Marketing campaigns should be established with an open, unbiased mind regarding final outcome. This allows for a high level of fluidity, and fluidity ignites efficiency. All too often I see teams plan a marketing strategy, design concepts and creatives, pick targets and decide on prices before they’ve ever served an Ad to their audience. This can mean a huge waste of manpower and ultimately a misuse of time and money resources. You may have heard of “The Lean Startup” by Eric Ries, it’s an exceptional read detailing an extremely solid approach to launching and running a business. Ries talks about a “build, measure, learn feedback loop” in which products are sold as soon as they are acceptable (without spending unnecessary resources trying to perfect them) and through customer interaction with this “Minimum Viable Product” real world knowledge is acquired and a superior product is developed. I encourage marketers to use the same basic blueprint to drive campaigns. Digital marketing is prime for this approach.
Marketing should be driven by data and not opinion. Businesses should use validated learning to find out what their audience wants rather than first deciding what they think the customer wants and creating what they believe are amazing campaigns based on this. It never ceases to amaze me how often my team and I can be completely wide of the mark as to how an Ad will perform; all too often the Ad we think will perform well performs poorly and vice versa. The future’s unknown, if you want to gamble do it on the racecourse, use a data-centric strategic method to win in business.
Build
You’ve probably heard the saying; “meetings are where ideas go to die”, this is a sad but true reality and often a reality based on a few people’s opinions rather than the feedback of many. When first launching a campaign, businesses should create a variety of different Ads and serve them to diverse target audiences; based on audience interaction optimal targets and high performing Ads can be discovered. Marketers should still use due diligence and have a certain level of pride in preparation, teams should still brainstorm to come up with ideas, but I often feel more ideas should weather the storm and make it into campaigns. A diversity of advertisements and targeting allows companies to find what works best. In the beginning, Ads should be run using small budgets, enough spending to allow for a sufficient volume of impressions (views) and thus a statistical significance in data without taking a huge chunk of the company’s funding. It’s like fishing with a net rather than a hook and line, there is a much higher chance of finding fish. Once the areas that “have fish” are discovered we can remove the net and fish with a few hooks, creating a more efficient campaign in terms of money and time spent. Smart data analysis can be used to eliminate guesswork and establish profitable campaigns.
Measure
If you’re unfamiliar with Facebook’s Ads manager here’s a quick cheat sheet; if you’ve already used it, feel free to skip the next paragraph.
Facebook Ads have a three-tier hierarchy. The first tier is the Campaign level, in which you choose the campaign objective (conversion goal), spending limits etc. The second tier is the Adset level, which includes targeting; the type of people who should see your Ad (based on what they have interacted with in the past), bid; the amount a conversion is worth to you (not the actual cost of conversion, which can be higher or lower than the bid price), budget etc. The Third Tier is the Ad level and as the name suggests includes the actual Ad (video or picture) and accompanying text etc. In gauging campaign strength, Ad level data indicates how well the Ad is performing and Adset level data unveils accuracy of audience targeting (have you chosen the correct audience).
Companies need to decide what metrics to look at to determine if the Ad has been successful. A good place to start is at the Ad level with Facebook’s “Relevance Score”, this shows if the Ad was well received by its audience. It’s calculated based on positive and negative feedback given by this audience. Relevance score should become your barometer as to whether the Ad should remain running or not. A high relevance score will mean cheaper Ads; given that all other conditions are the same, lower relevance scoring Ads would need an increased bid in order to obtain a proportionate impression volume as Ads with a high relevance score. This equates to a higher spend (not recommended, instead try to improve the relevance score). You’ll find that within an Adset, Ads with a higher relevance score will get most impressions. In this way just comparing the impression volume alone will give you an idea as to what Ads (and Adsets) are performing well.
Other factors to look at are click-through rate (CTR); a percentage value of Ad impressions to clicks and conversion rate (CVR); a percentage value of Ad clicks to conversions (sales or downloads etc). CTR and CVR are already taken into account when calculating the relevance score, so should be used as a way to dissect the Ad and see what specific areas need improvement. If the Ad has a high CTR but a low CVR maybe the landing page needs improvement or the Ads served don’t showcase your product clearly causing people to click on your Ad but not spend money on your product or service. If the CTR is low, the Ads or targeting probably need improving.
At the Adset level, we see data based on the combined performance of all Ads in that Adset, thus displaying data on the quality of targeting. If the CTR and/or CVR are low for all Ads in an Adset the target group might not be a good one and should either be removed or modified. Another two crucial metrics for any business are Revenue and LTV, or Lifetime Value (the total value of the customer), they mirror product quality and brand strength, however Ad targeting also plays an important role. Find the target groups that generate more income than their cost of acquisition and what targets are costing the company more to convert than revenue generated. Remove the targets that are creating a loss and increase budgets to scale profit generating targets, this will increase the company’s profit margins.
I recommend using Excel to construct reusable pivot tables and charts so that raw data from Facebook can be viewed clearly for all these metrics in just a few clicks. For more details on pivot tables and data analysis feel free to contact me on Linkedin.
Learn
After crunching the numbers for the relevant data points, extrapolating information needed to enhance the campaign is straightforward. Low performing Ads and targets can be stopped and high performing Ads run on a bigger budget. These high performing Ads should also be further improved by split (AB) testing. Continued testing is essential because Ads have a certain level of decay as they age if people keep seeing the same Ads either; Ad blindness will begin to occur in which the target audience ignores them, or worse click on the hide button, either way the relevance score drops. Advertising is far from a “set and forget” system and so the build, measure, learn feedback loop needs to continue throughout the campaign. Always split test high performing Ads and always test new Ad concepts and audience targets.
The focus of this article is Facebook marketing but the overall strategy is applicable to almost any type of advertising online. I recommend first mastering one platform and you’ll find using another is like a pleasant deja vu. Each platform or marketing vertical will have its own unique features but the basic concept and functionality are usually the same.
