Advice for Sales

Experimenting with product signals across the PLS cycle

How to experiment and reach out to PQLs in an early-stage Product-Led Sales motion.

Cassie Pallesen

January 5, 2023
·
 min read

With more than 10 years of experience in the B2B SaaS space, Pavel Hayes uncovered his sweet spot as a proven sales leader in hyper-growth start-up environments. His passion for hiring, training, mentoring, and scaling sales teams led him to PLG companies such as PandaDoc, where he drove ARR from $2M to $60M and grew the sales team from four AEs to 30. 

Below is an excerpt of a recent conversation we had about introducing, experimenting, and testing Product Qualified Leads (PQLs) throughout an early-stage Product-Led Sales (PLS) motion.

The evolution of an early sales cycle

You were one of PandaDoc’s earliest sales reps, evolving its self-serve motion over the course of six years. What did the early sales cycle look like in the early days and at what point did PQLs come into play?

At PandaDoc, self-serve is the lifeblood of the business—its main revenue driver. What’s interesting is that product signals and PQLs didn’t play a significant role until two or three years ago. The reason why is because our self-serve funnel was entirely inbound, with Sales touching every 14-day trial and demo request coming through. We put leads into automated cadences, reaching out to folks who opened emails or engaged with our outreach.

Similar to how PLS teams operate today, we positioned Sales as product specialists from the very beginning, and our conversations focused on the pain points people were trying to solve. Early on, our deal cycle was very transactional and quick, although it eventually expanded as we moved upmarket. At that point, multiple stakeholders, more product demos, and different businesses were entering the mix. Our inbound leads started to cool about four years in, which is when we turned to product data to build more pipeline.

Initiating sales conversations using product insights

I’d love to dig into that process a little more—how did you go about incorporating product data into your sales strategy? What types of product signals did you look for?

At the time, the warm leads coming into our funnel weren’t enough to cover every rep—the funnel shifted from 100% inbound to fluctuating between 50-70%. This meant reps had to recycle old opportunities and re-engage with folks they previously reached out to among other activities to supplement the pipeline gap. This opened up conversations about how we could use product data to guide this outreach. For example, what types of activities reflect someone who’d be receptive to a second sales outreach?

The first thing we looked at was whether an active user mapped to our ICP so we were focusing on the right people. If they did, we looked at their activity inside the product. Creating and sending documents, along with getting them signed, were the key actions we looked for here. This gave our sales reps an immediate signal to re-initiate a conversation. It also helped us quickly spot new folks who were actively engaging in the product, especially as we continued experimenting with different product signals

Testing and tracking product signals

Talk to me a little more about experimentation—what were the biggest success drivers for testing and experimenting with different kinds of product signals? What moved the needle the most?

For me, it really came down to three key elements: 

  • Defining success
  • Regularly tracking experiments
  • Collaborating with cross-functional stakeholders

For starters, defining what success actually means is critical to understanding which experiments are driving the right results. For example, is a successful experiment one that builds pipeline or one that drives revenue? At PandaDoc, we ran experiments that created a lot of pipeline which failed to convert to revenue. This meant digging deeper to figure out if it was a fault of Sales or pipeline that wasn’t real in the first place.

We also ran experiments for too long while not giving others enough time. I found that two months was a reasonable timeframe to understand what’s working well and what’s not. Weekly syncs with stakeholders across product, data, ops, sales, success, and marketing to review performance helped a lot here. A week gave us plenty of time to see how things developed and decide whether to make tweaks or let it run. Anything longer than a week made it harder for us to pivot direction or course correct.

Feature gating, outreach strategies, and team growth

Switching gears to life after PandaDoc, you joined another PLG company called CloudApp. Given all your PLG experience going into the new role, what did you focus on first?

For some quick background: CloudApp is an asynchronous communication tool that lets you record messages in lieu of sending an essay-long Slack response. When I first joined the company, anyone using the free tool could do almost everything in the tool—as in, there was zero incentive to upgrade to a paid tier. So, my first priority was working with the CRO, CEO and product team to limit the features in the free version so people had a reason to pay for the premium product.

Building out a sales outreach strategy was another initial focus, which is where I focused on product signals to identify three key user actions:

  • Creating a screenshot in the last 30 days
  • Recording a video
  • Dragging and dropping a media file into the app

If someone took one of those actions, we’d reach out for a conversation about the tool. Lastly, I spent a lot of time expanding the sales team. When I joined, we had one AE and one SDR running the sales process. By the time I left, I expanded the team to four mid-market AEs, three SMEs, and four SDRs, with each AE having a book of business they were responsible for when it came to upselling, cross-selling, or notifying accounts of a price increase upon renewal. 

Telling the right stories in the deal cycle

You’ve accomplished a ton of success as a PLS leader—any final words of wisdom or expert guidance you want to share for similar leaders at early-stage PLG companies?

Telling the right stories is key to selling new business and cross-selling. As long as you have stories to tell, you have everything you need to reach out to existing accounts and inquire about other areas of their business. I’ve found great success reaching out to different department heads, initiating conversations to uncover new stakeholders and use cases. I’d then use this information to sharpen our storytelling across the deal cycle.

For example, the teams using CloudApp the most are success, sales, product, and engineering, so we had tons of stories built around these personas. But when multiple departments are all using the tool, the director of IT becomes the main stakeholder we need to convince. Learning this helped us dig into the priorities that mattered most to them and then adapt our pitch accordingly: when upselling IT folks to our enterprise plan, we leaned into stories that focused on the importance of enterprise security.

