There is unlimited amount of data thrown off our digital existences. (Or to use sexy term du jour , we have big data!)
Our leaders (companies, agencies, teams) have to deal with an incredibly complex landscape, and they don't have enough time.
The very natural outcomes is this ask of us: "Can you make it simple? What's the one thing I should care about?"
And we oblige: "Conversion Rate, that's it." Or "don't worry about anything except Facebook Likes." Or, "I read this blog, Bounce Rate is the only one!" Or, "Profitability, it is so sexy, just focus on Customer Lifetime Value, no, sorry, I mean Profitability."
To be fair, it is not just our leaders. Because the combination of complexity, limited time and available time, everywhere in the organization people want the one thing to watch for.
Honestly, who can blame them.
But the problem is that single golden metrics hide valuable insights and, more often than not, drive bad behavior. Especially in medium and large size organization because responsibility gets fragmented pretty quickly. (In small organizations there is a lot more end-to-end ownership amongst the few employees, and if something is going awry things hit the fan pretty fast. This is a great behavior correcting mechanism.)
So, how do we fix this problem in a responsible manner?
Here's my proposal: If you are pushed to have a single golden metric, give it a partner. For each metric deemed to be critically important, identify an immediately adjacent contextual / OMG we are on the right track metric that will give more context while incentivizing the right behavior.
The key is the immediately adjacent part. The BFF metric you find should not be one that is very far away. It should be immediately adjacent.
The reason is that it is easy for every discussion to come down to: "Well, all we care about is Profit. Why not just measure Profit?" That is right, we will measure it. But as we strive to improve the many things that result in a massive digital success, we need to look at multiple facets of our existence and we need to look at a cluster of critical few metrics. For many of them, Profit is not the metric that will give valuable context.
You'll see this in action in this post.
Let's look through ten specific strategic and tactical examples that will help internalize the value of the approach I'm recommending. The examples cover elements we optimize for in our acquisition (what are we doing to attract traffic), behavior (what happens once they land on our website) and outcomes (did we end up making money, were the customers satisfied) strategies.
1. Click-through Rate <-> Bounce Rate.
There are many good acquisition metrics including impressions, clicks, delivery rate, share of voice, and on and on. One of my favorites is Click-through Rate (CTR).
I like it because CTR it immediately discourages spray and pray strategies so prevalent in our industry (particularly in display advertising). It says, you must also get clicks at a certain rate. That incentivizes a focus on the targeting strategy, the content in the ad, recency and frequency capping, and other such things. Better, more relevant ads will get more clicks.
So, great metric. Dare I say, a key performance indicator.
The problem is that it does not provide any incentive to the marketing team to ensure the rest of the experience for the user is great. How do we get them to care and not just dump people on your site (mobile or desktop)?
Simple. Give CTR a BFF. Find it the immediately adjacent contextual metric. I suggest Bounce Rate.
So the marketer is now incentivized to get lots of the right people to the site (better more relevant ads!) and get them to the right landing page that deliver on the promise made in the ad.
If users are sent to the right page, and they don't bounce, the Marketer should get her bonus.
See the magic? The BFF fixes a gap in the incentive/org structure.
Let's try another one.
2. Visits <-> Visitors.
(Or Sessions – Users)
You definitely want a lot of Visits. It delivers happiness!
But an obsession purely with Visits drives very short term thinking. You can get lots of terrible Visits on your site. Get a bit number. Up and to the right. But is the business really doing well? Are we adding value with our efforts?
That is unclear with Visits.
So. What's the immediately adjacent contextual metric? Visitors.
How many people did we manage to get to our site? Now things get interesting.
Consider this scenario: 50,000 Visits, 50,000 Visitors. And 50,000 Visits, 10,000 Visitors.
The people who see the data will ask very different questions.
For the first set perhaps they will ask… How come each person only visited once? Is that what we are solving for? How will our business survive?
For the second set perhaps they will ask… Wow, that is cool, each person visited five times on average. I wonder what the distribution looks like? Are there some outliers? What did the people who come back most consume?
Think of this scenario: 50,000, 60,000, 70,000 Visits.
How intriguing would be with the immediately adjacent contextual metric… 50,000 Visits and 50,000 Visitors, 60,000 Visits and 50,000 Visitors, 70,000 Visits and 50,000 Visitors.
The numbers could go either way, but having them together allows the recipient to just see the right amount of complexity. That is great.
Let's consider a more controversial, and behavioral, metric.
3. Time on Site <-> Page Views per Visit.
Time on Site is not a great metric in almost all circumstances. Time on the last page of a visit is not recorded (that also means time for bounced visits is not recorded).
But I still see it used, so let's get off that topic. (But think carefully before you use Time on Page or Time on Site .)
Say you communicate that the Average Time on Site is 60 seconds, 150 seconds, 98 seconds in the last three weeks (/months/days/years).
How much context is there to be able to separate the good from the bad? Not a whole lot.
What's the immediate adjacent contextual metric we can use? Why not use Page Views per Visit? Another indicator of activity during the Visit!
Now you might see 60 secs and 5 PVV, 150 secs and 20 PVV, 98 secs and 5 PVV.
