Solving the Revenue Funnel Data Challenge


Hi, everyone. I’m Arun from FunnelEnvy. Today I want to spend some time talking about how we solve the revenue funnel data challenge.

Of course, many of you are running multiple campaigns on lots of different channels. You have digital advertising, paid acquisition campaigns that are heavily optimized, and you spent a lot of time getting people into your marketing automation platform and running really personalized email nurture campaigns to them.

Now, of course, the main problem that we focus on at FunnelEnvy is that the main thing in the middle that glues a lot of that customer journey together is your website. Of course, for many of us, it’s a static experience. Unlike the other channels, prospects and customers keep coming back to that same static website. In many cases, it’s just not keeping up and dragging down your conversion rates and ultimately your pipeline that you’re delivering to the sales team.

Now, that’s a broad example of some very specific demand gen website problems that we commonly run into. You’re probably looking at website analytics, and if you’ve spent any time in there, it’s not easy to tell which experiences and offers, things like landing pages or other offers, content, are actually contributing to revenue. Maybe you’re running experiments and campaigns on your website, and it’s also hard to measure the pipeline and revenue impact from those website experiments. Of course, a static website that’s showing the same thing to every buyer at every stage isn’t very effective at moving prospects efficiently through the funnel.

Now, of course, the demand gen revenue journey is long and complex. This is just one example of someone coming to the site, downloading content, getting on an email sequence, attending a webinar, talking to sales who finally opens it up and later on closes the deal. The important thing to understand is that, of course, that revenue journey occurs in multiple different contexts, multiple different channels, and ultimately different platforms in our backend martech stack, from the website and the website analytics to the marketing automation platform and ultimately the sales team and the CRM platform.

Now, the net effect of this is we end up creating a lot of data silos in our stack. Website analytics, the CMS or the optimization tools, maybe third-party firmographic data providers, our backend systems like marketing automation and CRM, these are all silos of information that all have individual pieces of that entire customer journey. We can think of those individual pieces as pieces of the puzzle that, if we put together, can actually present a cohesive picture of those individuals and accounts and their opportunities as they move throughout that customer journey.

Now, the important thing is having that glue that can bring all of those puzzle pieces together and not only receive data from those individual systems, but also feed them and also feed our business intelligence or reporting. For now, we’re going to call that a customer data platform, or CDP. This is similar to a data warehouse, but it has some very specific features that we want to see to be able to solve some of those problems that we were talking about.

First off, it’s not just sufficient for this CDP to receive data. It needs to have bi-directional integration to a lot of these solutions so it can pass data between these different platforms. Certainly, it unifies all of these puzzle pieces, or that profile data, and because we’re talking about demand gen, it happens both at an individual and an account level. It has that full view of the customer journey, so unlike website analytics that’s only looking at what’s happening on site, it has that perspective tied to that unified customer profile of what happens not only on the website, but also down the funnel as that leads to account progress towards revenue. It allows for rich segmentation because we have those unified customer profiles and full perspective on that buyer journey, and because we’re talking about website optimization, allows for real-time resolution to be able to target those different audiences on the website.

Let’s get into some of the specific use cases of the customer data platform. First off, we can use it to enhance our existing web analytics. If you spend a lot of time looking at your website performance and in platforms like Google Analytics you know that you’re typically only looking at those vanity metrics, things like bounce rates and visits and exit rates. Unfortunately, a lot of the interesting data that we have lives in our marketing automation, our CRM, the leads, contacts, accounts, and opportunities that we’re really seeking to improve and understand.

By pumping this data into our CDP and unifying it and then feeding those down-funnel outcomes into our web analytics, as well as our reporting and dashboards, we get a much more complete picture of that revenue journey for that visitor. This lets us answer questions like which experiences and offers on my site are actually contributing to revenue, and how much? You see here a landing page report, but instead of only looking at your typical vanity metrics you see on the right, we actually can visualize the closed one revenue of each of those landing pages. Of course, you can take any of those dimensions that you’re typically looking at in a web analytics and understand the pipeline and revenue impact this way.

You can also answer ad hoc questions through dashboards, like my website traffic, not just in terms of numbers, but by different buyer stages and the expected revenue of each. This often starts the hypothesis process of how can I actually target those different buyer stages differently to accelerate them through that revenue funnel.

Once you actually get into running experiments and campaigns, it can be very difficult to understand how much pipeline and revenue those experiments and campaigns are actually generating, especially if you’re using a multi-touch attribution model, which many of you are. We can solve that problem by bringing, again, that lead, contact, account, and opportunity data, as well as the touchpoints in my attribution model and the test and campaigns from the optimization platform, and feeding those into my reporting and business intelligence environment.

By doing that, we can start looking at the amount of sourced pipeline using that attribution model that my website campaigns and experiments have delivered over time. We can start comparing campaigns, not just on their ability to get people to fill out a form, but based on the actual pipeline and revenue impact that they’ve had. We can look at individual campaigns and the variations in those campaigns and understand which variation is most effective, again, not just in terms of those onsite vanity metrics, but their ability to deliver results down the funnel.

Finally, when we’re talking about optimizing the revenue funnel, a lot of it is about targeting in real time and targeting the right offers to different personas, different groups of accounts, and my personal favorite, by buyer stage, to move prospects more efficiently through the funnel. Now, to do this, again, we’re bringing over that same marketing automation and CRM data, but potentially also that website behavior from web analytics, and in this case, feeding that into audiences in our content management system or our testing and optimization platform. In this case, this isn’t a reporting use case, so that audience has to be delivered in real time based on an individual visitor as they come into the site. If you can do that though, we can start doing things like personalizing those offers based on that buyer stage and where they are in the journey, or even differentiating offers and changing the experience in real time to present the best offer based on the persona that’s coming to the site.

With that, I want to leave you with some takeaways. If you’re a demand gen marketer and you’re looking at your website performance and even optimization of it, our recommendation is always not to settle for top-of-the-funnel KPIs. Those vanity metrics, those engagement metrics, even form conversions, they’re not sufficient. Really, you should be trying to align your efforts with the way the rest of the team and organization measure success. For a demand gen team, that’s based on pipeline and revenue impact.

Of course, demand gen sites, demand gen marketers, typically deal with long customer journeys. What that often means for your site is that you have a lot of return visitors. They’re coming back to the site at different stages in the buying journey with differentiated intent, but the measurement of pipeline and revenue and the ability to understand those different buyer stages and ultimately target them is really related to how efficiently you can bring that data together and activate it in real time.

Then finally, many of you may have already invested in a data lake or data warehouse to unify that data that you have in these different silos together. Typically, this is done for attribution use cases and other reporting and measurement use cases. It’s a great starting point. Our CDP often compliments existing data lakes or data warehouses, but the reason that we have to compliment it is that typically those are solutions that aren’t built for that real-time use case. If you get into the targeting and activation of that data in the milliseconds that are required by the time a visitor comes to the site and you deliver them that web experience, it requires a real-time source, in our case, the FunnelEnvy customer data platform.

