From strategy to tactics: using analytics to optimize profitability

Analytical management approaches can support the optimization of business processes and product strategy design and implementation with profitability as a key target. Gathering data on consumer behavior and developing reliable forecasting models are the first steps. But once that goal is achieved, companies face another challenge: how to embed these analytical assets in their tactical and operational processes to optimize profitability?

We often find companies that are savvy on using data analysis for their strategic decision-making processes, but that fail to connect these decisions to their operational and tactical planning. This results in systematically missing targets that would otherwise be achievable and are expected by senior stakeholders.

When deploying strategic targets to their operational teams, companies must ensure proper analytical methods to successfully breakdown targets into realistic and suitable day-to-day goals that teams can focus on fulfilling to drive successful strategy implementations.

While tacit knowledge is a key factor in developing the strategic vision of a company, having it supported and aligned with empirical data-based evidence is not only a best practice but also mandatory for companies to take the next step in their analytical growth process. For example, it is desirable for companies to connect their analytical modelling for consumer behavior with financial forecasting. By doing so, companies can generate reliable estimations of the required indicators that C-level executives will consider during the decision process.

The problem many companies face is that consumer, financial and supply chain knowledge are often spread across organizational and data silos, making it hard to efficiently coordinate tasks, analytical modeling, and outputs for seamless and agile strategic planning activities.

But why is strategic planning often more complex than forecasting the market reaction to a specific decision? There are two main reasons:

1. Decisions at one end of the value chain may significantly impact all other functions. For example, bringing in a new product may help increase sales, but it might also disrupt operations and thus increase costs and erode margins.
2. While multiple metrics, including sales, market share and bottom-line results, are simultaneously important to guide strategic decision making, some market strategies will often result in conflicting impacts towards downward objectives. Stakeholders must successfully prioritize business goals, while ensuring these are both reflected in the strategic decisions and communicated effectively towards operational implementation teams.

There is no doubt that profitability and strategic goals should be key factors guiding the decision process of all business tasks. It is important to make sure that all decision processes, from manufacturing to consumer sales, are always balancing short-term profitability with long-term strategic guidelines.

For that reason, companies should focus on leveraging data to estimate the impact of their core strategic decisions in the route-to-market, whether they include pricing, assortment planning, trade optimization, channel planning, product design, or supply chain design.

Analytical modeling needs to go hand in hand with corporate strategy, both when planning operational models and when designing organizational strategy.