Author: David Daniels, BrainKraft @ BrainKraft.com
How do you measure the impact product marketing has on your business? It’s a common question I get. Most of those questions are in response to pressure by bosses wanting attribution metrics. That is, data to prove that a given marketing effort has an impact on the business in a positive way.
Measuring a direct correlation is possible but it’s far easier to measure in the aggregate. That way you’re reporting on trends rather than incidents. You want to use KPIs that correlate to the business goals for the products you’re supporting. In other words, don’t measure things that don’t matter.
There are 5 basic KPIs I recommend you monitor. Each one brings something to the table to help you figure out what’s working and what’s not. This is a good starter list of KPIs, but it isn’t a complete list by a long shot. The list is limited in scope to what product marketing should focus on.
Customer Lifetime Value (LTV)
Customer Lifetime Value (LTV) is a high-level KPI. It’s a trending indicator, like your weight, cholesterol, or blood pressure. In this case though, a higher LTV is better. It means that each customer, on average, is spending more money with you. It means your business is getting healthier.
A declining LTV could be the result of competitive pressure or there could be excessive discounting. If all competitors are experiencing declining LTV it could be an indicator of a declining product category.
LTV = Sum (amount each customer spends until they leave) / # of customers that have left
LTV = ($250,000 + $375,000 + $225,000) / 3 = $850,000 / 3 = $283,333
What I like about LTV is it can be easily calculated from past data. Ask your Finance team to pull the data for you from the last few years and see where it’s trending.
The close rate tells you how many qualified leads actually close and become customers. A higher close rate is better. It means there is a higher efficiency in acquiring customers and it’s being done at a lower cost.
Lower close rates could be an indicator of a poor product-market fit or a sales enablement gap.
You need to decide where you begin the measurement of a close rate. Some organizations use a marketing qualified lead (MQL) as the starting point and some use a sales qualified lead (SQL) as a starting point.
To calculate the close rate using SQLs, your formula looks like this…
Close Rate = # deals closed / # SQLs
Close Rate = 320 deals closed / 1,000 SQLs = 32%
Average Cost per Lead (CPL)
The cost per lead is an indicator of lead gen performance. A lower cost per lead is good. A higher cost per lead is not as good. The goal is to generate the best leads at the lowest cost while increasing close rates.
You can calculate CPL based on MQL or SQL. The game for your sales team doesn’t start until there’s an MQL, so that’s the minimum threshold. Take the total cost of marketing programs to get MQLs and divided it by the number of MQLs acquired.
CPL = Money spent to acquire MQLs / # MQLs acquired
CPL = $500,000 / 850 = $588
Average Contract Value (ACV)
The average contract value is the average amount of money per transaction. Think of shopping at a grocery store. There are hundreds of people who go to the grocery store each day. If you take all the sales generated for the day and divided it by the number transactions, you get the average contract value.
ACV = Sum (value of deals closed) / # of deals
ACV = ($150,000 + $200,000 + $175,000) / 3 = $175,000
Customer Acquisition Cost (CAC)
Customer acquisition cost is like CPL only bigger. It adds the cost associated with going from awareness all the way to a close. A higher CAC is generally thought of as bad but that’s not always true. The CAC can rise as long as the corresponding ACV goes up too.
CAC = Sum (all marketing and sales costs) / # customers acquired
All marketing and sales cost includes salaries, promotional costs, commissions, and any other costs associated with acquiring a customer. If you calculate your CAC on a monthly basis, you can monitor trends. It’s not an exact science because there’s an assumption that near term marketing efforts turn into near term customers. It’s not perfect but it definitely trends over time.
CAC = Sum ($50,000 marketing cost + $25,000 sales cost) / 5 customers = $15,000
It’s bad when the CAC is greater than the ACV, like the parody of SliceLine on the TV show Silicon Valley. You can’t make money if the cost to deliver pizzas is $10 and cost to buy them is $9.
In the example, we’ve calculated a CAC of $15,000. If the ACV is below $15,000 then you have a business decision to make. It might be a strategic play to accept deals that are under water to optimize for profit later. It might be a bad business model. Or, there could be leaks that need to be plugged.
Putting it All Together
Think of this list of KPIs as basic diagnostics, like when you visit a doctor. The doctor gets measurements from you to evaluate the status of your health. You know from experience the initial collection of data can result in more diagnostic tests to pinpoint an illness.
The reason I’m using this analogy is that I just returned from the doctor’s office. I have a small scratch on my cornea which was diagnosed after a secondary set of diagnostic tests. It occurred to me this is a perfect metaphor for product marketing KPIs.
Using multiple KPIs is important. It allows you to compare how KPIs correlate to each another. One KPI can be great while another KPI is bad.
Assume that you have a Cost per Lead of $300. Your finance team has decided the cost per lead is too high and you need to bring the cost down. Over 6 months your team has dropped the CPL down to $50. High fives are in order because it’s the lowest CPL you’ve ever documented. The CFO is thrilled. But he’s noticed something else.
You now have a Close Rate of 10%, which by any measure is abysmal. It used to be better. In January the close rate was 30% and trending higher. What went wrong?
You can’t diagnose the problem yet, but you have data to analyze. You graph the CPL against the Close Rate from the past 6 months.
Uh-oh. You see where the problem started and can figure out the right treatment to help the sick patient.
We can’t be absolutely certain there is causation to the correlation, but it sure does stink. It requires more investigation but now you have data, not a guess.
If you were tracking CPL against the close rate on a monthly basis, you would have spotted the trend sooner.
There is an endless supply of relevant KPIs. Don’t get lost in analysis paralysis. Pick a handful of KPIs that are meaningful to your business model and to the goals of your organization.