5 key criteria for efficient retail analytics

Date Posted: May 26, 2020

Using point-of-sale and e-commerce data to prevent lost sales and share timely insights can help your brand stand out among the many suppliers vying for a buyers’ attention. But generating insights from the reams of data retailers share can be a cumbersome process, depending on how you manage the hurdles.

Some brands rely on analysts who pull and analyze the data in Excel every week. Some turn to companywide business intelligence tools, like Tableau or Looker, to help them visualize data. Yet others use more purpose-built retail analytics tools for data collection and reporting. And a growing number use Alloy for dynamic dashboards, predictive analytics and management by exception.

It’s not a simple “one size fits all” decision, especially if you’re taking into account the needs of different cross-functional teams – sales, supply chain and planning – who need to work together to get products on the shelf. When choosing the right solution for your business, consider these five factors that will impact the ROI of your investment.

1. Scalability

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

We see many brands switch to a dedicated retail analytics tool 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 combing through reports to identify insights and having relevant insights brought to each user. An efficient solution frees you from crunching numbers so you can focus on what matters most and quickly take action. That means automatically providing key performance indicators, smart recommendations and alerts to proactively flag issues and opportunities.

Intelligence is the difference between combing through reports to identify insights and having relevant insights brought to each user.
-

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. Are nuances like different retailer fiscal calendars and calculations for key metrics taken into account?

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 your solution is both easy to use for non-experts and robust enough for experts. You wouldn’t purchase a tool that only gave results in a foreign language and then hire translators to explain everything to the rest of the company, so don’t put your analysts or data scientists in that position.

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

4. Integration

Data is the starting point for analytics, so an efficient solution should also have an efficient method for ingesting and harmonizing data.

Keep in mind, ingesting and harmonizing are two different and equally important processes. Ingestion is the first step of bringing data from its original source into your analysis tool. For example, exporting data from Walmart RetailLink and into Excel, or downloading EDI feeds into your data lake.

Harmonization is a second, often overlooked step of modeling all the data into a common language, translating across different levels of granularity and retailer product identifiers (DPCIs, UPCs, ASINs, etc.) and hierarchies. It’s what enables you to easily total all your sell-through by product, or make apples-to-apples comparisons between channels.

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 insights while data is 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. A strong Customer Success team, trainings and templates are also important to help get users up and running quickly.

Finally, a modern solution is also cloud-based, so any software updates and new features are pushed automatically. If installation or maintenance requires its own special team or expert, it’s probably not a best-in-class platform.

Using these five points to assess your current solution and evaluate new options for retail analytics can help ensure your team gets the timely insights they need. For specific features to look for and a convenient reference sheet, download this Evaluation Checklist.

Related resources


Press

AI in logistics: Emerging startups, remaining challenges and new models

Artificial intelligence is hitting new adoption levels each year, with industries like supply chain management and logistics taking center stage in the AI race.

Visit site
Article

An interview with Michael Hutzli, Engineering

Meet Michael Hutzli from Alloy's Vancouver office who has also worked as a software engineer at Addepar and AdNovum in Zurich, Switzerland.

Keep reading
Press

Readers’ Choice Survey 2019: Artificial Intelligence

The CGT community’s preferred providers of solutions leveraging artificial intelligence/machine learning software and services, either as a stand-alone tool or as an enhanced component of...

Visit site