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How data analytics can reinvent your business model

Navigate disruption in retail and supply chain with data analytics.

August 3, 2020

Layered superimposed images of binary data and light streams of the data analytics that can help you reinvent your business model

Now more than ever, B2C companies need exceptional ways to engage with their customers online. From the moment they decide to purchase, down the supply chain, to the time it arrives at the end customer, digital transformation must be at the forefront. Especially in the era of COVID-19, organisations cannot assume their customers will go into a brick-and-mortar store. In fact, they need to expand digital options to move forward with business momentum.

As business models change, data analytics and insights are imperative to support and fuel companies to be agile and successful. The extreme disruption from the COVID-19 pandemic has prevented in-person interactions and, therefore, has changed the way retailers are engaging with consumers.

Data that cultivates customer experiences

Demand for doorstep pickup and delivery options have skyrocketed, with companies like grocery stores shifting to offer online ordering and doorstep services. And with convenience and safety top of mind, customers are strategically making decisions on where they return to based on the customer experiences they're encountering—such as timing of deliveries, wait times or accuracy of picked products. At such a precarious time, companies can't afford to lose customers based on these small windows of interactions. Those who have been able to streamline their data strategy for an improved digital experience have reinvented the way they do business, like using data to know what time customers are reaching the store for pickup or enabling improved applications to select in-stock items. These retailers have been able to reach their customers regardless of the circumstances, and ultimately maximise revenue.

Shift revenue from in-store to online with data-driven recommendation engines

While some retailers have focused on changing how they offer products to consumers at the front door, others have looked to expand and maximise their e-commerce by building improved recommendation engines through machine-learning capabilities. As a customer browses online, sites can interpret data based on a user's unique choices, location and time of year and make relevant suggestions. Looking at kitchenware and home décor when you're located in London in the middle of winter? Perhaps you'd be interested in crockpots or a quality Dutch oven to make soups. Creating a wedding registry through a retailer? Maybe you'd like to see what other couples are putting on their registries. And as a retailer that has multiple companies operating under a core umbrella, you may have a variety of opportunities to upsell and increase revenue. Leverage the data correctly, and these recommendation engines will continuously become smarter over time, evolving into accurate tools that delight customers.

Navigate and enhance the supply chain with a clear data strategy

Pivoting business models are needed to succeed during times of disruption and data analytics plays a critical role in visibility and forecasting needs when it comes to an organisation's supply chain. Traditionally when producing a product, the process includes sourcing the raw materials, the actual production, packing, shipping and so on—and those managing the supply chain and logistics need visibility across the entire process in order to forecast the However, when disruption occurs that cannot be forecasted—such as increased customer panic-buying that many of us experienced at the break of the COVID-19 pandemic—the supply chain is significantly impacted, especially if organisations do not have a clearly defined data strategy. When unexpected increased demand spikes like this, it not only affects processing plants and suppliers, but retailers and, ultimately, the end customer. In a traditional retailer that has a business model based on a replenishment cycle, they may have visibility across their own supply chain, but that visibility may end at their suppliers—causing them to place orders for products that are unknowingly unavailable.

On top of these downstream effects, companies are experiencing massive transportation challenges with evenly distributing product that's sitting in different warehouses. For example, deciding which and how much product to put on a truck to minimise the cost of shipping. The benefit of data analysis is helping to make optimisation decisions on a wide scale. The significant amount of data coming out these experiences can help organisations improve their business models and understand where there are gaps across all layers of the supply chain.

Maximise the value of your data strategy to transform

Many companies have been forced to change business models and evolve the way they interact with customers. Those that have traditionally relied on in-person experiences are looking to own change. A well-devised data strategy can help you use data to reinvent, reengage and stay agile, all while engaging customers in meaningful ways.