LinksTry Benchmarks ExplorerLearn More About DataboxSubscribe to our newsletter for episode summaries, benchmark data, and moreWhy MQLs?If you spend any amount of time on LinkedIn, you might see any number of posts proclaiming that “the MQL is dead”. But Ollie and his marketing team at VanillaSoft don’t think so.In fact, MQLs are the primary metric Ollie works to move the needle on. They share a common metric with the sales team to ensure that they’re driving high value MQLs who have a higher likelihood of converting.To do that, they employ a number of channels, ranging from paid ads, to SEO, trade shows, and webinars. Until recently, they had never tried virtual events.How They Improved ItThe prior quarter, Ollie’s team had a big, scary MQL goal. They hit it, but only barely.Then, in the next quarter, the goal was raised significantly. Ollie knew they’d have to change their approach in order to hit it.So he decided to invest heavily into virtual events.In the prior quarter, Ollie’s team hosted an all-day virtual event. It was imperfect and exhausting, but they learned from it. He was determined to host another one (new and improved), in order to secure the new MQLs he needed.Here’s how he did it…He got great speakers, who could also help promote the event.He chose speakers who were incredibly smart and well-spoken. But more than that, they had to be able to help promote the event to a relevant audience, so the content would actually get seen.He made the event 1-month long.The first conference they ran was an 8-hour day, jam-packed with back-to-back sessions. That format was rough on both the team and attendees, so this time, they tried a new approach.They’d aim for 2 sessions a day, 30 minutes each session, for 1 month straight. That worked out to 45 total speakers, presenting 45 sessions, across 22 days.This new format took longer to plan, but provided 4 main benefits:Benefit 1: It was more relaxed.Attendees could consume events they were interested in all month, without giving up an entire day of work.Benefit 2: It provided ongoing content to market.Ollie found that with their single-day event: they promoted it, and it was done. By changing the format they were able to continually promote new material and build on the success of past sessions.Benefit 3: It provided social proof to help them secure additional speakers and sponsors.The day the conference launched with its initial lineup, Ollie was able to keep doing outreach and gain an additional 14 speakers and sponsors. Prospective speakers or sponsors were able to see what they’d be participating in. They could also opt-in late in the game, without feeling like they’d missed the opportunity.Benefit 4: It drove more attendance.With 45 speakers, if each speaker brought just a handful of their audience, Ollie knew they’d have great attendance.Ollie promoted 1 new speaker, every few days.He felt he couldn’t do justice to all 45 speakers if he tried to promote them all in 1 big announcement.So instead, he’d focus on promoting a new speaker every few days. This allowed him to properly highlight the skills, expertise, and session that each speaker was bringing to the table.They created generous, strategic sponsorships.Some of the sponsors came from ABM accounts. This gave them the ability to continue building those relationships, while offering them something of value. And some were friends of Ollie’s, who came from smaller companies.They didn’t charge these sponsors. Ollie wanted to be able to have the relationships be truly win-win. VanillaSoft would get the benefit of more promotion and attendees. And the Sponsors could gain leads and exposure without risking a huge budget.They used HeySummit to host an event website.This provided each speaker with their own landing & registration page, one place to house live and on-demand content, and a sponsors page.ResultsThe pace was exhausting but drove massive results:Ollie and his team exceeded their high quarterly goal, bringing in 1,200 new MQLs from the event.

