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There is no question that information is flowing at exceedingly rapid rates. This increased complexity often creates uncertainty, and in extreme cases, paralysis for organisations.
We often look for shortcuts or simplifications in this environment, and this is especially true of the data that is being generated as part of doing business. As a result, both business leaders and agencies often try to simplify data into a single key metric to measure goals and performance.
As pointed out by Avinash Kaushik, the danger of this approach is that single metrics can often hide valuable insights, and even worse drive bad behaviour.
The solution he proposes is to always ensure you partner up your 'golden metric' which is most important for the business with a paired metric (in essence acting as a backup and sense check to the data). The goal here is to ensure the partner metric is immediately adjacent or offers contextual value - it should add further insight into the specific primary goal you are focused on.
Below I'll go through several key paired metrics Avinash outlines as examples. Remember, all businesses are very different, and have largely differing goals, so use these as inspiration.
Conversion Rate (Revenue)
Conversion Rate is a heavily used golden metric, especially if the business is selling product online. More often than not it equals revenue, so it can be the big ticket number that the business leaders want to see in analysis.
On its own however, this metric can be hiding a lot of sub-optimal customer behaviours, or expose bad marketing strategies.
A good metric to pair Conversion Rate (CR) with is Average Order Value (AOV).
Compare these two scenarios when comparing data between periods:
- P1 = 2% CR / $26 AOV. P2 = 2.5% CR / $14 AOV
- P1 = 2% CR / $26 AOV. P2 = 2.5% CR / $40 AOV
In both cases, a focus on CR alone would look good for both. After all we drove a rise in Conversion. But by pairing this up with AOV we can see that scenario 1 was in fact very bad.
With this paired metric in place, you get a much better handle on weeding out activity that drives simple, low value conversions or things like badly implemented platform updates that fail to drive long term business growth.
Conversion Rate (Completion)
In some situations, Conversion may be better monitored with a qualitative metric. So we may need to measure if our users were able to complete a particular task on the platform.
A good metric to pair Conversions Rate in this instance is Task Completion Rate.
Conversion Rate really only shows you what a small fraction of your audience did on the platform when they came to buy. Task Completion Rate gives you a view of what 100% of your audience did, and if they achieved success regardless of the reasons they visited you.
Example, you have a 2% CR and a 14% Task Completion Rate. Something very bad is going on that needs attention.
[By Channel] Conversions
When optimising acquisition strategies, more often than not we fall back to last-click attribution. This is an extremely narrow and outdated view, as it doesn't take into account the new ways people move through the funnel (i.e. not in a predictable, linear path).
The metric then to pair up with [By Channel] Conversion is Assisted Conversions.
The goal here is to not only see what drove the conversion, but how many times the various activities helped with future conversions. With this information, you can make much better decisions about the various channels in your marketing portfolio.
Click-through rate is a great metric for ensuring acquisition activity doesn't focus on simple 'spray and pay' tactics. So this metric ensures you think about content, targeting strategies, recency and frequency. The biggest problem with this metric is on it's own, it doesn't make you think about behaviour once people arrive at the platform.
A good metric to pair up with Click-Through Rate is Bounce Rate.
With this in place, you are now incentivised to not only get lots of people to the platform, but ensure you are matching them to the right landing pages, or are delivering on the promise of the ad. If people are bouncing like crazy, click-through rate essentially becomes useless, so this provides a nice backup.
Nearly all of the time, we want to drive a lot of Visits (or Sessions) to our platforms. That's why we built them after all. But on its own, Visits may not be providing insight on the health of the platform or the business.
A good metric to pair up with Visits (Sessions) is Visitors (Users).
50,000 Visits / 50,000 Visitors vs 50,000 Visits / 10,000 Visitors.
These will result in very different questions when reviewing the data, which means we can get a better understanding of what behaviours we are driving.
In the first example, why did they only visit once? In the second example, they are coming back a lot, so what content diode they consume? And what is the distribution of these returns?
When it comes to apps, getting people to install the app is incredibly important. However this fails to show us an important behaviour - around 80-90% of all downloaded apps are only used once.
A good metric to pair Mobile Installs with then is 30-Day Active.
The 30-Day Active metric shows the unique users who have been active during a 30-day period. This allows you then to see your retention rate, and ensure users are not only installing, but coming back to the platform for long term success.
Switching to Rent strategies, on a platform like YouTube it can be very easy to only measure impacts by Video Views. We often here the desire to make something "viral", and so this metric is closely monitored - this however doesn't show if we are driving behaviour.
A good metric then to pair with Video Views are Subscribers.
If people are just viewing your video and not becoming a Subscriber to your channel, you are not building an Owned audience that you can engage with in a cost effective fashion over time. You may have got a temporary bump in publicity, but this can expose if you are approaching audience building in the wrong fashion.
One of the sad truths about Facebook is often the primary metric organisations become obsessed with are Likes. From being obsessed with your Page Like count in comparison to your closest competitor, or desperately asking fans to Like individual posts, the problem here is that this metric does not give you a great indication of the quality of your page.
At the page-level, a good metric to pair Page Likes with is People Talking About This.
You could have a huge number of fans on your page, but if your Talking About This number pretty much doesn't exist, you are the proud owner of a ghost town. Same with comparisons to competitors - they may have huge fan bases, but if you have an incredibly good Talking About This number, ultimately you could be in a much better spot (provided the conversation has been positive).
At the post-level, a good metric to pair Post Likes with is Amplification Rate (or post Shares).
Our goal on Facebook is not only to reach our fans, but also reach their friends. If they are not actively sharing the content we produce, we are missing out on reaching new audiences cost effectively and driving all of the good social proof.
Acquisition, Behaviour, Outcomes
With these examples in place, you can express this data in an Acquisition, Behaviour, Outcomes framework for a better snapshot for business leaders. Check out the example below.
This post continues my series on mental models.