The retail sector is becoming increasingly fast paced due to shortening product life cycles and evolving consumer expectations. Retail merchants must rely on data analytics to keep up with competitors, and empower them with the ability to predict retail merchandising challenges before they even happen.
Additionally, consumers demand seamless, omnichannel customer experiences that merchants can provide by ensuring their different business units are working together as efficiently as possible. Using merchandising analytics, retailers can position themselves to make better informed decisions across their operations that can make them more profitable and exceed customer expectations.
Use Cases For Merchandising Analytics Models
Retail merchants are responsible for product assortment, planning, negotiations and determining pricing. In the past, merchants leveraged back-ward looking historical data to inform decisions around these responsibilities, but as big data has evolved the industry, winning decisions are driven by analytics. Having access to unique, timely and accurate corporate data analytics amplifies retailers and investors abilities to identify and capitalize on strategic growth opportunities that without data would otherwise be hidden.
Some use cases for merchandising analytics include the following:
Top-Down Business Strategy & Planning
Insights from timely data and predictive retail analytics can guide retailers to make better informed high level decisions around performance goals, merchandising strategies, and business plans. By looking at competitive market trends with easy to use segmentation tools, shopping behavior for a variety of market categories and services can be revealed to broaden market research backed by accurate data.
Space & Assortment Planning
Analyzing data sets empowers merchants to tailor their product offerings and product categories based on customer preferences or local demand. Segmenting markets can reveal unique opportunities and help predict the best performing schematics for space planning purposes.
Pricing & Promotions
Predictive analytics can help inform decisions about pricing and promotions based on timely and accurate retail trends. Merchants can understand their pricing compared to their competitors in the marketplace in order to create competitive pricing, or determine optimal promotion strategies. Knowing exactly when to push items at the end of their life cycles out of stores to make room for new products can make all the difference against competitors.
Competitive Market Knowledge
Big data makes it easier to gain an understanding of spending behaviors at your store versus competitors without having to wait for SEC filings and other reports that are made publicly available at certain times of the year. With merchandising analytics, data is kept track of in real time so market research does not have to be bogged down by out of date data, and instead always be driven by the most up to date and accurate data available.
Inventory Management
Restocking products no longer needs to be a reactive process once a product is sold out or missing from store shelves. By leveraging data and increasing automation using AI and machine learning technologies, forecasting tools can trigger alerts so merchants can prioritize inventory management based on KPIs set well in advance before shelves are empty.
The Future of Predictive Merchandising Analytics
Big data has become more valuable than ever in 2020 as retailers have been pushed to adopt new digital business models and online strategies. Having access to data is only one side of the coin retailers must begin prioritizing as they make the shift to new business models. Ensuring the data you’re working with is of quality and properly managed is key to driving high quality insights.
Advanced analytics systems that use AI and machine learning will enable better and faster decision making overtime, with the potential to begin automating some manual tasks to unlock automated insights. With greater process efficiency, retailers can lower overhead costs and work with more streamlined merchant organizations.
Data also enables efficient asset allocation that both investors and retailers can use to measure and understand the monetary benefits of corporate decisions before they’re even made.
Envestnet | Yodlee is a market leader in data aggregation and data analytics solutions helping retailers and investors improve their strategic decision making by providing them unique, timely and accurate data. Economic trends offer a lot of value in determining whether or not working from home trends that have impacted foot traffic are here to stay. As retailers continue to navigate the crisis, this data can be used to help determine what products should be excluded online to help generate more foot traffic back to stores.
Without these insights data enables businesses to discover, retailers risk navigating the future with less research and information than their competitors.
