Data-Centric Strategies Driving Customer Loyalty in the Retail Sector

driving customer loyalty with data

The retail landscape in the UK and Europe has experienced major change in the last few years. Many of the big names like Toys R Us, Debenhams and New Look have been loyal to the high street and shopping centres for decades, but have since either cut back their physical operations, embarked on financially-challenging restructuring, or disappeared completely.

But it’s not all doom and gloom. While many have struggled, the high street has also been home to a number of successes – many retailers are thriving thanks to their use of innovative tools and technologies, maximising the value of their data sources and using analytics to make more informed and better predictive decisions. It’s also promising to see that the vast majority (82%) of retailers are committed to implementing a data-centric strategy in the next five years, with almost a third stating this is already in place. 

At the centre of this analytics shift is the now familiar debate around cloud and on-premise strategies. While there are merits to both, it’s important that a retailer’s long-term data strategy is flexible. That’s why many of the successful retailers today are opting for a hybrid cloud and on-premise approach. One that harnesses the almost limitless processing and storage capabilities of cloud platforms, such as AWS and Azure, while retaining the ability to interface with on-premise data and application resources. It also allows for the aggregation of data from external sources to further inform outcomes.  

This is important because retailers are looking not only for a competitive edge, but also for ways to better understand the customer, the marketplace, the supply chain and the evolution of trends. Gut feeling and experience alone are not enough anymore – data is essential to completing that equation.  

Using data for differentiation

A great example of a retailer embracing data analytics to enhance the customer experience is e-commerce platform Zalando. It specialises in fashion and is using data to deliver an “emotional and qualitative” marketing approach. Zalando is extracting smart insights that go far beyond the usual socio-economic and seasonal factors that drive the fashion sector, giving Zalando a level of differentiation that very few can match. 

An in-memory database is at the heart of Zalando’s analytics work, using internal data sources, and external ones such as social media, to transform business intelligence (BI) into powerful analytics. This helps the organisation anticipate and respond more quickly to customer behaviour, adapting its product range and go-to-market strategies along the way. On top of that, it’s flexible enough to work in the cloud, while easily fitting with any legacy data system on-premise. 

Data-centricity is key

It’s not just fashion retailers that are benefiting from this approach. European mail order company and e-commerce retailer Otto Group has undergone significant data analytics transformation in an effort to consolidate systems, data sources and be more agile in the face of a constantly changing marketplace.

Rather than just bringing in waves of technology alone, it aligned the need to be smarter with data alongside the cultural and operational shifts needed to make such a new strategy work. It ingrained data-centricity into every business unit within the Otto Group family.  

The retail sector continues to be pushed to the limits by the always-connected, digitally-minded consumers that want convenience, personalisation and value in the omnichannel world. Retailers are coping with razor-thin profit margins, increasing the pressure to deliver for customers at any time, any place, at the right cost, and personalised to them.

Customers don’t only wish for, but expect, an exceptional and consistent experience across all channels now. The new battle lies in better understanding and successfully anticipating what will appeal to individual customers. Winning that battle is reliant on having the right data strategy and exploiting the speed and depth of insight now made possible by in-memory analytical databases.

An in-memory database helps to keep up with changing customer demands and to help consistently provide the best experiences. This allows retailers to break down the barriers between disparate systems such as stock control, POS, staffing, supply chain, or provenance and it enables these individual data sources to be exploited as part of powerful data analytics that span an entire organisation. 

The key is ensuring the sector has the right tools, skills, processes and strategies in place to make best use of analytics-derived insights and information analysis, at both a business, and technical, level. And above all else, you need to have a database that’s built for the future of retail. 

Credit: Helena Schwenk, Market Intelligence Manager at Exasol.