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How retailers can use analytics to adapt as stores reopen

(Image credit: Image Credit: Shutterstock/Sergey Nivens)

Throughout the first six months of 2020 retailers have been met with extraordinary challenges. As markets have been flipped on their heads and consumers have adopted new purchasing behaviors some retailers have made recording-breaking losses, and others have made huge profits. Now, as lockdown measures and government restrictions begin to ease, retailers are once again having to shift to meet the newest demands of an ever-changing landscape as well as identify new areas of opportunity in order to recoup market position and grow revenue.

One of the primary ways in which retailers can and are doing this is through their use of data. But while large volumes of data are now commonplace for most retailers, many are still not using it to its full potential with the likes of Debenhams falling into administration twice in 2020. Gartner in its 2020 retail digitalization transformation report reveals that retail success hinges on the ability to use data prescriptively across the retail ecosystem The obstacles that retailers face today present the perfect catalyst for those looking to truly be data-led in their decisions. As markets are increasingly difficult to predict intuitively, applying data effectively is the only way to shine the light on how to navigate the ‘new normal’.

With this in mind, there are some key ways in which AI and search-driven analytics can be applied to the current challenges the industry is facing, as well as help propel retailers to succeed in the uncertain longer term.

Demand forecasting and inventory planning

As economies are reopening the new trends in demand for specific markets and products are yet to be fully determined. Fluctuation in interest for products has risen and fallen dramatically in a short space of time, meaning purchasing patterns have become harder to predict. As such, retailers need to be agile and quickly pivot to accommodate changing priorities for both business and consumer purchasers. Both high-street and online retailers are grappling with the same fluctuation, and as a result, there is a race to get on top of the so-called ‘new normal’ and gain an advantage over competitors.

Retailers can better navigate these challenges and customer needs in-person and online if demand and inventory forecasting are easier to access and communicate across the business.

Machine learning solutions are able to sift through huge volumes of data much quicker than any human can, while also having the context of previous searches and ongoing market changes from other data sets to compare against. This is vital for effective inventory planning in both the short and long-term. Using these insights businesses will be equipped with the data-led information to inform re-opening rates of their stores, bringing employees back who may have been furloughed, and planning for multiple sales forecasting scenarios and how each will impact following strategies.

Instead of needing to compute complicated data-flows or write a new piece of code to pull the relevant dataset, search-driven analytics allows for anyone, no matter their experience level, to access the relevant data simply by asking. This allows companies to course correct and shift strategies as and when they need to and not be beholden to a reporting schedule. This is often not the case with current practices, as data volumes are so large and the people who need access to it don’t have the technical skills to do so, ultimately delaying important decisions that result in losses.

Supply chain resiliency

Restriction of movement both nationally, and the closing of borders internationally, as well as mass work from home orders, have all led to global suppliers meeting new challenges. Retailers are having to find alternatives and solutions to shipping materials and goods across the globe and into their stores or warehouses.

With search-enabled analytics, companies facing these challenges have been able to quickly understand the everchanging supply landscape. Using the data already being collected, retailers have gained a clear picture into how the global supply chain in major regions such as the US and China would not be able to meet much of the previous production quotas given their respective numbers of Covid-19 cases.

Accessible forecast data incorporating Covid-19 cases reveals alternative suppliers in regions, allowing retailers to diversify and meet demand. Putting the data in the hands of those looking to solve real business challenges allows companies to leverage AI insights at speed and generate the information and insights they need to make important decisions using global data from within and outside of the business.

Branch planning for physical stores

Paramount to any business’s reopening strategy should be the safety of its customers. Since social distancing is a method to reduce the spread of Covid-19 and make customers feel safer in public settings, retail locations will need to put tighter safety measures in place and limit the number of customers who can visit their establishment at any one time.

Therefore, as stores are physically reopening, there are variables that need to be considered. Leaders need to understand which locations are most at risk, how many to reopen based on demand, the timing of each reopening, as well as what additional safety measures need to be put in place and in which stores. All of this information is often stored within disparate data sets, without the context of the business needs behind the questions - not to mention it’s also often inaccessible to the people making these decisions. As well as this, diverse data must be used. Combining proprietary and third-party data is essential to gaining rounded insights. The challenge here can be finding these data sets, loading them into the database, and computing the data to display value. These problems are common, but with the correct solutions in place, they can all be overcome.

With search driven analytics large volumes of data can be indexed to make them accessible for non-technical users to access. Retailers can use this to help identify the health risk of individual branches by searching for the most relevant data-points. For example, searching for the top 20 branches with the most in-person transactions, the most at-risk locations are identified. Further insight can be gained when searching for the age demographics within this top 20. This type of relational search allows for contextual information and therefore a more valuable level of insight - in this case, the level of risk involved with opening different branches would present. The ability to use internal data and link publicly available Covid-19 cases by location is important so that retailers can have peace of mind that they are doing their utmost to protect public safety.

Accessibility and flexibility is key

In a period of unpredictability and volatile market forces a need for the ability to be flexible is underpinned by accessible data. Having access to the data is one step one. However, being able to identify the key information, process it, and in the end draw value from it to inform business decisions will be a key difference between businesses navigating Covid-19 well - or not.

With search-analytics, retailers can ask questions relevant to their business needs and receive consistent and accurate answers across disparate data sets. This increased access and efficiency unlocks value in the data that has previously been inaccessible or far too time-consuming, and in-turn will help to inform decisions to adapt to the changing product and service demand, both now and in the future.

Spencer Tuttle, VP EMEA, ThoughtSpot (opens in new tab)

Spencer Tuttle, VP EMEA at ThoughtSpot.