And the tip from a Facebook™ Employee that made it all happen
Case study written by :
Abdul Rahman Abdul Hamid (firstname.lastname@example.org/www.2feetsolutions.com)
There I was, on yet another visa run, as most expats are accustomed to. Except this run was very different from all the others I had been on before.
This time, a chance encounter gave me the final piece I needed to complete the jigsaw puzzle I had been working on for the past year.
Before I get into the jigsaw puzzle, let me tell you a little about myself.
I’m Matteo, and like most of you, I provide digital marketing services to local businesses.
However, I’m not only a digital marketer, but also a Big Data Analyst.
In fact, I received my IBM Big Data Certification in 2016. This allows me to use my experience with big data to fine tune my targeting and reduce my costs per conversion.
And in this Case Study, I plan on helping you achieve the same exact thing for yourself, and your clients without all the studying and knowledge you’d need to get your own Big Data Cert.
Here’s what you can expect when you implement what you’re about to read.
- A list of Qualified High-Value Leads
- Elimination of Wasted Ad Spend on Low-Value Leads
- Refined and Segmented Audiences to be used for Re-Targeting
I will expand on each of these points at the end.
…So where do we begin?
Well, let’s go through an over the shoulder Case Study that will reveal every single step I took to achieve the results you saw posted in the Facebook™, which by the way, has over 700 comments since the writing of this.
Let’s jump right in.
Creating Content that Sells
Everyone talks about it, you know it, I know it, we all know it.
Your content matters.
Few disagree with that.
That’s not the problem. The problem is where do you find “good” content, and what makes content “good” anyways?
“Good” is subjective.
Do you know what isn’t?
Cold hard conversions baby!
That’s what matters most.
Not someone’s opinion on how “good” your content is.
How your audience interacts with your content is more important than how “good” you think it is.
As a big data analyst, I know anecdotal evidence is total bullshit. I can disprove it nine ways till Sunday.
So what did I do?
Jumped right into the data of course.
Using a Data Scraper, I mined all of the viral stories that trended on social media in 2015 and 2016 right up until November 2016 (I ran the campaign in Dec. ‘16).
I also utilized Facebook™ API to gather data from viral Facebook™ posts during that time period as well.
Once I had all that data, I began organizing it based on the engagement metrics they ranked for.
These are shares on the web, likes, comments, and shares on social media as well as other metrics.
Filter Results to Find the Best Content
As I compiled this data, I began filtering out many of the results which were obviously manipulated or boosted artificially in order to make them go viral.
The criteria I used in this filtration process were:
- skinny content,
- where the content was coming from,
- the social signals it across all channels
- the authority of the website it was published on, and
- engagement, especially sharing on social media, if it looked natural or artificial.
Once the content was filtered I was able to identify trends, from the headlines used, to the images, the ad copy, and even the pages the traffic was being directed to. After careful examination, I compiled a list of the top performing pieces of content that went viral– organically.
I studied the top three.
While sifting through all of this data, I began to pick up on certain trends and started to notice why things were being done in certain ways.
One of the biggest takeaways I got from this process was when I started asking myself one question…
Why were people drawn to these pieces of content in particular?
So I began reverse-engineering the content creation process.
The goal was to determine what content attracts our audience market enough to opt-in.
This would require its own article in and of itself to explain the ins-and-outs of the entire content creation process. If there’s a demand, I will schedule another Case Study to go over that process in-depth, which includes utilizing Facebook’s API.
For now, let’s move on to…
Your Greatest Source of Targeting Data
This part baffles me. I just don’t know why more people don’t do this.
It’s so obvious it hurts.
So, let’s pretend for a second that you’ve just closed a small business client that wants to market their business using Facebook™ Ads. You convinced them that you’re an expert that get them leads for their business using paid advertisement.
Now, you will usually advise them that the initial ad spend budget will be used to test your ads and fine tune your targeting settings.
But what if you could circumvent that entire process?
What if you already had an entire data set that could tell you exactly where your most interested visitors are from, and how much they’re interested in what you offer.
Now you’re probably wondering, Matteo, I don’t have that and where the hell do I get it?
I’d argue that you do have access to this data.
If your client has a website that is already running, then you already have all the data you need.
Google Analytics of course!
Who better to target than look-a-like audiences of the people that visit your site the most and spend the most time on it?
