Revenue Funnel Optimization Archives

How to Build a Culture of Experimentation That Doesn’t Fall Apart When People Leave

Let’s be honest: most experiments won’t “win.”

And that’s fine—because the real goal of enterprise experimentation isn’t a single A/B test. It’s building a system that consistently delivers insights, revenue, and resilience across teams.

In Part 2 of our latest podcast episode, Arun and David dig into what separates long-term experimentation success from short-term sparks that fizzle out when a single stakeholder leaves.

Here’s what we covered:

1. Wins Are the Fuel, But Culture Is the Engine

A few successful tests aren’t enough. If your entire experimentation program lives in one person’s head—or one team’s Google Drive—it won’t survive when that person leaves.

To scale this capability, enterprise orgs need:

Cross-functional teams that can act independently
Visibility into what’s being tested and what’s been learned
A center of excellence (not a patchwork of BU experiments with zero ownership)

Make experimentation a strategic asset, not a side project.

2. Start Small. Share Loudly.

Early wins matter—but so does how you socialize them.

Even if the first tests are on small, low-friction areas (like lead forms or paid landing pages), the learnings often apply far beyond that single channel. Those insights should be shared across the org, through:

Internal newsletters
Experimentation decks
Customer insight docs
Team-wide playbooks

This is how you earn buy-in before the next budgeting cycle.

3. Build a Team That Can Ship

If your experimentation team needs to ask 10 people for permission to test a button color—you’re dead in the water.

What works better:

An agile squad with analytics, dev, QA, and strategy in-house
Empowered autonomy from slow-moving parts of the org
A governance model with lightweight approvals and tight feedback loops

Don’t overthink it. Don’t wait for perfect data. Start testing, start sharing, and build from there.

4. Avoid the Trap: Complexity ≠ Maturity

Too many teams over-complicate experimentation with heavy processes and endless planning.

Here’s the play:

Start where you’ll see quick wins and minimal stakeholder blockers.
Share those results widely and often.
Build a center of excellence that spans orgs and departments.

And most importantly—don’t wait for perfect tracking. You don’t need flawless data to ship meaningful experiments. You need trust, velocity, and a tolerance for smart, managed risk.

Watch the full conversation on building enterprise-grade experimentation programs

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By Hitaishi Chaudhary|2025-07-28T07:11:43-07:00July 28th, 2025|Conversion Rate Optimization, Full-Funnel Optimization, Lead Management, Marketing Automation, Revenue Funnel Optimization, Website Optimization|0 Comments

Enterprise A/B Testing: Why It Fails and How to Build a Culture of Experimentation

It’s not budget.
It’s not headcount.
It’s not tech.

The biggest blockers to experimentation in enterprise organizations are cultural.

When experimentation is treated like a side project—or worse, a threat to the status quo—it’s no surprise that velocity stalls and progress dies in committee.

In this episode, Arun and David unpack exactly why legacy enterprise companies struggle to build a culture of experimentation, despite having all the resources in the world. And what separates the companies who succeed from the ones who stay stuck.

Why Enterprise Velocity Gets Crushed

There are two core ingredients that make experimentation successful:
– A willingness to tolerate managed risk
– The velocity to move fast and validate ideas

Both are usually missing in large orgs. Not because leaders don’t want experimentation—but because bureaucracy, slow release cycles, and internal politics make it nearly impossible.

Layer in misaligned stakeholders, global complexity, and siloed data, and suddenly even running a basic A/B test feels like moving mountains.

How to Start Small and Build Momentum

The smartest enterprise experimentation programs don’t start with the homepage.
They start where:

There’s less internal friction
There’s measurable business impact
There’s faster time-to-learn

That’s usually deeper in the funnel—lead forms, onboarding flows, paid landing pages. These are the areas where you can move quickly, validate hypotheses, and prove value without ruffling feathers.

Wins here earn trust, budget, and buy-in to scale the program.

Risk Mitigation: The Story Execs Will Listen To

Experimentation isn’t just about growth. It’s about reducing risk.
It’s a safer, faster, more controlled way to validate what works before you commit major resources.

Frame it this way, and suddenly even the most conservative stakeholders start paying attention.

TL;DR: If You Want to Build an Experimentation Culture in Enterprise…

Start where you can win fast.
Show how you save time and reduce risk.
Use those wins to scale the program into something sustainable.

Watch the full conversation on Episode 4, Part 1

Get the full breakdown on why experimentation struggles in enterprise—and how to fix it.

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By Hitaishi Chaudhary|2025-07-22T13:15:53-07:00July 22nd, 2025|Conversion Rate Optimization, Full-Funnel Optimization, Lead Management, Marketing Automation, Revenue Funnel Optimization, Website Optimization|0 Comments

How to Roll Out a Product-Led Growth (PLG) Strategy Without Breaking Your Funnel

Product-Led Growth (PLG) can be a revenue multiplier—but only if it’s rolled out right.

In Part 2 of this conversation, Arun Sivashankaran and David Janczyn lay out a pragmatic, experiment-led approach to launching PLG in B2B SaaS organizations. This isn’t just about free trials or surface-level A/B tests—it’s about using real signals, smarter data architecture, and practical tests to align PLG with long-term growth.

Here’s what they shared.

