There's no doubt that buyers have started becoming more guarded about how and where they deploy their budgets.
It's not to say people are panicking, but they are certainly being more conservative, and price is now playing a more prominent role in their decisions.
These factors point to the fact that PLG (Product-Led Growth) will become even more widespread than it has been.
Because buyers want to manage their cashflows, prevent unused spend from happening, and not be locked into something when they are unsure about how the next few months will play out.
Before going further, let's start with a definition of Product-led Growth (PLG).
What is Product-led Growth (PLG)?
Product-led Growth, or PLG, is a Go-to-Market strategy in which the methods of acquisition, retention, and expansion are built around the product. In this model, the end-user experience is the driver of Growth through every stage of the funnel, from awareness, consideration, and then finally to decision.
Key components of this strategy include accelerating the customer's time to value and self signup or freemium version of the product.
Source: RevOps Framework for Supporting Product-led Growth (PLG)
We've seen a big shift in the demographic of B2B buyers, which has created new challenges for businesses.
First, many buyers in management positions are now millennials or younger; they are the new software buyer. They grew up in a world where everything has been digitally purchased, from app stores to MP3s to physical goods on Amazon.
This new buyer knows this is the way they will be buying in the future; it's already the standard norm in the wake of COVID, and it's going to stick around, especially for a B2B.
In this environment, "try before you buy" will be a competitive advantage for those businesses trying to build more expensive software.
Evaluating a big purchasing decision has become more complicated because many more people are pushing back on spending.
Budgets have decreased, and there's more scrutiny over investing in tools and other resources.
Suppose there's an opportunity for these software buyers to try before buying or evaluating different freemium models. In that case, they will have a greater level of information to make a case for purchasing more expensive plans and programs.
And that's the real opportunity here.
There will be a lot more scrutiny around how much is being paid for existing contracts for those more prominent incumbents in software.
There will be a significant amount of renegotiating of those contracts, trying to lower the spend. This will force businesses to reevaluate whether or not they want to stick with their existing vendor or consider other options.
That opens the door for newer entrants to grow, and PLG is how they will win.
A trial or freemium offering gives prospects the opportunity to make side-by-side comparisons. By providing this opportunity, businesses investing in PLG can create a significant competitive advantage for themselves.
Let's look at the digital trends over the last 10 or 15 years.
In the first wave of analytics tools, we saw the emergence of Google Analytics.
It helped us build segments for buyer behavior, get deeper insights into our website traffic, and better understand why people were converting (or not converting).
But that was only good enough to get people into the front door.
It's the equivalent of measuring how many people entered the grocery store, but not the first thing they saw or if they took it home with them.
Continuing with that analogy, the really valuable information would answer questions like:
That information becomes the central source of insights for specific use-case; a challenge that has been solved by the emergence of Customer Data Platforms (CDP).
What is a Customer Data Platform (CDP)?
Customer Data Platforms, or CDP, is a software solution that gathers and combines first-party customer data from various sources to provide a single, coherent, and comprehensive view of each visitor, prospect, or user.
CDPs create data pipelines that gather these disparate data, cleans, normalizes, and validates them, storing them in a single source of truth for customer data.
In the CDP space, Segment has firmly established itself as a market leader, and enables companies to capture and deliver powerful of multi-touch information.
But when you introduce tools like Segment with specific use case tools such as Calixa or Correlated, sales teams working with a PLG motion are able to capture the user and intent data they need to quickly move on user that is showing buyer intent.
In a Product-led Growth world, the way someone finds value in your product is through a trial or having a freemium license. With tracking software in place, specific usage data starts to accumulate over time incurred on the account.
That's when they can use a buyer signal awareness tool to notify the sales team; Correlated does a great job of this.
We're beginning to see a renaissance with great tools for Product Managers and Inside Sales teams, helping them develop and grow their accounts.
Calixa is a fantastic tool to help you measure the success of certain types of segments, allowing you to cut into them and see them as specific Account-Based Models.
