Can big data analytics save the high street?

According to the Press Association, up to 50,000 jobs have either been made redundant or have been threatened in the last year, with large-scale retailers most at risk of being disrupted by the growing migration of consumers towards online platforms which offer a more convenient, streamlined and ultimately customer-friendly experience.

What’s Causing the Issue?

Whilst business rates, creeping inflation, and decreasing consumer confidence cannot be discounted as factors, the primary cause for the seemingly terminal decline in the relationship between consumers and producers within retail can fairly be directed towards the now well-established role of online platforms in circumventing the necessity to visit a fixed location in order to purchase a product.

Beyond the simple consumer-online-producer dynamic, the role of disruptive technology, and big data analytics specifically, has served to further empower such online platforms by providing producers with a previously unavailable insight into the character, motivation, needs and persuasions of their customer base. Such insights have served to strengthen the disconnect between consumers and the high street, limit brand loyalty, and ensure greater resources and investments are allocated to disruptive technology.

The need for Big Data

It is for this reason that Flavio Pereira, Senior Analyst, Customer Analytics Marketing, Debenhams, is addressing how big data can positively impact the individual shopper experience, improve in-store footfall, assist in decision-making processes in real time and refine the manner in which long-serving enterprises like Debenhams can improve the means by which they source, secure and interpret big data for business benefit. Currently, 59% of British brands cannot process customer data fast enough, and 52% say they collect too much data from too many sources. Clearly, the monolithic, legacy-ridden nature of the traditional British high street shop has much to address in order to
remain relevant.

So, what can be done and what are individuals like Flavio doing to address such concerns?

First, organisations must address how to use data properly by defining what their data needs to be, which is strategic, clean, diagnostic, connected, predictive, reactive and actionable. Built into the purpose, design, and impact of a successful big data analytics programme is the power of AI, which can amplify an organisations capacity to extract insights from data, overcome the issue of scalability and complexity, and enable a greater level of usage. In addition to this, AI can also better articulate different data and change the way in which to interact with such technology through the adoption of platforms which allow continuous sourcing and storing. Equally true is the ability to automate knowledge work to improve efficiency and allow more agile and predictive decision-making, which frees time to enable more strategic thinking.

Next Steps

Once in a position to realize the potential of big data analytics and AI, organisations can begin to appropriately segment their customer base, channel marketing in accordance with the characters it produces, from social chameleons to statement makers, and add personalisation to marketing strategies to ensure an effective approach which increases efficiencies and decreases the potential for misdirected production. Only then can department stores which have enjoyed a long-held position on the British high street begin to compete with new and emerging enterprises who have disruptive technology and agile processes at the heart of their activities.

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