Predictive Analytics

Imagine for the moment you could predict the future. Suppose you could foretell how your customers, employees or suppliers would behave during the next year or two. How would you use the information? Would you introduce a new finish to the market before anyone else? Would you let go of an employee that you knew would fail you in the coming year, or give a raise to one who was bound to be a superstar to make sure he stayed on your team? Would you tighten the credit on a customer that was going out of business? Would you give a little extra attention to a customer that was going to leave you for your competitor if you knew it was going to happen? How cool would that be?

 

Fact is … you can predict any of the above, and many companies already do. I am routinely amazed at how Google’s autocomplete function can complete my thought as I’m typing in my search criteria. It knows what I’m searching for before I even finish typing it. Based on my previous purchases, iTunes makes suggestions about what music might appeal to me and invites me to purchase those items. Amazon does the same thing with books. But it gets better. A major winery uses information it gathers from its customers to predict which wines will appeal to which demographics and then adapts its marketing initiatives. A well-known casino actually measures the number of times a customer smiles while engaging in an activity and then adjusts its customer loyalty programs accordingly.

 

What these companies are doing is called predictive analytics—and it is changing the way customers are served, markets are analyzed, and products are presented and sold. Predictive analytics, sometimes called business analytics, is the practice of analyzing the masses of data available to a business, drawing conclusions from the data, and then adjusting business strategies and customer experiences based on the conclusions drawn.

 

But why would this matter to a small- or medium-sized surface finisher or supplier? After all, the likes of Google and Amazon probably have office buildings full of geeky analytical types who write computer programs and algorithms that make useful predictions. There’s no way a smaller company could ever afford the type of resources necessary to apply predictive analytics to its business model, right?

 

Dead wrong. Smaller companies can benefit from predictive analytics every bit as much, and perhaps even more, than a huge corporation—and it doesn’t take an army of analysts to make it work.

 

Consider a medium-sized coatings industry exhibitor at the 2010 Fabtech show that gathered data about each and every attendee that expressed interest in the company’s products. Following the show, it analyzed this data and determined that the majority of its leads came from stamping and fabricating companies that also have finishing departments. For the 2011 edition of Fabtech, this company positioned its booth in an area that was more likely to see foot traffic from these types of attendees. Predictive analytics.

 

I know of a custom coater that was having a difficult time hiring paint line employees who had the attention to detail necessary to inspect customer product at the line. This company hired an outside firm to complete a personality profile for each of its employees—based on a questionnaire completed by each employee. The company then reviewed the personality traits of those employees that performed well in this part of the paint line job to find the “ideal profile” for the position. Today when this company interviews a candidate to work on the paint line, the candidate also completes the questionnaire and his or her personality profile is compared to the “ideal profile” to make sure the candidate shares the personality traits that will make him or her successful in the position. Predictive analytics.

 

A supplier of paint and powder coating consumables analyzed the frequency with which its sales team members visited its customers against its customer retention information. The data revealed that a customer was 70 percent less likely to move its business to another supplier if the customer received a visit from a company representative at least once a month. Guess what adjustment the company made as a result.

 

Interested in getting started? Begin by making a list of what types of information your company has available that only you have. Information about the ordering habits of your customers, seasonality in your business and accounts receivable write-offs by customer industry or geography are just a few examples. Next ask yourself how this data could be used to predict future behavior, then use the conclusions to adjust your tactics and strategies.

 

Predictive analytics, it’s not just for Google and Amazon.

 

To learn more visit American Finishing Resources.