Have you ever wondered how you could turn PLG concepts like the freemium model into a fast-growing revenue driver for your company? Former Head of PLG and founder and CEO of Clinch, Uday Chakravarthi, shares insights into how to do just that. With a PLG-heavy background, first working at Microsoft Azure and again with Atlassian, the PLG pioneers, he gives insights into leveraging PLG for the growth of your organization.
How PLG Evolved
First, let’s start with PLG and its evolution. How did it all start? Let’s use this example of a car to explain. When you want to buy a car, you test drive it, look at the stats, the steering and handling, the air conditioning, the quality of the sound, and how you feel about it. Then, after you experience the value, you decide to buy it. That’s PLG.
Let’s break down the definition of PLG into a few components.
It’s an end-user-focused growth model where your product drives acquisition, activation, expansion, and retention.
Why is it end user-focused? Because the end user starts using the product, often through a self-serve motion. They get value, make a decision, and buy. It’s a growth model and GTM strategy.
PLG is responsible for different parts of the funnel. From a PLG perspective, if you make it easy for users to start using your product, any interaction with a rep or customer service person is a bad user experience. You want customers to understand the product and pricing and start using it on their own.
Sales-Led vs. Product-Led vs. Hybrid
What are the pros and cons of different motions? Let’s start with a sales-led motion, which is traditionally how B2B software has been sold. They prioritize direct selling and relationships over allowing customers to go and buy directly.
If you have a high-ticket customer, this is an advantageous model where you can customize the product to their needs. It’s also time-consuming because the buying decision doesn’t happen right away.
Hybrid combines product-led and sales-led motions, and they work closely together. You’re likely not having demos with customers because they’re exploring the product independently, so customer acquisition costs are low. The challenge with hybrid is that it’s difficult to implement without close collaboration, and everyone must understand how product drives growth.
The last is product-led. Typically, you have individual buyers who make decisions about a product. The product usually isn’t expensive because you can’t buy a $500k product with a credit card, and freemium and self-serve are popular ways of doing this.
Freemium Models with Feature Gates vs. Free Trials
Many companies either have free trials or freemium models with feature gates. Whichever you choose will be a strategic decision. A freemium version can work well if you want to gain market share quickly. Atlassian, Microsoft Azure, and Zoom are good examples of that.
If you’re on a limited trial, customers have to purchase eventually, so this strategy is more about monetizing customers as Zapier does.
The Differences Between PLG and Product Led Sales
In PLG, you’re an individual going into the product and deciding to try it. In the product-led sales world, you use that data from freemium users to pitch to your customers. So, in PLG, you’re going in by yourself as an end user and making a purchasing decision, and no sales team is involved.
With product-led sales, they still go in and experience the product, but then that information is used to tailor a pitch to a CRO, Head of RevOps, Head of Marketing, etc. It’s not fully self-serve.
Let’s look at an example from Atlassian. The first screenshot is a signup for a product where the individual can go through a self-serve motion, sign up for a free trial, not even with a credit card, and start seeing value right away.
One of the core principles of PLG is virality. As you start to use the product, they’re often built in a way where you invite people to collaborate with you. The slide below shows how you can get value and invite team members after you see that value. As the team uses it more, you’ll see user limits or feature gates to pay to get to the next edition.
How does that collaboration and hand-off with sales work? If you have product-led sales, those reps can use the data from existing users to see what features they’re using and their job personas and tailor a pitch to the organization.
What PLG Signals Can Sales Use to Sell Software?
There are three broad categories of signals.
- Product usage
- Behavior
- Volume
For product signals, each product should have some value-driving signals or aha moments where you know that if they’ve used that feature, they’ve seen value. If you have data scientists or ML teams, you can correlate the probability of upsells and cross-sells if someone has used that aha feature. You can create product signal dashboards and give sales teams access to them to understand how the products are being used.
A behavior signal could be a user visiting or trying a feature in the next edition. This is important information for a sales rep because it signals that the user has tried an advanced feature and might have some buying intent.
The third type of signal is a volume signal. This could be an increasing number of active users in an account. A lot of storage companies, like Dropbox, have storage limits. Imagine if a salesperson knew a user had hit 90% of their storage limit. They could proactively reach out to that customer and have an expansion conversation.
These signals are powerful to arm your sales teams with because they allow them to tailor conversations to the customer and close more deals.
How to Leverage Product Signals
You can identify signals, but what do you do with them? You can leverage them to come up with your own playbook to drive growth. Some ways you can leverage signals are:
- Identify high-value signals, or signals within signals, that show a customer is more likely to convert.
- Identify buyers or decision-makers within a persona.
- Tailor messaging to that persona.
- Build triggers to integrate into your CRM systems.
- Generate outreach campaigns based on sequences.
- Enable sales routing
You have signals and did some automation to integrate them into CRM systems. Then, you can do sales routing based on those signals and who the buyer is.
Examples of Using Signals to Drive Upsell and Cross-sell Opportunities
What does this look like in action? Let’s look at some examples.
Support Requests
Having access to signals is important for companies where support is a key factor in users making decisions between free vs. freemium vs. standard editions. You can provide your sales teams access to the support data, see the support request volume and evolution time, and tailor outreach to specific customers to upsell a higher plan.
Daily Logins
Say you have some power users and some users who are not using your product as frequently. If you have information on those ten users using the product daily, you can segment them and create a campaign or upsell motion to sell advanced features to them.
Team Size
Understanding how many users within an account or team use your product is also great for segmentation. You want to work with your analytics or data science team to ensure they’re tracking at that domain level.
You can use that for upsell strategies once you know how many customers are in a team. Sales can use the information to pitch more collaborative features or to introduce feature gates when you hit a certain number of users.
Feature Usage
This is another interesting data point to keep in mind for cross and upsells within a multi-product company. Often, you have a feature map where you can correlate feature usage from one product to another, and then you can tailor outreach to customers to cross-sell.
If you have 15-20 different products and are launching a new product, cross-selling is essential for early adoption, and feature usage data can support that effort.
How to Use AI to Drive Sales
There are five key areas where AI can be helpful, and they are:
- Lead generation
- Lead scoring
- Sales forecasting
- Personalized engagement
- Churn prediction
Lead Generation
Predictive analytics can help identify who the right leaders are and what they’re searching for. This data can help identify high-propensity leads, whether free-to-paid leads, cross-sell, or upsell.
Lead Scoring
Lead generation identifies the potential lead, and lead scoring is about assigning a propensity score to it.
Sales Forecasting
Having insights into your customers based on what they have done in the past, how they use your product, and how much they’ve spent, and overlaying that with the macro conditions is helpful to determine how much a customer can generate.
Personalized Engagement
If you know who the customer is, or you have keywords or strategies that have been effective, many GenAI platforms or LLMs can tailor conversations and messaging to them based on that information. Having the ability to craft personalized content is huge for maximizing engagement.
Churn Prediction
You want to be able to identify at-risk customers, especially when your company is focused on revenue preservation. AI can create churn drivers, and that data can be useful for customer success or for sales teams to have proactive conversations with customers.
These are some of the trends happening now, and you’ll see more of how AI can help drive sales in the organization as time goes on and the products get better.
Key Takeaways
- It’s imperative to maximize and measure product usage. The more data you have, the more insights you have to tailor pitches and sell to customers.
- All teams should have a product-led mindset.
- Use tools and automation to speed up the process and reduce manual efforts.
- Leverage AI to assist sales. Are there features you can arm your sales teams with to make them more productive? Find and use them to drive customer acquisition, expansion, and retention.
