The Conversion Report

Benchmarks for trials, freemium, and conversion in 2026

Executive summary

Last month I partnered with ChartMogul and ProductLed to analyze new conversion data from 200 B2B software products. The findings demystified the economics of free products and the user journey in 2026.

The TL;DR if you missed it:

  • 57% of products have a free trial as their primary landing point for new customers, more than twice the rate of freemium (26%).
  • The most common trial length is 14 days (62% of products).
  • Among free trial products, 20% require users to provide a credit card upfront.
  • The median free-to-paid conversion rate across all products is 8%. But very few products actually have an 8% conversion rate.
  • Free trials convert at slightly higher rates than freemium; however, this difference gets wiped out upon accounting for signup rates.
  • Free trials that require a credit card see 30% free-to-paid conversion – more than 5x ones that don’t require one.
Three parallel set plots detailing different funnels starting from traffic, to sign-up rate, to converted. The chart as a whole is titled 'The free user journey in 2026'. The first one is labelled 'Freemium funnel', and starts off with 1,000 visitors segmented in 380 organic users, 170 paid, 150 product, 120 partners, 70 sales, and 110 other. 90 of them sign up, of which 5 start paying. The second plot is labelled 'Free trial funnel', which starts with 350 organic users, 180 paid, 150 product, 130 partners, 110 sales, and 80 other. Of these, 45 sign up, and in turn 4 of those start paying. The last plot is labelled 'CC-required trial funnel'. This chart starts with the same numbers of users as the 'Free trial funnel' plot, but results in 35 signups, of which 11 start paying.

But this data only tells half the story. Readers also wanted to know how to improve free-to-paid conversion, which remains one of the highest-leverage ways to grow revenue. Even a modest 1 percentage point improvement in free-to-paid conversion equates to a roughly 15% increase in new revenue per trial.

This follow-up report takes a data-driven look at what’s working to improve conversion with insights on tactics, tech, marketing channels, pricing, and sales touchpoints. Most of the tactics highlighted here don’t require more marketing spend or new headcount.

Kyle Poyar

Kyle is the Analyst-in-Residence at ChartMogul. He has spent the past 15 years helping software startups fuel growth, monetize their products, and become category leaders.

Kyle also writes the popular Growth Unhinged weekly newsletter where he explores the unexpected behind today's fastest-growing software startups. He is based in Boston, Massachusetts.

What’s working to improve conversion

The survey was conducted in January 2026 and included 200 software products. A typical respondent is between $1 to $10 million ARR with an average revenue per customer of $50-$249 per month and a year-on-year growth rate between 25-50%.

43% of survey participants improved free-to-paid conversion over the past 12 months. They most often saw modest gains of 10-25% (one-third of participants) although one-in-ten improved conversion by 25% or more.

We asked participants for their highest-impact growth experiment or tactic, collecting 30 experiment ideas after consolidating duplicates.

A diagram labelled 'What's working to improve free-to-paid conversion'. A subtitle reads '30 impactful growth experiments from the past year'. It branches off into 5 sub-categories. The first category is 'More targeted acquisition', below which 6 bubbles are found: 'Targeted the right ICP', 'Better precision with Meta ads', 'Better paid search campaigns', 'Established a reseller channel', 'Repositioned to a new ICP', and 'Better homepage messaging'. The second category is 'Attracting high intent users'. Below it are 'New template library for SEO', 'Technical SEO experiment', 'Better LLM visibility', 'AI translation of marketing', 'New referral program', and 'Community-led growth'. The third category reads 'Faster time to value', and below it are 'Shortened onboarding', 'Added interactive demos', 'Gamified onboarding', 'More obvious upgrade CTAs', 'Use case-based onboarding' and 'New AI features'. The fourth category is 'Better plans and pricing', and contains 'Changed plans', 'Adjusted rate limits', 'Raised monthly prices', 'Added a free tier', 'Gated features', 'Added a reverse trial'. The fifth and final category is 'Smarter human touchpoints', and has 'Added 1:1 onboarding calls', 'Redirected ENT accounts to AEs', 'Required a demo before trial', 'Added interactive landing page', 'Removed self-serve purchasing', and 'Focused on sales-assisted motion'. The graphic as a whole has the original survey question in the footer: 'What was you most impactful growth experiment or tactic from the past 12 months?'

