Dynamic customer profiles that evolve as your customers evolve
Written by Conor Simpson ·
Some of the leading tech giants such as Amazon and Uber are adopting and incorporating data analytics to help steer strategy and meet objectives and many retailers are following in their footsteps. Following this trajectory is allowing them to specifically channel consumer behaviour, shopping patterns and understand what demands are trending both geographically and industry-specific so they can sustain their dominant competitive market position.
Dynamic Data Evolution
But where did this journey begin for retailers? Well, it varies for each retailer. We decided to look at the types of data relevant for retailers, the origins and which data trends are shaping retail for the future in the below infographic.
As you can see, the types of data have evolved over time. However in recent years, it has become evident that one singular data source is not enough in providing a full 360 view of what is happening in the retail ecosystem.
Through combining various data sets, we begin to build a clearer image of what is occurring inside and outside our points of sale and uncover the factors influencing its performance.
A recent partnership with Experian delivering dynamic customer segmentation
Our recent partnership with Experian has added another data set to our Location Management Platform offering dynamic customer profiles that evolve as your customers evolve.
Data can be used by a variety of teams to meet objectives set by retailers, however these are presented with a set of challenges when using a singular static data set as changes in the retail environment occur frequently.Marketing teams with objectives such as identifying potential customers or deciding which point of sale to activate for new products or pricing, we address how these can be overcome with dynamic data.For retailers who have expansion/optimisation objectives, the need for dynamic data is even more important. For example, physical stores need to anticipate footfall outside their POS. This data needs to be granular and dynamic as datasets that quickly become outdated, can have a detrimental effect on forecasting sales. This was recently evident during the pandemic, with many consumers opting for shorter journey times or visiting local convenience stores, resulting in less footfall within city centres. Changes in consumer behaviour can have a long term impact, especially when POS are tied up in lengthy rental agreements. Footfall as a singular dataset will only take you so far, being able to profile the people passing by your store is the next step.
Having a dynamic dataset to understand which consumer profiles are visiting your store, where they came from and typically how long they spend in your store, allows retailers to anticipate trends and react accordingly.
We look at some of the other challenges of using a singular data set below;
Micro-segmentation powering the new retail
Consumers are taking a multi-channel approach when it comes to how they interact with products. Recent worldwide events combined with a surplus of choice have caused consumers to become even more selective—they expect the services and products marketed to them to be relevant. This means that blanket strategies executed across retail networks are not enough to generate sales transactions.
Companies operating in the retail ecosystem will have to dive deeper. Tailoring value propositions and product offerings more precisely on a customer-by-channel basis. Shifting strategies from macro to micro is essential to successfully reach different consumer segments in ways that are aligned to their behaviour and preferences and, ultimately, boost conversions.