Every company constantly seeks ways to increase profitability. How can we be smart as we grapple with new technology, innovation, and new channels such as mobile devices, and new markets? How can we be sure we are making decisions that will increase profitability?
Making profitable decisions requires data that reveals the sources of profitability (and unprofitability) as well as the causes of profit differences. Whether you are segmenting markets; making the right customer acquisition and retention decisions; designing new products and services; setting the right prices; planning capacity; reducing spend; predicting future revenues and costs; or setting accurate profit targets, you need relevant information about profitability. You also need analytical tools to understand profit variation, model the impact of change decisions, and predict the impact of these decisions on future profitability.
These analytical capabilities are necessary to guide any company to long-term profitability. Unfortunately, most companies do not have this information. Financial systems meet regulatory requirements for accurate, audited financial information. But these requirements do not include dimensional profit information about the company’s business such as reports on customers, channels, and segments. In other words, financial systems do not report the information needed to make profitable decisions, nor do they forecast future profitability by business dimension.
The solution—or at least part of the solution—is to create a data-driven dimensional profit model. This model accepts financial and nonfinancial data and transforms it into information about the profitability of processes, products, services, customers, segments, channels, locations and other business dimensions for which decisions and plans are made.
At CT Global, the accumulated evidence from over thirty years of building profit models is compelling in terms of the positive impact on profitability. In the case of the Deluxe Corporation, for example, the company created a profit analytics model to guide the implementation of a new strategy. The new strategy delivered an increase in return on sales from 2 percent to 26.9 percent over a four-year period.
How to Successfully Grow Profit
Deluxe is one of many companies that have leveraged profit analytics to the benefit of the bottom line, but not all companies share this success. Many companies fail to build their own profit analytic capability; still others build a profit model but underutilize it. Why is that the case, and what can be done to increase the chances of success?
Increase Profit Analytics Maturity. Companies may wish to grow profits rapidly but may be limited in their ability to make profitable decisions and plans. The start of their journey may be bounded by the limitations of the financial systems, by the absence of dimensional profit information and the lack of analytical tools to use this information to make profitable decisions and plans. Decision makers and planners may also lack the knowledge needed for profit analytics.
Correcting these limitations requires deliberate investment in capability and a phased implementation where the organization can grow and learn at each stage and build the profit analytics capability of a highly mature organization.
There are three phases on the journey to profitability maturity, and successful companies build from one stage to the next until they reach full maturity. (See Figure 1).
- Phase 1 establishes a basic understanding of the sources of profit and loss, and their causes. The goal is to create a foundational model to support business needs in Phases 2 and 3.
- Phase 2 predicts what profit will look like in the future. This can include forecasts of current profitability, as well as the profitability of different decision and planning scenarios. Phase 2 helps management understand, and if necessary, increase profitability to meet current and future profit goals.
- Phase 3 leverages this basic understanding and builds profit information into the algorithms and decision models in marketing, sales, supply chain, operations and other parts of the organization. Phase 3 is accompanied by increases in profitability and a healthy ROI for the profit analytics initiative. It is the highest level of profitability maturity
All three phases are accompanied by increased profitability. Phase 1 will be opportunistic. Phase 2 places profitability at the center of business planning, measurement, and analysis. Phase 3 builds on this initial success and systematically increases profits through more effective decision making. While all three phases positively impact profitability, there is evidence that the highest level of profit margin and growth are associated with Phase 3 organizations that are mature users of profit analytics. A recent study showed that Phase 3 organizations are nearly three times more likely to report above-average net profit margins and profit growth than low maturity organizations.
The Profit Analytics Road Map
What does an implementation plan for profit analytics look like? What does each phase look like? We will start with a description of the purpose of each phase, followed by a summary of the content of each phase. Lastly, we will share some strategies for each phase of the Road Map to achieving profit analytics maturity.
- Phase 1: Foundation – Design and build a foundational cost and profit model
- Phase 2: Integration with budgeting and planning — Create forward-looking planning models for revenues and expenses which can be used for budget, target, scenario, and decision analysis.
- Phase 3: Decisions — Enable sustainable decision-making using information from the cost and profit model combined with decision analytics. The decision areas of focus will vary from company to company but often include segmentation; customer acquisition and retention decisions; product design; pricing; reduction of cost in operations; profitable use of capacity; and other relevant decisions.