With that, I want to thank you for listening today. Bye.


Don’t Fear a Cookieless World, Instead Shore Up Your First-Party Data to Optimize Your Funnel

If you haven’t yet heard, the cookie is on the outs — much to the cookie monster’s chagrin. The death nell was sounded by Google’s announcement of Privacy Sandbox, which is basically their plan to create a set of privacy standards.

This plan includes improving how cookies are classified, clearing up the details behind each person’s cookie settings, and plans to aggressively block fingerprinting. A fingerprint is created by stitching together a bunch of tiny signals about a person to create a full profile, and since people can’t access or delete their fingerprint, Google’s basically going to make it impossible to create them.

All of these intentions add up to one pretty plausible result — third-party cookies (the type used to make fingerprints, and fuel activities like retargeting) won’t be around much longer.

There’s another type of cookie though that’s not going anywhere — the first-party cookie, which allows marketers to collect first-party data. Focusing on shoring up your first-party data will not only prepare you for the death of the third-party cookie, but result in a stronger marketing strategy overall, regardless of the third-party cookie’s fate.

In this article, we’ll talk about the difference between the first and third-party cookie, why the first-party data is more valuable anyway, and how to use it to optimize your demand generation funnel.

First-party cookies vs. third-party cookies

Before we get into exactly how and why you should focus on first-party data, let’s straighten out the two types of cookies:

  • A first-party cookie is created and stored by the website you’re visiting; the one in the address bar. If you’re a site owner, first-party cookies allow you to collect data like customer analytics, language settings, the user journey, and other information that can assist you in improving your customer experience on-site.
  • A third-party cookie is created by sites other than the one you’re currently visiting. These other sites own some of the content, like ads or images, that you see on the site you’re currently visiting, and can therefore collect information about you while you’re there.

For example, say you’re shoe shopping with popular retailer DSW. When you visit and shop for boots, you might not purchase right away. During that first visit, the homepage looks like this:


The next time you visit their site, there’s a new section of the homepage that displays the shoes you clicked on during your last visit. DSW dropped a first-party cookie on their site in order to remember that you were interested in buying boots. They then used this information to personalize your experience the next time you visited their site.


During this second visit, you made a purchase and provided your email. Two days later, DSW sends you an email about an upcoming boot sale. That’s first-party data. DSW used a combination of first-party cookies and personally identifiable information (PII), namely your email address, in order to personalize your experience.


Third-party cookies are most often used to retarget you on sites other than Perhaps after shopping for boots, you head over to to read up on the news. As you’re reading an article, you see a Google-owned banner ad advertising the shoes you just looked at:


A lot of data exchange went on behind the scenes for you to see this ad. First, DSW partnered with Google and started using Google Ad Manager to serve ads around the web. The New York Times also partnered with Google to display ads on their site, in order to monetize their content.

Google then dropped a third-party cookie on DSW’s site to collect data on your visit, and DSW retargeted you on in the hopes of capturing your attention, and bringing you back to your site.

These are the types of cookies that Google is looking to guard against, and they’re the ones that are likely to die in the coming year.

Your first-party is data more valuable than third-party data anyway

The thought that third-party cookies are on the way out has caused a bit of a panic among marketers, mostly because they’ll have to come up with new ways to retarget site visitors.

But the thing is, focusing on first-party data is way more lucrative than scaling retargeting campaigns based on third-party data. First off, you collected that data directly from a person, and you know it’s accurate. Second, because you collected that data while that person was visiting your site, you know they’re actually interested.

Let’s go back to our shoe example — which interaction with a potential consumer would you find more valuable — the one on your owned website, or the display ad impression they probably didn’t see?

We bet your answer is the former.

First-party data is more valuable because it’s the best indicator of buyer stage, and therefore intent. Someone visiting your website has a much higher intent to interact with your brand than that of someone who saw a display ad.

For demand generation marketers, buyers go through many stages in their journey, so it’s really important that the data you’re collecting on those buyers captures their intent at each stage.

FunnelEnvy combines first-party data insights and offers personalization in order to align the offers on your website to the intent the buyer has at the time they’re visiting. This way, you move them down the funnel every time they visit, leading to better conversion rates and ultimately more revenue.

Here’s an example from Fitch Solutions. They guide their clients in making clear-sighted decisions through data, research and analytics on the capital markets and the macroeconomic environment.

Like many B2B technology companies, they thought of their homepage as a type of welcome center where they introduced themselves to people getting to know them for the first time:


But, also like many B2B technology companies, they saw a lot of returning traffic, which is often a result of having a longer buyer journey. A “welcome center” isn’t an optimal experience for someone you’ve already welcomed.

FunnelEnvy worked with Fitch Solutions to personalize their homepage experience for each visit, and for returning visitors, they surfaced a relevant offer in place of their welcome message:


This change resulted in a 55% increase in conversion on site. By doubling down on optimizing their website using first-party data, Fitch Solutions made a huge impact on their funnel.

Optimizing the demand gen funnel with first-party data

So, how do you get from collecting first party data to activating it with a personalized experience on-site? The biggest challenge for the demand generation marketer facing the death of the third-party cookie is that first-party data is often siloed away in places like your customer resource management (CRM) software, marketing automation and in website analytics. 

If you want to truly personalize an experience, you need to bring all of that data together for a holistic view of the consumer journey.

The effort is well worth it — in fact, 77% of B2B sales and marketing professionals believe that personalization builds better customer relationships.

But to get there, something needs to bring all of that siloed data together so that you can target accordingly by buyer stage. The FunnelEnvy Backstage platform brings together these data sources, website analytics and experience tools to create a unified customer profile.

If you have the data, you can get sophisticated with offer personalization. You can attribute different user experiences to revenue, target by buyer stage and scale revenue.

Here’s an example from TIBCO Jaspersoft. They had one static product page that contained multiple offers for different personas within the organization. 


When they tried to squeeze multiple offers on a single page, offers competed for attention and blended in, which put the onus on the user to determine which was right for them. 

We worked with them to target specific personas with a single offer, based on data they had stored in their marketing automation platform. Through testing variations that replaced the default experience with a single focused offer, we saw an almost 50% improvement in revenue per visitor.

Conclusion: the death of the cookie is nothing to lament

While the death of the third-party cookie will mean a shift in strategy, there is a huge silver lining — as it’s phased out, demand gen marketers can use the opportunity to shore up their first-party data strategies, which are likely to result in a much larger impact on their funnel.

We’ll see less focus on (admittedly crappy) ad buys and retargeting campaigns, and a larger focus on leveraging first-party data insights better at home.

If you’re stuck on where to start when it comes to shoring up your first-party data strategy, we can help. Apply now to get started.