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Advice for Sales

Experimenting with product signals across the PLS cycle

How to experiment and reach out to PQLs in an early-stage Product-Led Sales motion.

Cassie Pallesen
|
Head of Marketing
|
Calixa

Subscribe to High Intent

Your PLG roundup in 5 minutes.

January 5, 2023
ReadTime

With more than 10 years of experience in the B2B SaaS space, Pavel Hayes uncovered his sweet spot as a proven sales leader in hyper-growth start-up environments. His passion for hiring, training, mentoring, and scaling sales teams led him to PLG companies such as PandaDoc, where he drove ARR from $2M to $60M and grew the sales team from four AEs to 30. 

Below is an excerpt of a recent conversation we had about introducing, experimenting, and testing Product Qualified Leads (PQLs) throughout an early-stage Product-Led Sales (PLS) motion.

The evolution of an early sales cycle

You were one of PandaDoc’s earliest sales reps, evolving its self-serve motion over the course of six years. What did the early sales cycle look like in the early days and at what point did PQLs come into play?

At PandaDoc, self-serve is the lifeblood of the business—its main revenue driver. What’s interesting is that product signals and PQLs didn’t play a significant role until two or three years ago. The reason why is because our self-serve funnel was entirely inbound, with Sales touching every 14-day trial and demo request coming through. We put leads into automated cadences, reaching out to folks who opened emails or engaged with our outreach.

Similar to how PLS teams operate today, we positioned Sales as product specialists from the very beginning, and our conversations focused on the pain points people were trying to solve. Early on, our deal cycle was very transactional and quick, although it eventually expanded as we moved upmarket. At that point, multiple stakeholders, more product demos, and different businesses were entering the mix. Our inbound leads started to cool about four years in, which is when we turned to product data to build more pipeline.

Initiating sales conversations using product insights

I’d love to dig into that process a little more—how did you go about incorporating product data into your sales strategy? What types of product signals did you look for?

At the time, the warm leads coming into our funnel weren’t enough to cover every rep—the funnel shifted from 100% inbound to fluctuating between 50-70%. This meant reps had to recycle old opportunities and re-engage with folks they previously reached out to among other activities to supplement the pipeline gap. This opened up conversations about how we could use product data to guide this outreach. For example, what types of activities reflect someone who’d be receptive to a second sales outreach?

The first thing we looked at was whether an active user mapped to our ICP so we were focusing on the right people. If they did, we looked at their activity inside the product. Creating and sending documents, along with getting them signed, were the key actions we looked for here. This gave our sales reps an immediate signal to re-initiate a conversation. It also helped us quickly spot new folks who were actively engaging in the product, especially as we continued experimenting with different product signals

Testing and tracking product signals

Talk to me a little more about experimentation—what were the biggest success drivers for testing and experimenting with different kinds of product signals? What moved the needle the most?

For me, it really came down to three key elements: 

  • Defining success
  • Regularly tracking experiments
  • Collaborating with cross-functional stakeholders

For starters, defining what success actually means is critical to understanding which experiments are driving the right results. For example, is a successful experiment one that builds pipeline or one that drives revenue? At PandaDoc, we ran experiments that created a lot of pipeline which failed to convert to revenue. This meant digging deeper to figure out if it was a fault of Sales or pipeline that wasn’t real in the first place.

We also ran experiments for too long while not giving others enough time. I found that two months was a reasonable timeframe to understand what’s working well and what’s not. Weekly syncs with stakeholders across product, data, ops, sales, success, and marketing to review performance helped a lot here. A week gave us plenty of time to see how things developed and decide whether to make tweaks or let it run. Anything longer than a week made it harder for us to pivot direction or course correct.

Feature gating, outreach strategies, and team growth

Switching gears to life after PandaDoc, you joined another PLG company called CloudApp. Given all your PLG experience going into the new role, what did you focus on first?

For some quick background: CloudApp is an asynchronous communication tool that lets you record messages in lieu of sending an essay-long Slack response. When I first joined the company, anyone using the free tool could do almost everything in the tool—as in, there was zero incentive to upgrade to a paid tier. So, my first priority was working with the CRO, CEO and product team to limit the features in the free version so people had a reason to pay for the premium product.

Building out a sales outreach strategy was another initial focus, which is where I focused on product signals to identify three key user actions:

  • Creating a screenshot in the last 30 days
  • Recording a video
  • Dragging and dropping a media file into the app

If someone took one of those actions, we’d reach out for a conversation about the tool. Lastly, I spent a lot of time expanding the sales team. When I joined, we had one AE and one SDR running the sales process. By the time I left, I expanded the team to four mid-market AEs, three SMEs, and four SDRs, with each AE having a book of business they were responsible for when it came to upselling, cross-selling, or notifying accounts of a price increase upon renewal. 

Telling the right stories in the deal cycle

You’ve accomplished a ton of success as a PLS leader—any final words of wisdom or expert guidance you want to share for similar leaders at early-stage PLG companies?

Telling the right stories is key to selling new business and cross-selling. As long as you have stories to tell, you have everything you need to reach out to existing accounts and inquire about other areas of their business. I’ve found great success reaching out to different department heads, initiating conversations to uncover new stakeholders and use cases. I’d then use this information to sharpen our storytelling across the deal cycle.

For example, the teams using CloudApp the most are success, sales, product, and engineering, so we had tons of stories built around these personas. But when multiple departments are all using the tool, the director of IT becomes the main stakeholder we need to convince. Learning this helped us dig into the priorities that mattered most to them and then adapt our pitch accordingly: when upselling IT folks to our enterprise plan, we leaned into stories that focused on the importance of enterprise security.