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5 ways analytics are helping businesses master post-pandemic retail transformation

Pandemic disruption may be winding down. But for retailers, the real transformation is just beginning. Here’s how analytics can help.

It’s been a challenging couple of years for the retail industry. The pandemic triggered huge shifts in customer preferences and buying behaviours, with restrictions transforming how we were able to shop overnight.

Fortunately, the worst of the disruption is now behind us. But while retailers have been weathering the storm and adapting to an unprecedented global crisis, the need to digitally transform their core operations has only grown. There’s a new environment to master, new behaviours to understand, and new expectations that all retailers need to meet if they want to achieve their goals in 2021 and beyond.

Here are five ways that analytics are helping retailers lead powerful post-pandemic transformations, and drive success in today’s new retail environment:

#1: Re-evaluating customer behaviour

Even during a typical 2-year period, customer behaviours and shopping habits can transform massively. But the last couple of years have been anything but typical. To learn what today’s customers want, and address evolving behaviours, retailers need to capture as much data as they can, use analytics to better understand it, and then turn those insights into experience and commercial optimisations.

For example, a recent digital audit conducted by The Smart Cube for a UK- based ecommerce client, demonstrates the impact of using data effectively on cross-selling and revenue. 

Our in-depth audit and assessment of the existing infrastructure and architecture of the digital landscape covered factors including data layer assessment of the ecommerce website, tags/events configuration, features and ads positioning, and product impressions. From the GAP analysis, we made recommendations focused on capturing data effectively by improving data layer and tags configuration, and optimal placements of features and ads on the web pages, based on customers’ browsing patterns.

By gathering and analysing thousands of different data points to build up a complete view of how customers were using the platform, we found that very few were engaging with the company’s product recommendations, due to their position on the page. Equipped with that insight, the company moved recommendations into an area that saw consistently higher engagement – increasing uptake of recommendations and driving a significant increase in cross-selling and revenue.

#2: Helping sales teams adapt to change

When market and behavioural forces change the way people buy, retailers need to change the way they sell. That doesn’t just mean optimising and activating channels in new ways – it also means changing the roles and priorities of salespeople. Those salespeople need to be supported through the change, and analytics can help.

We recently worked with a large US-based company that was going through an enterprise-wide transition from direct selling, to moving all customers through its ecommerce platform. That meant huge changes for the company’s thousands of salespeople.

To help, we broke down silos and leveraged data from multiple ERP systems to analyse sales rep performance and presented it all through an intuitive Tableau-based dashboard. Using the dashboard, the company could see at a glance which salespeople were struggling to adapt to their new KPIs, and provide targeted support where it was needed most.

#3: Better understanding stock shortages to create a more sophisticated supply chain

Between the pandemic, the Suez canal crisis, geopolitical events like Brexit, and a global shipping container shortage, retailers around the world are feeling the pressures of stock shortages. Analytics can’t fill those gaps and keep products on shelves. But, they can help retailers better understand how shortages are impacting them, which SKUs need to be sourced as a priority, and where shortages are hitting customer experiences the hardest.

By monitoring capabilities like stock level notifications – the emails customers can sign up to receive when a product is back in stock – retailers can build up a clearer picture of how stock shortages are impacting customers and revenue. If notification sign-ups surge for a particular product, that’s a good indicator that it should be sourced as a priority – and a clear sign that short supply is leading to unsatisfying customer experiences.

Elsewhere in the supply chain, analytics can also be applied to monitor supplier performance. As with any crisis, some suppliers will be hit harder than others, and real-time analytics can give retailers a dashboard-based view of which suppliers aren’t able to meet the terms laid out in their contracts, so they can re-evaluate their viability.

#4: Innovative merchandising through the power of AI

In the highly-connected world of omnichannel retail, the competitive landscape is constantly shifting. Every day, new products and competitors emerge, offering similar products at different price points – each with the power to undermine even the most well-planned product, price, and range strategies.

For large retailers, trying to keep up with those new entrants and manually making shifts in competitive product offerings isn’t an option. But, by applying the power of AI to merchandising analytics, it’s more than possible.

It’s a concept we recently explored in detail in The Smart Cube’s AI Lab. Using a large set of Amazon review data, we used advanced Natural Language Processing techniques to create a product matching model capable of finding matches across hundreds of thousands of products incredibly quickly.

Retailers can use similar models to transform how they conduct competitive range analysis – creating new opportunities to optimise product ranges and pricing, and gain a valuable edge in an exceptionally competitive market.

#5: Digital revenue forecasting

The last couple of years have been something of an anomaly for all of us. For retailers especially, there’s a very good chance that their forecasted revenues for 2020 and 2021 look very different to actual earned figures.

Now, building strong forecasts that account for the shifts seen in the last two years is an important part of every retailer’s return to normality. For those that have previously relied on traditional forecasting methods, it’s also a great opportunity to modernise forecasting, and gain the benefits that analytics models and approaches can deliver.

As part of a recent project in the US, The Smart Cube helped a large company build a granular model for forecasting that enabled it to accurately forecast at the organisational, team, and customer levels. Empowered with that insight, the company can now clearly track target versus actual performance across markets, territories, and even for individual sales reps – all through a single intuitive dashboard.

Ready to talk shop? We’ll be at Shoptalk

With so much changing, and digital transformation back at the top of leaders’ agendas, it’s a hugely exciting time to be in retail – and there’s a lot to talk about. That’s why we’re attending the Shoptalk Europe Fall Meetup this week.

If you’d like the chance to talk about how analytics can help your organisation master the shifting retail landscape and kickstart your post-pandemic transformation plans, please get in touch to learn more about our Merchandising Analytics solution.