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We’re often asked by customers and companies who are considering purchasing Correlated: how are you different from other solutions?
It’s probably the most common question we get so we have a lot of experience thinking about the answer. In my view, our answer gets to the core of why we’re building Correlated the way we’re building it and why we believe we’re right in our approach.
In short, we think some of the other solutions in the market are “doing it wrong” and we have a fundamentally different view on how to tackle Product Led Sales (PLS) and modern GTM in 2023.
Before we go into our recommended approach, let’s start from the beginning.
Why did we start Correlated?
We started Correlated after directly experiencing the problem at an open source database company called Timescale that had thousands of monthly free sign ups. The issue was a prioritization problem. Our small team couldn’t talk to every free sign up so we needed to segment those self-serve users and target the best ones.
To solve the problem, we combined key product usage trends with enrichment around the company using the problem. In our case, this was database software so we looked at things like:
- How much data was stored in the database?
- Was that changing over time and how quickly?
- What kind of infrastructure was the customer using (the larger, the better the signal)?
- Was the company a fast growing startup?
- Was the company a F500 or government entity?
We took this data and created manually spreadsheets at first and then started to pull some of this into Salesforce.
Even these manual efforts led to a much better hit rate and eventually allowed us to source millions of dollars in revenue pipeline.
As we were manually building out processes at Timescale, we imagined what a more robust solution might look to scale these efforts. We always wanted a way to easily push insights around the highest propensity accounts into our GTM stack:
- Alerts in Slack when a F500 company started to see a big increase in data stored
- Emails triggered with relevant docs and onboarding resources when users within an account that started using a bigger deployment on AWS
- Fresh data in Salesforce related to product usage and high propensity scores.
- Users entered into campaigns in Hubspot when they hit certain onboarding, activation and monetization triggers.
This was incredibly hard to build due to data being spread out across multiple systems and not having a single place to define these playbook triggers and action off of them.
What’s wrong with Product Led Sales platforms today?
Since starting Correlated in late 2020 to tackle the problems we directly experienced, a number of other companies were founded to help solve the problems I outlined above. Many of these solutions take an approach that we feel is the wrong way to tackle the problem.
Ultimately, the goal of a PLS solution should be to:
- Centralize your customer data
- Synthesize that data into insights
- Allow GTM teams to easily take action on those insights
Most PLS solutions today allow you to get some of the way to solving 1), have some functionality to support 2) and fail completely at 3). Let’s look at each bucket.
Centralize customer data
Other PLS solutions have connected to key customer data sources like your CRM, your data warehouse, product analytics, etc. Many have “checked the box” here, but I would argue that
Correlated has gone above and beyond by enabling the best self-serve onboarding for data sources as well as a data catalog that allows you to categorize and define the data you’re sending us. No other solution has that breadth of functionality and as a result it can be challenging and take a lot of time to onboard your data and implement other tools.
Synthesize customer data into insights
Other PLS tools ask you to define your own scores and figure out which customers are highest propensity. They also ask end users to log in to monitor their accounts and individual users to manually enroll them into playbooks like email sequences or tasks.
Correlated has developed a ML platform that helps you define the goals you’re trying to achieve (free to paid conversion, expansion, cross-sell, churn prevention) and then automatically surfaces those Accounts or Users when they hit a high propensity.
Allow GTM teams to easily take action on those insights
As discussed above, the approach is to have the SDR, sales or CS teams log into their platform, sift through the data and manually take action based on their judgment on what to do.
There are a few problems with this approach:
- Sales teams often have many priorities beyond prospecting including working active opportunities, demos, post-call follow-up etc. Asking them to actively sort through their accounts and prospect doesn’t always yield results.
- It’s not always clear what the playbook should be. Asking salespeople or CSMs to “choose their own adventure” when they identify a potentially promising prospect is a lower odds of success approach.
- Manual actions aren’t as timely so “speed to lead” is often sacrificed.
Correlated enables you to drive Playbooks off of high intent triggers proactively. You never have to worry about your sales team logging in to take action. You can update Salesforce, send a sequence in Outreach, trigger a Smart Campaign in Marketo and more right within Correlated Playbooks. Most importantly, it can all happen automatically.
Where is PLS heading in 2023?
We believe centralizing your customer data, identifying high propensity accounts to target based on different lifecycle stages and then driving automated actions based on high intent triggers is the right way to build a PLS platform in 2023.
Our customers like Intercom, Tailscale, RainforestQA and others have adopted this approach of using Correlated as the middle layer between their customer data and their GTM tools and teams to great success so far.
As we’ll discuss in our upcoming webinar with Intercom and ZoomInfo, we’re able to drive substantial increases in revenue pipeline by taking this trigger based approach.
Ultimately we believe really strongly that we are architecting Correlated to solve this problem the right way. If you’re curious to learn more about whether a PLS platform can help you and you’d like to hear more about our approach, feel free to drop us a line.