These tools now enable more of an intimate setting in an environment that is very non-intimate. It's like a stranger walking through your front door trying out your software - they don't know who you are, why you created this software or your history.
But they are doing a fitness test of the product. So the best way to maximize their experience and identify if they are a good fit is to deliver this data through one of these CDPs, pushing it along to those additional tools for greater granularity.
This kind of insight puts a company at a significant advantage in a competitive environment because they understand the value their users are finding and how best to lead them to it as quickly as possible.
Over the years, the most significant difficulty with Product Analytics tools has required a lot of configuration and customization.
Industry incumbents, such as Gainsight, have a reputation for their configuration difficulty and the amount of time, effort, and resources they require to implement.
But newer tools in this space - Calixa, for example - come ready to deploy out of the box.
This is because the first iteration of analytics tools were built before the self-serve mindset took hold. As a result, no one considered the concept of self-onboarding.
But the proliferation of PLG as a mindset has filtered down to the tools that support it.
The Product Analytics platforms that were created to facilitate a PLG motion are themselves optimized for PLG.
As we continue down the road of PLG dominance, we will see higher adoption of CDPs with more self-serve-friendly tools emerging.
There are many roles in a company in which Product Analytics are vital, like Sales, Product, Design, and Engineering.
And there is now a Product Analytics tool for each of those roles, and they are sold, packaged, and measured in different ways.
Tools like LogRocket help developers measure a product's usage and track its success or failure over time. This helps them report, debug, and monitor the experience of using the product.
In the case of the product designer, we have tools like FullStory, which give them the ability to see where users are adapting well or where they are "rage-clicking."
This information becomes helpful for designers to know where to spend more time on improving small details that have a significant impact.
Then you move into the more generalized platforms that provide information about how people are adopting certain products in your setup, as well as helping you build different workflows associated with that deployment, such as Heap.
Heap provides a full-stack solution with a low level of implementation difficulty. In addition, it allows you to track different segments, actions, and workflows to understand where value is being found in your product.
That's useful for product managers looking to see how much value is delivered back and forth between the end-user.
Similar to tools like FullStory and LogRocket, these systems are set up to enable the end-user to configure it to their needs with minimal developer resources.
You then have more revenue-focused roles, such as Customer Success, who are looking for their product analytics. For these roles, product adoption is the key indicator that they are tracking with an eye on identifying opportunities for increasing revenue.
The older generation of incumbents in this space, such as Gainsight, did not build their products with these different use-cases in mind.
But you're seeing newer players in the space going down specific verticals. The first one is Calixa, which tackles more of the PLG SMB market, trying to help people better understand their users.
And this is great for companies with an open front door and trying to grow those accounts. They are empowered with usage data coming from Segment and other data sources such as Stripe.
Then you have Correlated helping sales teams get those signals in a way that is much more accessible to a non-developer so that they can take more significant action on users who are most likely to convert to paid or increase their LTV.
The first time someone interacts with your product, they are likely not yet in buying mode and if you don't have feedback in your user's full lifecycle, you can never make the experience better for them the next time they come back.
But suppose you could get direct feedback from them, such as sending them an email with a questionnaire for a survey or asking them to review their experience on a scale of 1-5.
In that case, you'll allow them to explicitly say what they expected to see but didn't, which can feed into your product roadmap, helping you build a process for creating value discovery.
Many companies suffer from this challenge in product feedback tracking, where they don't understand the immediate value that the customer is looking for.
As a result, they're not optimizing the first-time user experience, not making the real value easily discoverable.
While many Product Analytics tools measure and track users as they are active within your platform, what we learn from this has to be inferred rather than explicitly stated by the user.
But Product Analytics also means getting contextual feedback about what's happening in the tool, and that feedback can come from any data source.
Productboard enables a way to crowdsource a lot of this data from support systems, et cetera, and helps you build out playbooks for the individuals you're working with. In addition, it gives you analytics that you can use for building more robust road maps.
What is a Free Trial?
A free trial, in the context of Software, refers to giving users access to a platform for a limited amount of time. The level of feature access (full or limited) and the length of time vary, but all trials, by definition, are for a finite period of time.