Why the Pricing Page Is a PLG Power Move

One of the most overlooked PLG opportunities? Your pricing page.

Every SaaS company has one, but few use it to actively segment high-intent, high-value visitors from those better served by self-service.

By running experiments that route users toward the right experiences—self-serve options for smaller customers and sales engagement for enterprise prospects—you can start gathering data that feeds your PLG motion immediately.

This isn’t about overhauling your funnel. It’s about intelligently opening new lanes.

PLG Doesn’t Work Without This One Thing

A clear hypothesis.

Fivetran’s PLG success story illustrates this well. Their team hypothesized that they were losing revenue from smaller organizations who needed the product but were being ignored by sales. They didn’t guess—they measured:

Conversion rates by path
Average contract value by segment
Payback periods across touchpoints

By identifying these metrics and rolling out with experimentation at the core, they turned PLG into 20% of their net new revenue.

The lesson? PLG works when it’s treated like a strategic experiment—not a side project.

First-Party Data > Third-Party Noise

Many B2B marketers chase intent data from third-party sources like 6sense. But the real gold often sits within your own walls.

The way your visitors engage with your site, forms, interactive demos, and onboarding flows? That’s first-party intent—and it’s faster, cleaner, and more actionable.

This is especially critical when your funnel shifts from static demand gen to interactive PLG motions.

Your Data Infrastructure Has to Evolve

Rolling out PLG without rethinking your data flow is like racing with the wrong fuel.

PLG relies heavily on product data—data that typically lives outside of your CRM and marketing automation tools. To make it actionable, you need:

A centralized data warehouse (Snowflake, BigQuery, etc.)
Reverse ETL tools like Hightouch or Census to push signals back into your marketing stack
Coordination between product, marketing, and sales ops

The takeaway? Your source of truth has to evolve if you want PLG to scale.

How to Start Small with PLG

You don’t need to rearchitect your funnel overnight.

Instead:

Leverage your existing lead forms to identify PLG-ready prospects
Add interactive or on-demand demos to TOFU pages
Test routes via your pricing page that surface different options based on user behavior

These low-lift tests validate assumptions, show results, and build internal buy-in—without burning cycles.

Final Takeaways

Here are 3 key steps to a successful PLG rollout:

Map the customer journey: From website visit to in-product action. Identify where intent shows up and what signals to track.
Experiment your way forward: Never roll out without proper measurement. Every PLG element—demo, form, route—should be tested.
Invest in the right data infrastructure: PLG isn’t just a strategy shift; it’s a data shift. You need systems that unify product, marketing, and sales insights.

If you’re treating PLG like just another campaign, it won’t work.

But if you build it on experimentation, align it with buyer behavior, and ground it in real data—your sales team will thank you.

Watch Part 2 of Episode 3 of the FunnelEnvy Podcast

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By Hitaishi Chaudhary|2025-07-14T06:30:13-07:00July 14th, 2025|Conversion Rate Optimization, Full-Funnel Optimization, Lead Management, Marketing Automation, Revenue Funnel Optimization, Website Optimization|0 Comments

Product-Led Growth in B2B SaaS: How to Design a Scalable Funnel, Identify Intent Signals & Align Sales

Buyers today don’t want to talk to your sales team. At least, not until they understand what your product does—and how it will deliver value.

In this episode of the FunnelEnvy podcast, Arun Sivashankaran and David Janczyn unpack a critical but often misunderstood strategy: Product-Led Growth (PLG) in B2B SaaS. If you’re navigating enterprise complexity or trying to make your funnel more efficient, this conversation is a must-listen.

What is Product-Led Growth (PLG)?

David defines PLG as an onboarding approach that allows customers—especially lower ACV ones—to engage with your product and achieve value without requiring a sales conversation upfront. Think free trials or freemium models.

Arun expands on this, calling PLG a strategic response to changing buyer behavior. Your prospects want value before they speak with a salesperson. PLG helps you deliver that value early, using the product itself as the primary driver of acquisition and conversion.

Why PLG Isn’t a Fit for Every SaaS Company

One key takeaway: PLG doesn’t work for everyone. If you sell a complex product that requires a consultative sales cycle or implementation services, it’s unlikely you’ll get far with a self-service experience.

But if your product offers a straightforward experience that allows users to ramp quickly—PLG can shine.

The Risk & Reward of a PLG Rollout

Rolling out PLG is risky.

You’re exposing part of your product to the market without the insulation of a lead form or salesperson. That’s why Arun emphasizes the importance of iterating through experiments, measuring outcomes, and optimizing accordingly.

David adds that cross-functional alignment is critical. Sales and marketing often operate with different KPIs—and a new PLG motion will impact lead volume, rep quotas, and expectations.

PLG Rollout Strategy: Use Your Forms

FunnelEnvy’s approach? Use multi-step forms and data-driven routing to separate low-intent leads and send them to a PLG experience, while keeping high-fit leads routed to sales.

This allows you to:

Reduce sales team frustration with low-quality leads
Improve efficiency by filtering in the right buyers
Introduce PLG without disrupting your entire GTM motion

Identifying Intent Through Engagement Data

Done right, PLG is an intent engine. Whether through form interactions or in-product behaviors, you can surface buying signals that warrant higher-touch sales engagement.

