Many retailers have reams of loyalty data, only for the analytics to tell them that their average customer spends £52.68 and has a 15% chance of being called Dave. It wasn’t always like this. Once upon a time, loyalty was big business for retailers. Tesco’s Clubcard tore up the rulebook on what you did and didn’t know about your customers.

It’s been vastly over-quoted, but I always remember when the chairman of Tesco at the time said “What scares me about this is that you know more about my customers after three months than I know after 30 years.”

But fast-forward to today and loyalty has fallen out of favour. In fact, big data, fast data – whatever kind of label you want to put on analytics – has become a bit unfashionable.

In many ways, I understand. I don’t want big data. I don’t want fast data. I want knowledge. I want competitor insight. I want something which will give me the edge and make my business successful.

And when – as a retailer – you have to combine in-store loyalty data with data from online purchasing, customer service data, complaints data, marketing data and data from your app, it’s easy to get bogged down in integrations and lose sight of the ultimate goal.

The problem is that retailers can only analyse their own customer silos. Payment processors are far better positioned to build platforms and crunch data at scale, becoming the new analytics agony aunt to solve customer knowledge woes.

Processors have mountains of data at hand which is usually unused by retailers, and can give so much more insight than a typical loyalty scheme can. Processors have access to information about shoppers who don’t hold Clubcards, for example, because they can see all purchasing data that flows through the store.

Payment processors can also help retailers plan their next store, and compare retail outlet performance to the competition, providing insight on SKUs which are underperforming, as well as demographic and geographic data – all of which is vital in helping retailers to tailor how they sell, and to whom.

This could give retailers the elusive single view of the store and customers that many crave.

With that single view of the store, retailers could also drill down into peaks and drops in performance, compared to competitors in a local area. So, a Pret outlet could look at the local Eat outlet and see how they’re performing against each other every day, week, month and year. Who are their customers and where are they coming from? Are they experiencing boosts in performance and if so, why is that? Do they have a promotion running or is it something else?

It also opens the door to do more targeted offers for all customers at specific locations to boost footfall, instead of just loyalty members. Processors can help to identify pockets of money that competitors are feeding from, as well as identify areas that are untapped areas for outlets.

In my mind, that’s how payment processors can truly stand out in the coming years. If just one processor can bring the puzzle together and help retailers to gain useful, insightful knowledge, then not only can they drive retailer profits, but they’ll also help themselves in the process.

And that’ll mean no more embarrassing letters to the problem pages.