The Secret to Making Your ABM Personalization Campaign a Success

For all the demand generation leaders out there, I’ll bet you’ve engrossed your partners, sales directors and higher level leadership team on the subject of account based marketing personalization. But be honest, do you really know what a successful outcome looks like?

visual screenshots of abm personalized accounts

ABM-based personalized campaigns are the targeted, personalized experiences that either speak one-to-one to an account, or to a group of important accounts. Per a definition found online,

“Personalized campaigns that are designed to engage each account based on the marketing message on the specific attributes and needs of that account.”

In following this logic, you’ve decided that certain accounts are more important than others, and therefore are worth the effort of this level of personalization. Great! Typically, demand generation marketers follow these steps:

  1. Identify targeted accounts
  2. Target those accounts in real time with personalized campaigns
  3. Measure the effects of those campaigns

Steps one and two are pretty straightforward, and certainly there are potential pitfalls, but step number three is critical. The truth is the wrong measurement strategy could doom your ABM personalization efforts to failure.


Choosing the Right ABM Measurement Strategy

A common way to evaluate the effect of an onsite experience is through an A/B test. This is the traditional conversion rate optimization (CRO) approach. You might run a randomized A/B test, evaluate the effect of personalizing your homepage against a baseline or control, and measure an on-site goal, like lead conversions. You can carry out this type of testing in a variety of platforms, measure the effect on your goals, and try to determine the overall impact on your personalization efforts. 


It’s important to note however, that this approach makes several assumptions. First, it requires a large sample size to establish confidence based on statistical significance. It also assumes that all of these conversions are the same.

If you’re only looking at the difference between lead conversion for the baseline and lead conversion for the personalized option, then you’re assuming the conversions carry the same weight, measuring impact based on the quantity of those conversions, but not necessarily the qualityWe should ask ourselves, are these assumptions consistent and compatible with our ABM strategy?

When we think about ABM, the fundamental goal is to capture more revenue from a smaller number of accounts. That’s how you can justify the investment in targeted ads, website personalization, or direct mail.


Keep in mind that the further up the ABM pyramid you climb, the smaller the number of accounts that can potentially drive value or expected revenue. In many cases, those white glove accounts could be worth a hundred times more value than an SMB account. Since those accounts are likely larger enterprises, they’re also likely to take longer to close.

What to Include when Measuring your ABM Personalization Campaign

Your ABM personalization strategy should include a quality metric, which includes pipeline and revenue, considered over a longer period of time.

Now this can conflict with the CRO approach that we mentioned earlier. This approach, however is very transactional, top-of-funnel, and assumes that each conversion is the same.

You may consider or are actively engaging in the traditional CRO approach to pass variation information back from your optimization platform to CRM for analysis against pipeline and revenue. Often times, this means embedding those variations that users saw in a hidden field on a lead capture form, and then building a custom report in your CRM data on the backend.

 simple attribution solution

Will this get you better insight into the down-funnel impact from your personalization experiences, like pipeline and revenue? It may, but you could also wind up with a one-off solution for the website experience that doesn’t align with the way the rest of your demand gen team measures and attributes revenue.

The Right Attribution Model for ABM Personalization

You may be running a multi-touch attribution model, either with an off-the-shelf tool like Visible, or a custom solution.

These work well for longer journeys by allocating revenue back to customer journey touchpoints, like the first touch, lead creation or opportunity created, granting credit to the triggers that influenced the conversion.

bizible full attribution model path

Image Source: What is a “Full Path” Marketing Attribution Model? (Bizible | Link)

B2B attribution solutions are inherently account-based and they’re typically used for channel and campaign analysis, but they can also be used to measure the effect of on-site activities like ABM personalization campaigns.

Now, the real advantage here is by integrating your website experiences with your multi-touch attribution model. You’re aligning your website activities to the measurement strategy used by the broader demand generation team. So how does this work?

Let’s say you have touchpoints established, and you’re allocating revenue in some proportion across each one. Here, the actual percentages don’t actually matter as long as you’re are allocating revenue back to new customer touchpoints.

abm personalization touch points

Once you know the revenue at a certain touch point over a period of time, with attribution, you’ll be able to assign credit back to the activities that influenced that touchpoint conversion. As I mentioned earlier, it’s typically done at a channel and ad campaign-level, but there’s no reason that an onsite experience can’t be an influence over a touchpoint as well.

Campaigns and variations are a couple of example of onsite influences, but you could also consider chatbots, content, and anything that influenced a touchpoint. Eventually, you’ll see pipeline and revenue credit associated with the influencing on-site experiences of those touch points. 

The Better the ABM Attribution, the Better Your Metrics

Rather than report on percentages like conversion lift on a superficial vanity metric, you can report on campaigns over time with respect to their sourced pipeline. These insights grant you a better understanding of the impact your on-site campaigns have on your business.

graph of results over time

Additionally, you’ll see the impact your campaigns have not only in terms of onsite metrics, but based on sourced, influenced pipeline and revenue, and closed deals. 

personalization by intent chart

Variations within your experiments will give you the view you need to report on uplift based on whether pipeline or revenue correlated directly with your attribution model.

testing variation chart funnelenvy

Let’s talk about other ways you can ensure your ABM personalization efforts are a success. Start your revenue insights journey by measuring your revenue contribution of existing offers. By aligning this with your attribution strategy, you’ll gain deeper insight into your on-site offers and assess their revenue contributions.

Don’t Forget About Segmentation

Segmentation is a big part of account-based personalization. The audiences that you create will have differentiated intent. You wouldn’t want to spend time creating audience segments that are effectively the same and trigger the same experiences.

Think about how your account clusters and account groups are differentiated, and what offers they’re going to want to see that are specific to their interests within their own groups of accounts. 

Now let’s pivot to the must-avoids. This includes what we call “vanity personalization,” where you add the name of a customer or account coming to the site. Instead, focus on the offers you’re putting in front of your visitor.

The ideal is to present a more relevant offer throughout the demand generation funnel to your visitor. When segmenting, think about the offers presented to these target accounts, and focus on how to increase their relevancy.

Remember, this isn’t just a top-of-funnel activity. Obviously we’re talking about demand gen marketing in this post, but consider your full customer journey as a long revenue funnel.

Once you enable yourself with the capability to personalize your ABM campaigns, think how your business can better align offer and optimize, not only for your top-of-funnel, but for each stage of your buyer journey. 

We Know Why Your Online Ads Aren’t Scaling Revenue (And How to Fix It)

When you put too much pressure on something, it cracks. 

Online advertising is no exception. The cracks in this ecosystem have turned into gaping holes, and those holes are why your paid ads aren’t scaling.

Three of the big ones are often in the headlines: the death of the third-party cookie, attribution as an almost impossible feat, and data privacy which is getting clearer, but still murky at best.

Yet, most of us put up with it. 84% of B2B marketers use paid distribution channels (read: Instagram, LinkedIn, Facebook, YouTube, etc.), which would be one thing if the value of those channels was clear, but a staggering 47% of us admit that we can’t measure ROI and 18% aren’t sure if they can or not.