OMG! What happened?
Try different combinations above and you'll see how these two BFFs works very nicely together.
From an incentive perspective, this is also pretty cool. Is a lot of time spent on the site good? Is very little time good? It is a difficult question to answer, but having the number of pages seen during the visit gives us immediate enough context to understand what is going on and where your initial focus should be.
That's it. That's all we are solving for.
[Bonus: While these are not as apparently adjacent, if you use Time on Page as you metric, try Page Value as its BFF. They are particularly good together!]
Let's switch to some outcomes metrics.
4. Conversion Rate <-> Average Order Value.
Our returning champion, Conversion Rate! Everyone loves Conversion Rate!!!
And they should. Conversion Rate is money, sometimes directly as revenue and other times indirectly via Leads collected.
But a pure obsession with Conversion Rate can incentivize sub-optimal behavior (not on purpose, but people react to incentives).
For example, a Marketer can focus on getting lots of simple, lower value, conversions because that will boost the rate up. Or they might prioritize fixes in the site design or experience that get people to a particular cluster decisions that will make Conversion Rate looks better but not solve for the longer term for the business.
One simple way to solve this is to use of my favorite immediately adjacent contextual metric, Average Order Value.
2% Conversion Rate, AOV = $26. 2.5% Conversion Rate, AOV = $14. Ouch.
2% Conversion Rate, AOV = $26. 2.5% Conversion Rate, AOV = $40. Goodness, bonuses all around!
Simple fix, right?
The purists amongst you might notice that I'm really using Revenue as the BFF. You are right.
5. Conversion Rate <-> Task Completion Rate.
The immediately adjacent contextual metric you choose will really depend not just on the type of business you have, but also the people you have, the size of your company, the incentives currently in place, and a number of such factors. So this blog post should simply serve as inspiration. Take the spirit, apply it to your unique circumstances.
Your immediately adjacent contextual metric can be a qualitative metric. At least some of the times, it likely should be!
Where I've implemented a simple where you able to complete your task qualitative data collection mechanism, I always pair Conversion Rate with Task Completion Rate.
Two simple reasons.
Conversion Rate solves for the company and Task Completion Rate solves for the customer. Such a delightfully nice approach to take.
Conversion Rate only tells you how a very small fraction of your users, who came to buy, did. Task Completion Rate shows you how 100% of your audience did, were they all successful regardless of why they came to the site.
When you see 2% Conversion Rate and 14% Task Completion Rate you will cry. Everyone in the company will cry. And they they will ask how come only 14% of the users completed their task! That will lead to a broader obsession by the digital team, almost always leading to big wins.
6. Revenue <-> Profitability
I have to admit this is a hard one.
None of the digital analytics tools make it easy to measure true profitability. And if you can pass that barrier (with, say, dimension widening using universal analytics), it is very hard to find this data inside the company (Finance department?), at a level of aggregation or granularity you need, and send it into your digital analytics tools.
The whole thing is so painful. But it is incredibly rewarding and if you want your digital analytics practice to reach the analytics.ninja state you need to do it. (Ask an authorized consultant to help you, you will get there faster: www.bit.ly/gaac).
Revenue is the ultimate goal. Lots and lots of Revenue!
But of course it is entirely possible for you to make lots of Revenue and go bankrupt. Simply sell products that are loss leaders or don't cross high enough above the hurdle of the Cost of Goods Sold.
The immediately adjacent contextual metric for us is Profitability.
Now when you report at a business/site level you can show that $54 mil in Revenue resulted in $40k in Profit. Or, at a campaign level you can show that while Twitter brings $5 mil in Revenue, that only results in $5k in Profit and while the Email Revenue is $1 mil the Profit from those campaigns is $700k because how how remarkably your campaigns are targeted.
As long as there is even $1 in Profit you should spend all the money on Twitter, but when it comes to making strategic decisions for the company, you might make different ones now that you know the profitability of email, and other acquisition efforts.
Think of how much fun it will be have this pair for the products you sell, the geographic locations of your customers, and so much more.
Revenue, meet your new BFF Profit!
[Bonus: Another sign you are an analysis.ninja is that you not only measure Profitability – session-level short-term metric – but pair it up with the immediately adjacent contextual metric of Customer Lifetime Value – person-level long-term metric. Shoot for the above first, then, if you are successful, get to this one because it is, obviously, much harder even if it is disproportionately more impactful.]
Let's step outside our owned platforms and on to rented platforms .
7. Video Views <-> Subscribers
Raise your hand if you've heard this: "How can we make our video viral?" Too often, right? And you know the moment those words are uttered you are dealing with a video/effort that is most definitely never going to become "viral."
Sad, but true.
And yet, the number of Video Views is a metric often elevated as the thing to solve for, the single golden metric for video powered digital initiatives. You won't be able to get away from reporting views, so why not find a partner for it?
The immediately adjacent contextual metric that works marvelously well is the number of new Subscribers.
You can solve for the short-term with Views, but if you are not converting them into Subscribers you are not really building an owned audience that you can engage with over time. If your last one million Views video got you only 25 new Subscribers, was it really a success? Even if you got a temporary bump in publicity?