That’s where Arun highlights the power of AI and machine learning. You don’t need to build static models anymore. With the right feedback loop, Machine Learning can help continuously evolve your ability to predict which behaviors correlate to revenue.

How to Measure PLG Success

The team stresses that revenue should be the north star. Even if you’re capturing incremental signups or engagement, it doesn’t matter unless those leads eventually convert.

That means:

Measuring cost vs. ROI of routing prospects into PLG
Tracking pipeline contribution and revenue impact
Validating PLG performance through real sales outcomes

Website Resources to Support PLG

To support your PLG strategy, Arun references Gartner research highlighting interactive demos as one of the most effective resources on a SaaS website. David adds that while interactive tools work, their quality matters.

Don’t launch underwhelming experiences that fail to communicate value. Consider:

Ungated demo videos
Product briefs
ROI calculators

Start with lightweight options. Validate interest. Then invest.

Final Takeaway: Optimize for Time to Value

Both Arun and David stress this: time to value is everything.

If users don’t immediately experience the value of your product, your PLG motion won’t succeed. And when users stray from the happy path, your data should alert your team to intervene.

Watch Part 1 of Episode 3 on YouTube

Relevant Resource: FunnelEnvy’s Done-for-You Custom Forms

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By Hitaishi Chaudhary|2025-07-07T17:46:56-07:00July 7th, 2025|Conversion Rate Optimization, Full-Funnel Optimization, Lead Management, Marketing Automation, Revenue Funnel Optimization, Website Optimization|0 Comments

How to Scale Account-Based Marketing (ABM) From Pilot to Org-Wide: What Actually Works in the Enterprise

You don’t need more leads. You need better ones.

Enterprise teams—especially in Account-Based Marketing (ABM)—can’t afford to drown in junk form fills, misrouted prospects, and manual lead sorting. Sales time is expensive. And without the right infrastructure, that time gets burned on noise instead of nurturing real opportunities.

Here’s how we helped an enterprise client build a smarter lead qualification experience, drive sales alignment, and set the stage for scalable ABM growth.

Don’t Let Unqualified Leads Into the Funnel

The biggest threat to ABM isn’t your targeting. It’s your qualification strategy—or lack thereof.

In this client’s case, a tidal wave of low-quality leads was flooding the inbound funnel. Sales had to manually vet each submission post-form—burning precious hours on leads that weren’t even in the right state or company size.

So we changed the game at the front door:

Built multi-step forms that embedded qualification questions
Used conditional logic to sort leads by tier
Redirected unqualified traffic to content, not the sales team
Implemented Reform custom forms to design high-conversion, logic-driven forms that match ABM tiers and audience segments without needing dev cycles

“There are different levels of an unqualified lead—and you need paths for all of them. Some might influence others. Some should just self-nurture. But none should clog your BDR queue.”

Fast-Track Qualified Buyers While You Have Their Attention

If a lead hits your form with the right intent and fit, don’t wait to follow up. Route them directly to a calendar booking experience with their assigned rep. The best time to book a call is while they’re still on the page—not three nurture emails later.

For this client, that meant:

Using progressive profiling to dynamically adapt form fields based on known user data
Connecting qualified leads to real-time scheduling links
Routing form data through an integrated stack—using Reform for real-time segmentation and pushing enriched data into CRM workflows.

Qualification Is Routing. But It’s Also Strategy.

Here’s what most teams miss: ABM isn’t just about who you target—it’s about what you do with the people who don’t fit.

We built three paths:

Disqualified → Send to relevant content and exit the funnel
Influencers → Enroll in a nurture sequence (word-of-mouth matters in B2B)
Qualified buyers → Book directly with sales

That’s how you create a frictionless, intelligent funnel—one that gets smarter over time.

Want to See How the Full Strategy Works?

This is just one part of a larger conversation.

Watch Part 2 of Episode 2 with Arun & David

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By Hitaishi Chaudhary|2025-06-30T09:48:57-07:00June 30th, 2025|Conversion Rate Optimization, Full-Funnel Optimization, Lead Management, Revenue Funnel Optimization|0 Comments

How to Launch an Account-Based Marketing (ABM) Pilot That Scales Without Breaking Your Org

In theory, Account-Based Marketing (ABM) sounds like a no-brainer.

Personalized campaigns. Sales-marketing alignment. Focused revenue growth.

In practice, it’s usually chaos.

That’s especially true when enterprises with multiple business units and fragmented tech stacks try to roll out ABM org-wide.

In this episode, we’re breaking down a smarter playbook—one that FunnelEnvy used with a global B2B org to build alignment, improve lead quality, and prove ABM value through a tightly scoped pilot.

Don’t Start with a Rollout. Start with the Problem.

This enterprise didn’t come to us asking “how do we run ABM?”

They came asking: how do we fix the disconnect between our sales and marketing?

Sales had long been identifying and prioritizing accounts on their own, handing them off to marketing like an afterthought. The result? Reactive campaigns. Mismatched goals. Low respect for marketing-sourced leads.

ABM was just the wrapper for the real goal: better alignment and impact.

Step One: Scope Down the Chaos

Company-wide rollout? Not a chance.

Different teams used the CRM differently. No global data rules. No shared processes.

