Think Before the Ad using Customer Data

Think Before the Ad using Customer Data

This guest post reveals how to capitalize on Customer Data, Campaign data and research to make informed marketing decisions.

Marketers wear many hats. Especially in today’s data-driven marketplace, they’re tasked with their fundamental duties as well as being customer experience experts and acute data analysts. The expectations are extraordinary, but they’re evolving because of the increasingly complex ways marketers are expected to bring in new customers. Currently, staying on top of your game to produce actionable results means using granular data insights to lay the groundwork to produce game-changing ad experiences. Every ad campaign that executes its goals to capture audiences must include a data-focused approach.

Distilling customer data and behind the scenes research simply better-informs the decision-making process. In fact, research is the first step to understanding your audience, concepts, and using development to refine them all towards your campaign goals.

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Countless types of ads haven’t worked out for various common-sense reasons, but more often than not the main factor behind campaign failure lies in the lack of research to power that ad campaign. The research is really intended to be the “why” behind the “what.” It’s designed to better understand your campaigns and understand who your target audience is.

If the brand or the company is developing advertisements to essentially drive purchases or awareness, the campaign requires some type of an objective. This is where you want to define the audience perspective  — be it core buyers or a segment to convert. You do that by zeroing in on three very simple ad-focused questions.

Can You See It?

When you have an ad concept you want it to be visible and have the ability to cut-through the current market standards. It won’t stand out if it’s one-of-many, but it has a better shot at breaking through if you can determine where to place it in the right context to provide the greatest impact.

Do You Like It?

While you might see an ad, if you don’t like it or it’s not resonating with you or it’s not hitting on the points you think are important, it’s not going to ignite a beneficial customer behavior.

Do You Understand It?

You need the ad to compute. On a gut level, you need to just get what you’re purchasing, and in broader marketer-speak a successful ad projects a clear value proposition.

Any good research project is not research for the sake of research, it’s coupled with additional crucial context. You might have a good sense of who those people are, and it might point to specific types of buyers by going a step further to identify shared attributes those people have.

Whereas plain data is very transactional, and you’re looking at a point in time to pinpoint what actual purchase might look like, the research is dynamic and can actually evolve based on the needs of the product life cycle. Further insights include who could be exposed to your ad, and in many cases who would engage with that ad. That way you can develop meaningful buyer segments and understand the full scope of who the people are that would buy certain products.

This initial research allows marketers to glean initial insights to get in the minds of consumers via swift and informative feedback. Whether it’s a straightforward negative reaction or that the message that doesn’t hit properly, it’s fairly obvious when you see these reactions in targeted markets that there wasn’t any type of pretesting time.

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In the pre-tested advertising campaigns, ideas that are not effective or can have a negative brand impact are rooted out before you’re in next stages and it’s too late to rethink your strategy.

There’s always a cyclical approach to campaign development. Ongoing steps might mean that the campaign continues because it’s so effective, the campaign is refined to add or subtract components, or there are different subgroups that you want to target. Whether you’re in the early stages of launching a product or the later stages of understanding how that product has been adopted — research supports all the different decisions that happen along the product life cycle.

With data and research analysis capabilities available to organizations via informative tools now more than than ever before, using data-backed research in today’s digital world must be an industry standard.

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