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    Objective vs. Subjective Data: Finding Accurate Farmer Information

    Posted by FMiD Team on Jan 5, 2021

    Objective vs. Subjective Data_ Finding Accurate Farmer Information

    As agribusinesses gear up for the planting season and plan out their marketing campaigns, we all realize that 2021 will continue to look different than many previous years.

    At least for the first half of the year, trade shows look like they're going to be cancelled. In-person, on-farm visits are not as frequent as they used to be. Yet the need to grow hasn't gone away.

    This is where digital marketing can play a major role in your agribusiness growth strategy.

    But if you want to invest in digital marketing, you have to have solid data and farmer information. 

    Without data, you don't know:

    • Who you're targeting
    • What they may be interested in
    • Where they're located
    • When they may want to be contacted

    But you may be asking yourself where you should go to find data. There are plenty of options out there, but generally they fall into one of two categories:

    1. There are data providers who provide subjectively collected data. This data comes from surveys, promotions, registration forms, and other similar sources. Most publishers collect data this way.
    2. Then there's objectively sourced data. This includes data from sources like the USDA, public and private data firms, geospatial insights and analysis and more. We collect our data this way.

    Here are some of the reasons why objective data will provide you with better farmer information and insights.

    Objective data prevents collection errors.

    Farmer information is only as good as the method by which that information was sourced. And there are plenty of problems that come up when sourcing data subjectively:

    • Incentives. Many surveys are incentivized, and only the farmers motivated by that incentive will fill out the survey. There will likely be gaps in the data that way.
    • Bias. How a farmer thinks their response will be perceived will impact how they answer a question.
    • Confusion. A farmer may simply not understand the question, or may think the researcher is asking something they're not.

    We recently uncovered this when conducting our buyer persona analysis. There are respondents who claimed to be a "Sustainable Farmer", but when asked how high a priority they placed on sustainably maintaining the land, said that was a low priority for them. Whether there was social acceptability bias in play, or whether they misunderstood what "sustainable" meant in that context, the point was that there was a potential error there.

    Our survey design had controls built in to account for these kinds of errors. But many other data sources don't have these kinds of controls built in.

    Objective data avoids these errors by cutting out the middle man. Instead of asking a farmer how much land they operate, we can just look at their fields and calculate the acreage from there.

    This can help avoid potential errors and confusion, as well as streamlining the overall process.

    Objective data tells the full story.

    Another weakness of surveys is that farmers will only answer the questions you ask them.

    When you compile data from a survey, you may have access to some valuable farmer information. But there is plenty more that you're just not seeing, simply because you didn't think to ask.

    The answer to this problem is not longer surveys. Farmers are already inundated with enough requests for information that adding even more fields to a survey will cut down your response rate. 

    With objective data, however, you don't have to go back to the well again and again to get the full story.

    A great example is data on relationships between growers. We know that families farm together, and we also know that farmers hire operators to manage their land. So wouldn't it be great to know who in the family is co-operating the farm, and who they've hired to help out as well?

    Instead of going to a farmer and asking them to list everyone associated with their farm operation (and let's be honest, they're not going to to do that), there are objective ways to identify these relationships. At Farm Market iD, we look at shared addresses, land, split subsidy payments, and more to identify all the grower relationships that you may want to know.

    Our data provides loads of detailed information, including crops, acres, field boundaries, Gross Farm Income, irrigation, soil, equipment, on-farm grain storage, real estate, persona, and much more.

    When you have access to all of these sources, you can tell the whole story of the operation. This is something that subjective data simply can't do.

    Objective data covers more ground (literally).

    We mentioned earlier that surveys are limited in terms of the volume of response. Even the biggest publishers won't have 100 percent market share in terms of subscribers. And an even smaller percentage of that would be willing to fill out surveys and provide their data.

    Objective data, on the other hand, doesn't share this limitation.

    One of the ways that we objectively source data at Farm Market iD is through geospatial analysis. Because we have access to attributed farm field boundaries, we can look at the whole of the farmland in the U.S. (our coverage is above 95 percent of the U.S. market).

    When we look at a particular farmer, we can see all of the fields they own and operate. We can segment these fields by crop, owner vs. operator status, soil type, irrigation status, and much more.

    With objective data, not only do you have more overall coverage, but you can drill down more specifically. It's a win-win.

    Objective data is often more up to date.

    One last thing to note about objective data is that more often than not, it's more current. 

    Since objective data comes from sources that themselves are routinely updated, the update process becomes itself routine. We go to the same sources at the same time to get the updates we need. That way, our clients will always have up-to-date farmer information.

    Subjective sources, on the other hand, can't update their data without going back to the well. And while many farmers may be happy to update their information, there are those who will not.

    This presents a real problem: data decay. No farm operation is completely static. While there's a lot that stays the same over time, things do change. Farmers sell off farmland. They buy new farms. They rotate crops. They switch crops. They retire. They hand off the reins to their kids.

    The older your data is, the more it will decay. Without a sure-fire way of updating that information, you risk basing your marketing campaigns on bad insights.

    If you need objective farmer data to power your 2021 marketing campaigns, click here to learn about all the ways we can help you. 

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