Related: How Fyxer ran 514 growth experiments in the last 12 months to grow from $1M to $30M ARR

Five key focus areas emerged to help you prioritize growth bets:

  1. More targeted acquisition
  2. Attracting high intent users
  3. Faster time to value
  4. Better plans and pricing
  5. Smarter human touchpoints

Marketing: More targeted acquisition, attracting high intent users

In my experience, the fastest path to higher conversion doesn’t involve changing the product itself. It’s about attracting more of the right users in the first place.

Organic signups from search, referral traffic, social media, and LLMs convert at the highest rates, according to the survey. Signups from paid marketing (ex: paid search, display, Meta ads) convert at the lowest rates. The immediate action items: (1) expand organic acquisition efforts to their max capacity (which is easy to say and hard to do as SEO has declined) and (2) get more precise with performance ad targeting and paid search campaigns.

LLM optimization (referred to as AEO) and mid-funnel SEO was mentioned by several survey participants. At Webflow, profiled earlier, ChatGPT traffic converts at 24% (!), which is 6x higher than Google. Some tangible examples from the survey for expanding organic acquisition efforts via SEO and AEO:

  • Generating a free template library for legal documents that lawyers use. This made a 40x impact on our organic SEO.
  • Investing in G2 has lifted all channels. (Note: G2 data is frequently cited by LLMs.)
  • Localizing the website with new landing pages and dedicated PPC campaigns plus improving our presence in LLM sources.
  • Running a structured data and technical SEO experiment focused on improving how our product, use cases, and content are interpreted by Google and LLMs. By making key pages more explicit and machine-readable, we increased rich search results, AI-generated mentions, and visibility on high-intent queries. The impact was meaningful growth in qualified organic traffic and demo requests without increasing content volume, and better brand inclusion in both traditional search and LLM responses.

Better performance advertising starts with reverse engineering the characteristics of the highest-converting users. Fyxer, for instance, noticed that work email signups had a 10x higher LTV compared to personal email users.

Campaigns can then be optimized to get far more precise at bringing in these exact users. In the words of one survey participant, “We invested in paid traffic with the right ICP. It was scary how precise the thing got.”

Growth: Faster time to value

New users are, in a word, impatient. There’s a narrow window to impress these users and convince them to invest sweat equity into the product.

Faster time to value comes down to (a) reducing friction and (b) increasing a user’s motivation to overcome friction.

  • Reducing friction experiments: (1) Shortened onboarding, (2) use case-based onboarding, (3) new AI-assisted onboarding.
  • Increasing motivation experiments: (1) Adding interactive demos to show-off what the product does, (2) a gamified onboarding experience (ex: giving users more AI credits when they complete onboarding steps), (3) more obvious calls to action (CTAs).

In my experience, the user onboarding that works for one product doesn’t necessarily transfer to a different context. It needs to be tested.

More importantly, this testing needs a dedicated owner who’s accountable for the number. This is where things get messy.

A table titled 'Who owns growth?' detailing team ownership over different growth KPIs. The columns represent different stages in a user journey: 'Signups/leads', 'Product activation', 'Free to paid conversion', 'Self-service revenue', 'Expansion revenue' and 'Retention'. The rows contain different teams reponsible for those stages: 'Marketing', 'Product', 'Growth', 'Sales', 'Customer Success', 'Ops', 'Other'. Each cell has a percentage in it, and the higher percentages are color coded red to indicate the team is primarily responsible. Secondary owners are color coded gray, and the other cells are transparent. The first column, 'Signups/leads', reads (from top to bottom) 65% (red), 4%, 14% (gray), 12% (gray), 0%, 1%, and 4%. The second column, 'Product activation', has 7%, 49% (red), 16% (gray), 13% (gray), 11% (gray), 1% and 3%. 'Free to paid conversion' has 14% (gray), 24% (gray), 26% (red), 28% (red), 4%, 1%, and 3%. The fourth column, 'Self-service revenue', has 18% (gray), 28% (red), 24% (gray), 12%, 8%, 4%, and 6%. Then 'Expansion revenue' has 9%, 14% (gray), 10% (gray), 30% (red), 29% (red), 2%, and 6%. Lastly, 'Retention' has 3%, 23% (gray), 8%, 12% (gray), 47% (red), 3%, and 4%. In the table's footer, it highlights that 'by ownership we mean strategic decision making and accountability to the number'.

