You already know that data analysis can provide a wealth of insight to your farmers’ buying habits and behaviors.
But there are limits to the information that data can provide. And there are limits to the types of information you’ll find useful and relevant to your business practices.
To get better information from your data, you need to ask the right questions. This will keep you from wasting time and getting bogged down in insignificant details.
Let’s look at some of the best kinds of questions that your data can answer.
How Am I Doing?
Periodic data checks can help you gauge your success in any area where you’re collecting data. Monitoring feedback from ad campaigns, for example, can show you which ads are performing well and which farmers they’re resonating with.
Data checks can help you before you launch a new campaign or product, as well. A/B or yes/no testing can give you the insight that you need to see how different options perform among certain demographic segments.
Who Are My Customers?
You can learn a lot about your existing farmers and new markets by analyzing data on farmers within your territory. Data will tell you not only who your customers are (or might be), but should also provide insight on what your farmers are growing, spending and earning.
Data analysis can be most useful when applied to which farmers are part of your ideal audience, and which are not. Knowing which market segments to focus on saves you time, money and energy.
What Do They Need?
In addition to telling you who your clients are, data analysis can also help you identify what they need. Are your current farmers in need of revamped solutions to protect their financial investments? Would a new fencing product bring in a certain type of farmer that lives in a heavily wooded area?
While pointed surveys can address some of these questions and should provide answers, they’re likely to be limited in scope and won’t be helpful beyond a certain point. Data sets with information about land perimeters, Gross Farm Income and crop data allow you to diversify your data questioning and pivot when necessary.
Is This Weird?
One of the most popular way to analyze data is to search for trends. But an equally helpful way to sift through your data is to ask “is this [factor] weird?”, “what are my outliers?” or “where are abnormalities occurring?”
Data sets reveal aberrations in the market or customer behavior by the information that they can’t tell. If you’re monitoring the success of a product in a retail outlet, for example, looking for data about which days people didn’t buy your product might tell you more than data regarding how much people bought on other days.
Often, looking for outliers and abnormalities invites treks down rabbit trails that may or may not prove fruitful, so it’s important to proceed with caution. Look for abnormalities once you’ve exhausted the normal questions and analysis to save time and frustration.