The recipe for effective decision-making hasn’t changed much: Collect relevant information, interpret it correctly and make an informed decision.
But one aspect has changed: Service leaders have exponentially more data at their fingertips, which makes it exponentially more difficult for them to make smart bets about new opportunities or markets to pursue. As a result, service leaders too often fail to use the best mix of data — or they fail to look at it in the correct way, says Michael Blumberg, president of Blumberg Advisory Group and founder of Field Service Insights.
Field Service Digital caught up with Blumberg to discuss why service leaders should be diligent about determining a new market’s size and customers’ future behavior before making rash decisions.
Is it fair to say that the majority of service leaders overlook important data when making big decisions?
Yes, many service leaders tend to have a myopic focus on primary data about their existing customers. For example, they may conduct voice of the customer surveys, interpret data from their CRM or rely on big data or AI to anticipate how customers will behave in the future. These approaches don’t necessarily help when trying to expand into a new market or offer new services to new customers. More importantly, these approaches don’t take into account economic trends or other external factors, which could impact future demand. Nor do they provide a precise or granular forecast of the true market spend for new services.
What’s the reason? Are they overwhelmed, or do they just not prioritize the data-gathering step?
Developing market size and forecast models takes time and effort. Many service companies lack the talent, human capital or know- how to build these models effectively. As a result, they often rely on the most expedient method of obtaining data that supports or validates their perspective on the market opportunity — for example, market surveys or CRM data.
It is important to develop a precise view of the market based on a thorough and granular analysis of current and forecasted market expenditures within a new market or for a new service. Otherwise, service leaders run the risk of making a bad investment decision.
Is there still a place for intuition and gut instinct in field service decision-making?
If there’s not much at stake financially, making a decision based on gut instinct and intuition is a low-risk proposition. However, it can be a very costly learning experience if the decision can result in significant losses in profits or market share.
You’ve written about the concept of an “economic model.” How should service leaders go about collecting the relevant data to put through this model?
Econometric modeling requires massive amounts of trend data. For example, if a service leader wanted to know the size and forecast of the market for service on MRI machines then he or she would need to have data on the installed base, the serviceable value of that installed base, and data on market penetration rates. Since this data is not always readily available, a market research analyst or econometric analyst would need to build this model by collecting data on historical and forecasted product sales, replacement rates, pricing and attachment rates. Knowledge of econometric modeling techniques and algorithms is also mandatory. One of the responsibilities of a good analyst is to know where these sources of data can be found and which modeling techniques are appropriate.
What role does the data that a company already collects on its own customers and operations serve in this model?
Building a reliable and valid econometric model is both a science and an art. The econometric analyst can provide the science through data and economic algorithms. However, eventually the analyst must seek input from an industry expert. For example, he or she will likely require business intelligence on replacement rates, buying trigger events, and attachment rates. Data, or more importantly insight, an experienced service leader already possesses on their customer base and service operations provides a proxy or surrogate if this data is not readily available from external sources.