Every agribusiness wants to be customer-centric. So how do you take aspiration and make it a reality?
As time goes on, service and expertise will be the deciding factor as to what makes a great agribusiness -- above and beyond the quality of the product itself.
It all starts with knowing who your customer is, what they’re dealing with, and how to position yourself as a helpful solution to their problem.So how do you take this level of personalization, context and empathy and scale it to fit your business needs?
One helpful tool is the buyer persona. It helps you:
- Aggregate your ideal customers into a handful of highly defined representative figures
- Build a better understanding of who you're doing business with -- and where else similar people may exist in the market
- Customize and tailor your messaging to the specific, contextual needs of the grower
- Decide quickly and easily which markets to go after, and which ones aren't the best use of your time and resources
This post is the first in a three-part series on how to build and leverage buyer personas to create a customer-centric strategy. In this post, we’ll focus on what a persona is and how to take the first steps toward building one.
What is a Buyer Persona?
Your agribusiness may do business with hundreds, if not thousands of farmers. Personalizing and contextualizing your communications with them is next to impossible to scale.
A buyer persona is a semi-fictional representation of your ideal customer that’s built on data. Simply put, it allows you to take the core attributes of your ideal customers and aggregate them into a limited number of fictional profiles.
The graphic below illustrates how this works:
In that image, you can see various shades of orange being aggregated to form an “orange” persona, green to green, and red to red.
There are hundreds of potential insights a persona can give you. Here are just a few:
- Persona name & description
- Preferred marketing channels
- Problems to solve
- Farm size & Gross Farm Income
- Crop types
- And much more
Each of these insights can help you better craft your marketing and sales communications so speak directly to your customers' needs, interests, problems, and pain.
Now that we know what a buyer persona is, let's quickly discuss what it isn't:
- A persona is not a profile of a single representative customer. While knowing your customers helps to build the personas, they’re meant to be aggregate representations and, as the definition implies, semi-fictional.
- A persona is not the same as a customer profile. Personas are used primarily to give you an idea of how the individual person is receiving your marketing content and how they may react to it. They typically don’t expand to consider the business operation itself, except to consider how it may impact how the persona thinks about what you’re writing or promoting.
- A persona is not something permanent, but evolving. As you learn more about your customers over time and you acquire new pieces of data, you’ll want to change, add, and remove personas from your list.
Understanding what a persona is, specifically, is crucial to creating a customer-centric strategy. Without it, you'll be basing your decisions on who you think your customers are, rather than who they actually are.
This could lead to lost opportunity and unrealized potential.
Using Data & Statistical Modeling to Build Your Personas
In order for a persona to be functional and helpful to your marketing and sales operation, it must be built on a foundation of data.
In the consumer world, this data includes behavioral, attitudinal, preferences, etc. In agriculture, we take it a step further and look at fields, crops, planted acres, Gross Farm Income, spend potential for inputs, real estate data and much more.
One helpful way to take all this data into account is through a predictive model. In essence, you can leverage data science and analysis to predict -- based on hundreds of demographics -- which persona the farmer most identifies with.
When using a predictive model for persona-building, or any task for that matter, you need to have two sets of data.
- The first is your training data. For this dataset, you've already correlated them to a particular persona or group of personas. This data, as the name implies, trains the model to accurately predict a given grower's persona based on the demographic data appended there.
- The second is the remainder of your prospect or customer data. This data is not correlated to a persona initially. Instead, this is the dataset that you feed into the model, allowing it to make a prediction for each prospect or customer.
So let’s say we wanted to predict who in a database of 100,000 farmers would identify with a particular group of personas. The model would run a step-by-step process:
- Create a small sample of customer data who already have one of your personas appended to them to function as the training data.
- Append as many demographics as possible to those customers for maximum accuracy.
- Run the training data through the model, which will enable the model to make future predictions.
- Once the model has been trained, run the rest of the data through (with the same demographics appended) to predict which ones demographically align with a particular persona or group of personas.
For now, it’s important to look at how to arrive at that initial training data set. There are two ways you could do that:
- Make your own assumptions about specific customers’ persona identification
- Run a survey to allow customers to self-identify as a persona
The first method allows for user error in your assignment of the personas. While you may have some insight into who they are and what they do, there could be some information you’re missing. A bad assumption in this stage will lead to bad insights later on.
The second allows for you to survey customers and let them self-identify. This method removes your guesswork out of the picture, and lets you draw insights from customers directly.
One note here: For statistically significant results, you should include a “none” or “other” option in your survey. The reason being that you only want people who strongly identify with a particular persona description to self-identify. Muddying the waters now could lead to unclear or inconclusive results later.
If you have a significant number of your customers who don’t self-identify with a particular persona, this is probably a sign that your current personas don't represent all your potential customers. You'll likely need to come up with new personas that represent these new customers.
Once you’ve assigned personas to your training data, you’re all set for the next step in the process: actually predicting future outcomes through predictive modeling. We’ll cover how this works and what it means for you in the next post in this series.
A persona takes time and upfront investment to build. But in the end, it provides a solid foundation for your marketing and sales initiatives that is nearly unparalleled. And it truly sets you up to become an agribusiness that is centered around the needs of your customers.