Use PPC Data to Create Good Content that Converts

Note: This post was originally featured on the Acquisio Blog.

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Everyone and anyone will tell you that you have to create ‘good’ content, but what the hooter does that even mean? Some of your content might be really good at explaining your product/service’s value-proposition, but who effing cares?

I mean, can you remember the last time you were like “Yeah, you know what I’m in the mood for right now? I’m in the mood for an internet salesman to knock on my browser window with an unsolicited advertorial in the form of a blog post or facebook ad.”?

What really makes a piece of content ‘good’ is how well it performs at what it’s intended to do. And if it’s supposed to generate awareness of and interest in your products/services, the best way it can do that is by addressing an immediate need that your clients/customers are already aware of — and not trying to convince them of a new one (whether it’s imagined or real).

Fortunately, we don’t have to be Chris fartin’ Angel to figure out the kinds of challenges our clients/customers face already. Readily available PPC data can tell us quite a bit about what kinds of content users are already searching for, giving us the kind of profound insight that content marketers usually have to ask Santa for.

Editorial, Editorial, Editorial

Content is a product you have to market, so before you can do that, you have to define its value proposition and target personas. And you do that by identifying the kinds of content that appeal to your clients/customers.

Just like newspapers had front, business, sports, and entertainment sections, your brand is gonna have to determine what kinds of content it’s going to use to build an audience of clients/customers. Newspapers made money off of the advertising because readers trusted the content to be objective, not because newspapers wrote about how great their advertisers’ products/service were. Similarly, your brand is gonna make money off of content because users can trust its insight and advice, and not because it’s telling them to buy something.

So just like Google doesn’t blog about the cancer-healing properties of Adwords, your brand shouldn’t blog about the colorectal health benefits of your SaaS offering or line of IoT devices.

That means that your brand is gonna have to think like a newspaper when it comes to its content strategy (but please, not when it comes to its revenue model). It’s going to have to figure out the different personas of its circulation/audience, or what we like to call in marketing, your ‘target market’.

And then you’re gonna have to identify the kinds of content that those different personas are interested in.

So if your brand is an outdoors product retailer, rather than blogging about tents, backpacks, and boats, you might try blogging about camping, hiking, and fishing. Similarly, if your brand sells ad management software, rather than blogging about your bid management or campaign management tools, you might blog about ecommerce strategies or landing page optimization because that’s what your potential customers need to know about, and that’s what you can help them with.

Develop a Content Keyword Narrative

Now that you think you know what kinds of content your target market wants — you cocksure, presumptuous snowflake, you 😉 — it’s time to put your money (literally) where your blowhole is, and put your assumptions up against the data (and separate the doers from the koolaid-drinkers).

And this is a not as rocket-surgery as it sounds.

Rather, it’s as simple as this: (1) figure out which relevant Content Themes your different personas are into, (2) run some basic search volume data on each Content Theme, and (3) make some editorial decisions based on which themes have the biggest, er, appeal.

Basically, if people are searching for certain kinds of content, it’s because it’s relevant to them, so that’s the kind of content you should probably be producing.

For example, if you were the aforementioned outdoors products retailer, you might want to look at the Keyword Planner or KeywordTool.io search data around:

• Camping tips/blog/advice
• Hiking tips/blog/advice
• and Fishing tips/blog/advice

And if you did that, you might want to add up the Search Volume for each Keyword Group and then multiply it by the Avg. CPC to get some kind of Value Score on the potential traffic from each Keyword Group. In other words, you’d want to do something along the following lines for each Keyword Group:

Total Search Volume x Total Avg. CPC = Value Score

From there, you’d want to compare the Value-Based Metric between different Content Themes, and determine how you should distribute your content marketing efforts between each Theme. For example, if you were the aforementioned outdoors products retailer, doing that might look like this table I created below:

So now you know the Value Score of Camping, Hiking, and Fishing traffic respectively, and can make a woke decision on how to distribute your efforts or budget or whatever.

Of course, you should also adjust these proportions based on your business goals/priorities. For instance, if only 5% of your inventory/sales is fishing gear, then you might want to reduce its importance and redistribute the bandwidth to a higher-priority Theme.

Drill Down, Repeat

The point, here, is that your brand can and should be building its content strategy around actual data, instead of the whims, hunches, or preferences of aloof micromanagers or bubbly social media coordinators. Doing so will allow it to reach customers on a level that actually leads to sign-ups, downloads, checkouts, and recurring sales.

Granted, once you’ve set your Content Themes and Editorial Calendar, it’ll be time to start routinely brainstorming specific topics within those Themes and according to that Calendar. And to do that, you can drill down within each Theme, mining the data for topic ideas and trends.

But that’s a blog post for another day. Besides, you’re all smart people and can probably figure it out in the meantime. You just gotta follow the trail of breadcrumbs drizzled in garlic butter.

Image Credits

Feature Image: Gratisography

Image 1: Flickr/Thomas Hawk

Image 2: Flickr/Kevin McShane

Image 3: Screenshot of Dan Ariely Twitter

Image 4: Screenshot of Calculated Risk Films YouTube