new year predictions

Lists are very popular these days. Any content marketer guru will tell you to make a list if you want a lot of click through activity. Heck, that’s why I titled this article “Top New Year’s Predictions.”

Market research is seldom considered very predictive, which I think is a bummer. At Landmark and Millennium, we work very hard to understand what is going on right now in order to accurately predict and shape the future.

Now virtually all of our work is proprietary and confidential, so I can’t tell you all the wonderful predictions we’ve made that have come true. One of the values of working with someone who has been in the business more than 20 years, though, is that we have the ability to check our work. We remember what we said would happen, and we can see when it has. We’ve recommended to companies how to lead change, and sometimes we’ve told them that change is coming, and helped them know how to ride the wave.

But on to my point.

I have held on to a story from Rural Lifestyle Dealer from late 2013. It’s titled 2014 Dealer Business Trends & Outlook. It’s a story summarizing the responses of their readers to an annual outlook survey. In it, John Deere and Kubota dealers were the most optimistic about 2014, with two-thirds of both dealer brands expecting sales increases of 2 to 7%. Deere dealers expected tractors with less than 40 HP to have the most growth potential.

According to an AEM December 2014 report, sales of less than 40 HP tractors across the industry increased 8.7%, and 40-100 HP tractors increased 7.2% in 2014. Dealer responses were accurate and predictive.

Of course, the burning question that we ask is WHY?

In this case, I think the answers are buried deep in the research data. I’m just looking at the article, not the data, but I think two key questions give us the why:

  1. Are customers shopping for a specific brand when they come to your store?
  2. How often do customers accept your recommendations?

Let’s break the numbers down in a very simple way:


If we do just a little math and multiply these two numbers, we get something I call “chance any customer will buy.” Let’s normalize it, to use a technical term, so we get a percentage. Here’s how the data looks now:


What this simple calculation tells us is that any time a customer walks into a John Deere dealership, there is a 66% likelihood he or she will walk out with a purchase from that dealer. For Massey Ferguson dealers, the odds are much lower, with only a 29% chance the customer will make a purchase.

The questionnaire didn’t address how many customers each dealership has, but let’s say both have 100 customers come in on any one day. The John Deere dealer will convert twice as many shoppers into buyers.

What does this have to do with making predictions? Well, it shows in general that several factors are needed to increase sales:

  1. Increasing store traffic is a good start, but increasing conversion rate is more important.
  2. Increasing brand preference has to start before a shopper enters the store.
  3. Creating a trust relationship leads more shoppers to become customers.

We can also look at specific brand data and make recommendations for specific brands. In this case, Kubota dealers say 70% of shoppers are looking for a specific brand. (The article doesn’t mention it, but let’s assume this specific brand is one the dealer carries). Seven of ten shoppers take the dealers recommendation. In this case, if the dealer can increase the percentage of people who take his recommendation, the dealership will increase sales significantly.

For other brands, like Case or Massey Ferguson, less than half of shoppers who come to the dealership have a specific brand in mind. That means the customers are either making price comparisons to what brand they really want, or they truly have an open mind and are considering all choices available to them. The strategy these brands should pursue is to create a desire for the brand before the shopper gets to the dealership and improve the dealer relationship that causes shoppers to become buyers.

How does this relate to making predictions? Without knowing how to look at hard facts, it’s difficult to make good recommendations that turn into great predictions.