How retailers can use their data to emerge stronger

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Alongside the pandemic, 2021 continued to be marred by a significant and ongoing supply chain crisis. Disruption to shipping, increasing freight prices, and shortages of everything from semiconductors to HGV drivers has led to empty shelves for consumers and rising costs for retailers. Even the UK government confirmed that Christmas was at risk due to shattered supply chains, with key products still inaccessible and stuck in transit. Normally, the festive shopping period is a time for merchants to make the bulk of their profits; instead, 2021 saw many struggling to ensure customers (opens in new tab) can get anything at all for the holidays.

About the author

Richard Timperlake is Senior Vice President of EMEA at Alteryx (opens in new tab).

To cope not only with these supply chain challenges but also with other pandemic-induced issues of the past year, retailers of all sizes have needed to be nimble and adaptable. In an effort to innovate and offer shoppers the best possible service while driving further efficiency gains, many merchants have invested in alternative digital solutions to respond at speed to changing market conditions.

While reshaping business models and pivoting to digital-first strategies has required a lot of energy and a significant monetary investment at a time when both were in short supply, the good news is that these efforts were not wasted. Retailers now have the option to capitalize on this customer data (opens in new tab) and further refine it to draw out insights needed to future-proof their strategies and emerge stronger and more agile.

Demystifying data through analytics

Retailers capture all types of data – from inventory and pricing information, to customer, transactional, and operational data. These same retailers, however, often struggle to make practical use of this resource to drive decisions, support strategies to beat market adversity and identify opportunities to meet the ever-changing preferences of customers.

The key here is data science, which can be used to transform raw information from point-of-sale data or from ecommerce (opens in new tab) baskets into dynamic customer consumption behavior insights to drive decision making. By applying data science and analytics to this information, all retailers can generate data-driven insights which can help better understand product line performance, create personalized promotions to boost sales, or even create a more engaging and rewarding customer experience (opens in new tab) across their entire buying journey.

Ensuring that goods are delivered on time, every time, is now more important than ever considering recent disruption. In meeting this goal, embedding analytics into processes and operations - all the way along the supply chain – is vital to helping ensure products flow from the manufacturer, to logistics partner, to customer, without delay. These analytics can help predict whether a shipment will arrive at its destination on time, as well as provide recommendations and alternatives to improve delivery performance on both a micro and macro level.

The past year has underlined just how fragile global supply chains can be, and retailers now need to remain vigilant ahead of the next crisis. Supply chain professionals are set to invest more in technology, prioritizing advanced analytics and artificial intelligence (opens in new tab) in the coming years to address this need, but more can be done on the side of the retailer, too.

By using software designed specifically to manipulate data to optimize their inventory or model and analyze their supply chain, retailers can access several advantages. For example, using previous forecasts to help train predictive models that can optimize inventory and shorten lead times to increase efficiency throughout their supply chain. Building this supply chain visibility can help retailers to plan contingencies, such as finding alternative suppliers if something happens to their regular source. The core theme here is identifying and implementing strategies to drive resilience – to assess demand in advance and improve their ability to supply.

By combining internal data with external sources such as real-time weather forecasts and shipping information, retailers can dynamically forecast demand to anticipate product shortages and buying trends. These insights can inform decisions such as deferring a planned mark down of winter stock days before a major cold front or setting contingencies for – and quickly reacting to - a potential supply chain disruption such as a shipping container blocking a major global supply artery.

Dealing with data

All retailers – in one way or another – have data that can be used for insights that can significantly impact business decisions, whether that’s through Point-of-Sale data, or even just measuring the volume of inbound queries from customers. It’s likely that most retailers are already using some rudimentary form of analytics, too… even something as simple as a spreadsheet (opens in new tab) for staff rotas. Some believe that in order to pull useful insights, you need huge amounts of data; however, many projects use small datasets to deliver huge value. The key is the quality – not the quantity.

By starting small, and developing a firm data foundation, retailers can begin to replicate and expand their data work into more complex analyses using advanced data techniques such as predictive or prescriptive analytics. In contrast, businesses that aren’t upskilling their people to perform data analysis will struggle to anticipate changing customer needs, strengthen supplier networks, and remain on top of logistics in a way where they can respond quickly in a crisis.

Retailers hoping to emerge from the current crisis in a stronger position need to harness and leverage the power of their data into their decision making. As an example, over the last five years, supermarket chain Sainsbury’s has implemented a Property Insight team who have been working hard to produce engaging and usable insights for the business. This team is using automated analytics to optimize their property locations and integrate Argos stores – and sales - into their network following a buyout. This has helped the company to reorganize and optimize their regional strategy – a process that once took weeks – in under a minute.

Many business may not realize it, but they are sitting on a goldmine of data. Once this data is accessed, analyzed and democratized, it will be the key to continually meeting the core agreement between consumer and seller – the reliable availability of product – even as supply chains continue to fracture.

Richard Timperlake is Senior Vice President of EMEA at Alteryx. Richard has over 25 years of sales and general management experience in the IT industry, with proven success in driving high-growth businesses.