Using Advertising Data Analysis to Remove Campaign Risk

If you’ve run an ad campaign before, then you have something invaluable.


This data is useful regardless of the success of your past campaigns. You can learn just as much from an ad set that was a complete failure as you can from one that generated a huge number of conversions – maybe even more. 

Analysing and interpreting data from past advertising efforts can help you minimise campaign risk going forward. But first, you have to know which metrics to use.

How Much Data Is Too Much?

For many companies, the customer data they have available largely outpaces their capacity to do anything useful with it. 

In a 2018 survey done by Blueshift and TechValidate, 54% of marketers cited insufficient data analysis capabilities as their #1 challenge for utilising customer metrics. 

Most businesses have all the metrics they need to make data-driven advertising decisions. There’s just a lot of it. And organizing it in an effective way isn’t easy.

Delivering the right messages to the right people at the right time is the goal of any successful ad campaign. Today, we’re going to dive into how to analyse your past data to create targeted, personalised ads that save money and increase efficiency. 

Ready? Let’s go.

Use Data to Learn Who Your Customers Are

Your existing customers are the most valuable data set you have. When you have a solid grasp of who they are, it’s easy to find and more customers like them. 

What that particular set of data looks like is different for every business. For example, if you’re a brand that only serves select geographical locations, the metrics that are important for your advertising efforts will differ from those of a global corporation. 

Here are some examples of customer data that’s helpful for advertising:

Transaction History

This includes what customers have purchased in the past, when they purchased, how often they purchase, and how long it’s been since they purchased last. 

If you know that your average customer buys three items from you every year, then that will help you determine how often to serve them ads, and when to stop.

Demographic Information

Factors such as where your customers live, their age, gender, race, education history and income will help you to create buyer personas and find lookalike audiences.


What else are your customers doing online? Which other brands are they engaging with? What causes do they care about? Knowing this will help you tailor messages and serve them ads based on behavior. 

If you know your customers tend to care about environmental issues, for example, you can serve ads with language about sustainability and target fans of green brands.


If you’ve conducted any customer surveys or solicited feedback after a purchase, the results can be incredibly useful for your ad campaigns. But go a step further and search for reviews online. 

Glowing customer reviews and testimonials make for great ad copy. 

Don’t forget customer service inquiries, either. If you know what questions your customers have frequently, or their pain points, you can use that information in your messaging. 

Customer Acquisition

This one is huge. Where do your customers come from?

If you’ve been pouring equal amounts into Facebook and Google, but 85% of your conversions are from Facebook, that means Google isn’t working for you. 

That may be because your Google Ads needs to be revamped, or it may mean that Facebook is just a better platform for you right now and you should shift the majority of your ad spend there.

Customer Lifetime Value

Everyone knows you have to spend money to make money. But if you’re spending $200 to acquire a customer who will likely spend $100 with you during the course of your relationship, then your ROI sucks.

By analysing your existing customer data, you can uncover valuable patterns that will help inform your advertising campaigns. And not just for targeting, but for messaging, too.

Analyse Past Campaign Performance to Improve Engagement and Conversions

Your past advertising campaigns are a goldmine of data. Where it gets tricky is collecting it all in one place to get a holistic view of performance across channels.

You can purchase tools that will help to automate the collection and analysis of multichannel data, hire someone to do it or outsource some or all of the process. It all comes down to your budget.

There are some obvious data points that you should be looking at, including:

  • Results of A/B testing
  • Performance of campaign assets, like images and video
  • Effectiveness of messaging
  • Audiences with the best and worst response
  • Which platforms performed best

There are some less obvious metrics, though, and taking a good look at them will help you to boost your success rate and minimise risk. 

The Customer Journey

When analysing ad campaign data, it’s important to look at where in the sales funnel you’re losing potential customers. This requires a deep analysis of what happened to people who clicked on or engaged with your ad, but didn’t convert.

Sometimes, figuring out why you lost a potential customer is easy. Let’s say you created an ad and A/B tested two different landing pages, with 5,000 people clicking on each ad. On one landing page, you got 2,000 conversions; on the other, 200. 

In the above case, you know the second landing page didn’t perform. So simply taking that landing page out of rotation solves your problem.

