Predicted Returns: Three metrics to measure paid acquisition performance in SaaS

For performance marketers, paid acquisition is an ever-changing jungle of opportunities and traps; a galaxy of ad networks, formats, channels & keywords, each with their own idiosyncrasies. What makes performance marketing so appealing is that every campaign, click, conversion & bid is trackable and analyzable. Tools like Google Analytics make it easy to comb through, filter, segment & visualize your campaigns, channels, costs & returns. Line your traffic sources by campaign up against your website engagement & conversion metrics and you can see how users behave after they click on each ad.

The problem with pay-per-click in B2B is choosing how to measure ROI. Looking at email generation optimizes for acquiring low-value emails, but the SaaS buyer journey may take weeks or months to convert to actual revenue, which is far too long and far too complex to measure. Performance marketers want to know which spend is yielding results (to double down) and which spend isn’t (to shut it off).

Generating few great leads with a high CPL yields much better results than generating many low-quality leads with a cheap CPL. SaaS marketers need an transaction metric that mimics eCommerce-styles transactions to measure their performance against, and of course we have just the solution.

Measure performance marketing with smarter metrics

The goal of this play is to make sure we are investing our ad dollars into channels that generated qualified pipeline. We want to see which campaigns return a positive ROI in the long-run (though we can cap this definition at, say, 12 months). All we need is MadKudu & Google Analytics (or any analytics solution that allows for segmentation by UTM tags) to get this done.

The play itself is quite basic. As always, at the core of modern marketing operations is leveraging historical customer data to build a model for what your best leads look like and do. We’re going to use MadKudu for this, since it’s easy to operationalize across the entire buyer journey and requires no in-house data scientists. You can read up on about the three stages of lead scoring to start building your own.

Once we’ve embedded MadKudu into our conversion points (lead generation forms, user signups, etc.) – potentially via MadKudu Fastlane – we’re going to send MadKudu data back to a website analytics tool like Google Analytics so that a visitor’s data can now includes its Madkudu score.

Once that’s set up, there are three new ways you can measure your performance:

Cost per Qualified Lead

If your PPC campaigns are pointing to a lead capture form (webinar, eBook, free tool), you can look at which channels & campaigns are bringing in qualified pipeline in an ROI positive way. Specifically, we want to compute the total spend on the campaign divided by the number of good vs. very good leads – some campaigns bring in many leads that don’t convert, while others bring in very good consistently qualified leads. Identify where your generating qualified pipeline efficiently might lead you to cut 50% of your paid ad spend (as it did for Drift).

Predicted Spend vs. Cost Per Lead

This may be more useful for campaigns that are leading to account creation or demo scheduling, where the rest of the buyer journey follows the traditional buyer journey. Here we’re leveraging MadKudu’s ability to map user’s not only to their likelihood to convert but to their predicted spend. This is calculated by looking at how much a lead resembles historical customers in each segment – “do they look like a pro plan?” With predicted spend, we can develop a threshold for cost per lead accordingly: we align our acceptable cost per lead (and therefore bidding threshold) based on the average predicted spend of leads acquired.

By averaging predicted spend across leads who are predicted to convert or not, we’ll be able to quickly optimize for campaigns that acquire any combination of lead values so long as we are not paying on average more than they are worth.

Predicted Value vs. Campaign Cost

The last way we can view performance marketing ROI is by leveraging the predicted value field that MadKudu provides, for example, to Facebook’s Ad Engine. Instead of feeding this data to Facebook to train its bidding engine to bid on our best leads, we can look at all channels of spend through the lens of this metric and answer the question: are the leads we’re acquiring predicted to spend more than what we’re paying for them?

We can look either at average predicted value vs. cost per qualified lead or we can look at the sum total of predicted value vs. the total campaign spend. In both cases, we’re getting a picture of whether the leads we’re acquiring today will be profitable down the line.

Since Predicted Value is designed by looking at predicted spend by plan multiplied by % chance of conversion, we’re accepting a 12 month CAC. We can adjust this either by calculating predicted spend as 6 Months of MRR instead of the plan sticker price (12 months of fully loaded MRR), or by dividing our predicted value by a variable as a function of the CAC threshold we want to set.

