Facebook has had a hard time in attracting advertisers to their site. Mostly because advertisers have not figured out how to monetize or receive as high an ROI as they do when they advertise on Google with AdWords. As it sits right now, Facebook really only offers display advertising without the demographics guessing game so it is not really a surprise that Facebook has not attracted as many local advertisers as they would like.
You might be asking “Why the heck would Facebook want to attract local advertisers?” The answer is simple: user experience. Not only do users not want to see ads like the ones you see below, but in the conversations I’ve had with people at Facebook, they don’t even like them.
Besides the fact that Facebook is trying to tell me I need to workout and go get my teeth whitened, it isn’t relevant, and therefore I can guarantee that they have poor click through rate. I don’t know about you, but the last time I checked poor click through rate and lousy user experience doesn’t usually equate to great revenue. So what should Facebook do? The answer is simple, make it easier for local advertisers and search marketers to target relevant users.
Data is King
Facebook is an information company just as much as they are a social networking company. Their biggest attraction as a company, from a marketing perspective, is the fact they know everything about their users. They know what kind of music they like, where they live, how old they are, where they went to school, who their friends are, and much much more. In fact, they could potentially be the most valuable market research company out there because users are volunteering this information and have no reason to lie about their answers. It is real time information where we can actually see and predict trends amongst demographics before they happen. They even track their users click paths, navigation summary, and as you can see below, if there are two ways to get to the same destination on the same page they track that too.
It starts with categorizing
Currently when advertisers go through the process of setting up a Facebook campaign they are not asked what kind of company they are, only what demographic they service. So why wouldn’t Facebook categorize their advertisers? Wouldn’t it make sense for advetisers to categorize themselves when they sign up or before they create a new campaign? Instead the only real categorization Facebook can make of its advertisers are amount spent, targeted demographics, and keywords. But that is not enough. Completely different companies could be targeting me (i.e. teeth whitening and free stimulus check companies) and two different companies could be targeting the same keywords.
If and when Facebook starts categorizing all their current and future advertisers they will be able to track performance across specific verticals, whether they are small businesses, national chains, or just scams like this ad:
They will be able to utilize this by implementing the my next suggestions: keyword and demographic information clouds.
Information Clouds
Yes I know, we all have had about enough of the hype about information clouds, cloud computing, or cloud anything for that matter. However, now that Facebook has all of its advertisers placed into categories we can take real advantage of that by letting advertisers help each other out. We now have the ability to let a small business with a $100 per month budget take advantage of information and data that would have cost 10’s of thousands of dollars to put in place. You may be asking how do we do this. It is simple, store all advertising data by advertiser category, demographic, economic, and city population.
Facebook first needs to overlay the United States with population data, demographic data, and economic data such as median income. All of which they can easily get from the census bureau. Yes,the census bureau data is a little old but it is a great starting point. After they do this they will easily be able to place Facebook user data over those information layers and determine their market shares per neighborhood, cities, etc. So what does this give us? This tells Facebook where their advertising will be most effective compared to other advertising sources due to market share.
Now that we have our data layers in order, let’s bring this all together. Let’s say that I am Widget, Inc. and I sell blue widgets to people . I only have a budget of $125 per month to advertise on Facebook and I can not afford anymore because of the recession. Why should Facebook rely on that user to know what demographic is going to work best for Widgets, Inc? You have no way of knowing that the current demographic Widgets, Inc is currently targeting will be more profitable or successful than any other demographic. The answer is simple: let the users tell you, and no, not by the little thumbs up and thumbs down icons, but by tracking click data and conversion rates.
You might be thinking “but we only have $125 spend and thats not enough for a good test size”. You’re right, it isn’t, but remember that Facebook’s strength is in its numbers. The entire crazy valuation it received was based on the number of users and the information it had about its users. So wouldn’t it make sense for Facebook to combine all their advertisers into one information cloud, into one database. If Facebook implemented the categorization method they would be able to track all companies that sell blue widgets and treat them as a whole. Now, instead of $125 in spend data we have $20,000 in data. Each company still holds unique traits such as economic climate, geographic are, etc. but now each advertiser can learn so much more and therefore they can earn so much more from their dollars. This new data set can go much further than any one widget company’s data could. In this solution, the advertisers help Facebook find out what demographics are perfect for specific verticals and users tell Facebook which companies and ads they like best.
Imagine if advertisers had a feature similar to the current “You might also know” section where Facebook suggests other people on Facebook you might also know and have not yet run into. Well what if we had a section similar to this entitled “Demographics you might also like” where advertisers will receive suggestions from Facebook itself based on data where Facebook has noticed great performance amongst the demographic and the category of the advertiser.
Template Ads
Ad copy and content is one of the biggest things that hold up marketing efforts for any company. So if you leave that to the advertisers to figure out, you are leaving money on the table. Not only are you leaving money on the table but you are also assuming that the advertisers ads are so good they will see ROI and spend more with Facebook. This is not a good strategy. Google recently launched templated display ads where users can simply pick out the ad they like and fill in the text right in the Google UI. This increased both user involvement and the number of ads made.
Facebook could come up with ad content and ad pictures that captivate their audience and encourage users to click through. This obviously would be done for each vertical and eventually specific verticals within geographic areas. Now we won’t stop there, after the ads are made we will help distribute them to our advertisers; letting them know we have completed tons of market research, design, and testing on there behalf, for free. We will then let the audience determine which ones they like best by analyzing click through rates and if they became a fan of that business on Facebook. The more hands on and easier it becomes for the advertisers to take advantage of the jump start that Facebook has provided the easier it will be to implement A/B tests internally without relying on the users to do so.
A/B Testing
A/B Testing is something that every company should do for every part of their marketing, but they don’t. So what does that tell us? We should not rely on customers doing this. Once companies utilize the tool above we will have a large enough amount of ads being used that are based on the same template framework to where we can now calculate internally which ads are performing best (i.e. Computer ad #1a or Computer ad #1b) without having customers individually run these A/B tests themselves.
This same type of internal testing methods can be used across all aspects of the advertising campaigns, not just the ads. It can tell what geographic areas respond to specific industries best, which education level becomes fans of businesses the most, and so on. With this testing being done and the amount of users Facebook has, they can easily become the smartest advertising platform around, and dare I say even smarter than Google once implemented .
Math doesn’t lie
The data really does go much deeper than click through rate and fan conversion rates. When referring back to demographics, population sizes, and economic data we can determine that in situations like 22-35 year old mothers with an average household income of $150,000 ads with a coupon offers are not necessarily the best, but where the median income is $75,000 it actually is the top performing ad based on click through rate. Math will not lie to us. Math is not opinionated and it is not emotional. Facebook will soon be able to say they provide the maximum amount of ROI possible to each user because Facebook will continue to grow and learn itself as advertisers spend more and more on Facebook. With the combination of best possible Maximum ROI, ease of use, and a built in marketing expert (the new system that is created) the question won’t be will advertisers spend money on Facebook, but how much.
