Can Big Data Lead to Big Profits?

One thing that all companies have in common is that they want to increase their profits.   There are a number of traditional ways to do this, and there’s an up-to-the-minute approach.

The first and most obvious way to increase profits is to cut costs. This can mean reducing variable costs, as well as capping or reducing fixed costs as sales increase. But reducing costs can be a slippery slope, and a business cannot necessarily cut its way to increased profitability. If not implemented properly, cost control and reduction can have the unwanted effect of reducing sales, thus negating savings. Downsizing staff is a popular strategy, particularly after mergers or when top management changes. Customer service staff members are often the first on the chopping block. But such efficiencies can put the company at a competitive disadvantage by irritating customers. How much do you enjoy navigating your way through interminable phone menus?

Another familiar cost-cutting measure is to eliminate staff members and divide up their responsibilities among the remaining staff. This tactic, of course, has become increasingly common since the recent recession, and risks overloading the existing employees. I have worked at several companies that did this, and I can attest to the negative effect such “redistribution” had on both morale and productivity. Early in my career I experienced another negative effect of downsizing—I was downsized before I had even reported for work! I left a large company to accept a new position with another company. Allowing myself three weeks between jobs, I was enjoying my break until, a week before I was to start my new job, I received a letter saying that my position had been eliminated as part of a larger downsizing and hiring freeze. They were kind enough to give me a severance check, and fortunately I soon found a position at another company, but the experience left me disillusioned and devastated.

Another popular target for cost-cutters is the marketing budget. This is particularly tempting because, compared to the hard costs involved in manufacturing, the cost-benefit ratio of marketing can be hard to pin down, and so marketing can been seen as an unnecessary frill. But for most companies marketing is a primary driver of sales, so companies reduce marketing budgets at their peril.  

A second approach to increasing profits is to increase prices. While competitive forces as well as price elasticity of demand ultimately determine a realistic pricing structure, it is important to understand whether the products and services are commodities or highly differentiated. In the case of commodities, higher prices result in a drop in the demand. However, with highly differentiated products or services, psychology factors in very strongly. For instance, there aren’t many Lamborghinis on the road, but that’s a big part of their appeal. In some cases higher prices will result in fewer customers but more profit. The trick is to determine the tipping point.

The third approach, and the one that’s only been possible the past few years, is the strategic use of Big Data. Information is being generated at an ever-accelerating pace, creating huge volumes of Big Data. Tapping into that information can produce significant insights.   Companies of all sizes are investing heavily in Big Data and Predictive Analytics. Software tools are getting more sophisticated and more user-friendly at the same time. While some of the best tools are so expensive that they are only available to large companies, there are lower cost solutions available to small and mid-size companies as well.

So how, exactly, do Big Data and Predictive Analytics improve profitability? For that to happen key executives, along with their trusted advisors and strategy consultants, must become expert as assessing the big picture for their company, savvy about identifying the relevant data sets, and adept about how to use the analytical tools.

The end result is using Predictive Models to produce insights from massive amounts of complex data. The movie “Moneyball” told the story of how data analysis was used in scouting and analyzing players to turn a losing baseball team around. It was a unique sports film in that the hero was a numbers geek rather than the muscular guys on the field who were getting cheered by the fans. The analytics that were used were not as sophisticated or as advanced as they are now, but they worked, and now baseball analytics are a routine part of the game, not only for scouting and analyzing players but also to determine batter-pitcher matchups, choose the lineup and starting pitchers, and select pinch hitters and relief pitchers. Batting averages have taken on a whole new meaning.

Some professions and industries have been using predictive analytics for years, and many of their algorithms and analytics have been custom made. Weather forecasters have been using sophisticated models to predict the weather, economists to forecast market trends, civil engineers to predict failure rates in structures. With the advent of off-the-shelf and easier to use tools, other industries have been quick to get on the predictive analytics bandwagon. The CDC is now using predictive analytics to try to curtail the spread of the Zika virus. Many lives will depend on their getting it right!

I have developed sophisticated forecasting models at a number of companies. I built the models in Excel and in online Business Intelligence systems. While they provided good forecasts, they did not incorporate the massive amount of data that is now available and use a multitude of forecasting algorithms. The predictive modeling tools have become much easier to use, and they tap into previously unimaginable amounts of data, both structured and unstructured.

Given the proven success of predictive modeling in a Big Data world, it isn’t surprising that companies are increasingly using it to forecast drivers of revenue growth and profitability. In addition, predictive models can now be used to identify hidden patterns and trends in data and get insights into customer and market behavior. This allows business leaders to be proactive and agile, and have much better forecasts. They can use this information to understand and respond to market opportunities and customer needs before their competition. Often the interpretation of the analytics is done by designated development or planning executives within a company. If there is not a person or team assigned to that role internally, smart companies work with trusted advisors or consultants.

By creating new opportunities for insights, Big Data, in conjunction with Predictive Analytics, is helping businesses create value, growth, and increased profitability.