Despite these shortcomings, when we need to scale, our first thought is often growing the budget for online ads, but will that really move the needle?

In this article, we’ll break down why your ads aren’t scaling revenue by analyzing their actual contribution to your sales funnel, calculating what would really happen if you had your dream budget, and how to fix the gaping hole in your funnel that paid channels leave unfulfilled.

What do Paid Ads Actually Contribute?

Pretty much everyone buys ads from Facebook and Google, but it’s also quite common for B2B marketers to buy ads from LinkedIn. The rest of the web is fragmented and even harder to navigate than these channels, so for the sake of argument, let’s focus on those three.

  • Search ads are effective, but have incredibly narrow margins, and quickly get costly if you’re not paying attention.
  • Facebook ads have better margins, but only if you can keep up with inevitable creative fatigue on behalf of your audience.
  • LinkedIn ads have the best margins of all, but they’re incredibly expensive, with an average cost of $5.26 a click versus Facebook’s $1.72.

So, they do work, but the margins are small and the cost is high. Because of this reality, most marketers make incremental investments focused on driving traffic to web pages.

We took a look at website traffic from two of our high-growth clients, and saw that those expensive investments were a drop in the bucket in comparison with organic channels. 

Direct and organic traffic made up 70% to 75% of all traffic, whereas traffic from a whopping five to six paid channels only made up 25% to 35%.

In these two examples, paid search specifically accounted for 13% and 9% respectively.

paid traffic example case study funnel envy 1 paid traffic example case study funnel envy 2

But what if that 13% of traffic is where all of the revenue came from? Even in that unlikely scenario, we can’t expect it to continue to grow if this marketing team tried to scale their paid search buys.

This analysis from Search Engine Land shows that on average, after hitting a certain inflection point, your margins get smaller and smaller. This is true for all marketing channels, and is also known as The Law of Shitty ClickThroughs.

paid search roi return on investment search engine land analysis

Image Source: Paid Search Portfolios: The Good, The Bad & The Ugly (Search Engine Land | Link)

This is not to say that paid advertisements don’t have a place in your marketing mix, but that alone, they won’t scale revenue profitable or quickly. 

Optimizing the entire journey, which requires analyzing all channels as a whole, is the only way to scale revenue optimally. And since website traffic comes from all channels and your website is where buyers at every stage of the funnel engage, you can only do that if your web analytics are measuring revenue instead of top of the funnel vanity metrics.

How do You Scale Website Traffic so that it Ends in Revenue?

Instead of narrowly focusing on incrementally increasing website traffic with paid ads, the answer to revenue at scale lies with focusing on improving website conversions across all channels and at every stage of the buyer journey.

The chart below is a snapshot traffic analysis spanning one quarter, from a FunnelEnvy customer. This B2B SaaS company currently sees a 5% conversion rate from paid channels, which is higher than their direct and organic channels — at 2.5% and 3% respectively.

It might therefore seem logical to focus on scaling their paid channels. Let’s see what happens when they give it a shot.

This B2B SaaS company’s marketing team tested spending 50% more on paid advertising one quarter, resulting in an additional 1,000 leads. When they instead focused on optimizing their website funnel across all channels, they saw an additional 1,755 leads, a 10% improvement over just scaling paid ads alone.

web funnel lead optimization funnel envy example paid ads not scaling

As we’ve seen, online ads are subject to the same laws of diminishing returns as any other channel. So, actually achieving 10% growth from your website (which accrues to all acquisition channels), is likely to be much easier than generating 50% or more growth from your paid channels.

Optimizing your website doesn’t have to be an exclusively lead generation focused activity. In fact, you’re likely to get much better results if you can optimize all the way down funnel to pipeline and revenue. To illustrate the impact, let’s compare what happens when you optimize your top of funnel (TOFU) conversion rate with bottom of the funnel (BOFU) metrics like opportunities and closed deals.

The following FunnelEnvy customer is a B2B SaaS company in the fintech space, looking to increase the number of closed deals per quarter and reduce their customer acquisition cost (CAC).

They average about 600,000 visits per quarter and spent about $450,000 across all paid channels. When they increased their lead generation focused conversion rate alone by 30%, they saw a corresponding increase in deals closed, and a 23% reduction in CAC. However when they extended conversion improvements all the way down the funnel they saw an even greater increase in closed deals (50%), and a larger (33%) reduction in CAC.

This “Revenue Funnel Optimization” requires you to identify visitors at different buying stages on your website, and to target them with more relevant offers and experiences. 

This table captures the impact of optimizing through various stages of the funnel — leads (TOFU), pipeline (opportunity creation) and closed or won deals (revenue):

web funnel funnel envy example paid ads not scaling TOFU BOFU

An Easier, More Scalable Path to Growth Exists Across All Channels, Not with Paid Ads

The bottom line — simply increasing your spend on paid channels is not the answer to scale, and there’s a lot more growth to be found if you take into consideration how other channels impact your funnel.

As the paid advertising ecosystem racks up more and more problems and gets increasingly expensive, this is a great time to focus on elevating the value from other channels that impact your funnel.

If you’re not quite sure where to start, you’re not alone. 68% of B2B companies haven’t even identified their sales funnel. FunnelEnvy’s solution helps you close the gap between website analytics and revenue and target buyers at each stage of their journey.

If you’re a demand gen marketer that needs to scale growth efficiently learn more about our solution for Revenue Funnel Optimization.

Revenue Funnel Optimization Focus on the Offers


Hi, everyone, I’m Arun from Funnel Envy.

We help demand gen marketers increase pipeline and revenue through revenue funnel optimization.

And today I want to talk about why you should really focus on the offers. I’ll explain what that means as we go through this.

Now, let’s take a step inside the data driven demand generation marketing team, maybe the top problem on the CMOS radar is that the acquisition costs are too high and they’re not going to hit their pipeline or revenue goals. And so she’s asking that the head of demand gen, you know, where’s the problem?

Now, looking at the data, being a good data driven marketer he comes back with, you know, they’re spending a lot of money on unpaid and organic traffic. The quality of that traffic is good, but it’s just not converting like it should be.

So, of course, the natural question is, what can we do about it?

Now very good answer and a common answer is to focus on website conversion rate optimization. And that typically involves running a lot of online experiments.

So you can budget that, make it a priority but recognize that those executives are probably going to want to see impact based on pipeline and revenue and probably want to see it fast.

So let’s dig into online experimentation, back in twenty seventeen the Harvard Business Review published this important study and article really talking about the power of online experimentation and correlating successful business outcomes to a culture of experimentation. They cited examples like this from Bing where bing tested multiple different colors on their site, ran experiments and realized an incremental 10 million dollars in annual revenue from these experiments.

Similarly, Google ran a test with 40 different shades of blue on their site, when they ran those experiments, they saw 200 million in incremental revenue. And given these results, should we as demand gen marketers be running the same kind of experiments?