If you have a site that’s been running for six months you have all the data you need in GA to create super specific campaigns combining cities, age, %age of returning visitors, mobile devices they are using, the interests in GA can also be used.
Here’s how we set this up.
I already knew the data I needed was stored inside the Website Data of my client’s Google Analytics account.
So I went in and began filtering the results by:
- Mobile Device’s List
- Technology – Browser & OS.
- Audience > Demographics > City
- Audience > Demographics > Age
- Affinity Categories – This allows us to expand our targeting.
- Session Duration – This indicates the level of engagement.
- Pages/Session by Category – This indicates the engagement level per page/piece of content.
- Behavior > Site Content We use this data to compare it with Pages/Session by Category.
- Benchmarking > Location – Looking for New Locations with above average levels of engagement.
You want to find the best ratio between a high amount of visitors from particular cities that coincide with higher than average session durations.
This is your audience targeting settings for your Facebook™ Ads.
For example, on one of my client’s campaigns, when we checked their websites Analytics, I could see that we were getting over 1,000 hits from Brisbane, Australia. Over 30% of those hits were from returning visitors, and it had a much higher Average Session Duration than other cities.
That is a clear indication that this is a segment of my audience that is interested in the content my client has put out on their website. They have demonstrated that they enjoy consuming the content and that they are most likely target customers for my client’s business.
I call this a buyer list.
Set this up in your audience targeting and add the interests you know work for your clients offer. No need to get too detailed here and I assume if you’re reading this you have a good grasp on setting your audience interests targeting.
Age, Gender, Affinity Categories, In-Market Segments, and Other Categories are all datasets that are available to within your Google Analytics. You can use this information to build lists and segment them to serve specific marketing campaigns.
Now comes the fun part.
Combining Google Analytics Data with Facebook™ Ads
Now that you have some data to work with, you’re no longer swinging in the dark.
But, there must be a way to at least verify whether the audience data we got from Google Analytics will provide us with a good audience for our Facebook™ Ads.
Through your Google Analytics data you’re able to identify your most engaged audience and translate that to your Facebook™ Ads audience.
When you look at the data that you extrapolated from Google Analytics, you want to compare it to the total reach that Facebook™ show’s you after you input your targeting settings.
For example, let’s say that after selecting your targeting settings for your ad, you have a potential audience of 70,000 on Facebook™. Now compare that with the traffic you have been getting from those sources in your Google Analytics. The difference of those numbers is the untapped audience you have yet to reach with your marketing efforts.
Next, you’ll want to ensure you’re tracking your efforts so that you can carefully segment your audiences for your retargeting campaigns. This way you can use targeted messages towards each audience segment, instead of generic ads targeting everyone.
In each campaign, I work with a URL builder in order to create different UTM (Urchin Tracking Module) codes which allow us to track the activity on the link within Google Analytics.
When you create UTM codes for your campaigns, be sure to shorten your URL’s using http://goo.gl, it will provide you with an analytics overview of your daily hits and traffic numbers.
The idea here is to do a little reverse engineering on your targets.
Let’s say you want to get 100 conversions.
If you’re estimating a 10% conversion rate, that means you need to get in front of 10 people for every conversion.
So, in order to get 100 conversions, you need to reach a minimum of 1,000 people to make it happen.
This is a very rudimentary look at how this works, but it’s the framework of which you use to determine how to budget your ad spend and set targets you can reach.
How do you determine the budget?
Once you know the desired rate of conversions you’re looking for, you now reverse engineer that number to give you the reach necessary.
Check back with Facebook™ and their calculated Total Reach of your campaign.
Set your budget to get you to the reach you need. The only variable is time and how long you want to keep your ads running.
So you can stretch out your ads with lower daily spends, or ramp them up with higher daily spends for quicker results.
During this test, I was spending $5/day and generated 5-6 Leads daily!
Leads for $1!
…That’s it! ^___^
There you have it. The secret sauce that you can use to generate leads for $1.
What about the tip I got from the Facebook™ employee that changed everything?
The missing jigsaw puzzle piece?
You remember that?
Well, I guess if you’ve gotten this far into reading this Case Study, then I might as well cough it up and give you the tip that changed everything in the way I approached Facebook™ Ads.
I’ll also be going over the results we achieved for our clients when we were running these tests for this Case Study.
