While data collection in the retail and consumer goods sector is nothing new, detailed data analysis using innovative solutions based on big data and machine learning now make it possible to go much further. Not just in terms of anticipating consumer wishes and behaviours, but also in terms of the efficiency of support functions. In 2021, more than ever before, the retail and consumer section will look to data to improve its performance.
Mass expansion of data ushers brands and retail into new era
In retail marketing departments, the study of sales receipts has for many years provided a certain amount of information on the purchasing practices of individuals and in particular purchases that are generally correlated. In practical terms, retailers know that a consumer who buys one product generally buys a particular other product at the same time. This first level of analytics, far from being new, has made it possible to make connections that help with sales. But with the massive growth of data, retail has entered a new era, which offers more opportunities for the entire value chain.
We recently covered how luxury fashion brands stand to gain from client data by leveraging it to build loyalty, gain repeat sales and perhaps even let clients influence designs. Read our report, Conscious, collaborative, connected, here.
Today, thanks to loyalty cards and the rise of e-commerce – a phenomenon clearly amplified by the pandemic – it is now possible to "chain" consumption habits more precisely. In other words, businesses can observe – and therefore anticipate – what customers buy over time and the purchase path they follow.
Uninterrupted, abundant, and constantly growing, the flow of data collected by brands, retailers and internet traders is now too large and fast-moving to be interpreted in a usable timescale by human intelligence alone. This is why the combination of big data and artificial intelligence today appears to be the key to strengthening the capacity of teams and the competitiveness of retail players.
Data has something to offer the whole value chain but also creates new challenges
As well as product personalisation, improving the customer experience and the sophistication of marketing actions, data benefits all areas of retail and performance management. The cross-functionality required by omnichannel operation has led retailers to decompartmentalise their structures and information systems. For management control, this is a far-reaching transformation which has an impact on many aspects: the evolution of monitoring and forecasting processes and indicators, data modelling and business rules, support solutions, HR management, and more. Clearly, retail management control today needs to capitalise on big data and modernise its various tools (logistics, storage, for instance) to facilitate flow management.
Data also allows retail players to respond more effectively to new regulatory requirements, such as reducing their environmental impact by optimising supply chains. Ultimately, this new era highlights two major challenges for retail players.
- The first concerns customers: the collection and use of personal data must be subject to informed consent and in strict compliance with the GDPR.
- The second relates to all departments (HR, logistics, finance, marketing, and more) that will now have to learn to work together with data in an agile way. A broad programme should allow the most reactive players to get off to a good start.
Capitalise on predictive and prescriptive analytics to move ahead
Predictive analytics is the practical output from big data and business intelligence and should not be confused with statistical analysis. This first level of data exploitation involves learning from the past to predict the future and optimise actions to be planned.
For instance, in the case of retail it can provide global information on the consumption of an item, taking seasonality into account. This allows retailers to estimate sales of their products and implement the right supply chain so they can have the right amount on the shelves and in stock at the right time. Currently in the testing phase, new solutions based on machine learning algorithms have proven to be successful, giving better results than traditional approaches using time-series modelling.
Personalised marketing strategies
To remain competitive, retailers have every interest in developing their tools and, at the least, take advantage of predictive analysis to improve the reliability and overall efficiency of their logistics flows.
With even greater added value, prescriptive analytics allows an in-depth rethinking of the sales strategy by designing tailor-made shopping experiences. This is something that consumers are increasingly coming to expect as they demand personalisation, both of the products they buy and of the marketing strategies with which they are targeted.
Innovative marketing tools and technologies used by Hermes and Tiffany (among others) are bringing increased personalisation and building loyalty in the luxury fashion world. For more on the new luxury business model, read our Conscious, collaborative, connected report here.
While in the past it was necessary to make compromises between mass expansion and targeting, new approaches now make it possible to implement marketing that is both scalable and personalised. So, where the industry used to construct distinct marketing strategies for some twenty different segments, data now makes it possible to place customers in thousands of micro-segments, each of which can be the subject of highly targeted marketing campaigns (promotions, newsletters, invitations, for instance.)
In addition, the personalised experience is not just a case of offering the right products, but also about offering them at the right time to avoid advertising being perceived as intrusive. This allows both the short-term transformation rate and the satisfaction rate to be optimised, and with both comes increased loyalty. Such times could include those when the customer is in store, in particular when they visit a given department, when a personalised and highly specific offer can be pushed. In 2021, the geolocation of customers in store could become a new personalisation tool allowing consumption habits to be better understood and offers more effectively personalised.
Detect opportunities and anticipate trends
The ability to detect opportunities, anticipate trends and future best-sellers, model increasingly complex customer paths, and optimise sales through automated decision making puts it beyond doubt that predictive and prescriptive analytics add undeniable value retailers and brands’ strategies. While a number of lessons can be drawn from our knowledge of data, it is clear that many aspects and uses remain to be explored in more depth.
Nevertheless, it remains imperative that these technologies, which reinforce human abilities, are firmly rooted in increasingly accountable and transparent practices, to enable retailers and brands to win the lasting trust and preference of consumers. After all, wouldn't that be the whole point of the data?
An earlier version of this article was published by Mazars in France – go here to read it.