You can spend a lifetime analyzing to death endless data points, charts, trends, correlations, but as the saying goes: "If you don't know where you are going... you'll end up somewhere else"
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These days "Data is King".
All successful companies made it their second nature to extract, collect and analyze more and more data relating to their activities.
However, after spending some time jumping up and down in joy in the fresh data water, the scary truth always reveals itself - you can easily drown.
You can spend a lifetime analyzing to death endless data points, charts, trends, correlations, but as the saying goes:
"If you don't know where you are going... you'll end up somewhere else" / Lewis Carroll
This is one of the reasons the framework of OKRs (Objectives, and Key Results) became so popular in the past few years.
In theory, the framework is very simple:
Define your Objectives
Measure Key Results
Act accordingly
[To find out more about the OKR framework you can click here]
However, in too many cases, the "O" in the OKR framework is treated somewhat lightly and there is a tendency to move too quickly to defining the metrics and goals.
To do it right, and focus the organization on what matters, start with a crystal clear definition of where you want to go, before building the metrics that help you measure where you are in the journey.
Start with a crystal clear definition of where you want to go. Your Objectives.
And it's not easy, and never generic.
The "Don't Do"s for OKRs
The obvious choice for many companies would be to use revenues, the number of paying customers, or productivity and efficiency metrics as their top-level objectives. Mmmmm, think again.
"O" shoud not stand for revenues, # of customers and/or CAC/LTV
"O" should NOT stand for:
Revenue
# of Customers
CAC [Customer Acquisition Costs]
LTV [Customer Lifetime Value]
They are all lagging indicators.
If you do things right, revenues will grow, customers will come knocking on your doors, you'll minimize your CAC (Customer Acquisition Costs) and maximize your customers' LTV (Lifetime Value). But if you use these KPIs as your main objectives, you may find out too late in the game that you are doing things completely wrong.
The "Do"s for OKRs
"O" should stand for the pain you solve and/or the value you create for your customers
"O" should stand for the pain you solve for your customers and the value you create for them.
Easier said than done, I know, but this is the core of the OKRs exercise.
Your customers don't care about your revenue, your costs, and your efficiency. Your customers are evaluating and using your product because it solves a problem that they have and provides value. If you build your OKRs right, you will make sure your entire organization is focused on optimizing for what your customers need and care about.
"O" can be expressed as a function of E (engagement) x F (Frequency) x N (Number of customers engaging)
In many cases, "O" can be expressed as a function of:
"O" = E x F x N
Whereas:
E stands for the Engagement with your product that provides value for your customer
F stands for the Frequency of engagement within the measured time unit
N stands for the Number of customers/users engaging
The tricky part here is to define correctly which engagement is the right one. Note that many times, the right type of engagement, the "AHA" moment, will be relatively deep into the product.
For example, opening an account is normally not where the value is for the customer, it's just a step in the way. Finalizing the first design, if you are providing an app for DIY design, is.
Each stage in the customer lifecycle will have a different "O"
The customer pain points and goals vary, depanding on the stage of the custoemr lifecycle we are looking at (aquisition, adoption, retention and expansion), and so should the "O" vary.
For every stage of the customer lifecycle, there is probably a different "O".
This is because the customer pain points and goals in each stage are different.
In the Acquisition stage, the customers are normally looking to find out as quickly as possible if and how the product solves their pain and provides solutions to their needs.
In the Adoption stage, the customers are looking to find out how to use the product and extract the most value from it.
In the Retention stage, the customers are focusing on using the product and benefiting from it
In the Expansion stage, the customers are exploring how they can extract even more value from the product.
Now what?
Now it's time to jump right into the data ocean and start swimming.
You'll find out very quickly that this is an iterative process.
You start measuring key results, optimizing towards your "O" and then repeat the entire process, to fine-tune and re-assess.
Now what? Start measuring, optimize and hit repeat :)
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