LinksTry Benchmarks ExplorerLearn More About DataboxSubscribe to our newsletter for episode summaries, benchmark data, and moreInsights on Driving SQLs via LinkedIn Ads:1. Use benchmarks to identify competitive advantages.Look for instances where SQL rate (lead to meeting ratio) is 2-3x higher than average, and come up with a hypothesis as to what caused that growth.Then, see if you can duplicate it in another campaign.Once Remotion finds meaningful variance, they determine if the higher performance was owing to factors that can be duplicated.If so, they’ll increase the budget and spin off a new campaign where they lean into the things that made the former campaign perform so well.2. Get qualitative insights into ad performance.His team uses common sense, and deep familiarity with their clients to create a hypothesis of why a campaign saw meaningful change in performance.By talking with clients, they’re able to learn what’s happening that might be affecting performance.For example, after talking with the client they learn that John – the company’s best SDR – is on vacation.This is the cause of lower results down-funnel.And since they have access to their client’s CRMs accounts, they’re able to be proactive in looking at any other factors that impact ad results that they might not have seen by staring at performance numbers in the Ad platform.3. Remain “strategy agnostic” until you find what works for you.Gabriel has seen a lot of commonly accepted truisms fall on their face when applied to Clients in different countries or industries.4. Look at performance often enough to derive insights, but avoid knee-jerk decisions.Remotion gives each client gets their own real-time dashboard which shows:CPLLast 7-day CPLTrend over last 30 daysComparison to previous 30 days… and more.They know what a client’s current CPL is every day.But for metrics further down the funnel, they evaluate them monthly. This helps them make more informed decisions based on proven trends, and avoid knee-jerk reactions to a bad (or good) week.5. Determine your campaign goals early.Most companies run 2 types of campaigns:Direct response = promote your product, get someone to talk to youContent = promote your POV & provide valueEach generates different outcomes, so choose the one that best serves your goals. If you aren’t sure which type of campaign you want to run, Gabriel advises running both, then determining the CPL. Benchmark your performance to know if one is higher than the industry average. If it is, use that one.6. What companies get wrong about LinkedIn ads:Under-investingBeing inconsistentBeing unclear about their goalNot having messaging honed inHaving an inactive audience on LinkedInNot being ready (too early, no product-market fit)7. Testing messaging through LinkedIn ads can be costly.You need a lot of data to see how it impacts SQO rate.For example, you want to run 2 tests: so you need 50 leads on each (100 total, to have enough meaningful data) > and your CPL is $100.That means 1 test costs 10k. This can be great if you have a 100k budget. But if your budget is 15k/mo, then it means you spent almost an entire month testing 1 message variant.If that’s worth it, that’s great. But Remotion finds that often, it’s not significant enough to justify the investment.

LinksTry Benchmarks ExplorerLearn More About DataboxSubscribe to our newsletter for episode summaries, benchmark data, and moreWhy Qualified Pipeline via Inbound?The whole team is focused on driving net new logo revenue. So Pete focuses his team on qualified pipeline to contribute to that goal, and be aligned with the rest of the team.They define “pipeline” as “total booked revenue” (= when a scheduled demo meeting takes place). Since they aim for a win rate of ≥ 20%, this allows Pete to work backward from their revenue goals, and determine how much pipeline he needs to drive to help meet it.How They Improved It:They focused on understanding their audience better.They get on sales and customer calls weekly. This gives them insights around pain points and needs prospective customers have.These insights help them continually optimize their messaging, in order to be more helpful and relevant. It also helps them learn where their target customers spend time or pay attention.This means that when they make big bets on channels to invest in, they aren’t guessing.They invested heavily into refining their messaging & positioning.In a world where features are easily copied, Pete invested in differentiating Alyce by crafting a unique point of view and go-to-market message. This positioning provides a source for the entire team to draw from when they need to craft messaging or marketing creative.They brought in Dave Gerhardt, who helped them further refine how they thought about positioning and messaging for Alyce in a new and unique way.And once they had some concepts, they tested the new messaging on their homepage using Wynter, in order to look for leading indicators that the messaging would be successful and land the right way.They focused on harvesting more existing demand.They leverage about 25% of their resources and team on capturing existing demand. This includes using intent data to trigger more targeted outreach, retargeting on social, and a mix of branded & non-branded PPC programs.They focused on creating new demand.The biggest bet they’ve made is finding ways to create new customers and generate demand. They’ve done that in 2 broad steps:1. Create relevant and insightful content2. Distribute that content everywhere their target audience isThey made big bets on:Events (micro & virtual)Social (paid & organic)Co-marketing with other brandsInvesting in evangelists who speak on podcastsAnd communitiesBecause they’re tracking self-submitted, qualitative attribution, they’re able to see the efficacy of these channels and find the ones that are most effective.So far, it’s paying dividends. Top attributed channels are LinkedIn, Google Search, and Communities/Events.They approached communities with 2 main focuses:Bring value & education (don’t just talk about Alyce)Find ways to let members experience Alyce’s giftingFor example, members might be sent gifts upon joining or completing certain milestones. This allows these marketers (= the community members they’re reaching) to experience the value of Alyce in a more natural and generous way.ResultsAlyce had great momentum from past marketing leaders. With that as the foundation, this framework helped them increase the % of inbound qualified pipeline to 70%.Full episode here.

