Consumer goods brands that are looking to grow in 2019 and beyond need to take a closer look at their retail analytics. Also sometimes referred to as Point-of-Sale analytics, the insight they provide is critical to keeping up with fast-changing consumer demand, as well as keeping up with retailers that are becoming increasingly sophisticated with their use of data.
According to McKinsey, many buyers already have a wealth of customer information from both offline and online channels at their fingertips. As a result, “no longer will it be enough to generate insights at the national, channel, or customer level on a weekly or monthly basis. Retailers will expect store-specific, real-time insights tailored to their strategic priorities.”
So, with that in mind, let's take a look at the trends in retail analytics in 2019 and beyond. Whether your existing solution is Excel spreadsheets or a purpose-built platform, these points should help you assess whether you’re maximizing the value of retail data to boost your sales performance.
What Are Retail Analytics?
By definition, retail analytics refers to the process by which analytical data is provided, and analyzed, on various aspects of the retail supply chain process. The relevant data typically includes sell-through, returns, inventory levels, product movements, and more — all of which is needed to understand product performance and efficiently serve consumer demand everywhere it exists. This information is used by many teams at brand manufacturers, including sales, marketing, and supply chain, to shape their sales strategy, grow their top line, increase the ROI of marketing activities, optimize the supply chain, and increase collaboration internally and across trading partners.
Retail Analytics Trend #1: Predictive Insights
The first trend we observe in retail analytics is predictive and prescriptive analytics. Going beyond simply summarizing what has happened in the past (descriptive analytics), brands are looking to answer questions like “what will happen?” and “what should we do in response?” by using data and conducting robust analysis. That means preparing analyses like forward-looking demand forecasts and weeks of supply calculations to complement backward-looking KPIs.
Focusing on predictive insights enables companies to shift from reacting to customer orders to proactively shaping demand and ensuring the right product is in the right place, at the right time. Not only will consumers have an improved experience, but you will see an impact on the bottom line due to fewer in-demand products being out-of-stock and fewer overstock situations of less profitable and popular items. Predictive insights can also guide new product development and launches, providing fast feedback on consumer tastes and preferences.
Retail Analytics Trend #2: Data Management
Big data is more than just a trendy buzzword — it's the lifeblood of today's sales, marketing, and supply chain efforts. In manufacturing, real-time data supports the ability to better forecast products and production, deliver faster servicing and support for customers, and improve interaction with suppliers, among other user cases.
But while these volumes of data used to be like the Wild West, now the management of it is receiving increasing focus. Management includes:
- Governance that clearly defines roles for managing data
- Security to monitor and protect the data
- Access and permissioning that puts data in the hands of those who can action it
According to Deloitte, elite performers — brand leaders who reported a revenue increase of at least 10% in the past fiscal year — focus on data management at nearly 2x higher rates than underperformers across all areas. They call putting data in the hands of headquarter and store personnel “nothing short of a brand’s superpower, as it enables companies to know shoppers in the moment and grow intelligence with every step.”
Retail Analytics Trend #3: Curated Relationships Between Suppliers and Retailers
The trends in retail analytics are enabling ever more tailored conversations between suppliers and retailers. For consumer brands, that means shifting their strategy and “language” when talking to each retailer to focus on what the customers in each of their stores respond to. Although Target, WalMart, and Home Depot can all be considered “big box stores,” their shoppers will vary widely in their demographics and behavior, not to mention regional variations within a given retailer.
By analyzing performance at a store-by-store level, brands can better position themselves as a strategic partner to retailers, who understands their unique customer base. Accordingly, they can curate the product mix, usage of and types of promotions, and even store employee training efforts, to the benefit of both the supplier and retailer.
These are just a few of the trends in retail analytics that we expect to become more prominent this year and beyond. It’s clear that the industry is continuing to evolve, and given the benefits, it’s worth the time to review the latest solutions. You could be taking advantage of more modern technology capabilities to better manage your data, generate predictive and prescriptive insights, and drive curated relationships with retailers, with bottom line impacts.