This article is Part 1 in a series on leveraging intent data to book more meetings, accelerate deals, and ultimately crush quota!
In this article, you’ll learn how to use the freemium model to book more enterprise meetings.
Let’s get started…
Introduction to Intent Data
As technology continues to evolve, buyers have access to more information than ever before. This means more emphasis on independent research, and a diminished need to engage with a sales rep during the discovery stage of the buyer’s journey.
Because of this trend, sales organizations must strive to align their sales process with the buyer’s purchasing process. This alignment starts with creating a system for identifying when an account has entered the engagement stage of the buyer’s journey.
The solution: intent data.
Leveraging the Freemium Model to Book More Enterprise Meetings
The freemium model is nothing new to the SaaS industry. In fact, I’d say it has become the standard in recent years. Companies like DropBox, Zapier, and Hubspot all leverage a freemium model to promote adoption.
In addition to acting as a great conversion tool for marketing, the freemium model generates a wealth of intent data just waiting to be harnessed by the intrepid sales rep.
Though this guide is focused specifically on leveraging freemium data in the enterprise segment, this process can be applied to any segment with a few minor adjustments.
Sales Development Representative: Book more qualified enterprise opportunities for your account executives… and promptly crush quota!
Sales Development Manager: Reduce process fragmentation among your team and empower new reps by coaching to a structured system that reliably produces results.
Marketing/Data Science: Provide an accessible framework for the sales team to capitalize on your hard-earned data.
Freemium user data is arguably the most compelling form of intent data. By tuning into this data in real time, we can systematically identify and target the personas in our territory who are most likely to convert to customers.
- Collect Freemium User Data
- Segment Accounts
- Define Target Personas
- Build Your Lists
- Craft Tailored Content
- Run Plays
Collect Freemium User Data
This step of the process can vary greatly, depending on the data restrictions in place at your company. If you have access to a private database, preferably with the ability to export to a CSV format, you can easily aggregate, analyze, and segment freemium user data based on relevant fields.
If you have restricted visibility into this data, you will need to either request access to the appropriate database, or request to have a report emailed to you. Requests will likely be processed by one of the following teams:
- Data Science / BI
- Marketing (or specifically ABM)
It’s important to make sure your request is processed for your account executives’ territories. Otherwise you will need to filter the report by account executive, which can be time consuming if your company has a large sales organization.
Assuming you’ve gained access to your freemium user data, and you have successfully exported it to Excel, you should now be staring at a mess of rows and columns…
So far so good!
Now that we have the raw data, it’s time to begin transforming it into actionable information. From my experience, this is where most sales organizations struggle. Aggregating data is important, but it’s completely useless if we don’t apply a framework for interpretation.
To start, we’ll segment this data at the account level.
The goal of this step is to determine the accounts in our territory most in need of our solution, and thus most likely to engage with us.
In order to do this, we need to define a set of indicators that imply the degree of need. There are several indicators we could reference for this step, but let’s focus on two of the most accessible:
- Velocity – the number of total freemium signups from an account. Accounts with a high scoring “velocity” are likely experiencing consistent and wide-spread pain.
- Acceleration – the net-new number of freemium signups over a set period of time. Accounts with a high scoring “acceleration” are likely experiencing increased pain or awareness of your solution.
- Apply a COUNTIF formula to all duplicate values in the Account Domain column.
- Filter the Account Domain column in descending order.
- Note the top 5–10 accounts in this filtered list.
- Filter the Signup Date column for the past 1–3 months.
- Repeat steps 1–3 of the Velocity Filter.
You should now have two separate lists with 5–10 account names each. Our goal is to focus on the top 5.
To do this, cross-reference your lists — any accounts that make both lists will be our priority. If you don’t have at least 5 accounts after cross-referencing, prioritize the accounts with the most acceleration.
After doing this, you should have a list of the 5 accounts in your territory with the highest potential to engage with an outbound campaign.
Important Note: As SDRs, it’s our responsibility to generate net-new business. This means you should likely cross-reference your CRM to make sure none of your top 5 accounts are already customers generating revenue above the threshold for “new business.” This will be completely dependent on your company’s criteria for SDR quota relief.
Define Target Personas
Now that we’ve identified the 5 accounts in our territory most in need of our solution, we need to drill down a layer to understand exactly where that need is focused within the organization.
Think of this like triage at the ER. We’ve already identified which patients are presenting the most urgent symptoms. Now we need to evaluate each patient to diagnose the cause and locality. After we know who we’re helping, where the pain is centralized, and the root cause, we can prescribe a unique solution for each case.
Our goal is to build a profile of the personas in an account who share a common root cause. This process is often referred to in the marketing world as “needs-based segmentation.”
We do this by defining buyer personas and identifying how these personas are distributed within the freemium user base.
Tool Warning: This step is going to require access to a contact database like ZoomInfo, DiscoverOrg, Seamless.ai, or LinkedIn Sales Navigator. As I feel it’s the most widely accessible tool, I’ll describe how to execute this process with Sales Navigator.
- Filter the Account Domain column so you’re only seeing results for one of your top 5 accounts.
- Filter the Signup Date column to only show signups from the last 1–2 fiscal quarters.
- Open Navigator and click the “Advanced Search” tab to the right of the top search bar.
- Choose “Search for leads” from the dropdown menu.
- Enter the first and last names from your filtered report in the “Keywords” search field.
- Enter the account name in the “Company” search field, then click “Search.”
You should have a list of LinkedIn profiles that correspond to the freemium signups from your target account. From here, we can define buyer personas and identify the persona trends in our freemium signups.