LinksTry Benchmarks ExplorerLearn More About DataboxSubscribe to our newsletter for episode summaries, benchmark data, and moreWhy Free-to-Paid Conversion?Amanda had a gut sense that since “audience research” was still pretty early and search volume was relatively low, they needed to nail the onboarding experience when people did give them a try.She started working with Forget The Funnel, who helped them identify 2 big opportunities:1) Increase free-to-paid conversion.2) Improve the onboarding experience.So they focused on growing their free-to-paid conversion %, and got to work improving onboarding.How They Improved ItImproving the onboarding flow.At the time, SparkToro's onboarding flow had 15 (or so) steps and a 10% completion rate, which was above average. Despite that, they were still addressing churn and getting questions about the product, so Amanda knew there was room for improvement.So she worked with Ramli John, who helped them improve the sequence. They did this by reducing it to 8 steps, and having Rand Fishkin (founder) trim his welcome video from 5 minutes to 2.Next, they updated the onboarding messaging, making it more concise and using a more active voice.Finally, they made sure every onboarding step mapped to 1 single feature. Previously, they had introduced new users to "list creation" and "outreach" in the same onboarding step. Amanda noticed that not as many people were creating lists, so they broke these into 2 steps.These changes alone took their onboarding completion rate from 10% to 15%. And even though Amanda couldn't prove users were actually consuming the messaging more than before, she knew it was easier to understand, quicker to get through, and it drove users to complete 1 successful search so they could reach the “aha” moment faster.Creating a behavior-based email sequence.In the early days, Rand would send personalized welcome emails himself. Later, this became 1 email welcoming users to the product, with subsequent emails being sent to remind users of upcoming monthly charges.Amanda knew there was lots of room for improvement here, but she faced one major challenge: SparkToro had a very wide use case.It's used by a lot of different companies, for a lot of different things. This meant that doing "1 size fits all" onboarding emails wasn't going to cut it.So she created a "behavior-based" email sequence, with 3 main goals:1. Get people to "value realization" as quickly as possible2. Help users get into the habit of using SparkToro more often3. Get users to use more features, and realize it's powerShe worked with Casey Henry, SparkToro's co-founder, to build out workflows that would group users into logic-based cohorts. Depending on the cohort/segment they were in, they'd experience a slightly different onboarding email sequence.For example, the first action someone takes is signing up for a free account. Ideally, the 2nd action they’d take is running a search. So if someone performed a search, they'd get a welcome email that would suggest other searches they might try. But if they signed up and didn't make a search, they'd get an email suggesting first searches to try, that might be beneficial to them (based on inputs from the customer).This allowed them to provide specific help, based on prospective customers' needs and use cases.Launching "Office Hours"Amanda started a series of live sessions where anyone could ask questions and get answers in real-time on a consistent day/time every week.They'd promote this to new users in onboarding emails, but it was open to anyone. They regularly get 1,000 people watching, with as many as 1,300 on some sessions.This provided a way for curious, would-be customers to learn more about the product in a no-pressure environment. And existing customers can get answers to specific questions they faced while trying to adopt the product for their own use.ResultsIn 4-6 months, the team doubled their free-to-paid conversion, adding more revenue without any new inbound channels.

