Times have changed. Quickly. Just a year ago, you may not have expected a store to offer curbside or in-store pick-up. Today, that may be a non-starter.
Customers expect the shopping experience to be seamless, simple, and fast. To deliver the experience customers want and to remain competitive, companies must harness the power or retail analytics. In this blog, we will cover the 3 top trends in retail analytics that retailers are using to get ahead.
How can retailers use data analytics?
From a customer-centric perspective, adopting a retail analytics approach will equip companies with the tools and technology needed to create and automate seamless customer experiences between online channels and brick-and-mortar stores. The companies who proactively utilize retail analytics are already seeing significant improvement in business results by quickly acting upon key insights drawn from the data they own.
The most successful retailers are also finding effective ways to utilize second- and third-party datasets to propel their business strategy. Lisa Aguilar, Director of Product Marketing at ThoughtSpot, elaborated on this approach during Accelerate 2021, stating that “those who are going to capitalize, must not only rely on their internal data, but their external data for timely signals and comprehensive insights, to completely outperform their counterparts.”
Establishing a data foundation that combines the internal and external data that provides the necessary details of the customer experience is the critical first step on this journey.
What are the major trends in retailing?
Below you will find 3 key trends in retail analytics that companies are utilizing to gain a competitive edge. These include personalizing the customer experience, predictive analysis of spending and demand, and dynamic pricing models. Such capabilities in retail analytics are enabled only when you bring all your data into a single unified source of truth. To get ahead, many retailers have turned to data analytics consultants (like those at Wavicle) to help build and deploy the required modern data infrastructure. But once your organization has adopted a retail analytics approach, the following trends are within reach.
Create hyper-personalized experiences for a market of one.
In 2017, Epsilon surveyed 1,000 consumers aged 18-64 with the aim to help brands find ways to enhance relationships and increase customer loyalty. The research revealed that 80% of respondents are more likely to do business with a company if it offers personalized experiences. This demand for personalization has only increased since then. In a 2020 article on driving differentiation in retail, McKinsey states that the best retail experiences “make the customer part of the dialogue and leverage data to create one-to-one personalization.”
Of course, this is only possible through the lens of Customer 360, a comprehensive view of a customer’s data across every interaction they have had with the brand. Such interactions may include transactional data, customer feedback, shopping preferences, website and mobile app activity, and much more.
Tracking customer interactions on such a granular level allows management to gain a much deeper understanding of core shopper needs and expectations. This means retailers can finally create the unified experiences customers demand while communicating unique offers that target highly refined segments. As a result, creating hyper-personalized retail experiences for your customers can lead to a lift in total sales by driving up loyalty and share-of-wallet.
To implement and scale this level of personalization, your organization must have adopted a unified data and analytics platform. With a Customer 360 foundation, the depth of information retail management can uncover provides unprecedented value that goes well beyond the scope of personalization. Retail analytics has the proven potential to improve business decisions in areas such as innovation, marketing, merchandising, supply chain management, customer service, and more.
Predict spending and forecast demand.
Retailers are exploiting advanced analytics that use algorithms and machine learning to make predictions based on trends discovered in customer data. These advanced algorithmic models let retailers know how much of a specific product or service customers will want to purchase during a defined time period.
Business leaders are leveraging demand forecasting to get their most profitable customers back into the store through timely notifications and valuable offers on relevant products. As a result, retailers can ensure they are timing shipments to get the products their customers want on the shelves while improving their supply chain in the process.
In a recent Forbes article, Patrick McDonald, Director of Data Science at Wavicle Data Solutions, talks in-depth about how using advanced analytics can provide more potential opportunities for retailers to dynamically increase the profit margin of their inventory while ramping up sales:
“Looking at forecasts from the perspective of stochastic analysis … allows us to better optimize inventory decisions. This is the process of analyzing future events by looking at alternative possible outcomes. It doesn’t attempt to show a precise view of the future, but instead presents multiple alternative future developments. As a result, analysts can see a range of possible future outcomes and calculate the optimal inventory.”
Retail leaders are even using predictive data analytics to calculate the lifetime value of each of their customers with the goal of increasing retention.
Develop automated, dynamic pricing models.
Retailers often need to keep a percentage of their prices very low to stay competitive. These low-priced products or doorbusters and key value items (KVIs) are often the top sellers and traffic generators that shape a retailer’s price image. As a result, KVIs can account for up to 80% of revenue but only half of a retail company’s profit. To make up for the low margin on KVIs, retailers tend to raise the prices of their higher-margin items and place them strategically alongside doorbusters and KVIs in creative ways to encourage shoppers to add higher-margin products to their carts.
By optimizing product prices to raise the profit margin, retailers sustain themselves and drive growth. With this in mind, dynamic pricing algorithms in particular have been a game-changer for retailers. Dynamic pricing models will automatically make recommendations on prices, which frees up management to make more informed and timely decisions that improve the company’s bottom line. To be truly effective, we strongly advise partnering with a data analytics consulting firm to build a custom solution that is developed in the context of the retail company’s business objectives, operational processes, and customer base.
For example, one of our national retail clients was facing several challenges that put the company at serious risk of losing market share to competitors with more advanced data infrastructure, enabling them to have lower prices across a better selection of products. To compete with this kind of capability, the client partnered with Wavicle’s data analytics consultants to build a custom cloud-based order management portal to make it “faster and easier to evaluate and optimize prices, search and order products, and manage inventories.”
A word to the wise
It cannot be understated: retailers must prioritize the adoption of a customer-centric data and analytics approach to compete against e-commerce giants and local brick-and-mortar stores. As new technologies arise and AI models continue to evolve through advanced machine learning algorithms, retailers will need to leverage retail analytics to discover key insights that will lead to new ways to increase customer loyalty.
Data analytics consultants are a profitable resource
Does your company lack the bandwidth or expertise needed to adopt a retail analytics approach? Leveraging the services of experienced data analytics consultants can help you align your strategy to your desired business outcomes.