In a PLG motion, most companies rely on either a trial or a freemium offering to get users into their funnel. This is because PLG is focused on showing the value of the product upfront and letting it do the selling for you.
What Does "Freemium" Mean?
Freemium, as opposed to a free trial, generally refers to a software package that includes limited feature access for an indefinite period of time.
Restricting access to the product with payment requirements, such as upfront monthly or annual fees, can inhibit your PLG pipeline.
However, once a trial or freemium user has found value and converts into a paying customer, you have to consider how to best structure your agreement with them to facilitate the account's growth.
When deciding whether to use a freemium model or a free trial, there are several things you must consider:
When you decide on these different mechanics, you can distinguish what's more valuable for somebody trying before buying and then determine what's best for your demographic.
Where we land at RevOps is that we found that time-based trials may not always be successful with some of our users. We have seen that often they're not ready to buy in the next 30 days.
Or the next 60 days.
They sometimes don't even buy in the next 90-120 days because they have many other priorities in their business.
When companies buy us as the second piece of software, they need to know what value we provide that extends the value of the CRM platforms they are looking at - especially those CRMs that offer competing products.
We've spent a lot of time thinking about how they can discover value of our product for themselves with minimal commitment required.
For example, we offer free licensing for under two users without a credit card. And then, when they upgrade to beyond a second user, we require payment.
This makes it possible for them to try before they buy without a credit card, so they can understand and ultimately get full access to the platform.
It's a valuable technique for separating the value between a trial and a subscription.
When we move users of our product into an upgrade scenario, there are SaaS pricing model options for those who cannot fully commit, whether for budget reasons or otherwise.
Providing this kind of pricing flexibility is a key competitive advantage for companies in a time where purse strings are just a little bit tighter than they were before.
One of the easiest ways to move a trial user into a paid subscription is through a"pay-as-you-go" plan; it gives them the flexibility of ownership.
Some people prefer that because they're not ready to commit an entire year; they don't know if this will work yet.
It's a great way to continue a trial-type experience while still extracting revenue.
What is Pay-as-you-go (PAYGO) Pricing?
Pay-as-you-go, or "PAYGO," is a pricing model in which a customer is not locked into a contract for a set period of time, but pays an agreed upon price with the option to leave at any time.
Pay-as-you-go pricing in SaaS can be a flat rate on a monthly basis, a set rate per seat, or based on usage.
Pay-as-you-go means that the user is not committed for any period of time and is able to leave at any time. However, in a PLG model, instituting a PAYGO model that is on a per-seat or usage basis enables the customer to grow their account as they grow.
Interestingly, if you compare trials and monthly subscriptions, they're similar marketing funnels; to grow elastic revenue into sticky revenue.
Sticky revenue is a contract, whereas elastic means they can leave at any time.
What is Ramp Pricing?
Ramp Pricing refers to a deal that includes multiple time-intervals. Generally, prices will "Ramp" from one interval to the next; whether in absolute price or a decrease in discount.
In the scenario in which someone knows that their team will be growing in the near term, but they are not ready to commit to that amount of licenses upfront, leveraging "ramps" in your pricing gives both parties the best of both worlds.
Ramping means that you have agreed to some terms to grow your platform usage over a certain period.
Ramped pricing allows growing companies to raise their spend on your product over time instead of doing it all at once.
You might do a quarterly-based ramp of five users this quarter, ten users next quarter, and 15 users another quarter.
Or you might be paying upfront for an annual payment schedule so that for all four quarters, it's X for all these users, which is very traditional and contract-based pricing.
Simliar to the previous scenario, you have a user that wants to lock in their rate because they know that your platform will be more expensive next year, but they have concerns about committing to a number of seat licenses upfront that may go to waste.
This scenario lends itself to things like annual payment schedules and even multiyear payment schedules where people pay on a yearly to multi-annual basis to lock in those discounts.
They want to also lock in terms of support and other kinds of things, not to mention finance doesn't want to pay the bill every month because it costs them time for accounts-payables and different types of resources.
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