How agricultural marketers are using your data

You farm better by studying the stacks of data you collect about your crops. Ag suppliers sell better by studying the stacks of data they collect about YOU

Using customer knowledge to outperform the competition has been a critical survival strategy for small-town businesses as they struggle to fend off competition from mega-retailers.

Now, those mega-retailers are showing they can get to know their customers too. They can even get to know them better than the small shops.

Don’t believe it? Then just think about your last time on the ’net. When you went to amazon.ca to buy a specific book, you got offered great deals on all sorts of other merchandise too.

And somehow, even when you only go online to do a routine Google search, the ads that keep appearing are always about new trucks or airfares to the southern U.S.

The ads are no accident. Corporate retailers have figured out how to mine the huge amount of data they’re getting from the Internet in order to produce scientifically proven marketing programs that put their pitches in front of the people who are most likely to open their wallets for their specific products.

Now, agricultural retailers are also getting more sophisticated at individualizing their customer interactions too. The digital age has created a whole new world of opportunities for them to collect more information from farmers than ever before.

Yet in agriculture, the “hands-on” approach is still vitally important, even among suppliers that are critically dependent on data analysis.

By itself, Krystal Kolodziejak’s job title at Farm Credit Canada says a mouthful. Kolodziejak is manager of market insights, and she explains that FCC basically has two separate divisions dedicated to scrutinizing customer information. The job of the first is to build and maintain a customer experience index that compiles feedback that comes directly from clients and computes it into one number that represents how well they were served.

“It’s our way of monitoring throughout the relationship with a customer whether or not we are delivering value to them when we are working with them,” Kolodziejak says. “Based on how that score can fluctuate throughout the year, we will provide coaching to the team of that sales area.”

The other division, the one that Kolodziejak manages and that includes FCC’s Vision Panel, is dedicated to monitoring the impacts of changes generated either within FCC or from the industry at large, and then directing the company’s response to those changes.

Both divisions depend primarily on survey results, which are still physically mailed in many cases, as well as direct interviews of producers either as individuals or via small groups.

The only thing that has really changed for Kolodziejak’s work in more recent years is the ability to collect most of the information she needs online.

“Our FCC Vision Panel is made up of over 5,000 people across Canada, including primary producers, agribusiness, students, and people who serve ag clients,” Kolodziejak says. “We’ll go to the Vision Panel about a specific business decision or when we want to get the pulse of the industry, and we can use that information to get a better understanding of what our customers are facing.”

Kolodziejak says sometimes a survey fails to tell the whole story, so when she needs to dig deeper into a particular issue, that’s when they form focus groups at five or six locations across the country where a selection of growers will meet together personally.

Barry Nelson tells me this is pretty reflective of what they do at John Deere too.

In fact, John Deere is responsible for generating even more numbers than a credit supplier such as FCC. Diagnostic software can pull a lot of information out of the modern tractor, and many producers are using features which transmit information automatically.

“The customer is able to take all this data, crop yield, moisture, population, and now can wirelessly transfer this to a cloud — we call it the Operations Centre — and can use that to analyze all this data so that they’re making wiser decisions,” Nelson says.

It’s a real learning curve for farmers, who need to determine how to read and apply all of this new information.

Yet it’s a learning curve for retailers too, not least because they have to bring in new kinds of employees, such as software technicians, and because they have to figure out how to develop and market new kinds of services, such as consulting services that help farmers make use of their data.

It has also made Deere look at whether the value of participating in the new data-based farm equipment world makes it worth rethinking some of its most cherished paradigms, such as whether Deere should open its doors to research projects and components that originate from other workplaces.

“In the old days, everything we put on the tractor was ‘John Deere,’” Nelson says. “In this new arena, in order to take care of our customers, we need to open up our platform, John Deere Operations Centre for example, so a software developer knows what it takes to work within the John Deere Operations System.”

The big difference

Even so, there’s a big difference between showing other developers how the system works versus showing them the data the system collects.

Nelson says at no time does the data uploaded from farmers become property of John Deere. Privacy protections won’t even allow John Deere to use the data by anonymizing the farm it came from.

Unless customers opt to give them access, Nelson says, John Deere can’t touch their information.

So, how does John Deere get customer information for its marketing department and for making strategic business decisions?

As old-school as it seems, when it comes to customer insight, John Deere still relies on the feedback in its customer satisfaction surveys.

Deere’s secondary avenue, like FCC’s, is to host focus meetings.

“Before some products are even developed, we have customer focus meetings all the time,” Nelson says. “We even have our own research group that takes a look at all of that, because we have to try and figure out 10, 20, 30 years from now, where is agriculture headed and what are the needs in the future?”

