From internal buy-in to PLS roll-out in 5 steps (template included!)

Launching a PLS motion is a lot like composing a symphony.

Your go-to-market team is your orchestra. Each function plays a different role — like the strings, woodwinds, brass, and percussion — yet together they create a unified melody. In a PLS symphony, you’re composing a few different melodies (your playbooks) depending on your revenue goals and the opportunities you’re going after with PLS.

At the risk of stretching this metaphor… one of the challenges of successfully launching PLS is alignment, not just cross-functionally, like your orchestra sections, but inter-functionally. For example, if your reps are the “strings” section of your orchestra, then you’ve got to make sure that the violins, violas, cellos, and double basses (sales-assist, enterprise reps, AEs, etc) have scores that go well together — and that they know how to read and play them.

To get to this stage, it all starts with internal buy-in. I don’t mean just getting the green light from your exec board (that’s step one). Beyond the initial “yes,” you need to build up org-wide excitement. From our experience with customers, one of the biggest indicators of speed and efficiency during PLS roll-out is if leaders from RevOps, data, and sales are actively involved and fully bought in.

I often hear from revenue leaders that this initial step is a challenge. Especially in PLG, you’ve got to do a thorough job convincing the board that investing in a higher-touch sales approach will improve the customer experience, not detract. Then, comes the internal selling of the vision so that each stakeholder is ready to enable their team.

Launch your PLS motion in 5 steps

While there is no specific formula to launch PLS — the motion varies widely across products and markets — we’ve narrowed down the ideal launch plan into five key steps that will help you define and implement your PLS motion.

Step 1: Internal buy-in and PLS objectives

Digging deep into the why of your PLS motion is an essential step to structure your playbooks and get everybody on board. What areas of your current customer journey could PLS significantly improve? Is it at the top of the funnel? Is it more about retention? Analyze your pipeline metrics and pick one revenue goal you want to focus on, for example, net new revenue acquisition (ARR), expansion, net revenue retention (NRR), or churn prevention.

This initial goal will help you build your case, validate success, and eventually; branch out into other areas of the funnel based on your findings. 

How do you build a convincing case for PLS? By outlining where you currently stand on your goal and doing a little data-backed forecasting on how your playbooks will make a positive impact. 

You need to draft a proposal (not necessarily a lengthy one, our template is a one-pager!) that clearly answers:

1. Your PLS objective: How will PLS help overall company goals?
2. Strategy: How will we implement PLS? Who needs to be on the initial team? What other resources are required?
3. Roadmap: What will we deliver in the first 30 days? 60 days? 90 days? What key milestones are important?
4. Metrics: How will you measure success? What primary metrics will be impacted? What secondary metrics will be impacted?
5. Costs: Are any new resources or tooling required?

In short, craft a document that aligns your objective to your GTM strategy, plot out the roadmap for PLS implementation, choose the metrics you’re going to measure, and account for the associated resources and costs. Once you’ve compiled your findings, outline expected impact to your GTM org structure, compensation plans, and current processes.

Complete transparency on lift and involvement is crucial. A PLS motion is made up of many moving parts — if the foundation isn’t set up properly you won’t achieve the results you’re looking for. This is why it’s so necessary to take the time to write out all of the areas that will be impacted. 

For example, you could get the go-ahead on PLS by pointing out that self-serve conversions should be higher because too many ICP users are dropping-off early after signing up. This is an excellent reason to launch a sales-assist playbook, targeting ICP sign-ups with human touchpoints to remove friction, provide education, and help them get value before they churn. It might not be so hard convincing executive leadership of the benefits of sales-assist in this case. But, if they say yes without the full context, you might run into problems later on if you find yourself without the level of access you need to usage data, or the technical resources to build scoring models, and so on.

My advice is to be upfront on the current gaps you need to fill to launch PLS. If you run into some pushback (which is very likely, especially if you need new tooling, or new hires) then, create a PLS 0.5 plan. Lack of organization readiness is a major pitfall for many PLG orgs who are excited about PLS, but don’t have the infrastructure required to run it at scale. If you’re in this boat, part of your buy-in strategy should be to work within your current resources, DIY some PLS experiments, report on impact, and then advocate for what you need to scale.

