Solving for hard things: How better data led to better sales at Mulberry
Principal Data Scientist, Datatonic
Sometimes you can have too much of a good thing. In our new series, learn how partners like Datatonic are helping Mulberry and other retailers make sense of their bagfuls of data.
Welcome to Solving for Hard Things, a new series that showcases why business and technical partners are one of the most important components of any successful innovation initiative — often just as important as the technology itself. In our first edition, we hear from Sebastian Wolf, principal data scientist at Datatonic, on how the company helped Mulberry get its arms around a mountain of customer data to boost sales.
If you know fashion and you know bags, you likely know Mulberry. This quintessentially British fashion house offers made-to-last accessories available in 120 owned and partner stores in 25 countries. That’s a lot of bags — and with each sale comes a lot of data.
Such information has become incredibly valuable to modern businesses, particularly those in the retail space. Data collection gives retailers the ability to draw deeper insights than ever before, both on the micro and macro level, with far more detailed information available.
All of this new data can cause added headaches, however.
If teams start receiving exponentially more data points, it can become exponentially harder to gather all of this information, let alone draw insights from it. And when data is too chaotic, it can lead to data silos and swamps, which gives you an incomplete picture of your information, leading to poor business decisions.
It was a problem Neill Randall, Mulberry's cloud solutions architect, came to know all too well.
“All the data at Mulberry was coming in at different times, from different silos, in different formats, into different systems, making it impossible to gain end-to-end visibility," Randall said. "To create a global view of our stock, products, and customers, we needed to bring all that information together."
If this sounds familiar, you're far from alone. Datatonic recently worked with Mulberry to help it get more out of the data it's gathered from the 2.7 million customers who have shopped at those 120 owned and partner stores, as well as online transactions and other venues.
Here's three key areas where we worked together on improving analytics, which other enterprises might consider, too:
Searchable, networked databases — When data is spread out, it’s much harder to see patterns, especially if it leads to data silos. With cloud-based data warehouses like BigQuery, retail businesses can quickly track, sort, and analyze data from anywhere, all in one place.
Machine learning — Sorting the data of over two million customers can be done, but most of the patterns you’ll find are large, general ones. To really reach your audience, you need deeper trends – sometimes at the individual customer level. ML solutions like Datatonic's do the sorting for you, so you can go from info to insights to action quicker.
Enhanced targeting — Once Mulberry had deep insights, they were able to put out more personalized advertising. By speaking more to each customer’s needs, the click-through rate increased by 37% and return on ad spend improved by 110%.
By letting data and the cloud help drive their sales strategies, Mulberry saw a 25% increase in online sales.
If your business is gathering too much data too quickly to comprehend, these tactics may be able to help. With the right cloud service, data warehouse, and machine learning solutions, you can consolidate your data and continuously watch for new insights as customer needs change.