Trying to deploy ABM across the org would’ve crushed progress under politics and confusion. So we scoped it down:

A single business unit
A subset of the account list
A few willing sales reps (aka champions)

That made it possible to:

Test CRM workflows and lead routing logic
Roll out new sequences and touchpoints
Get buy-in through results, not slide decks

The Invisible ABM Experience Sales Actually Wants

You know an ABM program is working when sales doesn’t even notice it.

With the right setup, ABM should remove work from the sales team:

Pre-qualified leads
Pre-built sequences
Standardized touchpoints

The pilot was designed to give sales what they wanted: better conversations with real buyers, not more manual follow-up.

“The best ABM campaigns take work off the sales team’s plate by normalizing touch points and using data to guide the next best action.”

Run the Campaigns That Already Work

Here’s where most marketers go wrong: They default to status quo campaigns: LinkedIn ads, generic outreach, broad nurturing.

We started with what already performed: webinars.

Not because they were easy. Because they worked.

We dug through historical campaign data and found:

High-intent leads came from live events
Webinars supported complex, high-ACV sales cycles
Education-based engagement > ad impressions

So that’s what the pilot focused on: warming accounts through high-touch, educational experiences before the buying window.

Qualify Leads Through the Form, Not the BDR

Unqualified leads were a known issue.

The fix is to just add qualification into the form experience.

That meant:

Streamlined, multi-step forms
Embedded qualification questions
Logic-based routing tied to buyer fit and intent

Not only did this improve lead quality, it reduced manual sales effort. And yes—we built this with a Reform Custom Form as part of the stack.

Want to See the Full Breakdown?

This blog only scratches the surface of what we covered.

Watch Part 1 of the conversation between Arun and David to go deeper into how to:

Align cross-functional teams on ABM goals
Roll out campaigns without disrupting the org
Build a pilot that earns buy-in and scales the right way

🔗 Watch Part 1 of Episode 2 now

🔗 Run a smarter pilot with FunnelEnvy

🔗 Get Done-for-You Custom Forms here

By Hitaishi Chaudhary|2025-06-30T09:47:53-07:00June 23rd, 2025|Conversion Rate Optimization, Full-Funnel Optimization, Lead Management, Revenue Funnel Optimization|0 Comments

The MuleSoft example

Let’s look at an example of some common sense personalization ideas. We will use MuleSoft.com, a B2B provider of multiple software products, as an example.

First a disclaimer. MuleSoft is not a FunnelEnvy customer and I have no insider knowledge of their business or marketing. What I’m suggesting below are insights based on what I can determine from their website, with a healthy dose of assumptions included.

MuleSoft’s featured product, Anypoint Platform™, seems to follow a relatively standard SaaS buyer journey which includes a free trial. We can use this to put some definition around the activities that define our STDC intent clusters:

Cluster	Behavioral Criteria
See	New visitor coming to the site with no prior engagement history
Think	Visitor actively engaging with solution specific content
Do	Submitted free trial form or ask an expert form
Care	Paying customer who is having success

We can learn a lot from the technologies that MuleSoft is using on their site. They have Demandbase and Engagio, so it’s safe to say that Account Based Marketing (ABM) is a strategic priority. Since they’ve adopted ABM it’s also very likely that they have defined account tiers grouped by potential value to the business.

The navigation bar gives us clues about some of the other Account based attributes that they care about. Under the Solutions menu they list resources by initiative, integration, technology, and industry.

Digging around in MuleSoft’s training offerings helps us identify the individual roles within the accounts that they can market to as well.

With this information we can put together a contextual framework to evaluate MuleSoft’s website experience. As you can see there are a lot of variables to consider!

Rather than UX improvements or content suggestions, we’d like to personalize the entire experience – messaging, value propositions, and next best action based on an individual visitor context. Let’s look at how we might improve MuleSoft’s web experience with some of this context in mind.

The “See” Cluster

The home page is often one of the most highly trafficked pages, usually with a high volume of direct and organic (branded) search traffic. As a result, it generally has pretty generic top of the funnel content and often serves as a “traffic cop” – funnelling visitors to the sections of the site with more specific content.

What if instead of the headline, copy and CTA we could replace it with something that better reflected the visitor’s intent?

Visitor intent: Explore Government IT solutions

Visitor intent: Understand Salesforce integration possibilities

Visitor intent: Accelerate ecommerce integration

Visitor intent: Try Anypoint Platform

The three content boxes below the home page CTA could similarly be personalized based on intent. MuleSoft also has an extensive resource collection of case studies, ebooks, whitepapers and webinars. The featured content at the top of the page is prime real estate to showcase personalized content.

Accomplishing the “See” cluster

A common question that we get is how do we actually know enough about “anonymous” visitors (ones who haven’t filled out a form) to be able to personalize for them?

We’re looking for signals that could inform the right experience, and it turns out there are more than you might think. Think about how users get to the website. If you’re running ads you’re probably already segmenting based on intent and other relevant characteristics. It’s now become common for marketers to personalize landing pages, but keep in mind that visitors that hit your landing pages might browse to other areas of the site or return in subsequent sessions.