LinksTry Benchmarks ExplorerLearn More About DataboxSubscribe to our newsletter for episode summaries, benchmark data, and moreWhy HIRO Pipeline?Refine Labs helps drive demand and pipeline revenue for SaaS companies from Series A through D. When they hit 30~ Clients, the team went to measure how many pipeline dollars those clients would get for every $1 they spent in ads. Chris wanted to be able to show what the growth of their pipeline was (across all clients) from the time they started working with his team, to then.But he had a problem. He realized that out of all their clients, none defined pipeline the same. Every single one had different definitions. For exome companies, “pipeline” meant that the lead booked a meeting with an SDR, and was in a stage where the deals convert at < 10%. Others defined pipeline as a deal in a stage that converted at higher than 50%.So Chris created a new pipeline revenue metric that could be easily adapted by all clients (and non-clients who wanted to), and would allow them to benchmark their performance against others.They define HIRO Pipeline as leads that come in through a high intent source (book a call, schedule a demo, etc) with win rates greater than 3% from lead-to-win, and reach a deal stage in your pipeline that converts at a 25% rate for that cohort of opportunities.These two qualifiers mean that if the deal stage starts to close below 25%, marketing needs to change the stage that HIRO is defined by (to a 25% or higher one) so they’re always aligned to sales performance.It also helps align marketing to revenue, without having to wait for the lagging metric of actual revenue to come in – because it’s based on a secure win rate.How They Improved ItUsing qualitative attributionFirst, Chris believes it’s important to understand that HIRO Pipeline’s efficacy isn’t going to be clearly shown by traditional attribution software. It requires a blend of qualitative and quantitative attribution. For most, this can be done by adding a simple open text field in the onboarding process, asking prospective clients how they heard about you.Balancing focus and budget between creating demand, and capturing demandChris believes the main key to driving HIRO Pipeline is striking the right balance between creating demand and capturing demand.Capturing demand is waiting in channels where people have demonstrated intent, are “solution-aware”, and are actively looking to buy something. For example, Google Ads or product review sites like G2.Creating demand is spending time reinforcing your messaging in channels where people are not in the market for your product or service. They may not be “solution-aware”, and don’t have intent to buy what you’re selling.Chris believes the key is to have two different strategies to reach each of those audiences.Chris believes that because companies rely so heavily on attribution software, they’re only focused on the “channels that work”, which are all demand-capturing channels.This means there are only so many buyers these companies can reach, and worse still, they have no control over how that demand was generated. Instead, companies must focus more on creating demand (vs capturing it), so they have control of the flow of new buyers entering the market.This means most companies must change the way they think about and approach demand generation. For example, if you don’t draw a distinction between “demand capture” and “demand gen” channels, you’ll end up treating the audience in each of those buckets the same. Where in reality, one audience is ready to buy and wants to consume one type of content, while the audience in the other is not going to buy and is interested in an entirely different set of content.For Refine Labs, this means using “demand gen” channels to help clients amplify messaging that educates clients about:the categorythe business problems that the Client solvesHow that Client has driven success for other customersDifferentiating features the Client has, that competitors don’tetc.Chris believes marketing teams need to take those elements, say them in compelling ways, and serve that messaging up natively in demand-gen channels that prospective buyers are already in. The goal is not to convert the prospective customer in that moment, but rather to educate them and keep your company top of mind.This means that instead of driving a person on LinkedIn to download an e-book, you might run an impression-based video ad that showcases the growth you drove for a client, or a written post breaking down what people should know about the category you’re in.The idea is that you plant the seed in this person (who has consumed this messaging) in the belief that they will end up sharing your company in a Slack channel, with peers at an event, or through a LinkedIn DM. And then someone from their network or company will come directly to your site when they’re ready to buy.ResultsRefine Labs has been applying that framework for customers since Day 1.They researched a cohort of 20 customers from B2B SaaS companies that had clean, historical data for 6+ months before starting to work with them. They then compared the 6 months prior to working with Refine Labs to the 6 months after working with Refine Labs, specifically drilling down to the HIRO metric.Across those 20 clients, the median increase in pipeline was an impressive 76%. This means a series C or D company doing $2m in pipeline before Refine Labs was doing $3.5m~ after working with Chris’s team.View the full episode here.