Well, in our opinion and in our experience, no. You’re not Google or Bing, leaving aside traffic considerations, you’re trying to influence B2B buyer behavior over a long customer journey. And the reality is that groups of buyers that are considering enterprise solutions are not going to be influenced to buy based on the button color or other small cosmetic changes. And this is really important because, of course, experimentation comes with a cost. Not only do you have the people and the technology costs of running online experiments, it’s also an impact on your ability to make decisions as an organization. So, it’s really important that when you’re doing this, you focus on the elements that are actually going to deliver revenue and influence those B2B buyers.

Now, when we think about the B2B buying journey or the revenue funnel, you can think about it as various stages and as prospects progress through those stages, they do so through a series of exchanges. This is fundamentally the heart of marketing where you are offering something to that prospect in exchange for something else that could be a piece of content in exchange for their attention or their contact information, that could be an offer to attend an event, that could be an offer to talk to the sales team, it’s some offer through which they learn more about how your solution is going to benefit them.

So from our experience and in all of the testing that we’ve done, the highest value, the highest leverage use of experimentation for the demand gen org is to improve the relevance of those offers through that revenue funnel, through that buying journey and the ease of engaging with it. And of course, we always want to make sure we’re measuring the impact of those experiments based on the KPIs that matter, pipeline and revenue.

So what does it mean to be optimizing offers? Well, we like to focus on three main aspects.

One, of course, is the offer itself, that thing that you’re proposing to exchange with that visitor or prospect for them to better understand your solution. The more relevant it is to that visitor and their intent, the more effective your ability to convert them will be.

The other important aspect of the offer is the framing of the offer, and here we’re really talking about the headlines and CTA’s headline is really important because typically a visitor is going to spend five or 10 seconds, at the most, deciding if they want to stay on your site or hit the back button and go somewhere else. So the more effectively you can position that headline and entice them to continue reading and engaging with it, the more effective you’re going to be.

Third element of the offer is the mechanism of exchange, how they actually exchange what you want from them in exchange for the offer that you are putting in front of them. Typically, this is in the form of a web form, but it doesn’t have to be. We’re also seeing more chat bots, conversational marketing tools that accomplish the same thing, provide that medium of exchange for the offer.

So let’s look at some examples.

Landing pages are a great starting point. Many of you are probably running traffic to landing pages and putting an offer in front of those visitors hitting it. Now, in this case, we recognize working with a customer that they actually had three viable offers for those visitors coming through their paid campaigns to their landing pages. And rather than only showing them one, we use data to dynamically personalize the offer itself, but also the framing and the page layout to reflect what might be most relevant to that visitor. And doing this, we see an almost 44 percent improvement in revenue per visitor when we ran this experiment.

We spend a lot of time working on the home page and specifically that above the fold section of the homepage, at the top of the page where most of the eyeballs go. Now, many of you might have a site and a home page that kind of looks like the baseline experience here where you’re trying to introduce your solution at the very highest level to that first time visitor. But of course, you probably have a lot of return visitors, especially if you’re a SAAS solution, a lot of return visitors who are already familiar with your solution or your offering. And it probably doesn’t make sense to show them that same welcome offer. And in this case, we actually were able to identify visitors and the specific solutions that they were interested in and present that offer right on the home page that otherwise would have been further down in the in the website that they would have had to navigate to. So I presented them with an offer that they were more interested in and serving those as variations right on the homepage, we see an almost 50 five percent improvement in conversions coming through this page.

Now, you can do this based on buyer stage as well, in this example, we have a customer with a freemium model where visitors who are on the free plan come to the home page and see a call to action or a button that says upgrade your plan. When they click on it, the baseline experience was to take them to standard SAAS Plan tiers and they could select the one that they would upgrade to.

Now, using data, what we were able to do is identify the plan, which was most relevant for any individual visitor, and show them instead of a plan selection, show them the specific plan that they should upgrade to right on that home page, as well as the benefits they would get out of that plan. CTA was changed to from upgrade your plan to upgrade to a specific plan at a specific price point. And in doing this, we’re able to bypass the plan selection, kind of choose your own adventure experience and take them directly to the credit card entry and upgrade.

So by removing friction, presenting them with a more relevant offer, we’re able to see and almost 70 percent improvement in revenue per visitor coming through this experience.

Of course, that mechanism of exchange is often the form, and so we spend a lot of time optimizing forms. Now recognize there’s often a lot of friction on behalf of the visitor when they see a form and they start to enter personal information, even if they’re interested in the offer. The act of giving someone your email and other personal information often presents a big hurdle. And this is where you see a lot of drop off in terms of conversion. So one of the things that we like to do is take those contact forms and reinforce the benefit and the value to the visitor of filling out that form.

So you can see an example of that where we take a default contact form, which is kind of generic and make it very focused on the benefits and ran this experiment. Here we see about an 85 percent improvement in conversions coming through this form.

Final example, you know, if you have the data, you can get pretty sophisticated with this. Many times we see experiences like the baseline product homepage that you see here where it’s a solution that actually speaks to multiple personas within the organization. And your team actually has different offers for each of those personas.

Now, when you try to put it on a single page, they all compete for attention and kind of blend in and none of them gets the conversions or attention that they deserve. By using data, we were able to identify the offer that was most relevant to the specific visitor or persona coming to this page and replace that experience with multiple calls to action with a single focused offer that was most relevant to that persona, in this case, a developer or an analyst or a manager. Here running this experiment with those variations we see in almost 50 percent improvement in revenue per visitor.

So the offers are really important to focus on as the highest leverage area of experimentation for the Demand gen marketer on your site.

And I want to wrap up with some final points.

It’s really important before you undertake this kind of experimentation to make sure you have solid revenue insights. What that means is make sure you’re able to evaluate your existing offers based on their pipeline and revenue contribution and that you’re set up to measure your experiment not just based on onsite conversion, but based on their impact to pipeline and revenue.

You saw some examples of segmentation that I walked through and personalization.

Our recommendation is to prioritize your segmentation based on how the differentiation of intent across those segments and the size of your addressable audience. We often find that people are running segmentation only for like five or 10 percent of their audience. That’s not going to be as effective as if you can address 90, 95 percent of the visitors coming to your site. This is why we start with buyer stages as our starting point for segmentation, because it presents both great opportunities for differentiated Intent buyers at different stages, want to see different content and engage in different ways. And it maximizes your addressable audience. The vast majority of the visitors coming to your site fit into anonymous, known lead, active opportunity or customer.

Finally, there are a lot of common sense opportunities if you start thinking about buying stages for more relevant offers and some obvious gaps that you should be able to identify.

Start by asking yourself some simple questions.

Should a known lead see a lead capture form? Does that make any sense or can we repurpose those pixels and that experience for something that’s more relevant?

Similarly, should an existing customer see the requested demo call to action or talk to sales? Maybe not. Maybe there’s an opportunity to up sell them or, you know, get them to support or other resources that may be more relevant.