The Impact: -50% in Ad Spend

No matter which metric we optimize for – predicted value, predicted spend or cost per qualified lead – we’re arming ourselves with metrics that we can measure in real-time as our campaigns generate leads. Drift integrated MadKudu into Google Analytics and identified campaigns that were generating no qualified leads (although potentially many qualified leads). As a result, overnight they cut 50% of their ad spend and saw a major dip to their website traffic but no meaningful dip in their pipeline creation.

That 50% in recuperated Ad Spend can go into doubling down on existing campaign or creating new campaigns – either way, we’re increasing our performance marketing ROI.

Timing is everything: Surfacing sales-ready accounts & the right contacts to engage.

Identifying sales-ready accounts to reach out to is the heart of freemium sales acceleration. Freemium businesses rely largely on product adoption to trigger the a-ha moment that will ultimately lead to successful sales engagement, so quantifying that a-ha moment in the form of activity scoring is crucial.

For Sales SaaS reps with hundreds or thousands of accounts assigned to them, reaching out manually every three months yields little or no results. What are the odds that today is the day that an arbitrary account is ready to buy? Very little, right. Coupled with that is the fact that an account may have dozens of associated contacts to choose from and engage. Randomly picking based off of job titles and reaching out is a spray and pray strategy that yields equally unpredictable results.

Sales wants to know which accounts to reach out and who within that account to reach out. And we’ve got just the play for that.

Timing is everything: Surfacing sales-ready accounts & the right contacts to engage.

Our goal here is for sales reps to start every day with a list of accounts assigned to them that are ready to have a conversation. We’d also like to give that sales rep a filtered list of the contacts most likely to respond. That way sales reps spend all of their time crafting the most relevant message for their best leads.

This is a great play for freemium SaaS businesses with a large number of accounts where velocity & efficiency are key to success. This play is also good for products with a combination of low-touch and high-touch users – while you may have paying customers already, identifying when that paying account is sales-ready is key.

The bulk of this play is going to sit on MadKudu’s ability to build an accurate account-level behavioral scoring model. Structured data is going to be key – wecan’t build an account-level model if we don’t have account data. When we run this play with InVision, we use Segment for product data & HubSpot for marketing data. We’ll also need Salesforce for our sales data.

Once we have our data piping correctly, MadKudu is going to identify the features & activity that best predict sales readiness by looking at historical product & marketing engagement and the resulting sales outcome. Once the predictive model is build, MadKudu sucks down the latest activity data on a regular basis and looks for triggers.

When a sales-ready account is identified, MadKudu tags it in SalesForce and drops it into a daily report for sales reps. Within each sales-ready account, MadKudu’s models also look at the profile & activity of each contact in order to identify the contacts most likely to engage. Those contacts are recommended to the sales rep.

The Impact: +25% in Pipeline

InVision’s 25% increase in pipeline came from identifying accounts programmatically based on historical customer data. This came without any change to the product, just in optimizing for sales-readiness. If you’re looking at lead activity today, you may be leaving money on the table. With InVision, we identified accounts where no single lead achieved the activity threshold for an MQL, but the account as a whole hit the MQA threshold. It is the combined activity of several users that makes an account ready to talk to sales.

Once this MQA model is built, we can begin to add some of our other plays on top of it. Forcing the funnel by sales-ready accounts by customizing the app or website.  Reducing friction on forms or triggering chat for enterprise prospects with a Fastlane play. We can score accounts throughout the buyer journey.

We previously wrote about why activity scoring is so tricky and you can see slides here from a joint talk given by InVision & MadKudu at HubSpot Inbound 2018.

Activity Scoring & Enterprise Free Trials: how to do it right.

Redpoint Ventures Partner Tomasz Tunguz recently published the results of their Free Trial Survey, which included responses from 590 professionals working at freemium SaaS businesses of all sizes and shapes. The survey had many interesting takeaways and I recommend taking the time to dive into the slides that Tunguz shared at SaaStr Annual this year.

