About Arun Sivashankaran

I'm a tech entrepreneur who has been building, measuring and selling consumer and enterprise websites for years. Over the course of my career I've helped companies large and small increase revenue and engage customers as a manager, advisor and consultant.

Optimize your Revenue Funnel by Focusing on the Offers

Let’s take a step inside the data-driven demand generation marketing team. The biggest concerns on the CMOs radar are that the acquisition costs are too high and not hitting their pipeline or revenue goals.  Now looking at the data, we know that not only are they spending a lot on paid and organic traffic, but the quality of the traffic is good, and it’s not converting.

So, of course, the next question would be – what can they do about it? A common answer is to focus on website conversion rate optimization, which involves running online experiments. That’s something you can put a budget around and prioritize but recognize that your executives are going to want to see impact based on pipeline and revenue and probably want to see it fast.

Online Experimentation

Back in 2017, the Harvard business review published an important article digging into the power of online experimentation. In it, they correlated successful business outcomes to a culture of experimentation. 

harvard business review article title

Image Source: The Surprising Power of Online Experiments (Harvard Business Review | Link)

The article cited examples like the one below from Bing,  who tested multiple different colors on their site, ran experiments. and realized an incremental $10 million in annual revenue from these experiments. 

small changes with huge image image harvard business review article

Image Source: The Surprising Power of Online Experiments (Harvard Business Review | Link)

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

In our opinion and experience, no, you should not.

You’re not Google or Bing. Leaving aside traffic considerations, you’re trying to influence B2B buyer behavior over customer journeys. And the reality is that groups of buyers that consider enterprise solutions are not going to buy based on the button color or other small cosmetic changes.

This is important because experimentation comes with a cost. Not only do you have people and the technology costs of running online experiments, but also your organizational ability to make decisions. So, focus on the elements that would deliver revenue and influence those B2B buyers when you’re thinking about experimentation.

When we think about the B2B buying journey or the revenue funnel it’s common to conceptualize it as a series of buyer stages. As prospects progress through those stages, they do so through exchanges, in which you’re offering something to that prospect in exchange for something else. The offer could be some content in exchange for their attention, an event, or an opportunity to speak to the sales team in exchange for their contact information. Ultimately those offers are how they learn more about your solution and how it would benefit them. 

funnelenvy funnel image

From our experience and the testing that we’ve done, the highest leverage use of experimentation for the demand gen org is to improve the relevance of those offers and the ease of engaging with them throughout the buying journey. Of course, we always want to ensure we measure the impact of those experiments based on the KPIs that matter – pipeline and revenue.

Optimizing Offers

logistics transportation image of form

What does it mean to optimize offers? There are three components to an effective offer. One, of course, is the offer itself. That item you’re proposing to exchange with that visitor or prospect for them to understand your solution. The more relevant it is, the more effective your ability to convert them will be.

The second important aspect is how you frame it. Our primary focus here is the headline and Call to Action (CTA). Your headline is important because a visitor will spend five or ten seconds deciding if they want to stay on your site or hit the back button and go somewhere else. So, entice them to continue reading the content on the page.

Finally, the third element of the offer is the exchange and how they provide what you want. Most likely on your site this is a web form, but it doesn’t need to be. It’s increasingly common to see conversational marketing tools (chatbots) that accomplish the same thing by providing that medium of exchange for the offer.

Examples

Let’s look at some examples of how you could optimize your offers.

Landing pages are a great starting point for thinking about your offers. Many of you are probably running traffic to dedicated landing pages and putting an offer in front of the visitors hitting it. But not every visitor is interested in the same offer. In the example below, we recognized when working with a customer that they had three viable offers for those visitors coming through their paid campaigns. And rather than only showing them one, we use data to dynamically personalize the offer itself as well as framing and the page layout to reflect what might be most relevant to that visitor.

landing page offers comparison

When we ran the experiment against the static landing page we saw a 44% improvement in revenue per visitor. 

