#Marketing doesn’t sell products. It brings to light a solution that addresses a customer need. If you’re in marketing, you’ve likely heard some variation of this before. And while this golden rule is still true, many marketers haven’t caught up with that fact that digital marketing has helped take the role of marketing one step further.
As digital marketing continues to evolve, you not only have the ability to convey your solution to a customer, but the ability to gain valuable insights into customer behaviors that can both inform your marketing strategy and your business, while also working to help you anticipate customer needs.
What is it that you want to learn about your customers? Wanting to just learn new insights, in the general sense, is not a goal. You need to start with something far more specific than that, otherwise you’ll end up missing out on actionable, valuable information.
And that right there is the key—is what you are seeking going to be valuable and allow you to take action? In other words, what do you want to learn and why?
Let’s say your brand just released a new product. Your goal could be to learn more about the initial customer response, as well as likes and dislikes. Or maybe your brand wants to learn more about how they could improve an existing product in a future release. Then your goal would be to learn more about key issues with your current products.
Or your goal may be more complex—perhaps you are a retailer with an ecommerce site, as well as brick and mortar stores. Your #sales in the Midwest are exceeding expectations in your brick and mortar stores, but on the decline when it comes to ecommerce. Why are consumers more inclined to buy in-store than online for this specific group? Your goal could be to examine the difference between the in-store and online experience for this audience to better understand how they are interacting with the marketing materials involved in the purchasing path.
Identifying your initial goal can help you understand what you want to learn and why. But now it’s time to think about who in your audience you need to look to. Maybe you want to learn more about your customers who live in a specific geolocation. Or maybe you want to know more about customers who bought a certain product and haven’t made repeat purchases after that.
Once you’ve identified this target audience, think about where your digital marketing efforts are currently reaching them. This could include, but is not limited to your:
What are the numbers behind your standard metrics for each respective channel? For email, this could mean observing your opens, clicks, and top clicked content. For #social media this could mean seeing which Tweets or posts were the most shared or had the most comments. For online reviews this could be seeing how many are negative vs positive.
Now that you know what the numbers are, you can start to look at what they mean. If a particular piece of content was most clicked, what does that tell you about your audience? And what does that tell you about how they are responding to the larger goal at hand?
Let’s think about the example from earlier regarding the retailer who noticed that sales in their Midwest brick & mortar stores were solid, but sales on their e-comm site from those in the same geographic area were slow. You might look to see how email campaigns regarding in-store vs. online only perform with that audience. How are they responding to the content in those emails? What can you infer based on those interactions?
Essentially, what you’re trying to figure out, is whether or not you can correlate a pattern in interactions with marketing materials and consumer behavior.
Your analysis will (and should) lead to asking yourself a number of questions. Would this audience respond better if I did X or Y? What would happen if I ran a targeted ad campaign about Z just to this audience? Is my content falling flat with this audience and worth re-vamping? Etc.
No matter what you decide to test, make sure you follow proper A/B testing procedures. Start with a hypothesis—by doing A, I expect B to happen. Next, create your test. For standard A/B testing you’ll have version A and version B. For best results, one of those versions should be a status quo (also known as your control) while the other is changed to either prove, or disprove your hypothesis. Finally, run the test until you have statistical significance, i.e. you feel you have enough data to declare a winner.
And if results don’t come out the way you expected, don’t just consider it a loss. See what you can derive from it and test something new. It should be an on-going process.
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