How Data Analytics Empowers Retail Merchandisers to Thrive Through Disruption
If 2020 has taught us anything, it’s to expect the unexpected. Wildfires, hurricanes, civil unrest, economic uncertainty, and a global pandemic disrupted businesses across industries — and retail was no exception.
Brick-and-mortar retail chains saw their foot traffic plummet, online stores buckled under the strain of increasing transaction volume, and both had to deal with supply chain disruptions and the fallout of the US Postal Service crisis.
But every challenge brings opportunity. As the Stoic maxim goes, “The obstacle is the way.”
Merchandisers live at that critical intersection between retailers and consumers, supply and demand, and inventory and marketing. Savvy retail merchandisers are in a unique position to help their organizations navigate through disruption, mitigate losses, and even increase profits.
The rapidly changing retail landscape demands that companies be able to pick up on rising trends and patterns and change tactics at a moment’s notice. To do this effectively, modern data analytics are the key.
Data opens doors (and can keep the doors open)
While many of the platforms available to retailers can generate massive amounts of data, most retailers lack the analytics tools and know-how to translate that data into actionable insights. More often than not, data isn’t used to drive day-to-day decision making, but relegated to executive-level requests and one-off projects.
For retailers to really reap the benefits of their data, they must make near-real-time analysis accessible to as many of their decision-makers as possible. Every item, every transaction, every touchpoint, and every customer has the potential to yield rich insights that can shape direction and strategy. From the way individual stores are configured to the brands and SKUs that are carried each season, retail merchandisers can use data to help their organizations boost their bottom lines and minimize losses — no matter what disruptions come their way.
Here are 3 adaptable plays retail merchandisers can execute to help their organizations weather any storm and even come out on top.
Product Affinity and Market Basket Analysis
What it is
Product Affinity and Market Basket Analysis are data analysis techniques that retailers use to uncover the relationships between items. By analyzing the contents of transactions, retailers can uncover combinations of items that occur together frequently and better understand correlations between them to develop better cross-sell strategies.
Why it’s important
Larger retailers have been conducting market basket and product affinity analysis for quite some time, but today’s fast-moving and unpredictable retail environment has only increased its importance as consumer tastes have grown even more fickle. Additionally, easier and cheaper data mining tools have opened up this kind of analysis to smaller and mid-size retailers, enabling them to better compete with big box stores. A few advantages of product affinity and market basket analysis include:
- Targeted and personalized marketingBy understanding product relationships and purchase sequences, companies can identify and track customers who have bought similar products to deliver tailored messages and more effective marketing campaigns.
- More effective cross-sellingRevealing the connections and correlations between product groups helps stores better improve their recommendation engine rules and algorithms to increase conversions. It also helps merchandisers understand which products to pair together physically or online.
- Better merchandisingWhen consumer purchase patterns and product affinities can be identified and acted on, stores can create much more effective product promotions, shelf layouts, aisle displays, and more.
Why it’s hard to do now
- Overwhelming volumes of siloed data. The explosion in the volume and variety of data retailers are able to collect is a two-edged sword. Purchase history, transaction-level data, and product information are usually spread across multiple, discrete systems with no clear or easy way to integrate them.
- Compute power and scalability issues. Hundreds or thousands of products can easily generate billions of data points that take a staggering amount of computation power to analyze. Spreadsheets out near a few hundred thousand rows, requiring multiple spreadsheets and data extracts to keep up. This also results in only a subset of the data being analyzed at any one time, preventing merchandisers from getting the full picture. Capturing real-time PoS data, especially during peak seasons like Black Friday or Christmas, pushes systems to the brink.
- The need for speed. Even if systems are integrated and compute power is available, complicated BI tools that require coding and technical expertise usually leave decision-makers at the mercy of BI and data teams. As a small group supporting the diverse needs of an entire organization, it can take days or even weeks for BI teams to give retailer merchandisers the insights they need, causing organizations to miss out on the impact of timely insights. In the world of retail, decisions need to be made by the hour, not the week.
How to do it better
- Tap into a single source of truth. PoS data, online transactions, product information, clickstream, loyalty program, and omnichannel customer data can all be fed into your BI team’s cloud data platform, like Snowflake’s Data Cloud, providing your organization with a single source of truth. Multiple data formats can be stored and analyzed together, eliminating data silos.
- Embrace the cloud. Spreadsheets were not built to handle the volume and velocity of big data being generated by today’s applications. In contrast, modern data platforms that were designed to leverage the unlimited scale and speed of the cloud allow companies to seamlessly store, manage, and analyze billions of live data points in real-time.
- Get direct, real-time access to the data you need. There’s a new generation of analytics solutions that allow business teams like merchandisers and marketers to analyze and explore data at the same level as analysts — without having to know code. These tools take the form and function of tools merchandisers are already familiar with, like spreadsheets, to allow them to independently generate insights and take action without waiting on BI teams — all without sacrificing security, governance, or compliance.
A rare October snowstorm is forecasted to blanket the Northeast. While this would usually be a good opportunity for stores to advertise winter toys like sleds, a flu outbreak in the area has caused parents to restrict their children from going outside and interacting with each other. Instead, merchandisers at local stores dig into historical data to uncover the most common food pairings purchased during the winter season over the last five years. They shift their focus on creating displays with comforting treats that can be enjoyed at home, highlighting frequently co-occurring products like hot chocolate and marshmallows and apple cider and cinnamon sticks.
Examine a larger window of time. While impactful patterns can be found at the weekly or monthly scale, be sure to zoom out further for seasonal trends or those that have slowly grown over years. For example, milk from non-dairy sources has been steadily rising in popularity over the last several years. Initially thought by many to be a passing fad, sales of oat milk have been up 289% year-over-year and have rapidly accelerated through the COVID-19 pandemic. Grocery store chains with the ability to analyze data over time can use this information to merchandise stores and advertise accordingly.
Analyze abandoned carts. Incomplete online checkouts and what’s inside these abandoned carts can reveal many areas for improvement, including product discoverability, recommendations, payment processes, website speed, and more. In fact, it’s estimated that $260 billion worth of lost orders are recoverable solely through a better checkout flow and design.
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