Saturday, May 7, 2016

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.

Wednesday, May 4, 2016

Wrangling the Google Analytics Sampling Beast

After reading my last post, you have determined that you are really struggling with sampling in Google Analytics.  Now what do you do?

Your first option is to stick to the main reports on the left hand rail of Google Analytics.


The reports under these main categories rely heavily on roll-up tables in Google Analytics and therefore generally do not have sampling.  However, once you start adding secondary dimensions, filters, or custom segments, all bets are off and your sampling is likely to come roaring back.  While this can be very limiting, using these main reports is an option to remember.

A second option is to reduce the amount of data you are looking at, particularly by reducing your date range.  On the positive side this is a quick and easy way to deal with sampling.  The negatives are that this is really time consuming if you want to look at a large date range.  Second, you have to reduce your date ranges as your site grows.  Lastly, for some queries (e.g., unique users within say a month or a year), you may not be able to use this trick.  If you are looking at monthly unique users, you cannot chop the month into two date ranges because you don't know how much overlap you have between the two periods.

A related option is to use Analytics Canvas.  This tool basically takes the date range trick and automates it for you.  You can tell it to take a Google Analytics report for the past year and split it into 365 separate reports, each one day in length.  This is highly effective in dealing with sampling but again doesn't work for metrics like unique users.

The most comprehensive solution for dealing with sampling is upgrading to Google Analytics Premium.  There is no doubt about it - it is an expensive product at $150,000 per year (as of the time of this article).  In addition, to really exploit the data in GA Premium, you need to learn BigQuery, Google's Big Data tool.  If you know SQL, this isn't too hard, but there are some quirks for sure.  All these cons aside, the data that you can get out of GA Premium is amazing.  GA stores your data at a very granular level behind the scenes.  If you have worked with the GA API before, you will have seen a taste of what GA stores although the BigQuery interface to GA Premium exposes a far more extensive set of data that GA is collecting.  It can take some time to wrap your head around the data, but it gives you an almost limitless number of analyses you can run about your customers.

Sampling in Google Analytics can be a real drag, but now hopefully you will be able to wrangle that sampling beast into submission.