[Bonus: While Subscribers is my ultimate success metric for YouTube – I crave large owned audiences so that I can stop renting them from TV and/or Radio and/or Google and/or AOL – the other two that can also share key context are % Completes and Amplification Rate. Depending on your local circumstances, you could possibly consider those as well. Though if you want to make me happy, you’ll choose Subscribers!]
8. Likes – ?
People who don't know anything about Social Media use Facebook Likes to measure success.
There are so many of them, including your boss! Let me give you a virtual hug. There, there, it gets better.
First, be sure to mention that Likes simply represent people walking by us on the street who smiled at us. They meant nothing more. We need to make sure that we are creating content that is incredible and of value. That is what causes people who gave us a passing Like to come back again, engage with us, give us their precious attention.
Then we have to think about how do we give our dear boss, still obsessed with Likes, the immediately adjacent contextual that will help her/him make smarter decisions.
I recommend two different ones.
Likes are most commonly used at a page-level. For example, my Facebook brand page where I post daily analysis on an interesting topic has 19,789 Likes. The best immediately adjacent contextual metric for the page-level Likes is Talking About This. At the moment that number is 1,203. It is a decent tentative way to understand the engagement on Facebook.
The 1,203 is great context to have for the 19,789 for your boss.
[Bonus: For later reading, when you are attempting to be an analysis.ninja, see this post: Excellent Analytics Tip #25: Decrapify Search, Social Compound Metrics]
Likes are also present at a post-level. For example, my Facebook post on how to stand out from the crowd during an interview has 62 Likes. That is insufficient to indicate success because I not only want you to Like it, I also want you to amplify it to others so that I wonderful content (!) can reach others I can't reach myself. The best metric for that is Amplification Rate.
The above post only has one Share (the key ingredient in Amplification Rate). That is super-lame (and for such a good post!).
While another post, Global Views on Morality: Homosexuality, has 72 Likes and 40 Shares. Much, much stronger Amplification.
When your boss looks at the two posts he/she will now be able to recognize that one was more successful in terms of what's important (reaching new audiences) than the other. That's exactly why you want your metrics to have BFFs!
9. Mobile: Installs – 30-day Active.
We looked at YouTube, we looked at social, and so mobile can't be too far behind! Let's look at a quick one for mobile apps.
The most important metric our leadership cares about when it comes to apps? Number of Installs.
And it is important.
But if 80 to 90 percent of all downloaded apps are used only once, should we have an immediate adjacent contextual metric that will be more insightful?
I recommend also reporting 30-day active, the number of unique users who have been active during a 30-day period. You have some flexibility in how exactly you define it, but as long as you stay consistent it does not matter.
Now your boss is focused both on getting more new customers, and on keeping the ones you already have. Balance. It is what makes the world go round!
10. [By channel] Conversions– Assisted Conversions.
Let's close with a pairing for all of you analysis.ninjas.
It is common to segment Conversions, our beloved key metric, by the source of traffic. Earned, owned, or paid. Or, Google, Email, AOL etc. It helps your boss understand how best to optimize your acquisition strategy.
The challenge is every single analytics tool reports single-session conversions only (also known as last-click attribution). This is absolutely silly and leads to awful decisions.
The immediate adjacent contextual metric you need is Assisted Conversions – the number of times that same acquisition channel (earned, owned, or paid) was present in the customer journey that lead to a conversion but that channel was not the last-click.
Essentially, how often did that channel help with a future conversion?
Now you have excellent context for making smarter decisions about the full-value of each acquisition channel in your portfolio.
For example, Organic Search delivered 119k last-click Conversions and also assisted with another 73k Conversions that were delivered via other channels when looking at a last-click view.
[Bonus: For more awesome goodness on this yummy topic check out this post: Multi-Channel Attribution Modeling: The Good, Bad and Ugly Models.]
Ten short stories to help you internalize the incredible value of having an immediately adjacent contextual BFF for every critical metric you report to your management team.
Oh, and you can easily put all this together in a very simple dashboard…
Throw in your pretty pie charts (no!) and your stacked bar graphs and some lovely trend lines and you have yourself all the ingredients for creating an organization where data delivers the kind of insights that deliver big action!
Please consider the examples in this post, and the dashboard above, as a way of thinking I would love for you to embrace. The specific metrics you end up choosing will depend on many important factors. If you create your Digital Marketing and Measurement Model, you will know exactly what will go in the above dashboard for you and then all you have to do for each metric is find the BFF metric.
Friends don't let their KPIs not have BFFs. : )
As always, it's your turn now.
Do your current critical few metrics have an immediate adjacent contextual metric? Do you agree with the metric recommend in this post as the BFF metric? Would you have chosen something different for Time on Site or Visits or Likes? Are there metrics you are struggling with when it comes to identifying the BFF metric? Does your company dashboard provide all the necessary context to aid smart decision making?
Please share your insightful feedback, tips, omg don't do thats, and stories.
Excellent Analytics Tip #26: Every Critical Metric Should Have A BFF! is a post from: Occam's Razor by Avinash Kaushik