You have some data. What are you doing about it?
Agriculture is a peculiar market as it relates to marketing and data with much of the challenge stemming from the pre-eminent importance of the retailer in the middle of the sales process.
With the retailer owning the face-to-face contact and relationship with the buyers, manufacturers have been in a position to make the best of the potential choices as it relates to end-user data: don’t try, try through retail partners, try on their own, or find an alternative path.
Over the past couple of decades, the marketing push has been to amass first-party customer data. In some ways, it was encouraged so that brands could have some level of relationship. In other ways, it was preparing for eventual technology shifts (like cookie deprecation), so brands weren’t left with no way to market to their desired audiences.
That brings us to today, when most agricultural marketers have some data. Whether it is fresh, accurate, large, accessible or ultimately useful for marketing differs for every brand. While some of this is systemic, much stems from inexperience or a lack of awareness about what’s possible.
What data often looks like
Databases in agriculture typically fit into two categories, although the goal is some level of a third.
First, a database of farmer names, customers or prospects. This dataset is usually the most anemic, as it has names, addresses, some email addresses, and that’s about it. It could be used to message about an upcoming product release or rebate, but no one is confident it gets to all of the right people.
Second, a database of customer names cross-referenced to their retailer, offering a bit more demographics, like size of operation, type of herds or crops, etc. This is more useful in marketing because it allows for rudimentary persona work and segmentation within the database to better craft messages. But the data might be a couple of years old and it doesn’t house complete records.
A robust database
There is a third type of database that serves as the goal for most brands. Sometimes, it is seemingly an impossible goal, but the aspiration should be a robust database. Robust has a few requirements:
Expansive. A robust data set need not be complete, but it must cover a relevant percentage of the potential universe.
Accurate. A robust data set is clean of false data and is checked against other quality data sets to ensure ongoing cleanliness.
Full. A robust data set contains a level of completeness within each of its fields. Missing large chunks of first names, zip codes or crop types causes mistakes for those using the data.
Updated. A robust data set can’t live long past its expiration date. Farms change hands, farms grow and contract, and some farmers die. All of those things are important to know to maintain an informed database.
Connected. A robust data set needs to be connected to the life of the brand. This often includes sales and orders but may involve finance or other aspects of the business. The greater the connection, the more robust the data set.
Agile. A robust data set is able to constantly change. Not change at the foundation level, but ebb and flow as key data changes. Requirements around privacy, for instance, change and the database must adhere to those. But opt-in, opt-out recording; individual media engagement; new sku purchases; or new technology offerings should be able to flow into the mix.
The important next step
A robust database is really powerful in understanding customers and prospects. It allows brands to understand the type, size and geography of their best potential customers. But a database is merely a tool, and brands and even database vendors in agriculture often fail to wield the tool well.
Using the data to create segmentation, cross-brand correlations, persona development and other helpful results can fuel smarter sales and marketing. Running predictive models, evaluating potential churn, assigning accurate lifetime value equations, creating feedback loops to sales and product development based on in-market engagement are just a few powerful tools for those with a robust database.
Conclusion
Collecting customer and prospect data is challenging and important in agriculture. Most have started the process but fall short of robust, and the super power abilities a robust database offers.
In the next article, we’ll reveal how these robust databases are actually built—and the surprising, sometimes messy decisions that shape them.