One of the more interesting takeaways (that Tunguz discussed on his blog) was that activity scoring seems to negatively impact free trial conversions for high ACV leads. Tunguz found that Enterprise SaaS businesses using activity scoring see a 4% conversion rate for free trials vs. 15% for those not using activity scoring.

MadKudu has written a lot about free trials in the past, including the article that inspired Tunguz referenced in launching his survey, so it was natural for us to weigh in on this conclusion.

I asked for a few clarifications from Tunguz in preparing this article:

  • The survey defined activity scoring as lead scoring that leverages in-app activity (not email marketing, webinar engagement or other activities).
  • The conversion rate (4%/15%) is calculated against all leads, not leads that scored well, so we’re measuring the effectiveness of the funnel not the precision of the score itself.
  • We’re only looking at leads who participate in the free trial, not leads that schedule a demo or otherwise enter the funnel.

With that in mind, I believe there are two main takeaways and some data to support those conclusions.

Enterprise leads don’t want a Free Trial.

summary: our data shows Enterprise prefer to schedule a demo and self-serve prefer a free trial (if available). Putting either in their counterpart’s funnel negatively impacts their likelihood to convert.

Free trial products design enterprise buyer journey calls-to-action – “contact sales” “schedule a demo” &  “request a quote” – in order to entice enterprise prospects. As Tunguz pointed out in the analysis of his survey, enterprise leads don’t typically try before they buy. They may sign up for a free trial to get a feel for the interface and do some preliminary feature validation, but the buying process is more sophisticated than that and lasts longer than your free trial.

One hypothesis for why activity scoring decreases conversion for enterprise leads in free trials is that enterprise leads shouldn’t be running free trials – or at least, they shouldn’t be having the same free trial experience. It is worth reading Tunguz’s piece about assisted vs. unassisted free trials to dive deeper into this subject.

Supporting this hypothesis is an experiment run by Segment & MadKudu looking at the impact of a free trials & demo requests on the likelihood that a self service & enterprise lead would convert. Segment Forced the Funnel by dynamically qualifying & segmenting website traffic, personalizing the website based on the predicted spend. This allowed us to predict whether traffic was self-serve or enterprise.

“Self-serve traffic” would not see the option to schedule a demo while “enterprise traffic” would not see the option to sign up for a trial. They also ran a control to measure the impact on the funnel.

They found a negative correlation between self-serve conversion & requesting a demo. They also found a negative correlation between enterprise conversion & signing up for a free trial.  Each buyer segment has an ideal customer journey and deviating from it (even into another buyer segment’s ideal journey) negatively impacts conversion.

The converse is equally true: pushing leads into their ideal customer journey increases their conversion rate by 30%.

Startups using activity scoring on high ACV leads should work to get high ACV leads out of their free trial by identifying them early on. Algolia, for example, prompts self-serve trial signups who have a high ACV to get in touch with someone for an assisted free trial.

Scoring activity at the account level

For SaaS businesses that go up-market or sell exclusive to enterprise, activity scoring at the lead level may not be sufficient. We worked with InVision to identify sales opportunities at the account level, importing all activity data from HubSpot & Segment and merging at the account level. We analyzed the impact that various user personas had on the buyer journey and product experience.

Profiles that were more likely to be active in the product  – Marketing, analysts & designers – had a less than average impact on the likelihood to convert. Personas associated with higher likelihood to convert – Directors, founders, CEOs – had a smaller impact on activation.

Multiple personas are needed to create optimal conditions for an account to activate & convert on InVision. Their marketing team uses this knowledge to focus on post-signup engagements that will increase the Likelihood to Buy, the behavioral score built by MadKudu.

We see similar findings in the buyer journey as we examine how various personas’ involvement in an account impacts opportunity creation vs. opportunity closed-won. Opportunities are more likely to be created when marketers & designers are involved, but they are more likely to close when CEOs & Directors get involved.

For InVision, interestingly enough, founders have a smaller impact on opportunity closed-won than they do on product conversion.

