A Little Data Can Go A Long Way - Member Scholars Respond
In a number of recent CFO.You newsletters, subscribers replied to our themes with some learning filled responses.
A big thank you to the Contributors. Enjoy their lessons.
A Little Data Can Go A Long Way:
Sometimes we need data to recognize the obvious. That is why we discourage taking short cuts when analyzing your business. In terms of Roger Martin’s Design of Business, moving from the mysterious to the heuristic to the algorithmic is hard and valuable work.
We asked for the following and received some great responses to learn from:
Please share a story about how your analysis drove insight into finding a significant opportunity or solving a significant problem at your company (avoid taking short cuts):
Wayne Ackerman, Head CFO, Seaview CFO Services
Several years ago I was put in charge of a company that performed custom testing services for power generation equipment, pre-installation. The quote conversion rate was between 25%-30% but should have been double that due to the significant I.P. held. I downloaded and cleansed the past 24 months of inquiry to order cycle data and proceeded to work the data to identify trends. The first trend that jumped out of the data was that quotes issued within 24 hours of RFQ receipt had a win rate of near 73% and dropped quickly after 48 hours, but only 27% of the quotes were issued in that time-frame. We convinced the management team to add to the technical quoting staff of one, 2 ½ more trained employees and carefully monitor the impact.
After several months of new staff training, the overall quote conversation ratio increased to slightly over 50% and speeded the complete cycle for each project. We also learned that the one man historically preparing quotes had not increased pricing in several years and was leaving profits on the table. For the investment of 2.5 heads, the sales and gross profit increased dramatically. The analysis was done all in excel with pivot tables and some mid-level formula writing. As is often the case the most difficult part of the project was cleansing the data of errors and abnormalities and naturally upon presenting the data, several staff contributed that they understood this correlation all along. The facts we learned from the data supported the thesis and transformed the business.
Postscript:
When Wayne was asked, Why do you think you were able to see the value in doing the deep analytical dive when previous management missed the opportunity? His humble response below is a reminder of the adage ‘success is driven by 90% perspiration and 10% inspiration’:
“It was too simple. They just did not want to put in the work associated with the data extraction and cleansing.”
Sudheendra Rao, Finance Executive
My example of not taking a short cut is:
We had a global facility management contract with a well-known FMCG (Fast-moving consumer goods) customer. In the contract our fee was salary plus margin of all the staff deployed at that customer’s places. Once a year the client paid a top up fee based on the performance. In a year when I was involved for the first time I was asked to review the data and propose the top up fee numbers. I found that from one of the cost centers we had billed the client in excess, so I put across the facts in front of our management and my suggestion was to be open with the client and then claim whatever was actually due to them. Management agreed to this, but the question was how to approach client, would they start suspecting all other cost centers etc…We decided to spend a few thousand to engage an independent auditor to conduct a random review of all billing and give us a report. The same report was presented to our client and we admitted the excess billing due to his understanding of salary definition. Client was happy about our transparency and our approach. That client renewed our contract for 5 more years and paid the extra top up for that year.
If we have the data and present it correctly it can make magic; even errors can be used to create a more robust customer relationship.
Steve Rosvold, Founder, CFO.University
Not long ago I was helping a business that handled large volumes of product at skinny margins. When I arrived, there was a big concern regarding the P&L. The commercial managers were convinced the P&L was understated each month, mostly due to the accountant’s inability to explain the variances between the forecast and the actual results.
We quickly learned the forecasts were based on estimated freight rates and the actual freight costs were buried in the gross margin. As we peeled back the “freight cost” onion, gathering a significant number of data points, it became clear the freight rates used for the forecast were much lower than the actual freight rates being paid. The difference explained the deviation between the forecast and actual gross margin. Once this became known the team started working on the margin problem the business had instead of arguing about whether or not the P&L was correct.
It took a significant number of data points to make us comfortable our conclusion was valid. The extra work we performed digging through the details helped us create a more competitive business model and improved our data collection systems so we could easily capture and analyze critical business information.
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