Slicing and Dicing Your Way to Paid Advertising Nirvana

Digital marketing professionals who work in paid online marketing often get so focused on campaign and targeting level data that they forget to dig deeper and find the really interesting matches between those campaigns and their customer segments.  By doing so, they waste large amounts of their marketing budget. 

Let's say that you are running marketing campaigns in AdWords.  You almost certainly will be looking at your CPA (cost per action) at a campaign, keyword, and a device level (desktop, tablet or mobile).  If you know what you are doing, you hopefully have also thought about whether you want to target men or women and what age groups you want to go after.  In addition, with luck you thought about your geo targeting as well as whether there are certain times of the week when your product is going to sell better.

The next step, and it is a big one, is to link AdWords to your back end database so you can track campaigns and keywords all the way through to what customers spend over time.  How much do you spend on a given keyword compared to the LTV (lifetime customer value) you get from customers coming in on that keyword?  Were these new customers or existing customers coming back again?

Now most folks are thrilled to just get this far (and only about 5-10% of the folks I speak with make it this far).  However, the reality is that the fun is just starting at this point.  On the back-end you probably have additional information about your customers.  For example, their industry vertical, the product they purchased, their role, etc.  So you now have the following dimensions to slice and dice your data by:

• Campaign
• Keyword
• Ad
• Device type
• Geo
• Gender
• Age
• Time of day / day of week
• New customer vs returning
• Industry vertical
• Product purchased
• Role

Here is where the fun begins.  Most people look at the dimensions one at a time.  They optimize the ROI for a given keyword or they optimize the ROI for a given campaign.  However, the real efficiency gains can be had from analyzing multiple variables at once.  Maybe certain keywords only work with certain devices at certain times of the week for certain age groups.  Maybe certain keywords play better for existing customers who are interested in a specific product whereas other keywords work well with new customers in a given industry.  If you haven't looked at the data at this level, you are missing key insights.

When you start dicing up your marketing spend at this granular a level, you start to realize that have an enormous amount of fat in your marketing spend.  You will find certain combinations of the above dimensions where the ROI is extremely poor and you will find other segments where the ROI is excellent.  If you take this approach, you often realize that 10-25% of your marketing budget is completely ineffective.  Rebalancing the budget to double down on the most impact programs can have a huge impact.

Analyzing your data at this level definitely isn't easy.  This approach takes a tremendous amount of work to set up and process.  However, think about it this way.  If your manager was to come to you and offer you an increase of 10-25% in your paid marketing budget, you would probably be ecstatic.  If you slice up your data enough to find 10-25% fat in your budget, you are essentially getting a lot of extra money to spend each month.