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Analytics – giving retailers the edge and boosting sales
March 2019, Retail (Industry), Integrated Solutions

With the buzz from Black Friday and the festive season now in the rear-view mirror, retailers are looking ahead to ensure the continuation of ‘traffic’ into malls, high streets, pop-up stores, websites, apps and other channels. Today’s retailer is anywhere, anytime, and they’re providing greater levels of personalisation with their services.

But for those local retailers who want to up the ante in 2019 and position themselves for a radically different festive season this year, the concept of Big Data is often touted as a game-changer. But just how can data analytics impact retailers?

Simon Shaw
Simon Shaw

Attracting customers

Retailers have the opportunity to tap into real-time analytics from a number of sources: shopping centres, within their own stores, and more broadly in the consumer market. The goal? To better understand customer behaviour and preferences.

For instance, if you’re a clothing retailer, you can generate some powerful findings by connecting this season’s hottest Instagram trends with your own stock inventory.

If those insights tell one what to sell, then there are a number of data-led innovations that we can leverage to improve the way we sell. Within the customer experience itself, local retailers are getting excited by opportunities in areas like virtual and augmented reality (such as smart mirrors in stores that allow customers to virtually try on new outfits), smart checkouts that don’t require the individual scanning of items (no more queues) and interactive interfaces that instantly help shoppers to find what they’re looking for.

Moulding structured and unstructured for ultimate value

As we noted above, analytics can inform a far richer understanding of what’s driving customer behaviour. Among local retailers, we often find that the problem actually isn’t a lack of data, but rather a lack of expert data scientists to tease out meaningful insights (that can power a new direction for the business).

Over the coming years, as more specialists enter this field, we can expect an expansion of retailers’ vision and appetite for Big Data.

One of the most important considerations in a retailer’s data strategy should be how they unify a number of disparate data warehouses, into a single format and structure. Here, we’re talking about data from the likes of loyalty programmes, point-of-sale systems, surveillance cameras tracking footfall, and inventory management systems.

The next step? To take all of this internal, structured data and fuse it with the external, unstructured data from the likes of social media, reviews and shopping websites, eCommerce portals, and other pop-culture tastemakers that define customer trends. The true power of data lies in our ability to combine these insights, to see challenges and opportunities from a multitude of directions.

Responsive pricing

Newton's third law of motion states that 'for every action, there is an equal and opposite reaction', and so it should be for retailers: any change in pricing strategy from their competitors should be met with a promise to match.

This becomes a powerful marketing message, and a way to build high levels of trust and brand affinity among consumers. Today’s millennial shoppers tend to have less loyalty to brands and place more emphasis on things like price comparisons. It’s not uncommon for retailers to see potential consumers checking prices for competitor offerings, while standing on their own shop floor.

But to achieve truly dynamic and responsive pricing, retailers need to build in the scanning, analytics and automation tools, to automatically adjust pricing as the market changes. However, for many local retailers today, a sense of inertia is descending. They’re just not sure what technologies to adopt, what challenges to tackle, and what opportunities to pursue.

This is why it’s important to have a trusted partner that sees the data strategy as a longer-term play (and not a tactical solution). This partner must have vast data processing engines, into which massive volumes of data can be fed, to produce meaningful results.

But ultimately, each retailer’s data strategy must be customised, to meet the specific business goals that they’re chasing, and their unique market dynamics. After all, what may be a fairly irrelevant technology for one retailer may be an absolute game-changer for another.


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