Introduction

There are two major trends that are rapidly changing the B2B realm.

1. More than ever VCs expect profitability
2. Consumers expect more of a B2C experience

To accommodate these changes, companies are turning to product-led growth which allows them to be more efficient with their buying cycles and to dramatically improve their products based on user wants and needs.

PLG is a growing trend / preferred motion

Buyers more informed, closer to point of purchase

Enables faster shorter sales cycles, lower CAC, better enabled customers

Creates tighter feedback loops and faster platform evolution

In fact, a recent OpenView report found that product-led companies were twice as likely to grow more than 100% YoY compared to sales-led companies. The opportunity is astronomical.

But there are many challenges to running an effective product-led strategy, including team alignment, data realization and visualization, and sales enablement. In fact, many companies outright fail.

We created a maturity model that will not only show how effective PLG can be achieved but also give you the diagnostic tools to evaluate your current motions and understand what can be done to reach the next level.

The 4 Funnels of PLG

Most Companies approach their product-led with either a top down motion (sales led) or bottom up (freemium, free trial led) and connect the customer journey and more efficiently realize revenue.

Funnels Of
‍PLG Companies

Companies are very motivated in closing the gap between these motions because there is a massive amount of untapped pipeline sitting in a company’s product and/or their data warehouse. In fact, we recently polled a panel of CMOs and 67% reported that they are still trying to effectively monetize their product-led motions.

Sources of PLG Pipeline

Where are PLG Companies Leaving Money on the Table?

When you start looking into the data you’ll often find a combination of scenarios:

There are accounts that have signed up, are actively using your product and are prime for upsells but the sales team does not have access to user data or the correct signals.

Conversely, marketing may be feeding a high volume of user leads to sales teams that are not ready or willing to expand, creating frustration and wasting efforts.

There are also some users that have already become paying customers, swiping their corporate credit card and hoping to never talk to a sales rep and doing so will only alienate them.

These are common challenges even for the best PLG companies.

The Maturity Model

In a product-led world, sales need to be able to leverage insights from all user activities to make their outreach relevant.

This maturity model is meant to provide a quick assessment of where your company is at and how others have gotten to the next level. While the levels increment there is no need to go through all of them one by one.

Level 1 - No Product Data Available to Sales

How to recognize

Data is trapped in the backend of your product or within a data warehouse and therefore is unactionable

Why this is an issue for your business / pitfalls

You aren’t easily recognizing high potential users and are missing opportunities for upsell and expansion

Your sales team is forced to reach out to all or guess which users to engage with. Conversations are uninformed, redundant

Recommendation

A data strategy needs to be put in place at the exec level (CMO, CTO) in order to establish a roadmap to get data in front of go to market teams. This can be as simple as getting Amplitude instrumented and building some basic reports to share with the team.

Recommendation

Assemble a data team with representation from operations, sales, marketing, product and customer teams - very important that you have top down support throughout the process

Align on data definitions - make sure that across different teams you are aware of what you are tracking, are speaking in the same terms and have access to said data in your data warehouse

Level 2 - Dashboards in BI Tool

How to recognize

When companies start to build a data strategy, the first inclination is to bring on data scientists and to build in depth dashboards. However, they are going to build them in tools of their choice, not tools that sales or go-to-market teams use on a daily basis and the result is awesome reporting with little adoption. If teams are not accessing or are confused by the data available, then it is still useless in the PLG cycle.

Why this is an issue for your business / pitfalls

You’ve wasted valuable company resources on unused dashboards

Further looks, deep dives require more queries and data scientists support

You have to dedicate training, retraining and sales enablement time and resources to get teams up and running on new tools and dashboards

Recommendation

Sales team is your end user so in building your dashboards you need to consider how they will use them. Look to incorporate it in tools they use everyday and limit the information to the most relevant points. Ideally, companies scaling their PLG motion will skip this step altogether.

Some Important Next Steps

Embed the dashboards in Salesforce to avoid switching tools

Contextualize the reports to be specific to the account being considered

Ensure the reports are highly actionable

Level 3 - ReverseETL

How to recognize

The Data team bought a reverseELT tool and is pushing some aggregated product usage info into Salesforce.

Why this is an issue for your business / pitfalls

You run into overly complex workflows or Frankestack situations adjustments are dependent on engineering teams to maintain

You lose agility and timeliness with outreach

Recommendation

Operations or data teams should never define the go-to-market strategy. Instead, have sales share what data is most useful to them in their deal process and work to make those insights easily accessible in Salesforce or their tools.

Some Important Next Steps

Spend time with Sales to understand which data points are helpful and how they would use them

Spend time understanding what data is accessible and how it can be aggregated

From there work on putting together requirements for iterations

Level 4 - Data Driven Operations

How to recognize

Multiple aggregated data points are used across the customer journey to prioritize, classify and trigger operations.

Why this is an issue for your business / pitfalls

You will need strong alignment between product & sales to understand what important milestones to trigger off of
‍

Timeliness and strong messaging is key

Automation can help with your agility but with it, you can sacrifice personalization

Need to find a balance between Buddy A.I. and Police A.I.

Recommendation

Spend time with Product & Marketing to identify milestones of the customer journey (in product + education) in order to orchestrate outreach accordingly.

Some Important Next Steps

You’ll need to develop messaging and desired content for each milestone

Consider new channels for outreach and audience sizes

Figure our how to engage users the way they prefer to be engaged

Level 5 - Data Enlightenment

How to recognize

You made it, PLG nirvana. Data is used to power operations and Sales is provided data driven recommendations. Each new product user is classified against personas defined by enablement to be allocated into the right sequences. Data about engagement fuels personalization and triggers.

Recommendation

Don’t get too comfortable. GTM has moving targets, the product evolves, your motions as well. Ensure you have a high pace of iteration and keep learning

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MadKudu Product-Led Maturity Model