Making business personal: using data to become a customer centric organization

Retail life used to be a lot simpler and being personal was a natural part of doing business.  Walking into your local butcher, you’d be greeted with not only a smile and a hello, but you’d also be told about the special new venison sausages that you loved and not have to be asked how thick you like your ham sliced.  And, as such a loyal customer, you might find that every once in a while you’d be offered a free bone for your dog as a special thank you.

While there are plenty of local independent stores, and a renaissance of food markets, the whole shopping business has become a lot more complicated and, as a result, much less personal.  The vast majority of our shopping is done at multiple outlet retailers, who typically will stock tens of thousands of products and have thousands of customers walking through their doors each week.  To get back to the personal relationship with your customers requires a different approach – a data-driven approach to be precise.

Large retailers, like Sainsbury’s in the UK, have used market research for many years as a way to listen to their customers, either at the checkout or via email and telephone.  This provides a fantastic way to listen and react to customer feedback, but is limited to the hundreds or thousands of people who are able to be contacted.  Sainsbury’s talks about the access to the data from their loyalty program as a way of being able to extend this focus on the customer and enable them to listen to all of their millions of customers.

Loyalty programs offer a critical step in this path to customer centricity.  Loyalty programs have been around for many years.  But their purpose has evolved over the last 5-10 years to move from being a mechanism to engender and incentivize your loyalty to a particular brand, to being a means of capturing data on your customers.  This data, if used properly, can then become the mechanism itself for encouraging loyalty to a company.  The data enables CMOs to become personal again with their customers.  And the evidence is absolutely conclusive: being personal yields significantly better results.  The data itself can be used to prove it.  We have seen targeted communications out-perform mass by 4x, 5x, even 10x.

So, now that companies are capturing lots of data, where do they start?  Understanding your different customers, in particular, your best customers, is the most sensible place to set out.  Customer segmentation has been around for a while, but the sophistication of how we identify different groups of similar customers continues to advance.  Segmentations based on the maxim of “you are what you eat” have enabled fantastic understanding of customer lifestyle, price sensitivity and preferences.  But these would typically take several months to complete.  Today, in an age of productization of analytics, it is possible to build sophisticated segmentations in a matter of days or weeks.  This changes the game: segmentations can be developed for a particular category or campaign program.

The overall principle remains the same though: segmentations are most useful in terms of changing the mindset for the Executive team: there are several types of customer rather than one.  But in fact there are several ways to look at customer “type”, so segmentations work best when considered as different lenses on the customer which can be overlaid to give a richer picture: life stage, lifestyle, loyalty, share of wallet, price and promotion sensitivity and trip mission.

Again, we know this approach works, and have seen examples from across the world where data-driven insight out-performs traditional methods by 3-7%.

Making decisions on assortment, space, promotions, pricing, and store location will be more successful if they are driven by the understanding of customer segmentation and behavioral data.  You may be missing a group of products to appeal to your upmarket families.  You may have focused your promotions too much on just the deal-seekers, who have not brought incremental spend to your store during the offer.   Again, we know this approach works, and have seen examples from across the world where data-driven insight out-performs traditional methods by 3-7%.

When it comes to communications, these insights are still extremely relevant, but we should be aiming higher than five or six versions of the email campaign.  The target should be set at unique communications based on individual customer behaviour.  This is the only way to get back to the days of the local butcher and the ability to talk to each customer with relevance and personalization.