While a single lead may never surpass the activity threshold that correlated with sales readiness at InVision, scoring activity at the account level surfaced accounts that exceeded the account activity threshold. Both thresholds were defined by MadKudu & InVision using the same data sources.

The above slides are all from our HubSpont Inbound 2018 talk and are available here.

Measuring Scoring Model effectiveness

Looking at the results of experiments run with our customers and the data from Tunguz’s survey, it’s clear that activity scoring doesn’t work in a vacuum. Both our MQA model for InVision & our models for Segment require firmographic, technographic and intent data in combination with behavioral data in order to build a predictive model.

The impact that a model will have on sales efficiency & velociate depends on its ability to identify X% of leads that represent Y% of Outcomes. The power of this function increases as X tends to 0 and Y tends towards 100. “Outcomes” can represent opportunities created, opportunities won, pipeline created, or revenue, depending on the metric your sales strategy is optimizing for.

We similarly expect that the X% of leads will convert at a significantly higher percentage than lower-quality leads. As seen in the above graphic, a very good lead may be 17x as likely to convert than a low quality lead, which makes a strong case for sales teams prioritizing very good leads as defined by their predictive model – at least if they want to hit quota this quarter.

If you’re selling exclusively to enterprise leads, an assisted free trial operates a lot like a schedule a demo flow – you will score leads firmographically early on, evaluate the opportunity, and then assist them in onboarding to your product to trial it, scoring activity throughout the trial to evaluate likelihood to convert.

Most SaaS don’t sell exclusively offer free trials to high ACV leads, which is why activity scoring becomes crucial. A lead that is a great fit for self-service is also a bad fit for enterprise. Self-service leads convert quickly and consistently with a small to medium sized ACV, whereas enterprise leads have a small percentage chance of converting, but convert with a much higher ACV. Velocity sales requires a steady stream of quick conversions – a long sales cycle for low ACV is a loss – while enterprise sales can take months or years to close a single account while still being a success.

For customers with velocity & enterprise leads, MadKudu scores every lead against both models, which enables us to identify whether it’s a good fit for self-serve, enterprise, both or none (it’s almost never both).

Re-Get That Bread: Retarget qualified website traffic that just didn’t convert.

90% of your website traffic doesn’t convert, and there’s nothing worse than a missed opportunity. For b2b companies, retargeting is a no-brainer. It’s an easy way to make sure you’re always targeting an audience that has showed some intent to buy. However, the problem with retargeting anonymous website traffic broadly is that you don’t know who you are targeting and how qualified they are.

With just a high volume, many SaaS companies bid low on retargeting across their entire website traffic. They push their brand in front of their website traffic wherever they go. This spray and pray tactic means SaaS companies are only getting in front of traffic that other advertisers aren’t willing to pay more. Do you think your competitors may have a more focused strategy, outbidding you on your best leads and leaving the rest to you?

Click through rate is low because the quality filter is low. Conversion rate is low because most of your website traffic shouldn’t convert (candidates, low-quality leads, investors, analysts, perusers).

That is, of course, unless you only target leads that should convert in the first place. We already know MadKudu can handle qualifying anonymous traffic, so why not retarget it as well?

Re-Get That Bread: Identify, Qualify, Retarget

Our goal here is to focus our retargeting budget on the subsection of our website traffic that is worth the most to us. If we do that, we will be able to reallocate the budget we’re not spending on low-quality traffic to bidding more for our high-quality traffic.

We’ll need a few tools to Re-Get That Bread:

  • IP lookup: we’ll be using Clearbit Reveal for this.
  • Qualification: we’ll be using MadKudu for this.
  • Retargeting: we’ll be using Adroll for this.

As usual, we’ll be connecting this all through Segment.

