Analysis

GUEST COMMENT How to use big data to target and engage your customers better

This year, we have seen the collapse of BHS and Austin Reed, both sending shock waves through the British business establishment. Ever since the fall of these retailers hit the headlines, there has been speculation as to how two mainstays of the UK high street ended up failing.

Despite these headlines, the underlying cause as to why BHS and Austin Reed collapsed is relatively simple. Both companies struggled to connect with the changing needs of their customer base, and so their offerings became outdated.

These examples provide a reminder to all businesses to keep customers’ needs at the centre of their mission, regardless of size or sector. A failure to understand customers’ tastes, habits and preferences can be detrimental.

For marketers operating in today’s fast paced digital environment, the stakes can hardly be higher. At the heart of the modern retailer’s challenge is how to ensure you know your customers inside out and keep up with their needs 24/7. It is here that the spectre of big data provides both an opportunity and a challenge. It is crucial that marketers get to grips with the challenge of how to obtain and manage good quality data, analyse and develop valuable insights and then develop effective marketing campaigns off the back of them.

Creating valuable insight out of reams of data
The sheer amount of data that retailers acquire is phenomenal. Consider the challenge that faces big companies, such as a grocery giants selling millions of products to millions of British customers every week. That is a huge amount of data to make sense of. The fundamental principal that will determine success is taking time out to understand what data sources are available and figuring how to link all the information into a single view of the customer.

Chief data scientists and their teams of experts need to ensure there are feeds in place from all the data capture points and that they understand how they all link together. Most customers shop using multiple different channels, so a retailer needs a good understanding of how each of these channels vary and how you capture data. Once you have a good understanding how the data structure works, you must make it work from a customer point of view. The goal is to make a customer’s life easier or better, after all, the customer is king.

Electronic point of sale (EPOS) data is really valuable, as it tells you how customers are behaving. This is all done simply by understanding what customers are putting in their baskets and what they are spending. The key is to combine EPOS data with other data feeds – research data for instance. In addition, most big retailers will have a customer experience tracker where they get structured data through quantitative surveys and unstructured data through verbal feedback.

Once you make sense of the data and understand your customers, you can develop your marketing communications strategy to effectively target individuals. Retailers can tailor their communications and offers to their customers’ wants and needs based on their shopping habits.

Data analytics systems – segmenting customers
Data analytics systems that help segment customers are a core part of a retailers’ strategy in acting upon the drivers and motivations of different customers. Analytics software and systems can help to build the messages, channels and campaigns that will engage each customer segment, generate leads and build incremental sales.

The key requirement of any software system is to ensure it is dynamic, so that targeting and analysis is always as precise as possible. Systems that merely show a single, static snapshot in time are of no use in today’s fast paced world.

As with all segmentation tools, you need a system that can identify who your customers are and who and where your best prospects might be. Organisations need insights to accurately target relevant consumers with relevant messages to increase the number of qualified leads generated and reduce the costs per lead/acquisition.

Dynamic databases need to reflect changing consumer characteristics. People move between segments when they experience a dramatic change in affluence or a key life event. This includes setting up a home, getting married, having children, emptying the nest, entering retirement or reaching pensionable age. By segmenting target customers in this sophisticated, dynamic way, marketers can launch mini campaigns to each customer segment with optimal results.

Whatever software is used, it has to be able to handle huge amounts of data and quickly. It must be able to bring in different types of data sources, and include range modelling, price modelling and basket analysis.

Access to the right data
If a company has up to two to three years’ worth of transactional data, with current systems this data cannot be sliced and diced very quickly. The solution is for each individual retailer to build their own smaller area of data that is not only relevant to exactly what they are trying to achieve but will also be used on an ongoing basis, so that they have quick access to the right data.

Ongoing conversations are important, either on a weekly or daily basis. It is also helpful to create and update a set of data marks, a weekly suite of reports or dashboards that tap into a wider data warehouse, and help organisations quickly get to the heart of their progress.

Understanding your customers is key
Understanding your customers is the fundamental bedrock of success for any retail business. Know your customers, develop the products they need, and deliver the right propositions to the right audience.

Good quality data capture, analytics, systems and processes will go a long way to ensuring that retailers keep close to their customers. In this way, retailers can increase customer loyalty, improve the customer experience and ultimately build sales and gain a competitive edge.

Steve McNicholas is managing director, marketing solutions at the Callcredit Information Group

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