Building up excitement

I find that this step is often a missed opportunity. You need executive approval, but you also need cross-functional buy-in. A few concerns we often see come up across GTM are:

• Potential cannibalization with self-serve pipeline
• Adding unnecessary friction to the customer journey 
• Lack of clarity around data needs
  

This is where you need to start thinking about aligning incentives to impacted teams and addressing these objections. By giving your team visibility into workflow changes, setting thresholds for sales and self-serve, and making clear that the overarching goal is always to improve the customer experience, you can ensure that RevOps, sales, and data teams are ready to make PLS work — because they believe in it.

Get the template

Build your proposal in one page — use the prompts to guide you!

👉 Template: PLS buy-in and GTM alignment 👈

Step 2: Analyze usage, align on signals, and create PQA/PQL definitions

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I won’t spend too much time on this step, but you can read all about PQA and PQL scores in this guide. The bottom line is that by analyzing historical usage data, you can look for patterns that indicate your best-fit customers' actions and the "aha moments" that lead them to unlock value. 

These signals, or combination of signals, will help you craft scoring models to surface the best opportunities from your existing pipeline of users.

(Or use a tool like Pocus to speed things up :) )

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Step 3: Choose your playbook experiments

Once you know what you’re looking for, it’s time to create a workflow to act on these opportunities. These workflows are your playbooks — specific motions, triggered by data, that support the strategic goal. Playbooks surface leads for your reps to action (or for automated sequences). 

Start with your goal and a few hypotheses of the signals that make up the ideal PQA/PQL for your playbook. Map out your playbook experiments like this:

• Goal: i.e. upsell, free-to-paid-conversion, churn prevention, etc
• Target: who is eligible for this motion? 
• Outcome: what is the outcome the playbook is driving toward?
• Triggers: what criteria qualify a lead to be surfaced to a rep?
• Action: what rep or automated actions should be taken?

Step 4: Training and enablement

We’ve found that PLS motion launches go much smoother if there’s clear ownership of the project, typically by a RevOps or Sales Leader; and if there’s a smaller roll-out with a tiger team before team-wide adoption.

PLS is a new way of selling that requires analytical skills, product knowledge, and a data-driven mindset. Set up your team for success with meaningful data and documentation into why certain actions are considered signals. Ask for input on PQL/PQA definitions and encourage them to help, not sell. A PLS motion is, above all else, another layer to provide customers with value.

We recommend enlisting a tiger team to fine-tune and iterate on your playbooks. These initial stakeholders will be able to learn the ropes, help coach the rest of your team when you’re ready for a full roll-out, and make optimizations to your initial playbooks so you can hit the ground running.

Crawl, walk, run framework to launch PLS

Crawl
RevOps works with the data team to pull product usage data. RevOps and GTM work together to identify key product usage signals and customer fit signals and use that criteria to create spreadsheets for sales. Reps use spreadsheets or static data pushed to their CRM to inform prioritization.

Walk
RevOps works with the data team to push product usage data into a BI dashboard (or a tool like Pocus!) that is available to reps. Reps can use the dashboard to research and do account planning, without engineering help.

Run
RevOps and leadership are experimenting, running, and optimizing Product-Led Sales playbooks that are driven by product usage, customer fit, and intent data. Reps have a single source of truth for customer insights and can get proactively alerted on their top priorities. The GTM team is using a Revenue Data Platform as a central hub to orchestrate go-to-market strategy.

Step 5: Measure success, iterate, launch at scale

To measure PLS success and find areas for optimization, you need to look at metrics from two levels:

1. Overall sales metrics to help you understand the broader impact of PLS playbooks.
2. Playbook level conversions to give you insight into which playbooks to iterate.

The most helpful metrics are usually: playbook conversions, opportunities created, deal cycle velocity, and revenue attribution.

Launching PLS is hard work, but it’s definitely worth it. By giving your customers more options, generating qualified pipeline from your existing user-base, and having greater insights into how individual usage relates to team and account expansion, you’ll be well on your way to the next stage of revenue growth. 

Launch PLS with Pocus

Building sophisticated scoring models, optimizing playbook performance, and running increasingly targeted playbooks gets easier when you can invest in the right tooling to support it.

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