As an example MuleSoft is running search ads. Many of them provide clear signal as to the intent of the visitor who clicks through. These can be used to personalize not only the home page, but also the home page, content pages, and to take them deeper in the content journey.

Using data from incoming clicks doesn’t have to be limited to ads. Referring sites can be great indicators of customer context as well.

An article that links to MuleSoft.com. Visitors that come it are likely to be interested in MuleSoft’s Microservices offerings.

Organic search result that links to a specific MuleSoft content page. In this case the combination of referrer (Google) and landing page is a signal of customer interest.

There are third party data providers that can provide information on anonymous visitors as well. These include Demandbase (firmographic data from reverse IP lookup) and Bombora (B2B intent). If you have the budget these can also be incorporated into a model to inform personalized experiences. Even if you don’t have one of these data providers the underlying input (e.g. IP address) can be used as signal in a predictive model.

The “Think” Cluster

The requirement to be in the Think cluster is that the visitor is in the target market and has shown some commercial intent. In B2B that often means that they’ve returned to the site and engaged with more commercially oriented content, and likely filled out a gated content form. That could also mean that multiple visitors have come to the site from the same account.

We want to continue to provide these visitors with relevant content that continues to engage them, but also give them on-ramps to take the next step. In MuleSoft’s case, this “next best action” is either starting the free trial or talking to sales. Since we may also have information about the visitor’s account and role we can incorporate that into the experience and call to action. For example, we may want developers to start the trial, but IT managers at large accounts to talk to sales.

Changing the copy and CTA for a developer (end user) to encourage them to start the free trial.

If the visitor is an engaged decision maker we can present them with more specific content and a CTA that takes them directly to a Contact Sales form.

Accomplishing the “Think” cluster

As we’ve seen with behavioral data, the content that visitor engages with on site could be a strong indicator of customer intent. If a visitor has shown repeat engagement with content, and specifically engagement with content that indicates some commercial intent, they are likely to be in the “Think” cluster.

MuleSoft has a relatively large content library, and some it can be indicative of a higher intent to purchase.

A “thought leadership” ebook (left) vs an analyst report with vendor comparisons (right). The analyst report likely demonstrates higher commercial intent.

Remember that we don’t have to manually identify and evaluate each piece of content for commercial intent. We’re just looking for the machine to identify and correlate signals to outcomes. All we have to do is throw is therefore throw all of the content URLs into our model and evaluate which experiences actually convert.

Another rich set of data for the Think cluster is in our 1st party data platforms, specifically marketing automation and CRM. Most marketing automation platforms cookie every visitor which can be used to connect a website visitor to a lead record. The accounts in your CRM database can also be associated with visitors though it requires an extra step – at FunnelEnvy we usually make that connection using the marketing automation cookie or via the inferred domain from a reverse IP provider.

Website behavior as well as lead and account attributes evaluated against conversion outcomes can provide solid evidence that a customer is in the Think cluster.

The “Do” Cluster

Visitors in the “Do” cluster have shown strong commercial intent. This goes beyond filling out a form for a piece of content, they’ve demonstrated an interest in engaging in the sales process. Traditionally this is where marketing would have taken a “hands off” approach (it’s a sales problem now!) but that’s no longer sufficient.

For MuleSoft we’ve defined strong commercial intent as having submitted a Contact Us (sales) form or started the free trial. In the time between this conversion and a deal closing, the focus is often on continuing to educate the prospect, expand the champions in the account and alleviate concerns about value and cost. Effectively engaging customers in this cluster should result in higher deal velocity and overall conversion rate from qualified lead to revenue.

For a product like MuleSoft, the prospect will likely be asking certain questions depending on their role:

What support options are available relative to what I need?
What have effective implementations at similar companies looked like?
How much and what kind of training will our developers require?
What professional services or partner resources are available for implementation?

MuleSoft’s website has quite a bit of relevant content that can be both personalized and highlighted for these types of questions. All of the context that we’ve established up to this point can and should be used as well, including initiative, vertical and job function.

MuleSoft support plans can be personalized by highlight the recommended support plan and providing additional details based on the account.

MuleSoft has an opportunity to showcase partners based on what they know about the account and the specific opportunity being discussed.

Highlight training opportunities based on visitor role and surface them on higher traffic pages of the site.

Another relevant content option for customers who are considering purchasing Mulesoft might be to personalize the resources in the nav bar or replace the explainer video on the home page with recommended content related to these topics.

For prospects who have started a free trial one of the most effective strategies is to get them to engage and successfully complete certain tasks. In app engagement generally has a strong correlation to retention and in this case conversion to a paid plan. This seems to be true in MuleSoft’s case as they have a robust onboarding tutorial when a first time user logs into the trial.

Marketers often put a lot of effort into establishing intent before sign up but that doesn’t always carry over into the experience post conversion. If, for example, the visitor was interested in Salesforce integration the onboarding process could direct them towards relevant functionality once they were in the app.

Obviously not everyone is going to complete the onboarding and many will exit the app before completing a desired action. When these visitors come back to the site they could prompt visitors to sign back in and complete it.

Accomplishing the “Do” Cluster

In our example, visitors in the “Do” cluster have either filled out a contact sales form or started a free trial. These signals can be established behaviorally, but most likely you would integrate marketing automation, CRM or application data to the experience to incorporate a richer set of attributes.