But enough about me and my clients, you want to know how this damn puzzle piece fits into all of this, and more importantly, does it benefit you in any way?
If increasing your conversions while reducing your costs per conversion interests you, then I’m sure you’ll find a benefit in finding out how I leveraged this information.
So back to my story…….
I was on my way to Hong Kong to renew my visa, since I’m currently in Bangkok, Thailand.
While making my way to Hong Kong, I ended up having a chance encounter with a really great person.
We struck up a conversation and not shortly after I heard something that made my mind race with ideas, questions, and possibilities.
He told me he was a Facebook™ employee.
Immediately I wanted to talk about Facebook™ Ads, but I wanted to ease it into the conversation.
We had a great chat that spanned many different topics, and we did get to cover Facebook™ Ads.
When we parted ways he left me with one piece of advice, that has since stuck with me and I’ve ingrained it into how I build my Facebook™ Ads Campaigns now.
He told me:
” The FB Pixel needs at least 7-10 days to calibrate for it to calibrate completely”.
What does that mean?
Well, it means that in the first 7-10 days that a Pixel is activated on a page, that is when it is most sensitive to the traffic it’s picking up on the page. I’m guessing that Facebook™ has attributed some sort of quality score or metric applied to its pixel.
What does all of this mean for you?
If you’re getting signups right off the hop, meaning your lead generation campaign is successful, then your conversion rates on your re-targeting campaigns are going to increase.
This is obvious.
But what may not be so obvious, and lost by many that may read this, is that your campaigns should be created to specifically take advantage of the first 7-10 days of the campaign.
You want to generate traffic that converts as quickly as possible so that your pixel will work its best.
If you build out your campaigns with this in mind, you will start building powerful retargeting lists full of high-value leads that are qualified.
You can even build campaigns with a conversion window of 1-7 days in order to ramp up even more conversions within that window of time. This will give you data to determine how long you need to convert your audience, and at what step within the funnel do they convert within 1-7 days.
It’s shouldn’t be lost on you that the more targeted your audience, the better your Ad will perform.
The interesting part is how you can basically guarantee higher conversion rates on your re-targeting campaign if you funnel the right traffic to your initial Ad.
So let’s talk results.
What kind of results did we get?
…We generated 66 leads in 7 days on a $5/day budget!
In some of our other campaigns, we used a Daily Budget of $1/day!
We generated over 12 leads in 22 days which were later closed at an average sale price of $700-$7,000 (our client closed almost 50% of them).
Other results include:
- 1,735 likes on post and 200 shares on sponsored post.
- The ad reached over 52,953 people within 22 days of publishing.
- Generated retargeting audience through 1,590 Link Clicks from Ad to Website.
The best part about all of this: It’s SCALABLE!
…and that’s it in a nutshell.
I almost forgot. I promised to explain the results I listed at the beginning in more depth at the end of this article.
Here’s that list again:
1. A list of Qualified High-Value Leads
When you structure your campaigns the way I’ve described above, you will only be targeting an audience that has already shown a proven interest in you, your business, or product.
The most important thing to remember is the source of the traffic, which is current visitors that are engaging with your website, already!
With this source, we are able to target look-alike audiences and build even stronger qualified retargeting leads to send paid advertising campaigns to.
If you’re running an e-commerce site, you will want to identify user groups with the highest e-commerce conversion rate or revenue.
If you’re running a content focused site, you will want to identify user behavior groups with the highest engagement, which can be measured by “session duration”, “pageviews”, “pages/session”).
2. Elimination of Wasted Ad Spend on Low-Value Leads
Why waste money on impressions that won’t convert? People make the mistake of not considering wasted ad spend because they think a $20 budget doesn’t matter anyways.
With PPC Advertising, which FB Ads basically is, every penny counts.
So if you can identify your low-value audiences, you can eliminate wasting your money on them.
Do the exact same thing I showed you to find high-value audiences, and instead look in the other direction. Look for the lowest converting audiences and user groups and exclude them from seeing your future ads.
3. Refined and Segmented Audiences to be used for Re-Targeting
With your Google Analytics data you get, Age, Gender, Affinity Categories, In-Market Segments, and other categories from which to laser-focus your ad spend and identify your target markets.
When you use these pre-qualified audiences to target look-alike audiences and drive that traffic to your pixel, your retargeting audience will be made up exclusively of warm to hot leads.
What do you think about the results?
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