Today, new and emerging technologies are providing access to unprecedented levels of data, enabling us to understand and predict human behaviour in ways never experienced before, and creating huge potential to personalise and enhance customer journeys.
Thanks to market research, point-of-sale information, loyalty cards, trackable online and mobile transactions, location data, and social media insights, retailers are sitting on vast pools of customer data.
Yet many businesses struggle to manage this invaluable resource, as data has become increasingly complex, and is of limited use without intelligent analytics.
To derive value and gain a competitive edge, brands must not only gather and store data, but turn it into actionable insights.
When done correctly, the result is a win-win for shoppers and retailers alike. Shoppers benefit from a more relevant and helpful customer experience, and brands derive a boost in sales and profits.
Data-driven personalisation
In-store loyalty cards were once the cornerstone of customer analytics. By offering rewards in return for purchasing data, retailers could access insights to improve operations. But shopping channels have evolved and multiplied: consumers now engage with retailers online, in-store and via mobile, generating an exponential increase in data.
This presents exciting opportunities to track customers across platforms – but it comes at a price. Today’s consumers are much savvier about the value of their data, and in exchange, they expect even more relevant products and promotions. Critical to the success of these incentives is personalisation.
According to Monetate, 83% of customers expect personalised online shopping experiences. Many retailers have stepped up to the challenge. In 2015, Shop Direct launched a cutting-edge, data-driven service for Very.co.uk, creating 1.2 million iterations of the website, from which customers could create unique, personalised homepages.
While online shopping is in the advanced stages of personalisation, tomorrow’s task is customising the physical journey. Starbucks is looking to use a data-driven Artificial Intelligence (AI) algorithm to deliver bespoke barista services.Customers using an app containing buying preferences (and contextual variables, such as local weather), will receive targeted product and reward recommendations.
Fashion retailers are using smart mirrors in-store to read tags and display additional information while shoppers try on items. The future will see digital shelving replace static price tags, dynamic pricing reflecting personal buying patterns, and technology that scans tags as products hit the shopping basket.
Making content contextual
Personalisation is just one application of data analytics. Knowing consumers’ preferences and behaviours allows retailers to provide useful and relevant content, as well as to predict future purchases.
In 2015, The North Face piloted an online personal shopping platform powered by AI and IBM Watson. It worked by asking visitors a series of dialogue-based questions that mimicked the knowledge of an in-store expert. Interpreting and evaluating the answers led to targeted product and content recommendations, resulting in higher sales and more satisfied shoppers.
Elsewhere, eBay is using data to predict future customer needs.[v] Someone buying items for a new-born can be retargeted 12-months later with clothes and toys for a one-year-old. This shows that with detailed data analytics, marketers can be in the right place, at the right time.
Faster order fulfilment
Data has long allowed retailers to forecast demand, and this is now being enhanced by big data and advanced machine learning. In 2014, Amazon patented ‘anticipatory shipping’, which uses algorithms to predict what customers might buy. Products are then shipped in their general direction, all before a completed purchase, significantly reducing late delivery returns.
The shipping models of Amazon and other tech-giants have set new standards for all online retailers, and increased buyers’ expectations. According to Temando, 80% of shoppers expect same-day shipping, yet only 47% of retailers offer this. 77% of consumers want guaranteed weekend or after-hours delivery, but this is only available from a third of retailers.
Seamlessness with tomorrow’s technology
Next-generation connectivity is changing the way we shop. Beacon technology allows retailers to send push notifications to shoppers as they walk around a store, so promotions are both personalised and timely.
Amazon recently opened Amazon Go, a supermarket without checkouts. Instead of physical tills, mobile apps and location sensors track items picked up by shoppers, and when they leave, the app simply charges their Amazon Prime accounts.
But it’s not only the frontline that can benefit from innovation and intuitive technology; behind-the-scenes functions can also be transformed. SAP is testing digital eyewear in its warehouses, which puts information right in front of workers, instead of on a tablet or laptop, making things run smarter and faster.
So what may seem like futuristic technology is in fact here today. Further developments in the Internet of Things, smart home devices, and wearables will accelerate change in the retail landscape. Brands must consider how these disruptive technologies will impact consumer behaviour, and how they will respond using advanced analytics.
New ways with data
Data volumes are growing, and so too are its application in retail. The right insight can bring improvements in customer experience, personalisation, product delivery and cross-channel integration.
Going forward, brands that come up with bold ways to innovate will be the winners. Advanced sensor technology in particular has enormous potential to make in-store experiences as – if not more – engaging as digital. Revolutionising the customer journey has only just begun.
At The Smart Cube, we combine advanced analytics, data science and technology to solve our Retail customers’ most pressing problems, helping them to thrive in today’s competitive environment. Find out more about our analytics solutions, and to learn about the latest data science trends and the analytics projects we’re working on, explore our blog posts.