Qualifying traffic has become pretty easy with the advent of IP Lookup APIs – the most popular being Clearbit Reveal. Feed Clearbit an IP address and it returns (among other things) the domain of the company or of the individual visiting your website. This is enough to score an account. We’ll be scoring with MadKudu, but you can also do it with your homegrown Lamb or Duck lead scoring model. We’ll send MadKudu the domain name provided by Clearbit, which will return a few important data points for this play:

  • Customer Fit segment: very good, good, medium, low
  • Predicted Spend: custom based on your specific pricing plans, predicting which
  • Topical Segmentation: custom based on your target segments (e.g for Algolia: ecommerce, media, SaaS).

With this data we’re able to feed AdRoll a custom audience of qualified traffic to target. This can be a bit tricky since AdRoll requires a static audience, but a quick script to update a static audience on a daily basis will get us around that hiccough.

Based on predicted spend, we can even build separate audiences for our various plans, each with different budgets. If we add in Topical Segmentation, we can run targeted messaging to our various ICPs based on their needs at various price points. If we know the predicted value of the qualified traffic, we can calculate our maximum budget as a function of our acceptable CAC.

The Impact: +300% click-through rate

When Chris Rodriguez at Gliffy first began building this play, he was looking to get click through rate for his retargeting ads under control. When he saw it jump from the .7% industry average to 2-3% for qualified traffic, it became pretty clear that qualified traffic was worth the focus.

Bidding higher on a qualified audience is a no-brainer: we see it on ad networks that boast a qualified audience or a qualified system of manual segmentation. It only makes sense that we would apply the same logic to how we retarget our own audience: we want to spend more on the audience that matters, the ones that got away.

Identify, Qualify & Segment website visitors with a personalized website experience.

Your website is the story you choose to tell: to prospects, to candidates, to investors, to journalists & analysts. Everyone who wants to know how you talk about yourself goes to your website. Your website starts off simple: you speak authentically to your Ideal Customer Profile (ICP). You make it easy for them to understand your differentiation, pricing, and how to get in touch with you.

Then you grow and begin to sell to different businesses with different budgets and different needs. Telling a single story to a single user therefore becomes increasingly complicated. Should your core message focus on enterprise or self-serve? Should your CTAs direct to ‘create a free account’ or ‘schedule a demo’? How important is it to make pricing easily accessible vs. documentation for how to get started?

Identifying, qualifying & segmenting your prospects with personalized messaging can be a full-time job for SaaS companies. This MadKudu play, however, can take a lot of the pain out of rapid experimentation.

Force the Funnel: Identify, Qualify, Personalize.

Our goal with Force the Funnel is to provide the optimal website experience for every qualified account. This play is great for SaaS businesses selling both to self-serve & enterprise. It also helps if you’re targeting distinctly different customer segments (e.g: financial services & luxury goods). In order to achieve this play, we’ll need three things:

  • IP lookup: we’ll be using Clearbit Reveal for this.
  • Lead Scoring: we’ll be using MadKudu for this.
  • Website personalization: we’ll be using Intellimize for this.

We’ll also be connecting all of these through Segment as usual. Let’s dive in and see what happens:

Focus on qualified traffic

First and foremost, we are going to focusing our efforts on personalizing our site for qualified traffic. The two reasons behind that are:

  1. We don’t want to measure success based on how personalization affects unqualified traffic.
  2. We don’t want to spend resources trying to help unqualified traffic convert better.

Qualifying traffic has become pretty easy with the advent of IP Lookup APIs – the most popular being Clearbit Reveal. Feed Clearbit an IP address and it returns the visitor’s company. This is enough to score an account. We’ll be scoring with MadKudu, but you can also do it with your homegrown Lamb or Duck lead scoring model. We’ll send MadKudu the domain name provided by Clearbit, which will return three important data points:

  • Customer Fit segment: very good, good, medium, low
  • Predicted Spend: custom based on your specific pricing plans, predicting which
  • Topical Segmentation: custom based on your target segments (e.g for Algolia: ecommerce, media, SaaS).

Now that we’ve identified, qualified & segmented our audience, we’re ready to personalize our site. There are a lot of personalization/experimentation/testing platforms. We’re using Intellimize here because we want Intellimize to do all the heavy-lifting of designing and running experiments. Intellimize uses machine learning to generate, analyze & optimize experiments. They also pull up some pretty interesting insights around how different personas behave.

