How Forte Labs used subscriber segmentation—and reported 62% more sales from new leads

The starting point: 14 opt-ins, one product pitch

Forte Labs had built a large content ecosystem around note-taking, personal knowledge management, and productivity. Different lead magnets attracted people with different situations: someone starting a digital note-taking practice did not necessarily need the same next step as an experienced reader looking for community and ongoing support.

Yet Saxena’s account of the project describes one automated funnel connecting all 14 opt-ins to a pitch for Building a Second Brain Foundation. The system could use basic ConvertKit data such as subscription date, location, and purchase status, but it did not know enough about a new subscriber’s goals or experience to choose among products.

That is an important distinction. A business can have tags, automations, and a long welcome sequence without having a useful segmentation strategy. The test is not how much data sits on the subscriber record. The test is whether the data changes what that person receives.

For Forte Labs, the first high-value decision was product fit. Before rewriting individual paragraphs or subject lines, the team needed to know which broad route made sense.

The survey collected data the funnel could use

The survey appeared on the thank-you page immediately after someone submitted an email address. The opt-in was already complete, so the extra questions did not stand between the visitor and the initial signup. RightMessage could also adapt the page language to the lead magnet that brought the subscriber there.

Forte Labs did not ask one vague question such as “What are you interested in?” The survey built a practical picture from structured, mostly multiple-choice answers:

• Whether the subscriber took notes on paper, digitally, both, or not at all.
• The problem with their current approach or their satisfaction with it.
• Their main note-taking app.
• Their reason for seeking Forte Labs content.
• Whether they wanted to apply the material to work or personal projects.
• Their role and, where relevant, whether they worked for themselves or with a team.
• Whether they had read Tiago Forte’s books.
• Whether they preferred self-study or learning with a community and mentorship.

Conditional questions kept irrelevant branches out of the way. Someone who did not take digital notes was not asked to choose a note-taking app. Someone focused on personal projects did not have to answer a set of business-role questions.

The team reported that 96% of the questions shown on the thank-you-page survey were answered. That is a question-answer rate, not proof that 96% of all new subscribers completed every step. The distinction matters because a confident-looking percentage can easily grow beyond what the source actually measured.

Forte Labs also had subscribers who joined through channels that did not lead to the standard thank-you page. Those people received a welcome email linking to the survey. Saxena reported a 55% open rate and 13% click rate for that route—another reminder that the moment and placement of a question affect how much data you collect.

Survey answers selected one of three product paths

RightMessage transferred the answers into custom fields on each subscriber’s ConvertKit profile. That handoff turned a temporary form response into data the email program could use later.

Based on those answers, Forte Labs guided new subscribers toward one of three documented funnels:

The three-route design is the quiet strength of this customer segmentation case study. Forte Labs did not need hundreds of permanent audience boxes. It needed enough context to stop sending every new lead toward the same offer.

Within a product path, the sequence could acknowledge what the subscriber had said and emphasize the relevant problem, example, or learning format. The three-route design kept the system manageable while stopping every lead from receiving the same offer. The public accounts do not isolate how much of the reported lift came from routing rather than the changes inside those sequences.

This is what makes subscriber data commercially useful. The survey answer moves from page to contact record to automation to a visible recommendation. If that chain stops at a dashboard, the subscriber did extra work and received nothing in return.

For the underlying data model, see how to segment your email list without building a mess. The implementation details vary by email platform, but the principle survives: decide what the value means before writing it to a field.

Forte Labs reported 62% more sales from new leads

After the four-month project, Saxena reported a 62% increase in sales from new leads. RightMessage’s current course-creator page also carries her attributed statement:

The data RightMessage has collected about our new subscribers is enabling us to personalize how we welcome them to our email list and pitch them on a recommended product. So far, this has resulted in a 62% increase in sales from new subscribers.

That is customer-reported commercial evidence tied to a named mechanism: collect new-subscriber context, personalize the welcome, and recommend a product. It is stronger than a claim that “people liked the survey” or that the project accumulated a large number of answers.

It is not a universal conversion benchmark. The public accounts do not provide the number of leads, the exact baseline purchase rates, a holdout design, the attribution window, or a statistical-significance calculation. They also describe a broader four-month funnel project, not an isolated test where the survey was the only change.

Use the 62% as evidence that this specific Forte Labs system improved sales in the team’s report—not as a forecast for your funnel. The public record cannot tell you how much of the lift came from the survey, the routing, the email changes, or another part of the project.

What to borrow—and what not to assume

The Forte Labs setup offers a useful sequence for an established creator with several products:

1. Find the generic decision. Identify where every new lead currently gets the same recommendation despite arriving with different needs.
2. Choose a few real routes. Use products or next steps you already offer. Do not invent segments before you know what each one will receive.
3. Ask for the missing input. Put short, clear questions after signup and show conditional follow-ups only when they matter.
4. Store the answer deliberately. Write a stable value to the contact record your automations can read.
5. Use it quickly. Let the thank-you page, welcome email, or early sequence prove that the answer changed something.
6. Measure the business path. Track survey behavior, sequence engagement, and the purchase or other downstream action the route was meant to improve.

What should you not borrow? Forte Labs’ exact question list without first mapping it to your own decisions. If you sell one product, asking about ten dimensions may create an impressive profile and no better follow-up. If you sell three products for genuinely different situations, one well-chosen question might be enough to route people.

You also should not force an answer. The ordinary welcome path still needs to work for a person who skips the survey, joins through another channel, cannot be identified, or does not fit the available choices.

The thank-you-page survey guide covers that implementation in detail. Once the answer is available, an offer funnel can keep the recommended next step consistent across email and the website.

Start where one generic recommendation is already wasting attention. Pick one small campaign, define the two or three paths you can genuinely support, and ask for the one missing piece of context. Follow the answer into the email or page that changes, then compare the result with the ordinary path before adding another branch.

See how RightMessage builds surveys, quizzes, and conditional flows when you are ready to turn a subscriber’s answer into a more relevant next step.