Product activation, which includes new user onboarding, tends to be owned by the product team (49% of the time) with an assist from growth (16%), sales (13%), or customer success (11%). Yet few of these product teams are actually accountable for free-to-paid conversion. That usually falls to sales (28%) or growth (26%). This disconnect likely means product activation never gets the attention needed to really move the metrics.

People need the right tools to measure and improve these growth KPIs. You might wonder: which tools do I need today and which ones should I try next? We unpacked that question using the same dataset.

A graphic detailing the results for two of the survey questions. The first one is labelled 'Survey: Most impactful conversion tools (2026)'. It shows a list of companies, in order of popularity: 'Mixpanel as #1, then Amplitude, Google Analytics, ChartMogul, HubSpot, PostHog, Heap, Salesforce, Segment, VWO, Metabase, Pendo and Chameleon. The second part is labelled 'Survery: 'Must try' conversion tools (2026)' and has PostHog on #1. Then HubSpot, Clay, Statsig, Pendo, Amplitude, Segment, Intercom, Regie.ai, Customer.io, GrowthBook, Quarterzip, and Freckle. In the footer, the full questions are outlined: 'What are your most impactful tools for measuring and/or improving conversion?' and 'Are there any new tools you're excited to try for measuring and/or improving conversion?'

Based on the survey responses, the most impactful conversion tools were Mixpanel (#1 most cited), Amplitude (#2), Google Analytics (#3), and ChartMogul (#4). All four provide visibility into how free users behave and convert, making them core to the growth stack.

The ‘must try’ conversion tools were more varied. The top four were PostHog (#1 most cited), HubSpot (#2), Clay (#3), and Statsig (#4). The theme across the ‘must try’ tools is automating growth plays whether through lead enrichment, outreach, qualification, support, or user engagement.

You can use ChartMogul to get a complete picture of how free trials and free users behave, when they convert, and how they impact your recurring revenue. They offer a 14-day free trial and paid subscribers can save $50 per month through Unhinged Perks.

Pricing: Better plans and pricing

Pricing and packaging had the highest number of mentions when survey participants submitted their most impactful growth experiment from the past year. Perhaps no surprise here.

What’s confusing is figuring out which pricing and packaging changes can meaningfully move the needle on conversion. In my experience, simply raising or lowering prices tends to not change conversion rates very much, which is why price increases can make so much extra money.

Changing plans seemed to make a bigger difference. This includes adjusting limits and feature gates, introducing or removing plans, and adjusting trial mechanics. Some tangible examples that worked via the survey:

Adjust limits and feature gates

  • Giving free users limited AI credits every month, drove increase in activity (MAU)
  • Paywalling features with rate limits

Introduce or remove plans

  • Removing the option to pause a pain plan
  • Offering a self serve free tier with a major pricing change
  • Providing custom packages for partners

Adjust trial mechanics

  • Adding a dual CTA on the homepage to start freemium or a 14-day credit card required premium trial (it used to just be “Get started for free”). We saw a 26% improvement at creating premium trials
  • Instead of starting everyone on a low capability freemium, giving them the option of freemium or free trial
  • Offering feature trials rather than full plan trials for expansion
  • Introducing a free trial for existing customers

Other optimizations

  • Increasing pricing on our top tier’s monthly plan by $5, left the annual price the same which includes a 16% discount. It was impactful because demand increased and the share of annual subscribers has increased significantly.
  • Better pricing page
  • A/B price testing and discount campaigns

Sales: Smarter human touchpoints

Software companies used to have sharp lines between self-service and sales. Not anymore. Nearly every at-scale B2B company with a self-serve motion also has a large sales team.

