The retail landscape has changed dramatically in 2020. More people are shopping online than ever before, with online shopping growth now reaching 62.7 percent year-on-year (YOY). Businesses that provide groceries, household supplies and in-home entertainment have all seen increased spending.
In fact, Woolworths Group e-commerce sales jumped to $1.5 billion in the first quarter of fiscal 2021 and increased 87 percent from the corresponding period a year ago. At the same time, many businesses such as restaurants, consumer electronic shops, apparel retailers and hotels are bracing for lower spend.
The risk is this these trends will linger for some time even as the region recovers. And because of this, many ideas and assumptions that worked for businesses in the past no longer apply now and in the future.
Making data decisions without slowing down
In this uncertain climate where consumer confidence is in a constant state of flux, retailers must redefine the speed, accuracy and location with which they track, analyse and utilise customer data. Pre-defined queries, based on conditions of the past, will become less useful in an ever-changing business world. Retailers need the freedom to ask any question about their data, but there is rarely time to stop and ‘go do analytics’.
This is where embedded analytics can be key to making the best decisions without slowing down. It brings the power of analytics to the point of decision for every business user, within their applications, workflows and information services, where it can deliver immediate business value.
This helps retailers access and analyse data that is usually unused or difficult to locate, so that they have the right insights, in the right context to make accurate decisions quickly and accelerate their response to what’s happening in the market. Retailers that make decisions better, faster and with more accuracy will enhance their value to customers and gain their loyalty.
Explaining embedded analytics
Let’s use a food and beverage retailer for example. The retailer ramps up its online presence and offers home deliveries at the height of COVID. There are a few ways embedded analytics can help the retailer optimise operations and boost business.
To adapt to new buying patterns, the retailer embeds analytics into its inventory applications. When checking stock levels, branch managers automatically get insights on what items are selling more, so that they can better decide what to promote and what to stock up on for the week. They don’t have to wait for someone else in the organisation to tell them; they can access these insights and immediately decide themselves.
Then there’s the new delivery fleet processing online orders and home deliveries. Instead of monitoring the fleet and directing movements from a centralised control centre, the retailer embeds analytics into the fleet management software, giving staff more control over delivery times. They now know which drivers will become available and can prepare the orders just in time for the delivery schedules. This means the retailer becomes more responsive to customers and agile to changes in the market.
A grocery retailer for example can embed analytics into the app which consumers use to order their food. Visualisations or dashboards analysing the order history could show consumers when their favourite foods will be back in stock or on promotion, and which other items would complement them. This way, the retailer goes beyond just selling products to influencing consumers’ lifestyle.
Forecasting future demand
With embedded analytics, retailers are in a better position to weather disruptions and forecast future demand. Consumers might continue to order online or consume different products. Whatever the changes, embedded analytics can give the retailer the immediate insights it needs today to adapt to the market of tomorrow.
While 2020 has been a year of disruption, it can also become a year of transformation, especially for those retailers using embedded analytics. Their ability to uncover the data secrets and make the right decisions intuitively puts them into a better position to break previous assumptions that worked before, find new opportunities within this new landscape and create exceptional customer experiences – emerging stronger and more resilient.
Josh Good is vice president for product marketing (data analytics) at Qlik