Big Data and Retail
Many retailers are tapping into big data sources to enhance their customer understanding and future-proof their businesses. But why is big data and retail such a winning combination? Read on to find out…
Big data in retail: statistics
- 2.5 quintillion bytes of data are generated every day – a number that is steadily increasing.
- 90% of the world’s big data has been created in the last two years.
What are the biggest challenges facing the retail sector?
The way we buy and sell is evolving rapidly, both on and offline. The retail industry is facing major pressures, including the stratospheric rise of e-commerce, political and economic upheaval and the modern consumer’s growing preference for ‘experiences’ over ‘things’. Add to this astounding technological advances, limited brand loyalty and increasing competition as cross-categories become the norm, and you have a particularly challenging moment in time for retail across the globe.
In such a complex marketplace, it’s essential to understand your customers better than ever before. That’s where big data and data science comes in.
Why should retailers use big data?
Brands and retailers traditionally turn to market research and transaction data for insight into consumers, but these data sources have clear limitations.
Market research data is sourced from a small sample of people and can reflect biases, tending not to reflect people’s genuine opinions and passions. Questions can also be leading. Historic transaction data can be a useful source of information for large, transaction-heavy organisations such as supermarkets, but most organisations don’t have the volume of data to really understand what customers want and will buy, instead being left with a partial picture.
Luckily, retailers no longer need to rely on these traditional data sources; there is an infinite amount of information now available about customers’ behaviour.
Big data provides context around purchases, indicates emerging trends and showcases the passions and motivations driving new behaviours. In other words, it allows for a complete customer view.
Why is big data better than claimed data?
Powerful third-party data sources, such as social and geodemographic data, can enrich a retailer’s transaction data to reveal a much more granular picture of their customers. It allows you to see small changes in behaviour across millions of consumers rather than focusing only on your ‘best’ customers.
Segmenting your customers by their passions and motivations, as well as their purchasing behaviours, means letting the data dictate how certain products, brands, media or influencers group together, leading to an approach that’s much more customer centric.
By putting motivations and mindsets at the centre of customer insight, you can see what customers really love and find patterns around intention to buy.
Big data allows you to answers questions such as:
- Who are my high-value customers?
- What motivates my customers to buy more?
- How do my customers behave when they’re not shopping with me?
- What channels do my customers prefer?
- How should I speak to my customers?
Starcount’s platform, The Observatory, enables retailers to see insights from over one billion global consumers, tracking how their passions, motivations and mindsets change over time.
When should retailers use big data?
Big data and retail informed by analytics can be applied to improve every part of the customer journey and retail process, including:
- Customer acquisition
- Channel strategy
- Prices and promotions
- CRM and loyalty strategy
- Store location planning
- Supplier relationships
Is it GDPR compliant?
Yes – Starcount’s approach to customer insight, big data and retail is completely compliant with GDPR. Our data sources are aggregated and anonymised/pseudonymised.
How should retailers use big data?
- Focusing on the value of ‘one more product, one more visit’ – getting each customer to buy one more thing or visit one more time can lead to huge uplift.
- Personalising the in-store experience – building ‘the store of the future’ by analysing store behaviour to optimise merchandising tactics, personalise loyalty apps and create timely and relevant offers.
- Predicting trends – do deeper than social listening and sentiment analysis to understand what the silent majority on social media really care about. Using big data with our unique passion tags, Starcount can see which passions are growing for which consumer groups, and why.
- Optimise pricing and promotions – rather than reducing prices across the board during sale season, use big data to understand which customers are driven by pricing and which products are most attractive to them. A more sophisticated pricing strategy saves money and retains the customers who are motivated by discounts, while not losing potential profit from those who are happy to buy at full price.
- Store location planning – use big data and geodemographics to retain customers despite store closures and identify the locations where your store would be in demand. You can also use big data to break through in new and competitive global markets, identifying lookalike prospects and understanding how trends and tastes differ around the world.
- Informing CRM, loyalty and comms – develop an insight-led customer communication strategy by understanding what motivates different customer groups and tailoring language, channels and offers to suit their passions and needs.
- Improving supply chain – big data can be used to improve stock availability and supplier relationships by optimising product offerings for customers and creating a more efficient supply chain.
Are any retailers using big data as part of their strategy?
Yes, and the list is growing. Here are just a few of the retail clients we have worked with: