Retailers currently face many challenges in times of increased competition and the need for providing the ultimate shopping experience to attract customers. A diverse and unforgettable shopping experience is based on a great deal of information, but the collection of customer data also involves problems. This blog is about the mistreatment of data in the retail industry and we’ll discuss whether the use of data is compatible with the mindset of today’s customers and decision makers in companies.

The mistreatment of data

In times of the digital transformation and many touchpoints with brands and retailers, customers don’t want companies to spam them every two minutes. Surveys and other engagements on Facebook, Instagram and Twitter are common methods retailers often use to collect contact details of their followers and potential customers. The consequences are emails with general advertisement for various products, but rarely products customers really want to buy. The problem is that many retailers only use transactional data to forecast the demand of customers. They need new tools to understand demands of customers and preferences based on their in-store and online purchases with large datasets. It is therefore helpful to factor in external events and buying behaviour across various channels. The quality of this data obviously plays an important role in this customer centric strategy that brings up another key point.

Before they can use big data for analytics efforts, data scientists and analysts need to ensure that the information they are using is accurate, relevant and in the proper format for analysis. That slows the reporting process, but if enterprises don’t address data quality issues, they may find that the insights generated by their analytics are worthless. As mentioned in the last paragraph, unneeded and inaccurate data often leads to impersonal offers and a poor inventory management. The filtering of useful data, in turn, enables retailers to offer a personalised experience to their customers in real-time. Retailers need to invest in forecasting and data science competence to ensure that the collected data can be used correctly, but only a small number of companies have an entire data science team and the development shows that retailers become more sophisticated in forecasting.

As soon as retailers are aware of the needs of their customers, they should communicate this progress and reasons for the collection of people’s data, because the lack of transparency ensures a distrust of people with companies. Customers worry about mistreatment of data and retailers need to develop trust of customers and explain, how they will use the data and provide results as soon as possible, of course while mentioning all benefits for consumers.

In order to achieve this, a cultural change in the retail industry is essential. Many of the organisations that are utilising big data analytics don’t just want to get a little bit better at reporting, they want to use analytics to create a data-driven culture throughout the company. So far, only 32.4 percent of firms were reporting success on this front.

Conclusion

Collecting customer data has been notoriously loaded with a tangle of privacy pitfalls, but when done right, the benefit to the bottom line could outweigh the risks. The majority of UK customers actively favour companies who are able to capitalise on their data and say being sent personalised or exclusive offers and discounts has a direct influence on their loyalty. This shows that the conditions are given to cause a cultural change in companies, based on data.