But let’s say neither landing page converted. Or 1,000 people liked your sponsored post but only 20 people actually clicked. Then what?

If you’re bringing in potential customers at the awareness stage, or top of the funnel, and then losing them, it’s important to know whether you’re losing them in the middle of the funnel or the bottom of the funnel. That way, you can create a more personalized and effective message for moving them closer to conversion.

Timing of Conversions

When are your customers engaging with your ads? If you know when your audience is online, you can schedule ads to hit them at the right time.

If your audience is moms of small children, for example, when are they online? Probably in the early morning, before their kids wake up, or late in the evening, after bedtime. If you’re serving them ads from 9-5, they probably aren’t going to see them.

Alternatively, if you’re a B2B brand targeting decision makers, it’s imperative that they see your ads during the workday. 

By analysing when your ads performed best, you can narrow the window during which you serve them on future campaigns, so your audience sees them when they’re most likely to click. 

Which Devices Your Ads Are Being Seen On

Are people engaging with your ads on their phones or on desktop? It makes a big difference. 

Let’s look at retail. While most top-of-the-funnel (TOFU) activity happens on smartphones, most purchases aren’t made on mobile devices. It’s important to know when users are accessing your ads and on what devices.

If you are getting a large proportion of clicks from search ads on mobile, for example, but they aren’t converting, you’ll want to retarget those people who expressed interest at the TOFU stage again when they are using desktop, so they’ll convert.

Combining your timing data with device data gives you a powerful tool for meeting your target audience where they are – in a way that’s more likely to result in a conversion.

Ad Type Performance Across Channels

If your video ads are your top performers, you should just create video ads for every channel, right? Not so fast. 

Just because a video ad performed well on Facebook doesn’t mean it’s going to do gangbusters on Instagram. Don’t go all in on one type of ad just because past performance says it converted well. Assess each channel individually to find out what worked best for each one in the past.


There’s a sweet spot between a user seeing your ad enough times to make an impression and overexposure. That’s why it’s important to analyse how many times users are seeing your ads before they convert.

For example, maybe a user needs to see two different ads for the same product you offer before they purchase it. That’s vital information to have, and the only way you can get it is by diving into the metrics.

If you don’t get your frequency right, you run the risk of alienating potential customers. No one wants to see the same ad over and over again for a product they don’t want. But you might be able to bring them in later with an ad for another product if they don’t have a negative association with your brand. 

The One Metric You Can Ignore

I’m talking about reach. Forget it.

Who cares about how many people are exposed to your ad? Not me. 

Most of the people who are exposed to your ad won’t actually see it. Maybe they opened up Facebook and looked at the top two posts in their feed before they got a phone call, and yours was #3. Maybe they did a Google search for a relevant search term but scrolled right past your ad because they were looking for something specific.

If you have your targets right – and are utilising all of the data points mentioned above, then it doesn’t matter how many people are served your ad. Who actually sees your ad matters. And who engages with it really matters. And who converts really really matters.

Reach only matters if it’s too small or too large. If it’s too small, your targeting is too narrow. If the reach is too large, it’s too broad. If it’s somewhere in the middle, you don’t need to think about it. 

Past Data Leads to More Informed Testing

It’s important to remember that analysing data doesn’t provide you with a clearly defined path forward. 

“But wait, Phil,” you’re probably thinking. “Isn’t that what the whole point of this blog post was?”

Yes. And no. See, any advertising and marketing efforts rely on testing. 

First you test. Then you analyse. Then you refine. But you’re not done. 

You’re never done. 

Because then you do it all over again. And again. And just when you think you have it all figured out, your ads will stop converting. And you’ll need to go back through the data and look for more insights to help inform your strategy going forward.

Trends change. Customers change. The only thing that stays the same is that, in order to be successful, you have to give them what they want or need. And in order to do that, you have to find them. And deliver the right message at the right time. 

The more you analyse and refine, the better your ROI and the lower your risk. Big data isn’t the future of advertising – it’s already here. 

Get on board. 

What other tactics do you use to remove campaign risk from your paid ads? Leave me a note below sharing your thoughts:

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