For some of the examples in this cluster, an audience based approach combined with predictions can work well. A predictive model is going to show suboptimal experiences to some visitors, as in an A/B test that’s actually feature because you’re trying to explore and learn what correlates to conversion.

Sometimes you will want to restrict the range of possible “guesses” made by the predictive model, especially in the case where certain experiences clearly wouldn’t be applicable or there’s some other hard business constraint.

In situations where you have “hard constraints”, such as if a customer is in the free trial, the inherent error rate of the a purely predictive model may not be appropriate. In this case you could setup an audience for free trial users and then run a predictive decisioning model within that audience.

The “Care” Cluster

Customers in the “Care” cluster are your most loyal advocates. In SaaS solutions, not only are they paying for the solution but they’re also having demonstrable success with it. Visitors in this cluster are prime candidates for expansion and referrals, but may also need more advanced services and support.

As an organization pursuing Account-Based Marketing and Sales, MuleSoft has an opportunity to provide more value for and penetrate more deeply into their Care cluster accounts. When visitors in this cluster come to their website they could present a completely different homepage experience.

Salesforce changes the homepage experience between new visitors (left) and existing customers (right)

MuleSoft has several opportunities to deliver more value to existing customers through a personalized homepage experience. This could be in the form of:

Features that the customer are being underutilized and the customer could get more value out of.
Promoting services or partners that might be able to help the customer.
Highlighting training and certification options relevant to the visitor’s role.
Building the community by promoting location specific events.

KPIs that are relevant to the care cluster include engagement, expansions, renewals and referrals. Some of these may not be owned by the marketing team, but they’re certainly relevant to the company.

Accomplishing the “Care” Cluster

Once a customer is in the Care cluster you generally have a lot more first party data about them. This can include CRM data, but potentially also application behavior, customer support history, and success metrics. You’re trying to inform your decisions with a more holistic view of the customer, their interactions with your company and solution.

In Conclusion

If you’re struggling to understand why the same lead form and marketing automation nurture you’ve had on your website for years are not working as well as they once did take a step back because the rules of the game might have shifted underneath you.

We can’t assume the same uniformity of customer intent that we once could – and that has significant implications for experiences that we deliver across channels and particularly on the website. To deliver better outcomes it actually helps to go back to Marketing 101 – right message, right person, right time and identify the solutions and processes that will help us get there at scale.

By James Niehaus|2025-05-12T04:36:47-07:00May 11th, 2025|Conversion Rate Optimization, Revenue Funnel Optimization|0 Comments

What is PLG Routing?

PLG routing refers to the strategy of guiding users through a tailored journey based on product-led growth (PLG) principles. Instead of relying solely on traditional sales processes, PLG routing dynamically adjusts the user experience in real-time to enhance engagement and conversion opportunities. This approach ensures that users are always presented with the most relevant content and offers, optimizing the chances for success.

How PLG Routing Works

PLG routing leverages data from user interactions and behavior to determine the next best action, whether it’s directing users to specific features, offering personalized messaging, or adjusting the funnel stages accordingly. This allows businesses to create more fluid, responsive, and relevant user experiences that are guided by the individual needs and actions of each user.

The Impact of PLG Routing on Conversion Rates

By implementing PLG routing, companies can enhance their conversion strategies by continuously adapting to the user’s journey. This helps ensure that users are not overwhelmed with irrelevant content and are instead guided to the most impactful touchpoints, boosting engagement and conversion rates.

Why PLG Routing Matters for SaaS Businesses

For SaaS businesses, PLG routing is a game-changer in terms of user acquisition and retention. By focusing on personalized, product-centric interactions, businesses can effectively drive growth without the traditional heavy reliance on sales teams. This is especially important in the SaaS model, where a smooth, self-guided user journey can significantly impact long-term retention and customer satisfaction.

Watch the Full Video on PLG Routing

To gain a deeper understanding of how PLG routing can help your business grow, watch the video below where we explore the concept in more detail.

Conclusion

PLG routing is transforming how businesses approach user engagement, making it an essential strategy for any product-led company. By leveraging data-driven insights and dynamically guiding users along their journey, businesses can drive better results, improve conversions, and build stronger relationships with customers. Implementing this approach may be the key to optimizing your conversion rates and scaling your business effectively.

By David Janczyn|2025-05-12T04:37:20-07:00January 12th, 2025|Conversion Rate Optimization, Full-Funnel Optimization, Revenue Funnel Optimization|0 Comments

Improving Martech Observability to Reduce Costs and Boost Efficiency

In the world of marketing technology (Martech), analytics observability is often overlooked. Yet, without it, businesses risk inefficiencies, revenue losses, and operational setbacks. Martech observability focuses on maintaining a robust analytics infrastructure, ensuring data flows seamlessly and accurately. Let’s explore the costs of broken analytics and how improved observability can be the solution.

The Cost of Broken Analytics

Broken analytics systems are more than just an inconvenience—they are a financial burden. Without proper observability, businesses face a range of problems, including:

Revenue Loss: Incorrect or missing data results in missed opportunities and lost leads.
Operational Inefficiencies: Teams waste time and resources troubleshooting issues instead of focusing on strategic objectives.
Poor Decision-Making: Misaligned sales and marketing efforts stem from inaccurate data, leading to ineffective campaigns.
Brand Damage: Faulty campaigns delivered to the wrong audience can harm customer trust and retention.

