The real value of data analytics is what you don’t see

You have probably heard stories about how data analytics changes the whole customer experience, and how a store can personalize everything for you. The go-to example was Tesco, which was the role model for loyalty programs and customer analytics – until it encountered serious issues in its business and started to lose market share to competitors like Aldi and Lidl, neither of which had sexy customer analytics solutions.

This raises a question: Does this mean the promise of customer data analytics is a big illusion? Or is it just that many business people don’t understand how analytics actually works?

Data analytics is one of the most important technology and business areas. At the same time, it is one of the most difficult to really understand and utilize in business. Many people have a lot of ideas on how to analyze and utilize data, but there are few models to really monetize it. Really valuable analytics can be far different from fancy and sexy analytics stories. Put simply: quite often the real value can be found in the back office rather than the front line.

Analytics behind the scenes

As mentioned above, Tesco was a pioneer to utilize customer loyalty program data. The company realized that it is not enough to have a loyalty program to offer discounts or to collect points when most competitors do the same. It started to analyze customer purchasing behavior, make personalized offers and customer-specific discount vouchers. This was a big success story. The problem, of course, was that most of its competitors started to do the same.

In Europe the biggest winners in the retail market most recently have been German low-price chains Lidl and Aldi. Their approach has been to offer the same low prices to all customers all the time rather than try to personalize offers and discounts. So, on the surface, perhaps, we could say they don’t utilize data analytics. But that is not the whole story – there are many ways to utilize data.

The retail business includes many kinds of analytics use cases: optimize supply chain, optimize locations of stores, assortment planning for each store, pricing and price sensitivity for each store, product demand forecasting, basket analysis, and many other analyses. As we can see, it is not just used to analyze individual customers and their preferences – it is used to optimize the whole supply chain, pricing and shelf space. These kinds of analytics that are not visible to customers are crucial to provide low prices to all customers, all the time. The return on investment for these kinds of larger-scale analytics solutions can be even better than the ones that analyze customers, as it is typically more expensive to really utilize individual-level information in marketing and customer service.

The true value of data analytics

One problem with data analytics is that while people are very interested in it and like to innovate a lot of new applications, it is hard to find solutions with a clear path to monetization. Analytics results must be sufficiently actionable, and there must be solutions and processes to utilize the results – otherwise, analytics bring no real value. Analytics is also often something that’s slotted in the “nice to have” category when companies consider investments. It is much harder to find the urgency and business case for analytics. I have learned this the hard way – in my earlier companies, I spent seven years trying to convince companies to make analytics investments.

When I now look at data analytics startups and FinTech companies, quite a lot of them also work with those fancy and sexy customer interface solutions – even AI-based human-looking robots. They tell the same old stories – how to create the personalized customer experience, help customers to find products and prices, and find suitable, offerings for each customer. This can be relevant, for sure – but at the same time there are many further opportunities to utilize analytics in the background and for back office operations.

I have written earlier about how the invisible impact of FinTech is probably greater than the impact on visible services, and how the winners of FinTech will be the collaborators. In the same way, significant analytics is also often invisible, doing its work in back office functions and looking at how services are connected to one other.

Let’s take some examples:

1. Larger institutional investors need more cost-effective solutions to allocate their money to smaller ticket-size investments if they are connected to lending and investing platforms.Data analytics helps with investments and portfolio management.
2. Syndication of investments needs a combination of investors that have similar expectations and risk-return profile for the investment. Data analytics can help to find an optimal syndicate.
3. Compliance and risk management costs are significant for finance institutions. Data analytics can help to find areas and cases to focus on, put more effort on those and make the customer experience easier.

Some of those areas utilize old-fashioned data mining, but with modern data science solutions, companies can make it far more effective and automated.

Data analytics is becoming one of the most important technologies in most industries, especially with AI becoming increasingly linked to it. At the same time, however, many business, management and startup people don’t yet have a very good understanding about the reality of data analytics and especially how to utilize it.

It’s easy to go after simple stories: “Hey, let’s be like Amazon and recommend users things based on their earlier purchases.” But the big picture and realistic opportunities of analytics is much more than that. Often, the best return on analytics investments can be in those more invisible solutions that are harder to find and understand. Business people should do their homework on analytics, not just read headlines.