By Gregg Clark, firstname.lastname@example.org
Operating for nearly a century, a global retailer had enjoyed years of healthy sales, even through much of the financial downturn. But as the economic volatility continued, commodity prices rose and consumer habits shifted, the retailer faced unprecedented cost pressures that were compromising its competitive position in the market. A change at the leadership level was placing an additional strain on management to show accelerated performance improvements that would boost margins and satisfy restless shareholders.
The company was already collecting reams of data on its financial and operational activity, as well as its consumers. However, it had no idea how to use the information to make strategic, effective and timely decisions. The company was unable to turn its information into insights that could systematically link strategy, planning, execution and reporting. Instead, it had to rely on a combination of instinct and experience, supported by ad hoc analysis.
The situation this retailer faced was not unique. In a recent survey Ernst & Young commissioned of 285 senior executives globally from the consumer products and retail sector, 81% of executives who participated said they needed to improve their decision-making speed and level of insight. In particular, executives expressed that they were frustrated by the inability to get the level of insight they needed, and that the information they did receive was financially focused and to detailed. They also found that strategy, planning, resource allocation and reporting were not sufficiently linked. As a result, their organizations were making decisions based on intuition and not fact.
Retailers today are collecting more data than they ever before thought possible. Real-time retailer information, geo-positioning data from consumer smartphones, vast amounts of social media data all have to be managed and analyzed to drive insights and value for the business. Many companies have invested heavily in technology to collect the data, but then don’t know how to take it to the next level. Even companies with rigorous analysis capabilities find that their analytical methods are often not well-understood by decision-makers and don’t connect quickly enough with the organization.
Traditional approaches to decision-making tend to focus on financial statement line items and ad hoc reporting. However, without knowing the external and operational drivers, companies don’t have enough information to make fully informed decisions.
To improve their performance management capabilities and drive profitable growth, companies need to take a comprehensive approach that not only implements driver analytics, but also uses the analytics to logically link business strategies with the market, competitor, operational and financial forces that drive value and, by extension, good decision-making.
Here are five actions retail companies can take to improve their decision-making:
1. Define the value drivers. These can include market, competitor, operational and financial drivers. Drivers need to be quantified and linked to outcome metrics and other drivers. Logically tying outcome metrics to drivers creates a strong foundation for planning, reporting and decision-making.
2. Automate variance analyses to reveal root causes. These types of analyses focus on looking at the issues that lie beneath the surface, revealing root causes that may otherwise go unnoticed. For example, a driver-based analysis may determine that a revenue decline is the result of a unit decline rather than price. The unit decline comes from a smaller market size, but the company’s market share has exceeded plan. Knowing the root cause means the company can focus its strategic efforts on understanding the decline in market size rather than tactically adjusting its price to drive increased revenues.
3. Conduct “what if” scenarios. What if scenarios and sensitivity analyses can improve both strategic and tactical decision-making. Using the value drivers defined earlier, companies can conduct fact-based evaluations of business alternatives, and run risk-specific scenarios.
4. Simplify decision support and analysis. Traditional approaches make decision-making complicated because they require several layers of management to conduct ad hoc analysis and apply personal judgment. Driver insights simplify decision-making by creating a consistent structure and basing decisions on fact, not intuition.
5. Know the culture. How big and how far a company can go in transforming its performance management program ultimately depends on the company’s culture. To b