How adding sales turned $500 into $10,000 deals for Heap

Mitchell Tan5 min read

If you’re building a product-led company, you might be wondering why you would ever need a sales team.

This is especially the case if you have a smooth sailing self-serve motion. Your active users understand your product well, realize value in their regular usage, and upgrade to your higher tiers as their needs grow.

That’s exactly what Ravi Parikh believed in the early days of analytics infrastructure provider Heap, which he co-founded in 2013 with Matin Movassate.

Heap’s early successes with self-serve

When Heap was founded in 2013, the term ‘product-led’ wasn’t even common vocabulary. But Ravi and other founders had observed the phenomenal success that companies like Atlassian and Hubspot had built on freemium self-serve—and grasped first hand its logic when using SaaS tools like Mailchimp for themselves. Adopting a self-serve acquisition and expansion model for Heap was an obvious choice.

This served Heap extremely well in the company’s early days. Most of Heap’s early customers were other startups, and they fit well into either the free, $50, or $500 price-tiers. Activation and upgrade involved zero-touch from sales. Only in rare cases of much larger customers was more active customer engagement required—a small enough number that Ravi was able to do it himself.

Heap’s tiered pricing page in early 2019

Heap’s eureka moment with pricing strategy

In the face of increasingly positive market reception, Ravi and his team started thinking about how they could ratchet up revenue growth. One obvious approach was to see how much they could raise pricing at the different tiers and evaluate how it impacted sign-ups and conversion.

Then they observed something curious—no matter how they adjusted prices, conversion rates remained exactly the same. This was when they realized that they had so much untapped revenue potential in their user base.

“If we keep changing the prices, and literally nothing changes, we’re definitely leaving a lot of money on the table. And so we shifted more towards a model where we actually removed some of the prices from the page made it more random, and that’s how we closed our first couple of six figure contracts”

Ravi Parikh, Co-founder, Heap

Get weekly insights on best-practices at leading PLG companies like Heap

The key insight that Heap had chanced upon in its pricing experiments was that usage levels and limits—which was how the price tiers had been defined, were poor indicators of willingness to pay. For example, a 10-person social app startup might see an explosion of traffic, but be a lot less willing to pay for analytics on that traffic compared to a Fortune 500 insurance company which saw a trickle of traffic—but with each visitor potentially representing a sizeable deal.

To accurately price discriminate and capture value, a purely self-serve motion wasn’t going to cut it. Instead, by layering on sales-assist, Heap was able to accurately determine willingness to pay and price its tiered product appropriately for each customer. Accordingly, Heap changed its pricing page where prices for each tier were in fact not listed, but rather to be determined through consultation with sales.

Heap’s current pricing page

The outsized impact of adding sales-assist to Heap’s self-serve motion were immediately apparent. Sales assist sometimes made the difference between closing a $500 and a $10,000 deal. Without the consultative mechanics of sales outreach, Heap would have been leaving millions on the table.

When and how to layer on sales assist

Of course, layering on sales-assist may not have similar uplift on revenue for every self-serve product. So, who should do this and who should not?

When you have a complex product

One strong criteria would be if your product is highly complex, with many features and modules that your customers may not be sure if they really need or how to use. With sales-assist, you can help your customers figure out which of your many features might help deliver greater value immediately to their organization. In contrast, if you remained purely self-serve, customers may not be willing to invest the time to explore your product deeply and figure out whether there are specific features which might be especially applicable for their use case. By helping them identify features that would drive a significant step-up in value, you are able to better upsell the customer.

When your pricing is usage based

Sales-assist may also be particularly applicable if your pricing tiers are defined not on the basis of number of seats, but on some more arbitrary usage metrics. It’s often hard for customers to imagine what a higher tier would mean for them or how it would look like for their organization without the guidance of a sales rep. By helping to contextualize your usage tiers for customers’ specific needs, you deliver a stronger incentive to upgrade and are able to assess the appropriate price for the higher product tier based on the value it could unlock for the customer.

When your customers are not just SMBs

Finally, sales-assist may not be right for you if you are purely focused on SMBs and your average price point is $200/month or less. At that price point, it is hard to have a healthy customer acquisition cost to lifetime-value ratio with sales-assist and you should probably default to pure self-serve. But in almost all other cases, investing in a sales-assist layer in some form would be worth it in the revenue lift it will deliver.

Is your self-serve organization currently grappling with questions over how to further drive revenue growth and whether or not, or how to best layer on sales outreach? Do reach out! We’d love to chat and share what we’ve learnt in our many conversations with product-led growth leaders like Ravi.

In the meantime, don’t forget to subscribe below for weekly insights into best-in-class go-to-market practices adopted by leading product-led companies.

Readers from 100s of top PLG companies get our insights, tips, and best practices delivered weekly

Subscribe

Latest articles

Using machine learning to prioritize leads

Understanding what kind of ML approach works best for finding Product Qualified Leads, based on what the best companies do.

5 min read

Using machine learning to prioritize leads

Understanding what kind of ML approach works best for finding Product Qualified Leads, based on what the best companies do.

5 min read

HeadsUp raises $8m seed round to help GTM teams use data to accelerate revenue

Why we built HeadsUp and how we believe it will change the way go-to-market teams work with data

5 min read