Four Ways Data Can Improve Profits
by Brian Fitz-Gerald, CPA, Akseshen LLC –
July 19, 2017
Essential to running a successful business is knowing which customers are profitable and which are not. Many businesses perform customer profit assessments but are unaware of errors or misstatements distorting true customer profitability. Adopting a data-driven approach greatly reduces the chances of these errors and provides a more accurate and detailed view of customer profits. Here are four ways a data-driven approach can reduce the time it takes to turn insight into action to improve profits.
1. Actionable and Accurate
Data-driven customer profit analyses are action oriented because they use transaction-level information which can be quickly aggregated. The user can easily toggle between high-level observations and very detailed transactional data. Drilling into desired customers or products reduces the cycle time to turn insight to action and improve profits.
The transactional approach also results in more accurate gross profit calculations. The traditional customer profitability assessment uses summarized information and allocates costs which limit the level of detail and accuracy of the calculations. This traditional method uses three primary types of financial information — sales data, cost of goods sold and overhead information — and is typically summarized by quarter or year and higher-level revenue and expenses. This process obscures the details needed to yield actionable insights and also exposes data to a number of possible errors which can misstate the true economics of a particular customer or product. Most of these errors occur in the allocation of costs such as labor, raw materials, rent and so on. This significantly distorts profitability calculations because allocations apply average costs to all products and customers. In reality, not all products cost the same to manufacture and not all customers cost the same to serve. A transactional approach traces costs to specific products and customers without allocation. The result is a more accurate and insightful look into which products and customers drive profitability and which are mounting losses.
2. Break Down Data Silos
Traditional customer profit analyses only tell a small part of a customer profitability story while the data-driven approach tells the whole story. The traditional method relies heavily on data collected from disconnected departments or systems, and the data provided is often poor quality due to inconsistencies and insufficient data or blank values. In practice, these data issues can be as seemingly benign as multiple naming conventions for the same customer depending on which department collected and analyzed certain data.
In a data-driven approach, relationships between silos of data are thoroughly mapped through relational tables or algorithms which uncover duplicated data and inconsistencies. Once these relationships are correctly mapped out, the business has the ability to connect all these disparate data sources in a powerful manner. The ability to trace any transaction from raw material through a company’s procurement, manufacturing and sales processes for each customer is powerful and insightful.
3. Include Alternative Data Sources
Typically, the data housed in operational and financial systems represents only 20 percent of a business’s information. The remaining 80 percent is unstructured, held in forms such as email, documents and intranet pages. Customer profitability analyses are greatly enhanced when combined with these unstructured data types. For instance, consider the insights businesses can gain from not only knowing which customers are most profitable but which are satisfied and will repeat as customers by blending customer survey scores into the data.
It is now standard practice to include geo-targeted information with sales data to produce maps to easily visualize sales data by geography. For example, a business can blend internal supply chain and product distribution information with key demographic and consumer data. This dataset can produce an insightful map of the company’s areas of product supply compared to areas of demand.
4. Exploratory Analysis
A data-driven analysis enables business users to quickly perform what-if analyses on key variables. A traditional profitability analysis may lead a business to discontinue products and customers that accumulate sustained losses. However, in a data-driven approach, rather than discontinue an entire product line, businesses can see the exact combination of production and sales circumstances which resulted in the loss. This approach is much more nuanced and focuses on resolving the root cause to restore profitability.
Many businesses lose money because management cannot see when errors and bad data distort or misstate their customer profit analyses. Data-driven analyses enhance the accuracy of profit analyses and provide transparency into the profit and cost drivers of a business. Management can easily spot issues and then perform complex what-if analyses in a simple way to ensure every business process and decision is profitable.
Brian Fitz-Gerald, CPA, is the founder and CEO of Akseshen LLC, a boutique data management and analytics company helping accounting firms adopt data-driven solutions that increase profitability and efficiency in both back office and client facing operations.
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This article appeared in the July/August 2017 issue of New Jersey CPA magazine. Read the full issue.