Using Data Analytics to Understand Gross Margin Attribution


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Cloud Insights and Innovation

Companies across sectors and of all sizes face a common problem: understanding their profit margins and identifying the levers that are attributing to those margins. It’s as fundamental to business operations as you can get – if the margin isn’t there, you’re not going to have a viable business, and in an increasingly data-driven world, businesses that are not leveraging their data to make the best decisions are at a competitive disadvantage. The good news? If you know how to harness that data, you can uncover insights about your business that you never thought were possible, make them actionable, and make your business more profitable.

Start with the Basics: Revenue, Costs and What you Want to Know

Most organizations have a baseline understanding of the big picture of their business, but they’re not always able to dive deeper and slice those metrics in meaningful ways. In the example of third-party logistics, companies need to understand their gross margin per customer and then look at those numbers against overall cost – who is profitable and who isn’t? The answer to that question can have a huge impact on your business decisions, and in some cases, mean letting go of certain clients in favor of others. 

Key to the equation is understanding where the revenue is coming from – i.e. a certain product line, geographic concentrations, segmentation concentration across customers, etc. Is it revenue per product type, B2B, e-commerce? By identifying where your revenue comes from, you can then marry that data with your costs and begin to understand your key metrics.

Data Teams are the New Financial Teams

That all might sound fairly straightforward and is likely something that you’ve done with your financial team in Excel spreadsheets or other means. That should be good enough, right? Not exactly. The days of building and running those financial models based on static data that are days, weeks, or even months old, are over. The power in building your modern data analytics processes is that you’re able to view the data and results in real-time. Though it sounds simple, creating these models through a traditional accounting process is far too complex. A massive amount of data sets and sources go into giving you an accurate picture, and typically those sources aren’t in the same systems or speaking the same language. By leveraging cloud data analytics technology, you can acquire and blend together your data to get an internal 360 degrees view and then layer in various dimensions across revenue and cost to make better decisions, more quickly.

How to Get Started

These helpful hints are by no means exhaustive, but it’s a great starting point to begin your data analytics journey.

  1. Identify where the source of truth is for each piece of information – married to customer and operational information (where it’s being delivered / sourced from). 
  2. Select and build your modern data analytics technology. Select your data warehouse of choice and build the ecosystem around it to acquire that data, blend it in your cloud data warehouse, and then allow speed in delivering data sets to your users through your BI layer.
  3. Help teams analyze this data and make it actionable. Ultimately, you’re trying to find outliers. What do you see, what stands out, what needs to be fixed? A tenant of a successful data analytics program is that insights are interesting, but only actions make them valuable. 

The key to understanding the importance of data analytics focused financial models hinges on the fact that without real-time access, by the time you get the information it’s stale. Real-time means that you’re now accessing data from 15 minutes ago, so you can get immediate insights into the health of your business. And with immediate insight comes an immediate ability to pivot, fix problems and ultimately build a stronger business. 

Ready to uncover actionable insights about your business today? Contact our experts to get started! 

Dave Taddei

SVP, Data Analytics North American Practice Lead

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