5 key criteria for efficient retail analytics

May 26, 2020  |  5 min read

ChecklistUsing point-of-sale and e-commerce data to understand true demand and share insights with retailers can help your brand stand out among the dozens of suppliers vying for a buyers' attention. However, implementing an effective solution to take advantage of all the sales, inventory, and forecast data that retailers make available—in a timely manner—is not a simple “one size fits all” decision.

Generic business intelligence tools often don’t fully accommodate the specific needs of consumer goods companies. Even within platforms that are more purpose-built, different teams and individuals need to look at different metrics depending on their priorities and which partner they're talking to. If you want to use the data for forecasting in addition to descriptive analytics, that's another complicating factor. In short, there are many different types of solutions you can consider, from using an external partner like Alloy to handling it in-house, and like with any decision, there are multiple angles to consider.

As you evaluate your options, keep in mind these five core characteristics of any effective solution.

1. Scalability

When you grow, your needs naturally change, but who wants to go through the pain of switching solutions? Look for an option that will limit growing pains as you expand to more retailers and new regions, add new products, and grow your team. You volume of data and requirements for analysis will grow exponentially alongside this success.

We see many brands choose to switch to a dedicated tool for retail analytics not because they can't do it on their own, but because it's too ad hoc. They lack the building blocks described below to create valuable insights at the scale needed to support their businesses, every day. They needed a more scalable solution to keep up with all the requests from their teams.

2. Intelligence

Intelligence is the difference between your team combing through generic reports to identify insights and users having relevant insights brought to them. An effective solution moves you away from just crunching numbers all day, enabling you to focus on deciding what needs to be done and taking action in a timely and accurate fashion. That means automatically having key performance indicators calculated on top of the data, smart recommendations, and alerts to proactively flag issues and opportunities.

Because metrics and insights can vary so much from industry to industry, intelligence is often dependent on industry expertise and shared best practices among similar customers. In the world of consumer goods, it even extends to knowing retailer-specific logic for calculations, what metrics each one cares about, and nuances like their fiscal calendar.

3. Usability

Not everyone in your company has a PhD in data science, but everyone can benefit from making data-driven decisions. That’s why it’s important to have a platform that is both easy to use for non-experts and robust enough for experts. You wouldn’t purchase a solution that gave results only in a foreign language and then hire one or two translators to explain everything the entire company, so don’t put your analysts or data scientists in that position by using a complex system.

To take things a step further, choose a platform that’s simple for non-experts to actually work in , not just consume static reports— empower all your team members to customize what metrics they see and create their own data visualizations. This hands-on use will improve the adoption of data-driven thinking among all business levels.

4. Integration

Data is the starting point for any analytics, so an efficient solution should also have an efficient method for ingesting and integrating data. While most retailers provide point-of-sale data, the process for extracting it and getting it into a tool to be analyzed is often cumbersome. It also comes in a variety of formats, levels of granularity, and with retailer-specific identifiers that must be harmonized and the relationships mapped to make sense of the data (DPCIs, UPCs, ASINs, retailer-specific hierarchies, etc.).

A complete solution should take an end-to-end approach to retail analytics and handle all this data management for you. Otherwise, you may be stuck working with outdated data or holding up analysis while waiting for data to be loaded, verified, and harmonized.

5. Implementation

Unlike a jigsaw puzzle, half the fun of adopting a new solution is not assembling it yourself. Instead, look to take advantage of pre-built integrations into retailers and distributors for fast time-to-value, as well as templates, trainings, and a Customer Success team to get users up and running quickly.

A modern solution should also be cloud-based, so any software updates or new features are pushed automatically, instead of requiring manual downloads. If installation or maintenance requires its own special team or expert, it’s probably not a best-in-class platform.

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When evaluating options to support retail data and analytics, consider all the points above to help ensure you’re making a wise investment. For specific features to look for and an easy-to-use reference sheet, check out our Evaluation Checklist.

Posted by Alloy