There are many marketing productivity situations you will encounter where a change in the metrics, positive or negative, is being caused by something you or your area has no control over. For example, your marketing campaign generated a lot of leads but these leads failed to convert at an acceptable rate. Since this campaign has always generated good leads in the past, you ask yourself, “What happened? What was different this time?”

Upon further research, you find that the leads were of the same quality, but for some reason most of the leads failed to make it to the salespeople they were intended for, and instead went to a brand new salesperson in training. This “process failure” resulted in a low conversion rate, and is the root cause of the campaign failure. Here is the head’s up on root cause: make sure when analyzing campaigns and other metrics that you understand what “failure” you are really dealing with. Relentlessly search for root cause.

Getting to the Root of It All

I often speak about cross-functional teams in the management of the analytical culture, and the concept of root cause is the driving reason for this, as the road to root cause is often cross-functional in nature. Having a cross-functional mentality in place ensures that these complex root cause issues are addressed without finger-pointing. In a good cross-functional team, there is no “blame”, only learning and continuous improvement. If you don’t chase down the root cause of your issues, the problem likely will continue, creating pain for all members of the team.

We can look to the practice of Six Sigma for some help in performing Root Cause Analysis. A popular technique is known as “5 Why’s”. The “surface” problem or “effect” as it is called in Six Sigma is written down, specifically, at the top of the page. Then the team asks, “Why did this happen?” This answer, or “cause” in the language of Six Sigma, is written down underneath the effect. “But what caused this to happen?” is asked, answered and written down under the previous cause, and the process repeats until the team agrees that the “real”, or “root cause” of the problem has been arrived at.

If you solve this last problem, you will fix the root cause, which typically means you will solve all the other “causes” above it.

For some reason, the root cause is often reached around the 5th “Why”, hence the name. You may reach your root cause sooner or later depending on how well the problem is understood and defined in the first place.

Let’s take the example of the lead generation campaign above and run it through the 5 Why’s process:

Effect: The campaign failed to generate leads that converted at an acceptable rate.

Why?

Cause: Because the leads were distributed to a sales person who was in training

Why?

Cause: Because the sales manager who usually distributes the leads was out of town and the only person available to distribute leads did not know the leads are not supposed to go to sales people in training

Why?

Cause: Because there are no rules in place to guide somebody on how to distribute leads when the sales manager is not available

Why?

Root Cause: Because the sales manager has not provided specific instructions on how to distribute leads

Solution to problem of under-performing campaign:

Have sales manager create lead distribution rules and appoint a person to control distribution of leads in her absence. Probably a good idea to appoint someone to control distribution of leads if both the sales manager and the 1st level appointee are unavailable as well, so any “cascade” effect is stopped and campaign budget not wasted.

See how that works? The root cause doesn’t have much to do with web analytics or marketing, but the impact is felt there and as such, the cross-functional root cause for failure should be uncovered and repaired.

Now, as an analyst, you may want to “tweak” the “5 Why’s” process a bit. While useful in getting a discovery process going, particularly in cross-functional team settings, there is a real problem for the analyst. Do you see it?

Here it is: where is the analytical rigor in this process?  For each cause the team agrees to, I would ask for proof or evidence of the causal relationship, or else you might end up going down a dead end road. Examine this proof and determine if it completely explains the relationship between the effect and the cause.