The Impact: +30% conversion rate

Segment found that removing buttons linking to the pricing page for qualified enterprise accounts, they increased conversion to demo scheduling by 30%. We’re optimizing the upside by focusing on improving the buyer experience for qualified traffic. This dovetails nicely into other Fastlane plays via chatbots, lead capture forms & gated content.

If you’re running A/B tests on your entire traffic, you may be skewing your results & analysis in favor of what unqualified traffic does (see: Segmenting Funnel Analysis by Customer Fit). The key impact here is that we’re segmenting qualified traffic with AI-driven experimentation meant to optimize for the results we want: more demo requests, more signups, more leads captured.

Allow qualified demo requests to book a meeting with you

There is no better lead than an inbound sales request. The intent is high & clear: they want to evaluate you as a vendor. Studies show the last thing a prospect does before buying is talk to sales. The only real question SaaS companies ask themselves is “do we want them as a customer?”

Perhaps this is why most demo request forms act as intentional hurdles. They require qualified traffic to prove themselves worthy to speak to sales, because  reps don’t want to waste time on low-quality leads. This creates unnecessary friction in the buyer journey. Once leads fill in nine fields (on average), they wait 5-7 days before speaking with a rep. 50% of buyers say they choose the first vendor they talk to. Is that friction really something an organization can afford?

Imagine if we were to re-design the buyer journey to provide the best experience for high-quality leads. Undoubtedly we would ask for as little information as possible. It should be as easy as possible for leads to book a meeting with sales.

We can’t block low-quality leads from coming to our website, but MadKudu & Calendly make for a pretty powerful combo for this quintessential pipeline growth play: The Original Fastlane.

Removing friction & adding pipeline

The goal of this play is to give qualified leads access to a sales rep’s calendar so they can book time immediately. We want them to skip the form-filling and email back-and-forth. In order to execute this play, we’ll need to add MadKudu Fastlane to our demo request form, and we’ll need to leverage a scheduling tool like Calendly.


MadKudu needs an email address to score a lead, so we’ll want to make our email input one of the first fields in our demo request form. FastLane bundles two important steps together:

  1. It sends the email to MadKudu to score & qualify as the prospect fills in the form.
  2. If qualified, MadKudu FastLane triggers a message that let’s the buyer book a meeting with a sales rep directly.

Designing the buyer journey for your best leads

One of the more creative implementations for this comes from Outreach, who hides all unnecessary fields on their forms.

Outreach has hidden all fields that are non-essential to follow-up, leaving only email & phone number. If MadKudu qualifies the lead as a good fit, Outreach let’s the lead submit their slimmed down form. What a great buyer experience.

When MadKudu identifies an unqualified lead, MadKudu dynamically adds new fields to Outreach’s lead capture form. Unqualified traffic is often the result of a personal email address, so extra information may provide pertinent context.

The Impact: +60% Pipeline

Re-imagining lead forms is a quick win that can have a big impact. We underestimate the impact of operational friction between us & qualified leads. Reducing form fields and eliminating email tag means that you’re talking to more qualified leads faster. Segment increased their pipeline by 60%, and uDemy closed an enterprise client in 24 hours within weeks of deploying this play.

If you’re looking to hit your pipeline goals this quarter, start by looking at your existing forms: are your best leads getting the best experience?

Training Facebook to bid on your best leads

Facebook Ads has become the gold standard of paid acquisition because of Facebook’s powerful targeting algorithm. Retailers, for example, feed transactional data into Facebook’s algorithm to train its bidding engine. Then, Facebook optimize bidding for consumers who are most likely to buy from that retailer. The nearly instantaneously feedback loop enables fast iteration on paid acquisition strategies. You should never be bidding more on a lead than what they are worth to your business.

Facebook Ads have some limitations, though, which can make it less powerful for B2B companies. Facebook only holds onto data from the past 28 days. This means that purchase data from sales cycles that take longer than 28 days cannot be fed back into Facebook’s algorithm. Lastly, Facebook’s algorithm learns faster when events happen sooner, which again works against longer sales cycles.