The long-term cost of maintaining broken analytics systems often exceeds the initial cost of building them.

Why Observability Matters in Martech

Observability, a concept rooted in software development, has yet to gain widespread traction in the analytics world. However, it is the key to ensuring data reliability and system resilience. By incorporating observability practices, businesses can proactively identify and resolve issues before they escalate.

Key Strategies for Martech Observability

To strengthen your analytics infrastructure, consider two primary approaches to observability:

1. Synthetic Testing
Synthetic testing involves running scripted transactions and workflows through your systems to simulate user interactions. This proactive measure helps you spot inconsistencies or errors in data processing before they impact real users.

2. Real User Monitoring
Real user monitoring (RUM) captures real-time data from actual users navigating your systems. This method ensures data flows correctly, providing immediate insights into potential issues affecting your audience.

The Benefits of a Resilient Analytics Infrastructure

Implementing these observability practices provides numerous benefits, including:

Accurate Data Insights: Gain confidence in your analytics for smarter business decisions.
Operational Efficiency: Reduce troubleshooting costs and improve team productivity.
Stronger Brand Trust: Avoid damaging campaigns and maintain customer loyalty.
Informed Resource Allocation: Spend less time fixing data issues and more on growth-focused initiatives.

Final Thoughts: Observability as a Martech Imperative

Improving Martech observability is no longer optional—it’s essential for any business relying on data-driven decision-making. By integrating synthetic testing and real user monitoring into your analytics stack, you can ensure accuracy, prevent costly mistakes, and create a competitive edge.

Watch the video above for a deeper dive into these strategies, and start building a resilient analytics system today.

FAQs

What is Martech observability?
Martech observability is the practice of monitoring and maintaining the health of marketing analytics systems to ensure accurate and efficient data processing.

Why is broken analytics costly?
Broken analytics lead to revenue loss, operational inefficiencies, poor decision-making, and damaged customer trust, all of which negatively impact business growth.

What is synthetic testing in analytics?
Synthetic testing simulates user interactions through scripted transactions, allowing teams to proactively identify and fix issues in data systems.

How does real user monitoring help Martech systems?
Real user monitoring captures real-time data from actual users, ensuring the accuracy of analytics and helping to spot and address system issues promptly.

What are the benefits of improving observability in analytics?
Improved observability ensures data accuracy, reduces operational costs, strengthens brand trust, and allows for better resource allocation.

Is observability in Martech similar to software development?
Yes, observability in Martech borrows principles from software development, focusing on proactive system monitoring and real-time data analysis to enhance reliability.

By James Praise|2025-05-12T04:37:19-07:00January 7th, 2025|Conversion Rate Optimization, Attribution Modeling, Revenue Funnel Optimization|0 Comments

Dominating Data Overload: How to Get Actionable Insights from the B2B Marketing Funnel

Imagine this: Your content strategy is on point. You’ve meticulously crafted B2B marketing funnels, pouring effort into targeted campaigns, compelling content, demand generation, and strategic CTAs. Traffic flows steadily to your website, but converting leads needs to catch up.

You dive into your analytics dashboard, overwhelmed by a sea of marketing data points – website visits, bounce rates, and click-throughs. The list goes on. The challenge? You may be unsure how to get actionable insights from a B2B marketing funnel.

It’s not just about the data; it’s about transforming this data deluge into actionable information that illuminates what’s working (and what’s not) with your marketing efforts.

Here’s the good news: You’re not alone. Marketing automation and modern digital marketing tools generate a wealth of data, but extracting meaningful insights that translate to business growth can feel like searching for gold nuggets in a riverbed.

The key? Marketing teams should have a strategic selection process, allowing them to identify the data points that truly matter for optimizing the B2B marketing funnel and increasing that coveted conversion rate. Let’s look at how this works.

B2B Marketing Funnel Data Landscape

Your B2B marketing and sales funnel represents the customer journey, encompassing every touchpoint a potential lead has with your brand. Each stage in the marketing funnel strategy – Awareness, Consideration, and Decision – generates valuable data that sheds light on user behavior and engagement. Let’s look at the funnel stages and the key data points you can gather.

Awareness Stage

This stage is powered by content marketing strategies for creating awareness and is all about attracting potential customers. Search engine optimization can help get your brand and site noticed. Funnel metrics to track include website traffic sources (organic search, social media, referrals), customer data demographics (industry, company size), and content engagement metrics (time spent on the website, blog post views, video play rates).

Consideration Stage

As the action stage of the customer journey, visitors in this stage are actively researching solutions to their challenges.

Key data points to monitor include:

Landing page conversions (e.g., comprehensive guides and ebook downloads, webinar registrations),
Time spent on key content (product pages, case studies, frequently asked questions),
CTAs clicked (demo requests, free trial signups).

Lead nurturing email marketing campaigns can help move customers to the next phase.

Decision Stage

This stage in the journey is when qualified leads move towards a purchase decision, an essential stage in the sales process. Important data points include demo signups, free trial activations, and quote requests to gauge interest and sales readiness.