LinksTry Benchmarks ExplorerLearn More About DataboxSubscribe to our newsletter for episode summaries, benchmark data, and moreThe metric: Sales Efficiency RatioIn this episode, we’re covering Sales Efficiency Ratio: which the Referral Rock team defines as “the ratio of salesperson sales, vs non-salesperson generated sales.”John Bonini chats with Josh Ho, founder and CEO of Referral Rock, to learn how they improved their Sales Efficiency Ratio, and as a result:Onboarded customers fasterIncreased user’s “speed to launch” rateRemoved a bottleneck to growthAnd were able to do more, with lessWhy Sales Efficiency Ratio?There were two main reasons why Josh and the team decided to try and move the needle on this metric:Selling primarily through sales reps was successful at first, but eventually became a bottleneck for growthThey wanted to lean into product-led growth and grow the self-service side of their productBefore improving this metric, 90%~ of sales were closed by the sales team. This worked well for a while, but eventually became a bottleneck to growth.The problem was, as the leads increased, so did the need for trained sales reps. And for a bootstrapped company like Referral Rock, this was costly in time & money.It also made it challenging to test new channels because they didn’t have the ability to scale the sales team as fast as leads would come in. For example, if they decided to heavily invest in paid ad channels and it ended up driving lots of leads, they’d have to scramble to hire enough sales staff to help support the growth.Besides all that, they had been intrigued by PLG (product-led growth), and how much more efficient that model could be for a SaaS company like theirs.Up until that time, they were using traditional sales-led growth. The primary CTA drove users to book a demo, and 90% of their closed deals came from this sales-led method.There was a self-service option, where users could start a free trial and build their program without input or assistance from sales. But only 10% of closed deals came through that route.So they set out to improve their sales efficiency, by increasing the number of deals that were closed via non-sales methods.How They Improved ItThey made product improvements to foster more self-service.They began working on shipping more PLG-inspired features to improve their existing self-service onboarding and upgrade flow. This way, if users preferred to set up their own referral program or upgrade their account, the experience was smoother for them.They had CSMs lead group demos, then pass that group off to the product itself.Before, they’d try to get important internal stakeholders onto the call, and take a more heavy-handed sales approach via sales reps.With this “lighter touch” approach, they’d take a group of interested users, show them a demo and answer their questions, and then hand them off to the product ( = the self-service / PLG route they developed).They initially built this “group demo to product” team by stealing from their CS team.They started with one team member in particular who knew the product inside and out, and who had been on some sales calls before. He was critical in helping frame out the role, and make this new model more efficient.Once they had the process in place and a better idea of what the role looked like, they began hiring outside talent to expand the team.They promoted their two CTAs (“Book Demo” and “Start Free Trial”) equally.They updated the layout and design of their site to give equal primacy to the two main CTAs. This let users choose how they wanted to buy.They introduced intelligent routing which sent users to self-service or sales, depending on various criteria.First, they implemented various tools & workflows in the product to get a better idea of what stage the potential buyer was in, and what type of company they were.Then, based on those inputs, they’d put them in pre-set groups or buckets.From there, they could route them to either the lighter-touch sales method they had developed (which resulted in self-service), or the traditional sales method, depending on which would serve them better.In addition to that, they implemented a lead score mechanism to route the right leads to the right person.Finally, they retooled their workflows to better serve prospective customers depending on which bucket they fell into.For example, users in the CSM-led, "lighter-touch” sales group might get 1 series of emails and messaging. While users in the traditional sales bucket might get an entirely different set of messaging.ResultsIn 6~ months these efforts resulted in them changing their Sales Efficiency Ratio ratio from 90/10 to 50/50.In practical terms, this meant that:Customers were onboarding fasterThe team could expect the “speed to launch” rate (how fast customers get their referral programs launched) to increase significantlyHiring and training new sales reps was no longer a bottleneck to growth, or testing new channelsAnd ultimately, they were able to do more with less.Check out the full episode here.

