Why CEOs Must Change How Their Organizations Forecast

Forecasts are the foundation of all operational and strategic plans. If the forecasted expectations fail to align with reality, CEOs suffer the brunt of their decisions. The business literature is littered with dozens of examples of leading companies forced to concede missed expectations based on a failed forecast. The result is lost revenue growth and shareholder value, if not the CEO’s job.

This problem is acute and getting worse. Companies, on average, are missing their forecasts by an average of 13%, according to a KPMG survey. Altogether, they say, this adds up to more than $200 billion in projected revenue that was forecasted to materialize, but ultimately failed to happen.

Why do so many companies miss their targets? One answer is clear: Their CEOs are basing their decisions on half-baked assumptions, conclusions driven solely by the organization’s internal business data. The potential impact of external events is either generalized or disregarded in the analyses.

In an era of constant macroeconomic and geopolitical upheaval, creating a forecast leveraging just the company’s internal data is like predicting the temperature outside one’s house based on how warm it is inside. Yet, it’s this external information that can often make or break a forecast. No global company, for instance, is immune to the ongoing volatility in Asian markets. None can discount the effects of a weakened Euro, the gyrating cost of energy, or the rapid impact of innovative technologies on consumer behaviors.

Emerging economic trends in a geographic region may influence interest rates, inflation and credit capacity, resulting in higher than projected business expenses. Even changing weather patterns can disrupt supply chains and sharply curtail a country’s GDPt, snapping shut consumers’ wallets, when the forecast predicted rising disposable income.

This wide and growing range of potential outcomes from external events is lost in many of today’s forecasts, as they are focused on last year’s quarterly business data to guide next year’s quarterly projections. Target setting without external analyses is like tossing darts wearing a blindfold. Such dangerous forecasts lower the odds of a CEO making superior decisions on whether to enter or exit a market, develop a new product or stick with the current lineup, or engage a new geographic territory.

And when these decisions are made based on a forecast that turns out to be wildly off, the business media is sure to pounce, fangs protruding. Remember these past headlines?

• “Mattel’s CEO resigns, toymaker’s results miss expectations”
• “Pier 1: Botched Forecast, CFO Replaced”
• “Walgreen Shakeup Followed Bad Projection”

Why is this the case? One reason is that the Internet’s storehouse of geopolitical and macroeconomic information is simply too vast to easily access and inventory, much less make sense of. Millions upon millions of data sets cause a massive traffic jam bringing the analyses to a halt. Some of this data also is old and useless, misleading, redundant or just plain wrong.

Yet, somewhere in this jumble of external documents, files and records is real-time information on fast-changing population patterns and consumer demographics, regulatory initiatives in the works and even a change in anticipated rainfall. All this data has tremendous import to a company’s strategic and operational planning. Without it, many CEOs are flying blind in their decision-making.

The Case for Truly Predictive and Reliable Forecasts
The solution is to invest in a forecasting tool that leverages internal business metrics and external macro and microeconomic data in real time. This is true predictive data analytics, and it is already in place in a variety of businesses and industries for their respective needs.

Many retailers, for instance, are mining internal and external data to pinpoint emerging market trends and consumer purchase behaviors. They’re accessing data on a customer’s demographics, buying history, social media interactions, and Internet search and browsing activities, then blending this information with their internal business data to make more personal (and targeted) marketing pitches.

Real estate firms also are turning external data into a competitive advantage. Consider Zillow. The company is accessing previously unobtainable government information like census and permit data to make better projections of the market value of a house. This data was freed up by the government two years ago and made available to the public in an easily understandable and accessible format. Zillow correlated this external data with its internal metrics on the market value of a home, narrowing the forecast accuracy of a home’s final sale price from 13.6% to 6.9%.

These data analytics systems leverage internal and external data to make more informed and precise predictions. However, more than internal and external data is needed to accurately paint a picture of the future, insofar as projected business conditions, revenue growth and expenses. Forecasting solutions also must incorporate sophisticated algorithms to carefully correlate the potential impact of this aggregate data. For instance, a projected rise in consumer disposable income must be analyzed in relation to changing demographic data, indicating fewer customers will want your product.

Such insight is invaluable to a CEO planning a new product or market play, in terms of projecting the future business revenue and related costs. If the CEO is given access to this forward-looking data ahead of the company’s competitors, he or she can make better judgments to improve the chance of the venture succeeding. At the same time, the CEO can make more informed decisions on staffing, excess inventory, pricing and marketing.

This improved decision-making capacity optimizes working capital management to allocate resources to the activities forecasted to ignite more bang for the buck. More cash can be retained internally when needed to invest in R&D or launch a new product. And by better predicting interest rates and credit conditions, better decisions on when to borrow capital or seek investments in the business can be rendered.

The bottom line: CEOs can no longer rest comfortably, assured that their business forecasts are accurate or even useful to their decision-making. With their jobs increasingly on the line for missing Wall Street estimates, the time has come to invest in robust forecasting tools with predictive data analytics that take into account the world around us.