There are three key descriptors we’ll use to build our persona profiles:
- Function: Filter values include IT, Marketing, Operations, Sales, etc.
- Seniority: Filter values include C-Level, VP, Director, Manager, etc.
- Geography: Filter values include Country, State, City, etc.
For example, if I notice a majority of the sign-ups in a sales function, I can create content focused on my solution’s value proposition for people in a sales role. Alternatively, if I cross-reference Sales and Seniority, I may notice a majority of those Sales signups are Sales Directors. Now I can create targeted messaging with a value proposition that will resonate with people who manage a Sales Team and execute a 2- to 5-year strategy.
There are many options for layering these three filter values to define personas. The main limiting factor to this approach is volume. If you apply too many filters, and define personas too narrowly, there won’t be enough contacts in the organization to establish a reliable trend.
Defining too narrowly will also limit the scale of your outreach, because there may only be a handful of contacts who fit that persona.
If you define personas too broadly, your content will be less relevant and thus less effective. There is a “sweet spot” where you have an optimal balance of scale and relevance, it just takes some practice to find it.
Identifying Persona Trends:
On the left-hand side of the Navigator results page is a filter bar. Here you’ll find a filter button for each of our three key descriptors. When you click each of these buttons, you will see a drop-down menu of filter values. Next to each value should be a number in parenthesis. This tells you how many contacts that value applies to.
There’s no need for us to actually filter by these values. We just want to see which values are represented most frequently in our list.
Click each of these three key descriptor buttons and note which functions, seniority levels, and geographies have the highest frequency (number in parenthesis). The key descriptor values with the highest frequency will define the personas we target.
Let’s do an example as if we’re an SDR at ZoomInfo:
- Pull a report of the freemium sign-ups within our territory.
- Apply a COUNTIF formula to the Account Domain column to count the sign-ups from each account, then filter from high to low.
- Note the 10 accounts with the most total sign-ups.
- Now filter the Sign Up Date column to only show sign-ups from the past 4 weeks. Then repeat steps 2 and 3.
- If there are 4 accounts that appear on both lists, mark those 4 accounts on a separate list.
- Find the account with the most sign-ups over the last 4 weeks that isn’t already on our list of 4 accounts. That account is now number 5 on the separate list.
- Open the original freemium report and filter the Account Domain column so it only shows results from one account on our top 5 list.
- Filter the Sign Up Date column to only show sign-ups from the last 3 months.
- Copy the first and last names from this filtered list, and paste to Navigator’s “Keyword” search field under the “Search for more leads” tab. Enter the account name in the “Company” search field. Then click Search.
- Click on each of the three key descriptor filter buttons. Note the top 2–3 values for each filter.
- Because ZoomInfo is primarily used by sales people, we’d assume the highest frequency value for “Function” to be Sales. This is a broad persona that can likely be refined further.
- To refine the persona, apply the Sales filter in the Function drop-down menu.
- Now repeat step 10.
- We notice a majority of the sales sign-ups are from SDRs and SalesOps.
- Our target personas for this account are now defined as SDR or SalesOps at “X Company.”
Repeat this process for each of the top 5 accounts you identified while segmenting accounts in the previous step. This should yield 5–10 specific personas that have a high potential to engage with an outreach campaign.
Now we’re ready to build lists of all the contacts who fit our target personas.
Build Your Lists
Now that we’ve transformed our freemium data into a list of high potential accounts and personas, it’s time to build our roster.
The following process is a guide for building a list of contacts for one of the 5–10 personas we’ve identified. You will need to repeat this step for every persona on your list.
Tool Warning: This step is going to require access to a contact database like ZoomInfo, DiscoverOrg, Seamless.ai. Because I’m most familiar with it, I’ll describe how to execute this step with ZoomInfo.
- Open ZoomInfo.
- Click the “Contact Search” button on the tab on the left-hand side of the page.
- Enter the name of the account in the “Company” search field.
- Apply filters to the “Job Function,” “Management Level,” “Job Title,” or “Location” search fields. These filters should match your persona definition from the previous step.
- You now have a list of every employee who matches your target persona. You can either export this list to your CRM or to a CSV file.
Create Targeted Content
We’ve already done the hard work of segmenting our prospects by persona. Now we just need to create content for each persona and run separate outbound campaigns.
Before we continue, let’s take a second to review what we’ve accomplished so far. We have:
- Identified the top 5 accounts in our territory who are most likely to engage with an outreach campaign.
- Defined personas based on their roles at their respective companies.
- Identified which personas are most likely to engage with an outreach campaign.
- Built a list of contacts that correspond to each of our target personas.
The hard part, in my opinion, is over. Because our prospects have been segmented by job function and management level, we have effectively segmented our prospects by use case.
This means we can create email and cold call messaging with the same value proposition for every prospect in a campaign, and it will still be highly targeted.
I won’t go in-depth here on how to create email or cold call messaging because this is likely something you already know, and if not, there are plenty of resources online and on SalesHacker for you to reference.
Run Plays Leveraging Intent Data & the Freemium Model
This step will depend heavily on the standards at your organization. You may be encouraged to create sequences or cadences with several steps over the course of a month, or you may have free reign over your outreach. All that matters in terms of this process is that you create separate content and run separate sequences/cadences for each target persona.
The key is to use a freemium model to collect intent data that tells you when an account is engaged and ready to buy. Then run plays to book meetings with those accounts.
Applied correctly, this process will produce high-conversion campaigns, help you book more enterprise meetings, and lead you to a quota crushing quarter!