And with that, I want to thank you for listening today, bye.

Online Ads May Not Be the Most Efficient Way to Grow Your Demand Gen Funnel


Hi, everyone, I’m Arun, the founder of FunnelEnvy.

We help demand gen marketers increase pipeline and revenue through revenue funnel optimization

And today I want to spend a little bit time talking about why online ads might not be the most efficient way to grow your demand and a revenue funnel.

So this came about because I’ve been reading more in a lot of popular blogs, including Rand Fishkin, about something being wrong or, as he put it, rotten in the world of online advertising.

Now, in the article, he cites some pretty eye opening results from prominent brands like Chase and Uber, shedding a light on millions of dollars in an Uber’s case, about one hundred and fifty million dollars of wasted ad spend.

Now, if you bring a little bit closer to home, we work with a lot of B2B in demand gen marketers. This study suggests that about 75 percent of the advertising that B2B brands are doing are failing to produce long term growth.

So that seems like a problem. What do we do about it?

Well, let’s take a step back.

Let’s assume you’re a growth stage B2B demand gen organization, and you need to grow a pipeline by 30 percent and you need to do it fast in the next quarter or so.

Where do you invest your dollars? What do you spend on?

Well, of course, you’ve got the paid channels.

This is an obvious candidate, and the reason everyone loves them is because they’re very fast to add. But of course, the flip side of that is not only are they expensive, they’re also arguably much less efficient.

We’ll talk about why.

On the other hand, you’ve got your own channels, email, and organic search and social, these are cost effective in the long term, but of course, they’re harder and slower to scale.

Now, the one overlooked element in all of this is often the website, and the reason that it’s important is because all of these channels paid and owned funnel traffic to it. So it can be very efficient to scale, but it’s often the least optimized area. Everyone typically deals with static websites and it’s harder for organizations to execute on.

But let’s look at the impact of optimizing that web funnel.

Some data points, first off, when we look across our typical high growth customers, we usually see about 70 to 75 percent of the traffic coming to the site from direct and organic sources. And the remainder, about twenty five or thirty percent split across, you know, a handful of paid channels.

What that practically means is that if you’re trying to grow exclusively through a paid approach, you’re focusing on a single paid channel, you’re optimizing maybe 10 to 15 percent of your traffic. That makes it really hard to grow and optimize your entire funnel if you’re only dealing such a small subset of your traffic.

And of course, many of you know that at some point you start getting diminishing or negative marginal returns on that spend. A lot of the low hanging fruit, it gets carved away and you have to spend more on keywords and ad placements.

So it is possible to optimize your website funnel and grow with scale and speed.

Let’s look at a typical channel distribution in terms of traffic and conversion rates for our customers. And when we look at typical conversion rates across these channels, you get a sense of the lead volume per channel. If we were to spend our efforts growing exclusively through paid and achieve 50 percent growth through the paid channel that would be a good result. And of course, you see here that we’re getting, in this case, about a thousand more leads over the same time period.

But what Web funnel optimization allows you to do is actually distribute that growth across all of your channels. So let’s say you only produce 10 percent growth, but it’s spread across all of these different channels. You’re actually seeing a net increase above the paid channel strategy because you’re able to optimize all of your funnels.

So the point here is that improving that website funnel improves all channels and that can present a much easier path to growth.

Now, too often in the demand gen world and we think about optimizing the website, we only think about it as a top of the funnel activity.

Let’s look at what happens when you go further down funnel. Again here, we’re taking typical industry standard conversion rates through the entire funnel from visit to lead to opportunity to close one deal. And we put some numbers at the bottom that show the number of leads opportunities, close one deals and the resulting acquisition cost.

So if we take a top of the funnel strategy and you assume a 30 percent growth in the top of the funnel, the visit to lead conversion rate, and you make the big assumption of assuming that that 30 percent carries through to the entire funnel, which, by the way, is almost never the case. You get a significant improvement in close one deals and also a corresponding reduction in the acquisition costs.

But if we’re able to spread that improvement and actually improve the conversion rates down funnel from lead to opportunity as well as opportunity to deal, even if you do it in smaller amounts because you have less influence in that part of the funnel, you can see here that you get a much more significant improvement in revenue and a much more significant reduction in the acquisition costs.

So the point here is optimizing for the entire revenue funnel can generate significantly better incremental revenue than just focusing on top of the funnel. So don’t just think about it in terms of leads, think about it as optimizing the entire journey to revenue.

When we at funnel end we talk about revenue funnel optimization, this is our goal, optimize the entire customer journey to revenue.

So I want to leave you some takeaways here.

The first is that, of course, throwing money at paid channels is fast, and that’s why we do it. But it might not be very efficient, as we’ve seen today, and it could very well have diminishing returns over time.

The majority of your traffic is likely coming from direct or organic sources, and that also represents buyers at different stages. It’s not enough to just think about it as return traffic. You have buyers because your demand gen marketer coming at various different buying stages with differentiated intent.

And so if you’re able to optimize your website funnel across these buying stages, again, that’s what we call revenue funnel optimization, you can actually accelerate growth across every acquisition channel and have a much easier path to growth.

With that, I want to thank you for listening today, bye.

Use Google Analytics to Understand and Convert Leads

You’re already leveraging Google Analytics to drive website strategy. But you’re missing crucial context about the single most relevant factor for predicting and influencing visitor behavior: their stage in the buying process.

You use this context in your marketing automation campaigns – different messaging and different offers based on content consumed and lead score. You exclude existing customers from paid acquisition campaigns. But when you look at website data in Google Analytics, all of those visitors are lumped together.

The revenue opportunity for an existing customer is different from a new visitor. And the content and offers that are relevant to a new visitor are redundant for a lead in your marketing or sales pipeline.

Let’s take a look at how you can get data on leads into your Google Analytics reporting views, and what to look for once you’ve got it.

What’s a lead?

Leads are visitors who have identified themselves, but haven’t paid you money. They might be on a free trial, or they might have filled out a form to access gated content.

Getting lead data into Google Analytics

First, create a Custom Dimension to hold data about visitor stage.

Open up your Google Analytics account and click on the admin section.

Screenshot of 'Admin' option in Google Analytics

Next, go the “Property” panel and click on “Custom Definitions” then “Custom Dimensions.”



By |2020-07-31T08:12:28-07:00July 21st, 2020|Uncategorized|0 Comments

Attributing Campaigns to Sales Opportunities in Marketo

I’m writing this because it’s the year 2020 and we’re still having trouble attributing onsite campaigns/testing to Marketo MQLs/Opportunities/Revenue.

It is meant primarily for technical practitioners as a quick-start guide, and is so simple you’ll probably be able to get it hooked up today.

As a result, this guide is very tactical in nature, with the end goal of helping you answer the question ‘What impact is my onsite campaigns/tests having on revenue?”.