But that isn’t to say the data farmers share can’t be significant. “Right now farmers can opt in to allow us look at machinery issues,” Nelson offers as an example. “Say we’ve got 1,000 combines working out there and if we’re able to get the machinery information, maybe we find out 400 had this bearing fail in a certain time period.” That level of insight helps the company reassess specific shipments or parts suppliers and that’s the sort of customer knowledge that leads to build better machines moving forward.

The question, though, is whether Deere can generate similar kinds of analytics, not just on its machines, but on its farm customers too.

So far, that seems to be a step too far. Ag-chem companies, for instance, can crunch their sales numbers and figure out who their most valuable customers are, so they make sure they fight harder to retain their business by, for instance, sending agronomists to the farm, supplying extra technical support or perhaps offering great seats at an NHL playoff game.

They can also analyze their data to look for opportunities to up-sell or cross-sell.

But beyond that, the science hasn’t gone all that far.

Think ‘group’

It’s a point that Peter Gredig, a farmer and ag technology expert based near London, Ont., believes is very important for farmers to understand. One person’s information alone is not especially useful to anyone. But volume changes everything.

“A lot of the data that’s collected, so-called big data, they don’t care who it is,” Gredig says. “Marketers, advertisers, and manufacturers want to be able to aggregate it. And with that, comes power.”

Consider a phone app he helped to develop years ago called the Aphid Advisor. After scouting a soybean field for aphids, you input your observations and the app will tell you whether or not a spray application is warranted.

“If you check off ‘I’m OK to share,’ it generates a data point so that researchers know where, when and what the environmental conditions were on a map of Ontario,” Gredig says. “In real time they would be able to see where that pest is manifesting and, I mean, how would we do that otherwise?”

Where information sharing leads to greater good for everyone, Gredig says he has few concerns about giving access to his own information.

Understanding the intentions of anyone who asks for your data is the critical thing in his mind and not something anyone’s being very diligent in explaining up front.

“I’ve loaded apps or other pieces of software and the terms and conditions are in there, and I don’t think the lawyer who wrote them has read them,” Gredig admits. “If you’re going to hand off data to somebody else, at that moment, questions have to be asked; how are you using it, what access to copies will I get, and how will I be kept informed about it?”

Gredig says if you don’t like the answers you get, you don’t have to give your consent. But if there are benefits to be gained in exchange, think carefully.

And if your concern is someone else’s ability to profit by having it, well, chances are good they won’t because it doesn’t seem like anyone in the industry has figured out how to really profit from all this data yet.

“The ability to collect is well beyond our ability to assess,” Gredig assures me.

Basically, agribusiness right now is in the same spot crop producers were during the release of yield monitors in the late 1990s. “The idea was, we would just stare at these maps and all would be revealed,” Gredig recalls. “I have the feeling that agribusiness is going to end up staring at stuff and wondering how to get some value out of it.”

The limitations

Kolodziejak says the limitations of data management create a real hurdle for analyzing customer information. So much decision-making depends on the emergence of patterns, but it’s extremely difficult not only to see from the data when a few isolated events actually become a pattern, but also whether the pattern will eventually be influential.

Even if you could do that much, Kolodziejak says, you’d still be trying to sort out the complications that can arise from external factors, such as weather events, that have a big impact on the outlook of farming customers.

For instance, if a particular region got too much rain during fall harvest last fall, could that mean that their survey responses are artificially low? It’s her job to decipher when data like this is being skewed, or the company could respond to a business issue that isn’t really an issue at all.

Then there’s an additional complication.

Farmers who have good experiences in customer service outside of agriculture expect such services to be provided within the industry too. Sometimes, negative feedback isn’t because of bad customer service from an FCC transaction, Kolodziejak explains. “It is because they have the experience of buying a condo on an iPad, and those experiences are shaping their expectations of working with us.”

If customers continue to expect improved customer service, at accelerating rates, technology is going to have to take more of the manual labour out of her job going forward. “Excel is a great tool for so many things but if you have to go through and manually enter all of that information, the likelihood of you doing that is less,” she says.

New technology needs to be developed to collect information easily, as well as analyze it. “You see other examples of technology that are good cases,” Kolodziejak says. Her personal favourite is the Nest Smart thermostat. “So many of us have programmable thermostats, we might have taken the effort to do that initial setup but it’s cumbersome, and all of a sudden you go away for a week and you don’t change that program. The Nest will learn, based on motion sensors, if you’re even at home, will adjust the temperature based on that, and then it gives you a report that allows you to see the trends and patterns yourself to make decisions.

She wonders what an equivalent to this technology to help collect data in agriculture might look like and what more it could show her from the data she’s collecting.

It’s a huge opportunity, Kolodziejak believes, and it is definitely attracting application developers, tech companies, and venture capital to agriculture.

This article was originally published as, “Your data, their sale,” in the February 2, 2016, issue of Country Guide

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Amy Petherick is a Contributing Editor at Country Guide.

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