Building Executive Support

by Mar 18, 2020

Through­out this blog series, I have talked about the impor­tance of build­ing exec­u­tive support for your digital analyt­ics program. This started with the iden­ti­fi­ca­tion of busi­ness objec­tives so that your analyt­ics program could be aligned with the prior­i­ties of your exec­u­tives and the orga­ni­za­tion. The other tech­nique we discussed was making your team a profit center (or a ther­mo­stat) instead of being a cost center (or a ther­mome­ter). While these approaches will help, there is addi­tional work that you need to put in to get the full support of your exec­u­tives and stake­hold­ers. In this post, I will outline some strate­gies I have employed to do this in the past.

The first thing I like to do in the area of exec­u­tive support is to make sure the exec­u­tives know when the analyt­ics team can help and should be brought in. For example, exec­u­tives should know that if a new website project is being worked on, they should insist that the analyt­ics team be involved to show whether the project was success­ful or not. By build­ing this into the mindset of the exec­u­tives, you can avoid the common situ­a­tion of the analyt­ics team being brought in at the eleventh hour of a key project.

One way to do this is to make sure exec­u­tives under­stand for which data points the analyt­ics team is the system of record. For example, the analyt­ics team should be the system of record for metrics such as Visits, Unique Visi­tors, Campaign Visits, Customer Jour­neys, Website Fallout, etc. When there are cases where the same metric is stored in multi­ple appli­ca­tions, it is impor­tant that you clarify which one is the system of record. For example, when I worked at Sales­force, we had a metric called Lead Form Submit­ted, but the offi­cial lead count system of record was our inter­nal instance of Sales­force. There were many times that people attempted to use the Lead count in Adobe Analyt­ics as the offi­cial metric and I had to discour­age that and point them to Sales­force.

It is also impor­tant for your exec­u­tives to under­stand the key metrics for your digital prop­er­ties. While they don’t need to have fifty metrics memo­rized, you should get them to inter­nal­ize your most impor­tant metrics. When I worked at Sales­force, I was often frus­trated that the senior folks in the Market­ing depart­ment didn’t even know the basics of our website. So, one time, I gave them all a pop-quiz on our topline metrics and conver­sion rates. For example, I presented a multi­ple-choice ques­tion asking them to pick the correct website lead form conver­sion rate. I forced them all to raise their hands to answer A, B, C or D and most of them got it wrong. Many were embar­rassed that they didn’t know some­thing so impor­tant, but that was my point. Some­times, shaming people a bit can go a long way to getting their atten­tion!

One of the most power­ful tech­niques to attain­ing exec­u­tive buy-in is to incor­po­rate data that they care about in your digital analyt­ics imple­men­ta­tion. While website data is great, there are some data points that exec­u­tives care much more about. At Sales­force, for example, getting new customers was every­thing. While the website was useful to drive new leads, sales had the atten­tion of the exec­u­tives. Knowing this, I decided to look for ways that my team could posi­tively impact sales. One way I did this was by adding IP lookup data to Adobe Analyt­ics. This allowed me to see, in some cases, which orga­ni­za­tions were on the website and what they were looking at. I combined this with a list of target accounts from sales and was able to send alerts to the appro­pri­ate sales reps when their target compa­nies had hit the website and show them what they clicked on. This was very well received by sales who felt that it helped them “strike while the iron was hot” as they put it. This got the atten­tion of the sales lead­er­ship team and helped build my team’s percep­tion at the exec­u­tive level.

One other approach to exec­u­tive buy-in that I often advo­cate is putting digital analy­ses into finan­cial terms. This concept is best illus­trated through an example. Imagine that you work for an online retailer and you deter­mine that only about five percent of money being added to the shop­ping cart is being purchased:

While this doesn’t seem great, you could imagine an exec­u­tive shrug­ging this percent off and moving on to other things. But what if you showed the same figure in terms of finan­cial impact:

Imagine saying to your exec­u­tive, “our customers left over forty million dollars in the cart and I can show this to you by product or product cate­gory…” I think most exec­u­tives would stop in their tracks and think about what could be done to get some or all of this money. Even though it repre­sents the same five percent, the simple act of showing it in finan­cial terms will likely spur more action. You could even use this to justify more resources using the logic that recoup­ing just 1% of this is $400,000! Also, this tech­nique isn’t limited to eCom­merce. If you work for a travel company, you can show the amount of money researched in flights vs. booked flights. If you work in insur­ance, you can model how much money made it halfway through the quote process vs. how much was actu­ally bound to poli­cies. There are ways to employ this approach in almost every indus­try verti­cal.

Action Items

Your home­work for this post is to:

  • Make a chart or table that shows exec­u­tives which key metrics they should care about and iden­ti­fied the system of record for each.
  • Find ways to get exec­u­tives to know your most impor­tant metrics.
  • Deter­mine what data your exec­u­tives care about the most and see if there are ways to incor­po­rate it into your digital analyt­ics imple­men­ta­tion or at least incor­po­rate data that will help support those metrics.
  • Look for oppor­tu­ni­ties to show your digital analyt­ics data in finan­cial terms to get the atten­tion of your exec­u­tives.

In the next post, we will talk about build­ing and main­tain­ing your digital analyt­ics team.

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