What solution is for you

If you happen to already be using FunnelEnvy, then enabling our Marketo Integration is all you need to do. This not only enables attribution, but unlocks additional functionality, including the ability to target onsite users based on their Marketo classifications. It’s a paradigm shift in how you think about marketing automation and I encourage you to read our recent article to find out more.

If on the other hand you’re still using a web-based testing tool like Optimizely or Adobe Target to run your onsite campaigns then read on, as this guide is primarily for you. But first, a cautionary tale:

Always optimize for downfunnel outcomes, not onsite vanity conversions

Recently, FunnelEnvy ran a very visible experiment via Adobe Target for a well known SaaS company. Early results were trending downward and web analytic data showed a sharp downtrend in incremental conversions/leads. Talks were had about ending the test early as this was potential a huge economic impact.

Luckily, the attribution module you are about to see had already been installed, allowing us to query Marketo directly, telling a different story. We were able to calculate that the test was actually responsible for a significant increase in annual recurring revenue! Without this additional layer of information, we likely would have moved on, but.

But back to our discussion:

The Solution

We’re going to break this guide up into two main parts:

Part 1: Create/maintain a running list of campaigns/tests a user has seen

Part 2: Pipe this list into Marketo

Create/maintain a running list of campaigns a user has seen.

In order to automate this as much as possible, we’re going to create a centralized module that fires on every page. This allows us to just install it once (via GTM, Launch etc.) and not have to make any edits when new campaigns/tests are launched.

You’ll need to take advantage of Adobe’s Response Tokens or Optimizely’s client side object if you want to go this route. Users of other platforms like Google Optimize will need to follow a more manual approach, adding the module to every new campaign/test. The principles will stay the same, it just requires a bit more to upkeep.

The Code
(function () {
// Callback for Adobe Target response tokens
document.addEventListener(, function(e) {
var tokens = e.detail.responseTokens;

if (isEmpty(tokens)) {

var uniqueTokens = distinct(tokens);
} catch(err) {
The start of our module – An Adobe Response Token listener
[javascript] //Cycles through each token
uniqueTokens.forEach(function(token) {

var cookieName = token[""] + ‘ ‘ + token[""];

// Slugify the cookie name.
cookieName = cookieName.toLowerCase().replace(/\\((evar.*?)\\)|\\[(.*?)\\]/g, ”).trim().replace(/[^a-z0-9]+/g, ‘-‘);
Next we cycle through each response (there is 1 per active campaign) slugifying the campaign/variation name.
Find the existing cookie if it exists. Adds new campaign values to the front of the cookie
var existingCookie = _satellite.cookie.get(‘marketoCookie’) || ”;

if (existingCookie.indexOf(cookieName) === -1) {
var newCookie = cookieName + ‘|’ + existingCookie;

_satellite.cookie.set(‘marketoCookie’, newCookie, {expires: 30});
Since this module runs on every page load, we also need handle duplicates in the cookie name
[javascript]var checkLength = function checkLength() {
if (newCookie.length > 2000) {
newCookie = newCookie.split(‘|’);
newCookie = newCookie.join(‘|’);

Finally, Marketo inputs (you’ll set this up soon) have a character limit of 2000 characters, so we truncate older campaigns.

Here’s the reusable function in it’s entirety:
(function () {
// Callback for Adobe Target response tokens
document.addEventListener(, function(e) {
var tokens = e.detail.responseTokens;

if (isEmpty(tokens)) {

var uniqueTokens = distinct(tokens);

//Cycle through each token
uniqueTokens.forEach(function(token) {

var cookieName = token[""] + ‘ ‘ + token[""];

// Slugify the cookie name.
cookieName = cookieName.toLowerCase().replace(/\\((evar.*?)\\)|\\[(.*?)\\]/g, ”).trim().replace(/[^a-z0-9]+/g, ‘-‘);

Find the existing cookie if it exists.
Adds new campaign values to the front of the cookie
var existingCookie = _satellite.cookie.get(‘marketoCookie’) || ”;

if (existingCookie.indexOf(cookieName) === -1) {
var newCookie = cookieName + ‘|’ + existingCookie;
If above the 2000 Marketo input character limit,
truncate old campaign values
var checkLength = function checkLength() {
if (newCookie.length > 2000) {
newCookie = newCookie.split(‘|’);
newCookie = newCookie.join(‘|’);

_satellite.cookie.set(‘marketoCookie’, newCookie, {expires: 30});


function isEmpty(val) {
return (val === undefined || val == null || val.length <= 0) ? true : false;

function key(obj) {
return Object.keys(obj)
.map(function(k) { return k + "" + obj[k]; })

function distinct(arr) {
var result = arr.reduce(function(acc, e) {
acc[key(e)] = e;
return acc;
}, {});

return Object.keys(result)
.map(function(k) { return result[k]; });
} catch(err) {
console.log(‘Error in Target Marketo Cookie’);
A simple solution – copy and pasteable.

Create a hidden input field on all Marketo forms

Lastly, we need a way to get the running list into Marketo.

Marketo has out of the box functionality to create an input to ingest a cookies value. All you need to do is add this input to your forms, specify the cookie name (in our case, marketoCookie) and the rest happens by default.

You’ll now have a historical list of campaigns/tests an individual user saw whenever they submit a Marketo form.

Easy peasy.

By |2020-03-22T19:05:26-07:00March 22nd, 2020|Uncategorized|0 Comments

Why B2B Marketers Should Stop A/B Testing

B2B marketers currently face three main challenges with website experimentation as it is currently practiced:

  1.    It does not optimize the KPIs that matter well. – Experimentation does not easily accommodate down-funnel outcomes (revenue pipeline, LTV) or the complexity of B2B traffic and customer journey.
  1.    It is resource-intensive to do right. – Ensuring that you are generating long-term and meaningful business impact from experimentation requires more than just the ability to build and start tests.
  1.    It takes a long time to get results. – Traffic limitations, achieving statistical significance and a linear testing process makes getting results from experimentation a long process.

 I.  KPIs That Matter

The most important outcome to optimize for is revenue.  Ideally, that is the goal we are evaluating experiments against.

In practice, many B2B demand generation marketers are not using revenue as their primary KPI (because it is shared with the sales team), so it is often qualified leads, pipeline opportunities or marketing influenced revenue instead.  In a SaaS business it should be recurring revenue (LTV).

If you cannot measure it, then you cannot optimize it.  Most testing tools were built for B2C and have real problems measuring anything that happens after a lead is created and further down the funnel, off-website or over a longer period of time.

Many companies spend a great deal of resources on optimizing onsite conversions but make too many assumptions about what happens down funnel.  Just because you generate 20% more website form fills does not mean that you are going to see 20% more deals, revenue or LTV.

You can get visibility into down funnel impact through attribution, but in my experience, it tends to be cumbersome and the analysis is done post-hoc (once the experiment is completed), as opposed to being integrated into the testing process.

If you cannot optimize for the KPIs that matter, the effort that the team puts into setting up and managing tests will likely not yield your B2B company true ROI.