For most of us the most trafficked page on our site is the homepage. And on your homepage the “above the fold” section at the top gets most of the attention. Many of us think about our homepage in the context of welcoming the first time visitor and introducing your solution as in the example below.

fitch-solutions-landing-page

For SaaS and Demand Generation websites it’s common to have a lot of returning traffic. Since return visitors are familiar with your solution, it wouldn’t make sense to show them that same offer. In an experiment, we targeted these return visitors and the solutions they showed interest in and presented them on the homepage. In this case, those offers were buried in the site and require additional navigation. By presenting this offer they would likely be interested in and serving those directly on the homepage, we saw almost 55% improvement in conversions coming through this page.

fitch-solutions-home-page-offers

You can also target well-defined buyer stages. In the following example, we have a customer with a freemium model where visitors on the free plan come to the homepage and see a CTA or a button prompting them to “Upgrade Your Plan”. The baseline experience was to take them to a set of SaaS plan tiers where they could select the one that they would upgrade to. 

pricing-table-personalization-offer

Using this data, we can identify the specific plans most relevant for any individual and offer them directly on the homepage. The framing included the benefits and replaced the CTA with the cost of that specific plan we recommend. Since we recommend a single upgrade plan, we bypassed the plan selection (and the friction it created) and took them directly to the credit card to upgrade. By removing friction and presenting them with a more relevant offer, we saw an almost 70% improvement in revenue per visitor coming through this experience.

buyer-stage-changes-to-website

The most common mechanism of exchange for the offer is the web form, and as a result, we spent a lot of time optimizing them. It’s important to recognize that there’s a lot of friction for the visitor when they encounter one of these forms.Even if they’re interested in the offer, they face the prospect of handing over their email and other personal information, which often presents a big hurdle. Since it’s common to see drop-offs at this stage, we would like to take those contact forms and reinforce the benefit and the value to the visitor filling them out. In the following example, we tested an updated version of the form page resulting in an 85% improvement in conversions.

form-optimization

If you have the data, you can get sophisticated with offer personalization. It’s common to see pages like the one below. It is a product page that contains multiple offers for different personas within the organization. Unfortunately, when you try to put them all on a single page, they compete for attention and blend in, making it hard for users to know which one is relevant for them. 

TIBCO-homepage-before-personalization

In this case, we target specific personas visiting the page based on data we had in the marketing automation platform and identify the most relevant offer. By testing variations that replace the default experience with a single focused offer, we see an almost 50% improvement in revenue per visitor.

TIBCO-homepage-after-personalization

Final Thoughts

It’s possible to waste time, effort, and money optimizing inconsequential elements of your website. For demand generation marketers, the highest leverage things to focus on are the offers – specifically their relevance to the visitor and the ease of engaging with them.

Before you undertake this experimentation it’s important to make sure you have solid revenue insights. What that means is, evaluating your existing offers as well as future experiments based on their pipeline and revenue contribution.

Some of the personalized examples above require some segmentation. Our recommendation is to prioritize segmentation based on the differentiated intent and addressable size of those segments. We often find that marketers are running building audiences that can only address 5-10% of their audience, or ones that don’t have meaningfully different intent from one another. Ultimately those aren’t going to have much value when it comes to optimizing offers.

This is why we start with buyer stages as our starting point for segmentation because it a large set of well-understood segments with differentiated intent – buyers at different stages will naturally gravitate towards different offers. The vast majority of the visitors coming to a demand gen site fit into anonymous, known lead, active opportunity or existing customer.

Finally, when it comes to improving offers, start with common sense ideas. If you start thinking about your buyer stages, some opportunities should become apparent. For example, should a known lead see a lead capture form, or can we repurpose those pixels for something more relevant? Similarly, should existing customers see the “Request a Demo” or “Talk to Sales” CTA? Maybe there’s an opportunity to get them to support resources or event upsell them. 

What’s stopping you from generating more revenue by improving offers on your website? If you’re a Demand Gen marketer and need help, feel free to get in touch.

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

Transcript

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

Transcript 

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.

How to Effectively Personalize your Website using Account Data for Anonymous Traffic

Unlike consumer marketers, B2B revenue teams often reason about their market at an organization or account level. That may be based on specific named accounts or company size, industry or other related firmographic attributes.

Much of the traffic coming to the website is of course, “anonymous” meaning that they haven’t shared any contact information. There are however a number of vendors that sell firmographic data that can deliver this information even for anonymous traffic. This is possible because every web request must contain the source IP address and these vendors have mapped many (though far from all!) of them to specific accounts.

Traditionally used for sales teams, some of these solutions can be integrated in real-time into the website. This opens up some interesting opportunities to improve the traditionally static experience – such highlighting industry specific offerings, enterprise plans, or even targeting customers of competitors and highlighting differences.