The key elements in the phased implementation approach are:
- Phase 1: Automation; processing speed; multidimensional cost and profit model; basic reports for key stakeholders. These elements of the Phase 1 profit analytics solution create a foundation for the profit creation to come in Phases 2 and 3.
- Phase 2: Integration with budgeting, automation for budgeting integration, predictive financial models, scenario analysis, analytics for rolling forecasts. Useful for analyzing planned profitability, the results will be embedded in the decision focus of Phase 3.
- Phase 3: The focus of phase 3 depends on the business purpose, but may include customer profitability reports; customer profit scores; customer lifetime value (CLV); cost and profit models for pricing of shared services (e.g. information technology services); time and capacity analysis for managing repetitive activities; reporting and analysis to support decision making.
Phase 1 lays down a foundational model for current use and to support future phases. In this phase, the profit analytics model is designed to provide the information needed for targeted decisions in a powerful, easy to use solution. Targeted decisions will include cost reduction; product design and pricing; customer segmentation; customer retention and acquisition; and channel strategy. The model will incorporate best practices to automate the process, and to design a more accurate and powerful analytical tool.
The reporting element of the Phase 1 solution includes basic reports that will be consumed by Finance and utilized for analysis and dissemination to stakeholders as needed. The basic reports will also allow analysts to gain end-to-end visibility into assignments and drivers so questions raised by lines of business and other stakeholders may be answered with confidence.
Phase 2 is the integration of profit analytics with budgeting and planning. This involves linking the profit and budgetary models and automating inputs from one model to the other, as well as connecting to any needed external data sources. The value of integrating profit and budgeting models is the ability to create forward-looking profit models and scenarios for use in performance management and decision analysis. Phase 2 restructures the budget around dimensional profit information to measure profit targets and compare them with historical results from the profit analytics model.
Figure 2 depicts how one company integrated its profit and budget models. It helped them “goal seek” profit targets in each important process and business dimension. If these computed targets were below needed profit levels, the profit analysis and planning solution supported gap analysis and re-targeting based on new plans and different decisions.
Phase 3 is focused on decision support enabled by the expansion of the model’s reach. It builds on Phases 1 and 2 and adds additional elements such as customer profitability and profit scores; time and capacity analysis; and information technology modeling.
Customer profit scores is an excellent example of how the power of analytics is taken to a higher level in Phase 3. Profit scores are normalized measures of customer profitability that rank customers in terms of current and predicted future profitability. These scores are inserted into the organization’s customer relationship management (CRM) system and used in segmentation, acquisition, and retention decisions.
Time and capacity analysis in repetitive activities
Also included in Phase 3 is the use of time analysis and capacity modeling. Time analysis uses time-based drivers for processes with repetitive transactions and processing. This analysis provides time-based standards in areas such as document processing, call centers, bank teller activities, card services, internal support, customer-facing retail transactions, and other repetitive processes. This capability supports process improvement, cost reduction, performance measurement and resourcing decisions in these repetitive operations.
Capacity modeling is the quantification of the causal relationship between resource usage (and cost) and the volume and mix of each type of transaction processed by the resources. For example, a call center may handle five different types of calls each of which requires a different amount of time on the part of the people answering the calls. Knowing the predicted volume of each type of call and the average minutes per call enables a computation of the required call center capacity hours to meet predicted demand. Capacity analysis enables organizations to make more cost-effective operational decisions and set accurate plans and budgets for staffing. It allows them to compute variances from plan and make informed decisions to adjust the process and related measurements.
Modeling the cost and revenues associated with information technology (IT) is another area recommended for Phase 3, as IT services are growing rapidly in most organizations. These services include core systems, support systems, security and IT enabling technologies and tools. Our recommendation, based on best practices, is to analyze IT spend to determine IT sustaining costs; process sustaining costs; channel sustaining costs; infrastructure sustaining cost, and those IT costs that support internal organizations. Drivers for IT spend include types of hardware supported, technologies supported, the number of help tickets issued, and data-based measures such as CPU usage, and run times. This analysis can lead to insights into where to increase IT spend when new facilities and channels are added, or for future acquisitions.
 Deloitte Digital Transformation Executive Survey 2018, Deloitte Analysis.