II.  Achieving Long-term Impact from Experimentation is Hard and Resource-intensive

At a minimum, to be able to simply launch and interpret basic experiments, a testing team should have skills in UX, front-end development and analytics – and as it turns out, that is not even enough.

Testing platforms have greatly increased access for anyone to start experiments.  However, what most people do not realize is that the majority of ‘winning’ experiments are effectively worthless (80% per Qubit Research) and have no sustainable business impact. The minority that do make an impact tend to be relatively small in magnitude.

It is not uncommon for marketers to string together a series of “winning” experiments (positive, statistically significant change reported by the testing tool) and yet see no long-term impact to the overall conversion rate.  This can happen through testing errors or by simply changing business and traffic conditions.

As a result, companies with mature optimization programs will typically also need to invest heavily in statisticians and data scientists to validate and assess the long-term impact of test results.

Rules-based personalization requires even more resources to manage experimentation across multiple segments.  It is quite tedious for marketers to set up and manage audience definitions and ensure they stay relevant as data sources and traffic conditions change.

We have worked with large B2C sites with over 50 members on their optimization team.  In a high volume transactional site with homogeneous traffic, the investment can be justified.  For the B2B CMO, that is a much harder pill to swallow.

III. Experimentation Takes a Long Time

In addition to being resource intensive, getting B2B results (aka revenue) from website testing takes a long time.

In general, B2B websites have less traffic than their B2C counterparts.  Traffic does have a significant impact on the speed of your testing, however, for our purposes that is not something I am going to dwell on, as it is relatively well travelled ground.

Of course, you do things to increase traffic, but many of us sell B2B products in specific niches that are not going to have the broad reach of a consumer ecommerce site.

What is more interesting, is why we think traffic is important and the impact that has on the time to get results from testing.

You can wait weeks for significance on an onsite goal (which as I have discussed, has questionable value).  The effect that this has on our ability to generate long term outcomes, however, is profound. By nature, A/B testing is a sequential, iterative process, which should be followed deliberately to drive learnings and results.

The consequence of all of this is that you have to wait for tests to be complete and for results to be analyzed and discussed before you have substantive evidence to inform the next hypothesis.  Of course, tests are often run in parallel, but for any given set of hypotheses it is essentially a sequential effort that requires learnings be applied linearly.


This inherently linear nature of testing, combined with the time it takes to produce statistically significant results and the low experiment win rate, makes actually getting meaningful results from a B2B testing program a long process.

It is also worth noting that with audience-based personalization you will be dividing traffic across segments and experiments.  This means that you will have even less traffic for each individual experiment and it will take even longer for those experiments to reach significance.


Achieving “10X” improvements in today’s very crowded B2B marketplace requires shifts in approach, process and technology.  Our ability to get closer to customers is going to depend on better experiences that you can deliver to them, which makes the rapid application of validated learnings that much more important.

“Experimentation 1.0” approaches gave human marketers the important ability to test, measure and learn, but the application of these in a B2B context raises some significant obstacles to realizing ROI.

As marketers, we should not settle for secondary indicators of success or delivering subpar experiences.  Optimizing for a download or form fill and just assuming that is going to translate into revenue is not enough anymore.  Understand your complex traffic and customer journey realities to design better experiences that maximize meaningful results, instead of trying to squeeze more out of testing button colors or hero images.

Finally, B2B marketers should no longer wait for B2C oriented experimentation platforms to adopt B2B feature sets.  “Experimentation 2.0” will overcome our human limitations to let us realize radically better results with much lower investment.

New platforms that prioritize relevant data and take advantage of machine learning at scale will alleviate the limitations of A/B testing and rules-based personalization.  Solutions built on these can augment and inform the marketers’ creative ability to engage and convert customers at a scale that manual experimentation cannot approach

By |2020-01-16T11:52:51-08:00January 16th, 2020|Uncategorized, Experimentation|0 Comments

FunnelEnvy Personalization Without Rules defines personalization as:
“to design or tailor to meet an individual’s specifications, needs, or preferences:”

Yet, little about personalization as it’s currently touted in mar-tech is actually personalized to an individual’s needs & preferences. Instead of serving the optimized experiences for each individual, customers are merely getting segmented into audiences based on predefined rules.

Putting visitors into segmented audience groups is not personalization.

While serving one of five eBooks based on industry is likely improvement over a static site, it’s far off from personalized.

At FunnelEnvy, we define this approach to optimization as rules-based personalization. Let’s compare this to FunnelEnvy’s personalization without rules.

FunnelEnvy enables experiences that are personalized without rules.

Instead of having pre-defined audiences, FunnelEnvy evaluates each visitor and interaction on a 1:1 basis to determine optimized experiences. Our AI based platform is continuously gathering data and self-improving to ensure every touch is optimized for revenue.

The optimized experience that’s served is often completely different than what a rules-based approach, that is void of any relevant customer context, would serve. The reality is that while marketers can do their best to match an audience to an experience, an AI approach that’s not limited by audiences will almost always outperform a human.

Example to illustrate the between rules-based personalization and FunnelEnvy.

Let’s say you own a Fin-tech company that currently has two eBooks to offer potential customers:

A: ‘10 tips for reducing costs’
B: ‘How to maximize your website for growth’

Now let’s say there are three people that visit your website.

Steve: Works at 10 person chatbot startup
John: Works at 2000 person regional clothing company
Tom: Works at 10000 person multinational shipping company

How Rules-Based Personalization handles the visitors:

With a rules-based approach, you decide to create two predefined audiences. You think that companies under 1,000 employees should see one experience, while those above 1,000 should see another.

With the two segments defined:
Steve would see the eBook “10 tips for reducing costs”
John and Tom would see “How to maximize your website for growth”

After running this experiment you find:
Steve – Bought your product.
Tom – Bought your product.
John – Did Not buy your product.

How FunnelEnvy Personalization handles the visitors:

FunnelEnvy analyzes many traits about the three visitors including:
Evaluating historical data of similar companies to those of Tom, John, and Steve
Looking at Salesforce data to look how other individuals in similar funnel position
Evaluating the Behavior of the three individuals on your website.
Much more…

After analyzing all this available data in real time, FunnelEnvy decides that:
Steve would see the eBook “10 tips for reducing costs”
John and Tom would see “How to maximize your website for growth”

There is no predefined audience based on company size, as the AI determines simply picks the experience that will most likely lead to revenue.

After running this experiment you find:
Steve – Bought your product.
Tom – Bought your product.
John – Bought your product.

As we can see from this example, having a fluid system that can bring in data and evaluate optimized experiences on a 1:1 basis outperforms a predefined and stagnant audience approach. If you are serious about optimizing your website for revenue, consider the advantages of FunnelEnvy’s AI-based approach vs a rules-based one.

By |2018-09-28T15:47:21-07:00April 20th, 2018|Uncategorized|0 Comments
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