Can you confidently answer the following questions?
- What is the revenue collection rate for each of your customers?
- What is the actual expense to service each customer?
- What is the gross margin for each product?
- What is the variable carrying cost of inventory per month?
- How many days does it take for each product to turn over in inventory?
Depending on the value your company places on data, analytics could play a crucial role in determining a) whether you can answer those questions, and b) whether the answers are accurate. Regardless of the size of your business, you want to be able to answer these questions reliably and accurately—because the answers could affect the next decision you make.
In my work as a business consultant at Schneider Downs Meridian, an accounting and advisory firm headquartered in Pittsburgh, I’ve had a lot of opportunity to see how a company’s access to key data—or lack of it—has determined the competitiveness of the business. In this article, I’d like to discuss how simple data analysis revealed crucial business issues for three small/mid-market companies I’ve consulted for.
But first, let me say that analytics can work for any size of business. No, your company probably doesn’t have the resources of Google, GE or Amazon, nor does it have a business model predicated on moving margins 0.1%; but that doesn’t mean that your company shouldn’t be analyzing its data. Yet, small and middle-market companies often get stuck in thinking, “this is how we’ve always done it, and we’ve always done well.” However, I’ve found that companies that rely solely on established processes, and neglect basic performance metrics, will experience a disadvantage that affects their business’ growth and bottom line.
Perhaps one of the obstacles to performing basic analytics is that the word “analytics” carries a negative connotation with a lot of business managers, because they envision a person at a computer crunching statistical information, producing answers like: “the observed results fell within two standard deviations of the anticipated mean.” That kind of data has little to no meaning to the average business person. Yes, that type of analytics has a time and place in some organizations. But my goal is to show that analytics can be simple mathematical tools to help small and mid-sized businesses make better decisions and improve the bottom line.
I’ll walk through three examples where surprising information was revealed using basic analytics that required nothing more than a little time, business acumen, and rudimentary math/excel skills. In each case, a company had operated the same way for decades, and didn’t see a need to change. The problem was: the economic and business environment had changed, rendering their business practices inefficient and outdated. Simple analytics uncovered the issues, and shed light on the solutions.
Below I describe a misperception of costs at a trucking company, a new look at margins related to inventory at a manufacturing company, and a reversal of the sales team’s focus at an apparel wholesaler—all revealed through data analytics.
Case Study #1: Trucking Company
I recently consulted with a trucking company that had seen its margins deteriorate over the last decade. It had come to the point where the company was no longer profitable, and had to find a way to turn itself around. The source of the financial problems was management’s imperfect understanding of the company’s costs, particularly on longer hauls. The company charged a base price, plus a fuel surcharge. The fuel surcharge gave the company the perception it was insulated from rising fuel prices.
Unfortunately, that was not the case, because the hauler did not account for the fact that it only charged the fuel surcharge in one direction. So while the company diligently raised prices to cover non-fuel related expenses, prices were not raised to account for the higher cost of fuel for the return trip. The longer the trip, the more exacerbated the problem would become.
This company failed in pricing, because it did not go through the process of re-analyzing the cost of its service for different hauls to ensure that pricing increases covered expense increases.
Case Study #2: Steel Manufacturer
It is not uncommon for businesses to be carrying too much inventory. Even manufacturers that have evolved their model to be predominantly “just-in-time” end up with more inventory than is optimum due to customer-requested shipping delays and excess finished inventory for non-just-in-time customers and raw materials from vendors who are not capable of just-in-time delivery. What is uncommon is for a manufacturer to understand exactly what that inventory is costing.
Below is a breakdown of what inventorying product was costing one of my recent clients.
As one can see, this manufacturer lost roughly 2% of margin for every month it held a product in inventory. This problem is further exacerbated if the analysis is performed using cost of capital, rather than just the interest expense.
My client had failed to account for the cost of holding inventory and did not factor it into pricing with customers. After performing the analysis, the company began requiring payment at the time of production, and charged a monthly storage fee for any orders when the customer could not accept delivery at the time of completion.
Case Study #3: Apparel Wholesaler
Profitability by customer should be a key metric for any company; yet many companies do not look at customer profitability beyond a simple understanding of Customer 1 must be my most profitable because I sell my product at $10.00 to Customer 1 and I sell the same product at $9.50 to Customer 2. Unfortunately, the answer to how profitable a customer is can be more complicated, as is outlined in the simple example below.
As one can see, in the case of my client, the customer that paid less was actually more profitable to the company, when the company factored in returns, discounts and the terms in which the product was delivered and paid for. My client had never drilled into these factors and was instructing the sales force to sell to Customer 1 over Customer 2.
The three examples above show how simple analyses can guide critical business decisions. The important parts of this process are identifying the key factors that drive your company’s profitability, and through the cooperation of operational and financial managers, developing a process to collect the data and turn it into actionable information. With the help of relevant analytics, simple models can create solutions to many common financial problems. As we’ve seen, the companies each made a different decision based on new baseline information, which made a big difference for the health of the company.