Sales teams should remember that customer retention should be considered part of the sales cycle to capitalize on all the work done during customer acquisition. Be sure the customer service options are on point. Set up a system to review and analyze customer feedback regularly.

Remember: Data quality is paramount. Inaccurate data leads to faulty insights. As a B2B marketer, it’s your job to ensure that your analytics tools are properly configured and integrated to collect clean, reliable data. This data is the foundation for trustworthy analysis and the key to your success.

Goal-Driven Data Selection

Data is powerful, but without a clear direction, it can become overwhelming. Here’s where the magic happens – aligning your data selection with your specific marketing funnel goals.

Setting SMART Goals

The foundation of practical data analysis is establishing clear, actionable SMART goals.

Most of us are familiar with the SMART goals framework. Here’s a breakdown:

Specific: Define your goals precisely. Don’t settle for “increase website traffic.” Instead, aim to “increase qualified leads generated from organic search by 20% within Q3.”

Measurable: Ensure you can quantify your goals with relevant metrics.

Attainable: Set ambitious yet achievable goals within your resources and timeline.

Relevant: Align your goals with your overall marketing strategy and business objectives.

Time-bound: Establish a clear timeframe for achieving your goals.

Aligning Data with Goals

Choosing the right data points becomes straightforward once you have defined your SMART goals. The fundamental principle is to select metrics directly related to your funnel goals.

For example, if the goal is to increase brand awareness among decision-makers in the healthcare industry, the data point would be organic traffic from healthcare industry publications.

In another example, the chart below gives a broad overview of the metrics B2B marketers should track in 2024 to evaluate content performance.

Actionable Insights from Your Marketing Funnel

Source: Content Marketing Institute

Prioritize Ruthlessly

It’s tempting to track every possible data point. However, information overload can lead to analysis paralysis. Prioritize a limited set of high-impact metrics that provide the most valuable insights for achieving your goals. Focus on metrics directly influencing conversions and answer specific questions about your funnel’s effectiveness.

How to Get Actionable Insights From a B2B Marketing Funnel

Data without interpretation is like a treasure chest without a key. Here’s a step-by-step process to unlock valuable insights from your B2B marketing funnel data:

Step 1: Data Visualization

The human brain thrives on visuals. Charts, graphs, and other data visualizations make complex information digestible, revealing trends and patterns that might otherwise go unnoticed. Leverage tools within your analytics platform or consider data visualization software to create clear and compelling representations of your chosen data points.

Step 2: Identify Trends and Patterns

With your data visualizations, it’s time to delve deeper into analyzing trends and patterns within the data sets. Ask yourself:

Are there significant drops in traffic at specific points in the funnel?
Which content pieces generate the most qualified leads?
Are there particular CTAs underperforming?

Identifying these patterns will highlight areas for improvement within your funnel.

Step 3: Ask the Right Questions

Data analysis is all about asking the right questions. Here are some prompts to get you started:

Why are visitors dropping off after a specific page?
What content resonates most with qualified Consideration leads?
Are there any technical issues hindering conversions on landing pages?
How can we improve the user experience at different stages of the funnel?
For account-based marketing, consider including the following: How well does our current funnel content resonate with our ideal customer profile (ICP)?

By asking insightful questions, you unlock the true potential of your data, uncovering insights to optimize your B2B marketing funnel, such as identifying high-value leads for nurturing through lead scoring programs.

Step 4: Hypothesis and Testing

Data-driven insights are powerful, but they’re not crystal balls. Based on your findings, the next step is formulating hypotheses about funnel improvements. Teams should test these hypotheses to validate their impact.

Here’s an example:

Hypothesis: Upgrading the design and CTAs on our product page will lead to a 15% increase in free trial signups, moving more leads into our sales funnel.

Use A/B testing or other methodologies to test your hypotheses and measure the results. This data-driven approach allows you to continuously refine your funnel for optimal conversions.

B2B Funnel Optimization – From Insights to Action

You can transform your B2B marketing funnel from a data swamp into a goldmine of valuable information by implementing a strategic data selection process and following the outlined steps for generating insights. Remember, data is only as powerful as your ability to utilize it effectively.

Dominate Data Overload and Start Seeing Results

As you’ve seen, data wrangling can be complex and time-consuming. At Funnel Envy, we believe in focusing your marketing efforts with laser precision. Our Full Funnel Conversion Audit is the perfect starting point, providing an efficient way to identify areas for improvement and maximize your return on investment.

Here’s what you’ll gain from our Full Funnel Conversion Audit:

Holistic Optimization. Discover how to break down silos and optimize every stage of your funnel for peak performance.
Lead Generation Mastery. Learn how to convert more top-of-funnel prospects into qualified leads ready to close.
Marketing & Sales Alignment. Develop a shared understanding of lead qualification criteria to ensure seamless team handoff.
Actionable Roadmap. Get a 21-day plan tailored to your specific needs for optimizing your entire funnel.

Ready to stop data overload and start seeing real improvements in your conversion rates?Click here to learn more about our Full Funnel Conversion Audit and unlock the full potential of your B2B marketing efforts!

By Kala P|2025-05-12T04:37:14-07:00August 5th, 2024|Attribution Modeling, Revenue Funnel Optimization|0 Comments

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