Madkudu’s AI is training Facebook’s AI.

This raises a bit of an issue for SaaS companies. Most are spraying and praying ad dollars. They can only optimize on clicks instead of lead value, because they are unable to identify and optimize for high-quality leads.

Fast-growing SaaS companies like Drift are doing things differently. They feed MadKudu data into Facebook’s algorithm, enabling them to optimize bidding against leads which MadKudu would score high. In short, Madkudu’s AI is training Facebook’s AI.

Translating MadKudu data for Facebook Ads

The goal is to feed transactional data to Facebook that it can use to optimize bidding against leads that we want. MadKudu’s predictive score identifies a lead’s value at the top of the funnel. We just need to capture that lead data as early as possible and send it in a way that Facebook understands.

There are two main attributes that Facebook is looking for to train its AI – an individual and its “value.” For eCommerce, that typically means feeding a purchase back to Facebook; however, we need to adapt our value a bit.

MadKudu is good at predicting the amount that a lead will spend based on historical deal data. This helps us differentiate between self-serve and enterprise leads, for example. Of course, not all leads will convert (even the very good ones), so in order to create our predicted value to send back to Facebook, we can adjust the predicted spend by the likelihood to convert (two variables MadKudu generates natively for all leads). The result is the following:

Lead Value = % likelihood to convert  x Predicted Spend

If a lead has a 10% chance to convert at $30,000 in ARR, we can send Facebook a “transaction” worth $3,000 as soon as the lead gets generated. Now we can send data to Facebook nearly immediately to train its model. We’re training Facebook Ads algorithm to value the leads, without having to wait for the sales cycle to close.

To pass the information between MadKudu and the Facebook Pixel, we used the MadKudu FastLane, which Drift already had on their website. It’s a simple line of javascript that turns any lead form into a dynamic customer fit-driven lead capture device. The same mechanism that helps Drift convert more leads into demo calls is also training Facebook’s algorithm, no extra coding required.

The Impact: 300%

For Drift (in a test run in partnership with, the impact was clear and immediate: a 300% increase on conversion from Facebook. With MadKudu Fastlane sending transactional data back to the Facebook Pixel, Drift is enabling Facebook to only spend on leads that MadKudu scores well, which Drift already knows to work well in predicting conversion to customers.

By building its growth & marketing foundation on top of MadKudu as a unified metric for predicted success, Drift is able to extend MadKudu to its paid acquisition (in addition to their bottom of funnel use cases). Connecting MadKudu to the Facebook Pixel only takes a few minutes via the MadKudu FastLane.

Get in touch to learn more here.

Building a Shadow Funnel

Marketing is becoming an engineer’s game. Marketing tools come with Zapier integrations, webhooks and APIs. Growth engineers finely tune their funnel, each new experiment – an ebook, a webinar, ad copy or a free tool – plugging into or improving upon the funnel.

Growth engineers fill their top of their funnel by targeting prospects who look like they are a good fit for their product, but haven’t engaged yet. Guillaume Cabane, VP Growth at Drift, has been sharing his experiments leveraging intent data for years. Intent data allow Guillaume to discern the intentions of potential buyers by providing key data points into what they are doing or thinking about doing.

A quick review of the three main categories of Intent Data

  • Behavioral Intent: This includes 1st party review sites like G2Crowd, Capterra & GetApp, as well as Bombora, which aggregates data from industry publications & analysts. They provide Drift with data about which companies are researching their industry, their competitors, or Drift directly. (e.g: “Liam from MadKudu viewed Drift’s G2Crowd Page”)
  • Technographics: Datanyze, HGData & DemandMatrix provide data about companies that are installing & uninstalling technologies, tools or vendors (e.g: “MadKudu uninstalled Drift 30 days ago:)
  • Firmographics: Clearbit, Zoominfo & DiscoverOrg offer data enrichment tools starting from a website domain or email, providing everything from headquarter location to employee count.