A word of caution is warranted here, however. This approach to personalization is an investment that goes beyond just the vendor costs and we’ve seen a lot of campaigns where the return did not materialize. So let’s go into the more effective use cases, selecting a vendor and how to integrate it into FunnelEnvy audiences and predictive campaigns.

Beware Vanity Experiences

Personalization over email is useful because it helps the recipient understand that it’s not a mass-emailing robot on the other end and that the message has been tailored to them. Website visitors have different expectations and what works over email can cross the line or be creepy on site.

Website personalization is most effective when it helps the customer by presenting them with an offer (next best action) that’s most relevant for them in their journey. That offer could be content, starting a free trial, contacting the sales team or whatever is both most relevant for them and maximizes their likelihood of conversion.

Although it may sound obvious, where we’ve seen campaigns underperform with reverse IP personalization is where it doesn’t meet these customer-centric goals. Consider the following:

  • Injecting the account name in the copy – Doesn’t the visitor already know where they work?
  • Crafting experiences based on visitor industry when there’s no industry specific features to the product or service.
  • Serving pages that are specific to individual named accounts. The volume is generally too low to make a difference and again the customer already knows where they work!

These sorts of experiences are self-serving and what we call Vanity Experiences.

Vanity experiences, including one on FunnelEnvy.com. It’s no coincidence that these companies also sell the products that let you do it!

On the other hand reducing friction and targeting a more relevant direct-response offer based on firmographic data can be very effective. In some cases you could even skip steps in the journey – such as eliminating the pricing page for enterprise visitors.

Firmographic Vendor Considerations

There is no vendor that will be able to match all of your traffic, in fact match rates are typically in the 10-30% range. A variety of factors can influence that, but probably the most important factor is who you’re selling to and the types of accounts that are visiting your site.

Large enterprises, universities and governments often secure well known blocks of IP addresses which are much easier to identify. On the other hand smaller businesses often use shared office space and Internet Service Providers (ISPs) which make identifying them much harder. If you’re primarily selling to small business it’s unlikely that you’ll match enough accounts for this to be a cost-effective strategy.

If however you do sell to larger organizations then even if you have a low overall match rate it could still be worth pursuing. The effective match rate for the larger accounts is likely to be much higher and most B2B companies generate from enterprise customers.

Aside from the match rate the actual performance (time to return a match response) is an important factor on the website. If the integration is too slow the page may render before you have an opportunity to personalize, making for a sub-optimal “content flicker” on the all important first page view.

Activating Reverse IP Data with FunnelEnvy

FunnelEnvy’s platform has out of the box support for three leading firmographic vendors – Clearbit, Demandbase and Kickfire. Once you’ve selected one of these vendors, integrating into the platform and using the data for segmentation (audiences) and directly within 1:1 predictive campaigns can be done in a few minutes and requires no IT involvement.

Data Source Setup

Each of these firmographic vendors is a Data Source in FunnelEnvy, and activating any one of them is as simple as going into the integrations, locating the appropriate data source and clicking on the activate checkbox.

Once activated and saved the data source will appear in the list of active integrations.

Since these data sources return data in the browser the FunnelEnvy javascript snippet must also be present along with the reverse IP vendor snippet. Data collection happens automatically without additional setup and can be used immediately for creating audiences and in predictive campaigns.

Audiences

In the Audiences section of FunnelEnvy you can create conditions based on the activated provider. The rule builder will include all of the individual data attributes from the provider and rules can be AND or OR’d together for flexibility.

As with any of our data sources conditions can be combined with other sources (behavioral, Marketo, etc) to create audiences defined from multiple data sources.

From the audience builder you may want to report on visitor behavior from a particular firmographic segment (e.g. SMB or Enterprise visitors). This can be accomplished within the audience through the Google Analytics integration by setting either a custom dimension and / or sending an event.

For personalization Audiences can be used within the targeting section of campaigns operating in both A/B/n or predictive mode. If an Audience is selected in the campaign targetings visitors must meet both the page and audience targeting conditions to be eligible to see a variation.

Even if you don’t create any audiences the underlying firmographic data is used in our real-time predictive campaigns.