LinksTry Benchmarks ExplorerLearn More About DataboxSubscribe to our newsletter for episode summaries, benchmark data, and moreCallRail is an incredibly data-driven company. They transparently share performance metrics across the entire team, to foster accountability and ownership. By looking at the metrics regularly, they’re never surprised to see a low or high number at the end of the month. And they work together to try and identify negative (or positive) trends when they see them happening in real-time.In this episode, Jason Rozenblat (VP of Strategic Accounts) shares how CallRail grew Free Trial to Customer Conversion %: the percentage of total users who begin a free trial and end up becoming paid customers.While they have other metrics they obsess over (namely MRR, ARR, and ARPU), Jason and his team are especially focused on “Free Trial to Customer Conversion %”  because it directly impacts deal size and close rate.It’s also important, in that it’s a shared metric across the team. Jason works with the Demand Gen team to grow it, and it’s shared by any teams that touch the website because those teams also care about driving traffic and improving website conversion rate. The two go hand in hand.This includes engineering, product marketing, and customer marketing. So they obsess over this metric as an entire organization.How they grew itJason and his team found a number of things that have contributed to growing this metric.Changing from monthly to weekly cohorts.By measuring cohorts weekly rather than monthly, they were able to get much more granular and ask, “what happened this week, that was different than other weeks?”. This helped them spot causes of growth or decline in real-time, as opposed to waiting until the end of the month and looking at a post-mortem. They also started tracking cohorts in 5-week intervals (from 2-week intervals). Since they have a 2-week free trial, they used to analyze cohorts in 2-week intervals. But they started to realize that there was always a long tail of trials that would close way past the 2-week mark. Users would extend their free trials, or come back after a trial had expired in order to enter payment information. So by extending the sales cycle, and measuring cohorts in 5-week intervals, they got a clearer picture of what was going on and had better data to make decisions from.They changed the way they handled lead assignments.Before, if sales reps were going to be using PTO the following week, they’d be pulled out of rotation to ensure they weren’t assigned any new free trials to manage. But what they didn’t account for was all the free trials that were set to expire on while that rep would be out of office.So they changed the way they handled lead assignments, to make sure that whenever a free trial expired, it was passed off to a rep who would be in office during that time. They also started to view expired trials as viable leads and focused more on converting them.They focused on sales training and education.On top of all of this, they continued training sales reps on how to better handle objects, close deals, etc.ResultsThe results they saw were amazing. Some months, they increased their Free Trial to Customer Conversion rate as high as 10 points.With the traffic and free trial volume, they had at the time, that change alone could add an additional 100-150 customers or $100k of ARR.And in certain cohorts, they saw trial to conversion rates of 70-80%.View the full episode here.

