Predictive analytics can help retailers bring loyal customers back to physical stores – but retailers must gain the data science skills to make that happen.
Did you know that by 2040, artificial intelligence (AI) is expected to help increase retail profitability by 60%? After several years of falling profits and high-profile store closures, it’s cause for renewed optimism.
By harnessing data science and AI-powered predictive analytics, brick-and-mortar retailers can turn their data into a continuous source of strategic insight. That in turn can drive initiatives that boost sales, build loyalty, and bring shoppers back to physical stores.
In an age where shoppers are increasingly fickle and driven by experiences – not products – that insight pays off. A recent Microsoft study found that organisations using AI perform an average of 11.5% better than those who are not – up from 5% in 2018.
What do predictive analytics look like in retail?
Predictive analytics requires significant volumes of customer data and data science skills to build the models that will unlock accurate insights from it.
By applying predictive models to the data, retailers can anticipate changes in shopper habits and market trends, and experiment with new ways to build sales and loyalty.
For example, one underused source of data is in-store CCTV footage. With the right analytics tools, retailers can analyse historic or real-time video to surface shopper interests and footfall trends, and even generate insights based on which brands of clothing different demographics of store visitors wear.
Other reliable sources of customer data include point of sale systems (including card payments and loyalty programs); customer engagement on social media channels; and WiFi or mobile-enabled proximity beacons which respond when a customer’s compatible device comes within range.
The ‘human’ barrier to AI success
But while most retailers are sitting on a wealth of customer data, what’s missing today is the data science skills to build and apply predictive models to it.
Microsoft’s study found that retail is in the earliest stages of AI-led digital transformation, with only 43% of retail leaders using the technology compared to a national average of 56%.
Behind that lag is a lack of up to date skills. Microsoft found that only 11% of employees and 19% of leaders say they have completed training to improve their understanding of how to use AI in the workplace. What’s more, only 19% of leaders say people in their organisation can describe how AI can help achieve their business goals.
Without the knowledge and skills to make smart use of data and AI-powered modelling, retailers risk missing out on valuable new opportunities to define problems, respond at scale, and stay focused on the customer experience.
Data science upskilling at M&S
Marks & Spencer (M&S) recognised this and has launched its own Retail Data Academy to help build and train up “the most data-literate leadership team in retail”.
Created in partnership with a technology education specialist, the academy will take more than 1,000 employees from across every retail function in the business to upskill them with “an in-depth level of digital literacy as well as a Data Analytics qualification.”
Its overarching goal? To change its digital behaviours, mindsets and culture to make the business fit for the digital age.
As more retailers begin to recognise the value of predictive analytics, we expect many to follow M&S’s lead and forge partnerships with trusted data science consultancies.
Read more in our new white paper
To learn more about how predictive analytics can help you boost in-store sales, read our new white paper Regaining loyalty: How to use data to bring shoppers back to brick-and-mortar retail.
You’ll also learn how Adatis can help you build and maintain a data analytics infrastructure that continuously unlocks strategic insight from your data. If you’d like to talk today about how we can help, please do get in touch.