In a recent podcast with Startup Pittsburgh, a resource for young companies around the Steel City, we discussed the importance of data in your marketing efforts. If you don’t have data, you’re flailing around in the dark.
It’s a concept we’ve touched on in the past, back when we explained that content is still king, but it’s data that calls the shots—both for content and all of your other efforts.
At Poetica Marketing, we believe your marketing success depends on two factors:
If you have great data and make bad decisions, your marketing will suffer. Conversely, if you have bad data and great ideas, you execution will suffer.
Generating Great Marketing Data
To generate marketing data, you either need to tap into existing sources or start from scratch.
Those existing sources may exist right in front of you. Forgotten email lists. Your LinkedIn connections. The superfans on your Facebook page. Sometimes marketing opportunities are in front of us every day, but we don’t have the right perspective to catch their potential.
Creating your own data may require some leg work, but the results can be fantastic. LinkedIn, for example, is a fantastic tool for simple lead-generation—sort by location and profession, and the platform will spit out everything it has.
A/B testing (or split testing) is a terrific way to develop marketing data in a short amount of time. For example, our A/B testing ads for a clothing client has revealed a very specific type of image is outperforming other imagery in our tests. From here, we can begin drilling down by trying different versions of this image type—camera angles, colors, accessories, and more.
Developing your data takes time, but it’s a worthwhile endeavor. Once that data is in place, you can focus on making the right decisions.
Filtering Through the Data: Making the Right Decisions
We can go on and on in this section, but here’s the simplified version: We can’t make the right decisions if we don’t listen to the results we receive.
We were surprised by the data that came back from our tests with the clothing company. The results we saw went against our instincts, so we tested again to make sure our hypothesis was incorrect. The results didn't change—even across different marketing platforms.
We could keep spending money until we generate a result that proves our theory. With enough attempts, it’s bound to happen eventually. But the data now shows us that our target audience in this very moment prefers the other tactic. We’d be foolish to tell them what they really want.
If we don’t listen to what the data tells us, we’re ignoring our responsibility as marketers: Generate revenue for the company as effectively, efficiently, and as financially responsible as possible.