I’ve been developing products with millions of users for years. Some that got keynoted by Steve Jobs.
But it wasn’t until I took on the CMO role three months ago that I finally understood deep in my guts what this gif meant:
It’s easy to measure top-of-funnel metrics like social media followers or website traffic. But those metrics don’t tell the full story. And they’re not a good indicator of whether your top of funnel is effective.
Instead, we measure top of funnel based on people at accounts within our Total Addressable Market (TAM).
Here’s how I think of it: There are X number of accounts in my TAM. If those accounts don’t use Salesforce or HubSpot CRM, they’re automatically disqualified. I also have a higher chance of selling to them if they:
For each of these accounts I want to be able to have the ability for the following personas to become aware of our brand:
A “First Engagement” is the first touch we have with a qualified account.
And we’re able to measure First Engagements a few different ways:
This is the easiest to measure! When someone subscribes to our newsletter, we’re able to identify them and count them as a first engagement without the use of outside information.
When someone requests a demo, we’re able to use their IP address to figure out which channel was the first time they engaged with us, and attribute accordingly. (See #3 for more details on how this works).
After a webinar/in-person event, we upload the list to our CRM. Attendees from webinars/in person events are also going through this ‘Intent Model” that identifies first engagements and are plugged into our reports.
Here’s where things get complicated.
We use Snowplow (a Behavioral Data Platform) and Kickfire (a B2B Intent Data Provider) to identify those site visitors who haven’t yet raised their hand to identify themselves.
Here’s how that works:
When a site visitor comes to the Chili Piper website, Snowplow begins tracking user information, including the IP address of the visitor.
This user information is stored using an anonymized user identifier, which is stored as a first-party cookie on the client side.
If a website visitor provides their contact information by requesting a demo of our product or signing up for our various newsletters, the user has been successfully deanonymized as described above.
In cases where the user does not perform one of these actions, we automatically send the IP address information to Kickfire (a third-party B2B Intent Data Provider), who returns known information relating to the IP address.
We use the returned data to compare the information against our known companies and users from Snowflake (our existing data lake). In cases where a new company has been identified by Kickfire, we create a new company record in Salesforce (our CRM) using Hightouch (a reverse ETL tool).
At this point, the site visitor has been partially deanonymized (at the company level) and efforts are made to identify the person who has been visiting our website. These efforts include ad retargeting and monitoring of subsequent website sessions from the same identifier.
Measurement is still far from perfect — we can’t identify all our traffic, Kickfire only identifies a small percentage, and we don’t have perfect data for our communities or podcast streams.
But it still approximates our impact, especially when we calibrate it with our attribution model (which we’ll detail in a future article). It also allows us to see a breakdown of channels:
We are also exploring a tool called Phantom Buster to automate our manual counts when it comes to social media, but we’re still in early explorations with it.
This way we can have an approximate understanding of which motions are most effective and use that as a lever for our spend and resources.
For content that's ungated — in our case all of it — first engagements are about seeing what is most effective in creating brand awareness
Because we can convert them to first touches just from the visit, we're able to see if ads have a brand impact — even if they're not converting to hand-raisers or subscribers. We can't measure total eyeballs to the ad, but it's still a lot better than measuring nothing.
It also allows us to see which channels are saturated from a brand perspective and need to be nurtured instead.
But probably even more important it allows us to see down the funnel which channels are most effective to:
I'll cover our full-funnel metrics more in-depth in a separate post
We still do a lot of brand related things that are harder to measure — we're currently writing an article on how we built brand awareness at Chili Piper to give some insights into that.
Down the road there’s also a lot more specificity I want to add to this framework. For instance, I want to see how many touches are needed by persona and by product to funnel an account to what we’re all aiming for: Closed Won.
I’ve been developing products with millions of users for years. Some that got keynoted by Steve Jobs.
But it wasn’t until I took on the CMO role three months ago that I finally understood deep in my guts what this gif meant:
It’s easy to measure top-of-funnel metrics like social media followers or website traffic. But those metrics don’t tell the full story. And they’re not a good indicator of whether your top of funnel is effective.
Instead, we measure top of funnel based on people at accounts within our Total Addressable Market (TAM).
Here’s how I think of it: There are X number of accounts in my TAM. If those accounts don’t use Salesforce or HubSpot CRM, they’re automatically disqualified. I also have a higher chance of selling to them if they:
For each of these accounts I want to be able to have the ability for the following personas to become aware of our brand:
A “First Engagement” is the first touch we have with a qualified account.
And we’re able to measure First Engagements a few different ways:
This is the easiest to measure! When someone subscribes to our newsletter, we’re able to identify them and count them as a first engagement without the use of outside information.
When someone requests a demo, we’re able to use their IP address to figure out which channel was the first time they engaged with us, and attribute accordingly. (See #3 for more details on how this works).
After a webinar/in-person event, we upload the list to our CRM. Attendees from webinars/in person events are also going through this ‘Intent Model” that identifies first engagements and are plugged into our reports.
Here’s where things get complicated.
We use Snowplow (a Behavioral Data Platform) and Kickfire (a B2B Intent Data Provider) to identify those site visitors who haven’t yet raised their hand to identify themselves.
Here’s how that works:
When a site visitor comes to the Chili Piper website, Snowplow begins tracking user information, including the IP address of the visitor.
This user information is stored using an anonymized user identifier, which is stored as a first-party cookie on the client side.
If a website visitor provides their contact information by requesting a demo of our product or signing up for our various newsletters, the user has been successfully deanonymized as described above.
In cases where the user does not perform one of these actions, we automatically send the IP address information to Kickfire (a third-party B2B Intent Data Provider), who returns known information relating to the IP address.
We use the returned data to compare the information against our known companies and users from Snowflake (our existing data lake). In cases where a new company has been identified by Kickfire, we create a new company record in Salesforce (our CRM) using Hightouch (a reverse ETL tool).
At this point, the site visitor has been partially deanonymized (at the company level) and efforts are made to identify the person who has been visiting our website. These efforts include ad retargeting and monitoring of subsequent website sessions from the same identifier.
Measurement is still far from perfect — we can’t identify all our traffic, Kickfire only identifies a small percentage, and we don’t have perfect data for our communities or podcast streams.
But it still approximates our impact, especially when we calibrate it with our attribution model (which we’ll detail in a future article). It also allows us to see a breakdown of channels:
We are also exploring a tool called Phantom Buster to automate our manual counts when it comes to social media, but we’re still in early explorations with it.
This way we can have an approximate understanding of which motions are most effective and use that as a lever for our spend and resources.
For content that's ungated — in our case all of it — first engagements are about seeing what is most effective in creating brand awareness
Because we can convert them to first touches just from the visit, we're able to see if ads have a brand impact — even if they're not converting to hand-raisers or subscribers. We can't measure total eyeballs to the ad, but it's still a lot better than measuring nothing.
It also allows us to see which channels are saturated from a brand perspective and need to be nurtured instead.
But probably even more important it allows us to see down the funnel which channels are most effective to:
I'll cover our full-funnel metrics more in-depth in a separate post
We still do a lot of brand related things that are harder to measure — we're currently writing an article on how we built brand awareness at Chili Piper to give some insights into that.
Down the road there’s also a lot more specificity I want to add to this framework. For instance, I want to see how many touches are needed by persona and by product to funnel an account to what we’re all aiming for: Closed Won.