Sales reps can enhance the user’s experience by helping guide them through the buying process rather than forcing them to go it alone. Some buyers prefer to DIY; others want to talk to a human. Contract size may be a proxy for this, but it’s never perfect (and even free tools might get scrutinized if important data is involved). What really matters is having the ability to route people into the right experience (sales versus self-serve) and letting them move between those paths in a way that feels effortless.

A horizontal stacked relative bar chart labelled 'A middle ground between sales and self-serve', detailing the human touchpoints when an enterprise user self-serves onto a product. There are two stacks of bars; 'Free trial' and 'Freemium'. The former has 33% customer success, 18% sales-assist, 15% account exec, 14% account mgr, and 20% no human touchpoints. The 'Freemium' bar stack has 13% customer success, 22% sales-assist, 28% account exec, 6% account mgr, and 31% no human touchpoints. The footer shows the original survey question: 'If an enterprise user self-serves onto your product (aka doesn't select 'contact sales') are there any human touchpoints?'

Among freemium products, 69% have human touchpoints when an enterprise user self-serves onto the product. The touchpoints are usually owned by an account executive (28%) or sales-assist (22%). Sales-assist teams don’t have a traditional quota; they focus on product onboarding and then handing off leads to sales.

Among free trial products, 80% have human touchpoints for enterprise users. Interestingly, these are frequently led by customer success (33%) or sales-assist (18%). I suspect this approach drives better response rates and helps qualify enterprise opportunities. Account execs might multi-thread or go top-down to catalyze a larger deal with the decision maker.

The future of conversion: AI agents

The latest frontier is combining self-serve product experiences, AI agents (both in-product and outside of the product), and sales for efficient growth.

SafetyCulture, which I featured previously, gets 500,000 free signups per year and there wasn’t an easy way to identify the highest fit and highest potential customers for sales. This meant the sales team resorted to manually sifting through the backlog of leads, researched companies one by one, and wrote personalized custom outreach. They introduced an AI inbound BDR, which increased meeting bookings by 3x and opportunities created by 2x.

The AI inbound BDR handles (1) personalized outreach sequences, (2) knowledge base powered responses, and (3) meeting booking, synced with AE calendars. This is supported by AI-powered waterfall lead enrichment, calling five providers in parallel for greater accuracy and coverage.

The lines are already blurry between product and growth (typically focus on product touchpoints) versus sales and customer success (typically focus on 1:1 touchpoints over email or on calls). But the customer doesn’t care about these differences.

Everything that influences conversion should be fair game, and it should feel seamlessly connected. The winners will treat conversion as a team sport. And they’ll get an assist from AI.

Methodology and glossary

Data was collected via a Typeform survey and distributed via email, Slack, and social media channels to collect responses. The survey was conducted in January 2026.

The survey included 200 responses. The charts shown in this report may have a lower sample size depending on the specific audiences represented in the data (ex: excluding respondents that were not applicable to the question).

Definitions as follows:

  • Free trial - time-limited access to the full product
  • Freemium - free tier with usage or feature limits
  • Reverse trial - temporary access to premium features on a free plan
  • Interactive demo - guided hands-on product experience with dummy data
  • Paid trial - time-limited access with a special introductory price

Specific question wording as follows:

  • What is the primary way a new customer starts using your product?
  • Roughly what percentage of your website traffic converts to a lead or free signup? Please estimate to your best ability?
  • Roughly what percentage of your leads or free signups convert to become a paying customer within 6 months?
  • How has your conversion rate trended over the past 12 months?
  • Which team(s) own the following metrics? By ownership we mean strategic decision making and accountability to the number.
  • If an enterprise user self-serves onto your product (aka doesn’t select “contact sales”) are there any human touchpoints?
  • What was your most impactful growth experiment or tactic from the past 12 months?
  • What are your most impactful tools for measuring and/or improving conversion? Please list them below.
  • Are there any new tools you’re excited to try for measuring and/or improving conversion? Please list them below.

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