When introducing changes to organisations and communities, which aren’t simple, it is important to have an appropriate understanding of how they function and what the impact of these changes will be. We must be able to monitor the performance of the various flows over time (e.g. acquisition flow) so that we are able to identify the area that needs improvement the most and be able to measure how a certain change affects the work of the system.
In our experience, organisations often use data and metrics that are very high level or focus on sub-systems, which leads to missed opportunities to improve the sequence of activities, that generate value (value chains) in meaningful ways. The result is usually wasted effort and resources which is followed by the introduction of the next idea, that in turn carries the same characteristics, and eventually similar outcome.
Let’s look at an example that illustrates the problem well. Joe has an online shop that sells t-shirts. Joe is an artistic person and he designs the t-shirts himself. He then uses a drop-shipping provider who prints the designs and ships directly to the end customer. Joe advertises his shop on social media and various online advertisement networks. Joe tracks the total number of sales every month and monitors his profit. His sales have been growing for over a year but recently he spotted a downward trend with sales numbers well below his average.
Joe decides to spend more on advertising but for the next few months this only helped maintain the same number of sales. The problem is that Joe does not have sufficient visibility of the conversion rates of the steps in his sales generating system. If you only monitor vanity metrics like sales and profits you are likely to waste efforts and resources in addressing the wrong problem.
Let’s look at a better representation of the performance of Joe’s shop. Joe’s customers find his shop from three main sources — social media, Google adverts and blog post links. They then browse through his online shop and may or may not add items to their baskets. Sometimes they leave their baskets, perhaps distracted by something else and never return. Eventually those who stay on the site long enough complete the purchase and rarely return.
Since Joe does not track any of these events, he has no idea of what the numbers are and what specifically he can do about it. He only sees sales and profit so the moment they go down his reaction is to increase spend on marketing.
Let’s imagine that Joe’s conversion metrics for the last month look like this
The three tables represent actual numbers of visitors from each of the three main sources of traffic — Social Media, Google and Blog posts — along with conversion rate between steps and overall conversion.
These tables present us with a more insightful view and we can immediately spot that even though social media and Google ads drive the most customers to the site, actually very few of those customers complete a purchase. Based on this perhaps it isn’t very wise to invest more in Google ads. Perhaps Joe should focus on bringing more blog visitors since they seem to convert best out of his three most popular channels.
We can also spot that the conversation between steps seems to drop the most between “add items to basket” and “checkout”. This means that Joe’s customers might get distracted and forget that they ever added items to their baskets. To improve this step conversion Joe might want to look at sending reminders to his customers about the items they’ve put in their baskets or perhaps make the checkout more streamlined.
If Joe was to produce metrics like these on a monthly basis he could also monitor how the changes he makes impact the conversion and re-adjust his efforts if the anticipated positive impact does not materialise.
The metrics I used in the example above are more popular as Pirate Metrics, introduced by Dave McLure of 500 start-ups a few years ago.
The first step is Acquisition, which we have presented as Visitor in the example above. It is usually the first time you get in contact with your customer.
The second step is Activation and is defined as the activity that shows your customer the potential value of your product or service — in our case “Browse shop” and “Add items to basket” represent events that are typically associated as activation in a shop scenario.
The next step is Revenue, or when the customer purchases our product or service and in our case this is the “Checkout” step.
The next R stands for Retention and this step will have a different meaning depending on context. In our case of Joe’s shop, retention would mean a return to the shop for a repeat purchase.
And finally the last R is for Referral, or recommending the product or service to others who then visit the shop to purchase something. For simplicity I have excluded the referral step from the data in the tables above.
Tracking at this level of detail can help you make more informed decisions about the next experiment to run or the next thing you need to learn.
Making changes to a complex adaptive systems (like organisations and communities) without having an appropriate understanding of how the value chains will be impacted can be tricky and can lead you down the wrong path.
Thankfully, the pirate metrics for start-ups are relatively easy to implement and provide an appropriate level of insight and representation of the performance of your business.
If you would like to learn more about pirate metrics check our recent article The Tao of Running Lean.