In a standard buyer journey, the right message and medium depends on where a prospect is in the funnel:

  • Awareness: do they know about the problem you solve?
  • Consideration: are they evaluating how to solve a problem?
  • Decision: are they evaluating whether to use you to solve their problem?

Drift began looking at whether we could help them determine the next best action for every prospect and account in their total addressable market (TAM). TAM can be calculated as the sum of all qualified prospects who have engaged with you (MQLs) + all qualified prospects who have not engaged with you.


I’ll call the latter Shadow MQLs (SMQLs), more precisely defined as any prospect that is showing engagement in your industry or in one of your competitors, but not you.

Drift already leveraged MadKudu to determine when & how to engage with MQLs in their funnel, but they needed to automate the next best action for SMQLs. Should a sales person call them? Or should Drift send them a personalized gift through Sendoso?

Our strategy for determining the next best action involved mapping intent data to the standard buyer journey stages. By doing this, we could build what I call a Shadow Funnel.

For this experiment, we focused on four intent data providers:

  1. G2Crowd: a review site that helps buyers to find the perfect solution for their needs. They send Drift data about who is looking at their category (live chat) or Drift’s page.
  2. SEMRush: a tool that provides information about the paid marketing budget of accounts.
  3. Datanyze: this gives us information about what tech are being used on websites.
  4. (Clearbit) Reveal: tells us the accounts that are visiting our website.

In order to build our shadow funnel, we need to define Shadow stages of the buyer journey:

  • Awareness: understands the industry you operate in.
  • Consideration: looking at specific vendors (not you).
  • Decision: evaluating specific vendors (not you).

MadKudu’s role in this funnel is to determine whether the SMQL is showing High, Medium, or Low predicted conversion. Here is a table illustrating the data points we mapped to each stage & fit level:

By matching Datanyze & G2Crowd data, for example, Drift can identify accounts who have uninstalled one of Drift’s competitors in the past 30 days and have begun researching the competition. Without ever visiting a Drift property (which would, in turn, enter them into Drift’s real funnel), MadKudu predicts a high probability that this account is in the process of considering a new solution in their space.

With a traditional funnel, the goal is to fill it and optimize for conversion down-funnel. Awareness campaigns drive traffic, acquisition campaigns drive email capture, and conversion campaigns increase sales velocity & conversion.

The goal of the Shadow Funnel is the opposite. Drift wants the funnel to be empty and to have everyone who is in it churn out.

Rephrasing our previous TAM equation, we can state the following:

TAM = Funnel + Shadow Funnel

Anyone who is in your TAM that isn’t in your funnel is in your Shadow Funnel, and anyone who is in your TAM that isn’t in your Shadow Funnel is therefore in your Funnel.

The goal then becomes to move horizontally:

  • we want Shadow prospects to move from Shadow Aware (i.e: aware of the industry) to Aware (of you).
  • we want prospects at the Shadow Decision stage (i.e: deciding which tool to use, that isn’t yours) to move to the Decision phase (i.e: deciding whether or not to use you).
  • And so on.

Once you know where your target audience is in the buyer process, you can deliver targeting messaging to pull them from the Shadow Funnel into your funnel.

Next Steps: evaluating intent as predictive behavior.

For now, the Shadow Funnel is a proof of concept. Through this method, Drift identified 1,000+ new qualified accounts to engage with. Once we have some historical data ti play with, our next step will be to build a model to determine which intent data sources are best at predicting Shadow Funnel conversion. We’ll also want to look at which engagement methods show the most promise.

Can the same engagement tactics that work on the traditional funnel work on the Shadow Funnel? Does the thought leadership retargeting ad on LinkedIn have the same impact if an account has never engaged with you before? Does looking at a category on G2Crowd reliably predict whether you’re interested in considering our product?

We are excited to continue to explore this with Drift and other SaaS companies leveraging intent data to engage qualified prospects who need their product before prospects engage with them. This is a natural evolution of the B2C strategies that eCommerce & travel companies have been employing in previous years, but tailored towards helping companies looking for answers get those answers faster.

We’ll be talking more about this strategy with Drift & Segment on our upcoming webinar here.