LinksTry Benchmarks ExplorerLearn More About DataboxSubscribe to our newsletter for episode summaries, benchmark data, and moreIn this episode John Bonini chats with Adam Goyette, VP of Marketing at Help Scout, to learn how they grew their website conversion rate, and why that metric became a priority for the team.You’ll learn…How the Help Scout executive team sets annual goalsHow the marketing team identifies where to invest their time and moneyHow improving the website conversion rate became a priorityWhy Website Conversion Rate?Each year, the Help Scout executive team works together to identify what their annual revenue goal is, and what growth % they’re aiming to hit in the upcoming year.Once they have this number and growth %, they work backward to find how many signups and new customers they’ll need in order to hit that goal. Although the revenue goal is set annually, they’ll adjust the forecast of what they’re expecting quarter by quarter. This allows them to make smaller adjustments in real-time, as they see how things are trending.Transparently sharing their performanceEvery day, an email is delivered to the team, showing them how they’re progressing towards that month’s goal. It includes a breakdown of trials, sales opportunities, and projected MRR. On top of that, individual teams meet weekly to review their own numbers. And executive teams meet once a month, reviewing what happened in the last month and where they’re going next month. They feel that having the numbers transparently in front of the entire team, encourages everyone to be creative and offer up solutions to help improve that number. Looking for growth opportunitiesOnce they have their growth goals in place, it’s time to figure out how to achieve them. Their goal is to find the right set of “levers” to pull, in order to move the needle on traffic, free trials, demo requests, weighted pipeline, and more.And there are dozens of levers they could pull. It’s probably the same challenge you face at your company. Do you try new channels, or grow existing ones? Do you send more traffic, or improve conversion with the traffic you have? To help narrow things down, they look how each stage of their funnel is performing, and how each channel is performing. Then they ask questions like, “what channels can we reasonably expect to grow?” and “what channels might be worth investing in?”They also look for the easiest wins. For example, if paid channels are steeped in competition with deeper pockets, they’ll look for a channel where they can be more competitive. Or they might find they can get significant results just by doubling down on an existing channel.Why they chose Website Conversion RateAdam and the team found that the free trial to paid conversion rate was 20%+, which was already high for the industry. But if they looked higher up in the funnel, they found they were getting 500,000 visitors every month. They decided it would be far easier and more impactful for them to increase the website’s conversion rate (driving more free trial signups), than it would to increase an already high “trial to conversion rate” of 20%.So they set to work running experiments.How They Improved ItThey view their website content in 2 main buckets: “High-Intent Pages” and “Low-Intent Pages”.High intent pages include the homepage or product pages, where people are visiting with the intent to explore and potentially sign up for the product. Here, the call to action is “sign up” or “start free trial”. Low intent pages include content like a blog or thought leadership content, so they make the call to action something like “join the newsletter”. This encourages visitors to stay in the Help Scout ecosystem, without forcing them to take an action they have no intention of taking. When setting out to measure and improve their website conversion rate, they only looked at conversion on the “high intent pages”. This helped them get a more accurate number of how they were performing on pages that had a set call to action of conversion.Their current “website to free trial” conversion rate was 2% on those high intent pages, and their conversion rate from “free trial to active paying customer” was 20%. They assembled a “task force” of stakeholdersWebsite conversion is often “cross-functional”. In other words, it’s owned or used by multiple teams. Brand marketing might own the messaging. Product marketing might own the positioning. And growth marketing might own SEO or conversion rate.So Adam and the team put together an “optimization squad”, comprised of all the stakeholders who needed to have input on the website. This ensured that every stakeholder had seat at the table, and could work together on improving conversion without stepping on each other’s toes.They tested everythingThey experimented with just about everything. But they saw the biggest results by adding personalization. Helpscout has a wide base of customers, so by understanding who their buyers are, they’re able to present more helpful messaging tailored to those personas.For example, they found that smaller companies have to convert better by going through self-serve, while bigger companies almost always convert better when they speak with sales. The optimization squad would take an insight like this, and present a dynamic (changing) call to action, depending on the person viewing the site. Someone from a company with more than 500 employees might see “talk with sales” while someone from a small company sees “start free trial”. They’d also highlight different use cases of the product, and change the way they talked about Help Scout. For example, if a 500+ person organization read, “Help desk software for small business”, they might feel Help Scout wasn’t for them. By personalizing the team could show different customer logos (that looked like the visitor’s company), make the language more relevant, and be more helpful with use cases that visitor was interested in.In total, personalization included dynamically presenting the call to action, headlines, length of free trial, use cases and social proof.ResultsIn just 6 months, they saw a 56% increase in demo requests and a 6% increase in trials.Check out the episode summary here.

In this episode of Metrics & Chill, KlientBoost’s founder, Jonathan Dane joins the show to talk about how identifying and improving one metric helped boost customer engagement, customer retention, productivity, employee happiness, and so much more.

In this episode of Metrics & Chill podcast, find out how Databox reduced median first response time from more than 3 hours to just 17 minutes. Learn what the team did, how they did it, and measures that have been implemented to ensure this success is long-term and sustainable.