Should we continue selling a low-profit product line? Are we overspending on administrative expenses?
Businesses every day wrestle with these decisions.
But is ownership/management making a decision based on data and external insights? Or, are decisions based on intuition and feel vs. hard data?
Hierarchy in Quality When it Comes to Decision Making
The first step to getting good data is insure that your accounting is done properly and that it is kept current and is done consistently. Essentially, when you have more properly organized data, you have a better ability to analyze and apply third party insights; and create an optimal scenario to make a high-quality data-driven decision.
Case in point, one of our clients experienced the following scenario with their two main product lines:
- One product line generated 95% of the company’s revenue, and significant profit.
- The other product line generated 5% of the company’s revenue, and significant losses.
- They needed help deciding whether or not they should move forward with the second product line that was generating significant losses.
- Our first step was to clean up their accounting and operations data. Once we had a clean view, we were able the show client that they weren’t losing as much as they thought.
- Our second step was to understand the operations. The two product lines shared some equipment and processes that wouldn’t go away by getting rid of the losing product line. If you allocated these costs appropriately, the running losses were even smaller.
- The third step was speaking with their customers. After interviewing customers, we realized that “5%” product line could be considered a gateway to the “95%” business. If our client shut down or sold that business, competitors would pick it up. Those competitors would use that business to curry favor with customers and eventually take away the real prize.
With the right data (smaller losses than the client thought) and good external insights, we were able to show the client that the losses from this business unit should be considered more of a “sales expense” for the prime business unit than a stand alone loss. If we had stopped at either the 1st or the 2nd stop of the analysis, we may have recommended what would have ultimately been a poor decision. And it would likely have put the entire business in jeopardy.
Additionally, we were able to do some detailed pricing analysis that we pulled from their database, we determined there was room to increase pricing on a certain set of SKUs. This couldn’t be done quickly, but over time could get the smaller unit up to a break even point with limited risk.
Metrics are Easy; Insight is Hard
But what if you organized and cull your data, but do not know how to analyze it to make the proper decision?
As discussed in the blog “Metrics are Easy; Insight is Hard“, Irfan Kamal explains that while data is abundant, it is difficult to find the skilled resources to interpret the data and make valuable business recommendations.
“Even with great data and tools, insights can be exceptionally tough to come by." — Irfan Kamal
Companies must make a commitment to developing a culture of data-driven decision making. This means hiring the skilled resources, implementing required technology and making the collection and analysis of data part of your regular decision-making process.
So commit to high-quality data, commit to being data-driven and commit to making it a part of your company culture for making important decisions.