1:1 Predictions with Firmographic Data

Firmographic data sets are excellent for our predictive campaigns because they’re generalizable and often highly correlated to experiences and outcomes. There are only so many audiences you’ll be able to create but every data point from these providers can be used by our algorithms to predict which experience is most effective on a 1:1 visitor basis.

You can see the effect of this in our campaign signal report which shows how strong the predictive signals are and how much they correlate to uplift and revenue. Individual firmographic attributes are often highly represented in successful campaigns.

A great example of this in action is a homepage campaign with different variations for the SMB or Enterprise journeys. Since reverse IP data is available even on the first page visit, our model can identify patterns in the visitor profile and serve more relevant experiences to your customers.

Getting Started

It is possible to improve upon static website experiences with reverse IP firmographic data and help your customers while at the same time increasing your conversion and revenue KPIs. If you’re a FunnelEnvy customer and want to explore firmographic personalization for anonymous traffic let us know.

If you need help selecting a vendor we can help with that too.  You can contact us here anytime: https://www.funnelenvy.com/contact/

 

Personalizing the Revenue Journey with Segment Data

Accelerate your customers journey to revenue with FunnelEnvy, now powered with Segment.

Segment helps their customers instrument, store and unify data about their visitors and the actions they take all the way to revenue. Now with the FunnelEnvy Segment integration you can deliver personalized, 1:1 website experiences and optimize for revenue using all of that rich customer data that you’re already collecting in Segment.

What does this mean? Segment customers will be able to run more effective campaigns using better data with less custom code required.

Check out our integration on Segment

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How Not Picking an Experiment Winner Led to a 227% Increase in Revenue

By now most marketers are familiar with the process of experimentation, identify a hypothesis, design a test that splits the population across one or more variants and select a winning variation based on a success metric. This “winner” has a heavy responsibility – we’re assuming that it confers the improvement in revenue and conversion that we measured during the experiment.

Is this always the case? As marketers we’re often told to look at the scientific community as the gold standard for rigorous experimental methodology. But it’s informative to take a look at where even medical testing has come up short.

For years women have been chronically underrepresented in medical trials, which disproportionately favors males in the testing population. This selection bias in medical testing extends back to pre-clinical stages – the majority of drug development research being done on male-only lab animals.

And this testing bias has had real-world consequences. A 2001 report found that 80% of the FDA-approved drugs pulled from the market for “unacceptable health risks” were found to be more harmful to women than to men. In 2013 the FDA announced revised dosing recommendations of the sleep aid Ambien, after finding that women were susceptible to risks resulting from slower metabolism of the medication.

This is a specific example of the problem of external validity in experimentation which poses a risk even if a randomized experiment is conducted appropriately and it’s possible to infer cause and effect conclusions (internal validity.) If the sampled population does not represent the broader population, then those conclusions are likely to be compromised.

Although they’re unlikely to pose a life-or-death scenario, external validity threats are very real risks to marketing experimentation. That triple digit improvement you saw within the test likely won’t produce the expected return when implemented. Ensuring test validity can be a challenging and resource intensive process, fortunately however it’s possible to decouple your return from many of these external threats entirely.

The experiments that you run have to result in better decisions, and ultimately ROI. Further down we’ll look at a situation where an external validity threat in the form of a separate campaign would have invalidated the results of a traditional A/B test. In addition, I’ll show how we were able to adjust and even exploit this external factor using a predictive optimization approach which resulted in a Customer Lifetime Value (LTV) increase of almost 70%.

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Understanding the CMOs Data-Driven Decision Delusion

The term cobra effect describes an incentive policy that causes unintended consequences and results in the opposite effect intended. In the 1800s the colonial government of India (as the story goes), becoming increasingly concerned about the population of cobras in Delhi, offered a bounty for every dead cobra brought in.

Although it was initially successful, the cobra population increased as entrepreneurs of the day started breeding cobras to take advantage of the bounty. When the government realized the new policy wasn’t working as intended they ended the program, causing the cobra farmers to release the snakes and further exacerbating the very problem the government wanted to solve.

I recently read Chief Marketing Officers at Work, a fantastic series of interviews of CMOs from prominent companies like PayPal, Zendesk, Domo and SurveyMonkey. These marketing leaders unequivocally championed their data-driven marketing strategies, and emphasized the need for further data-driven investment and skills in their organizations.

The root cause of many cobra effect problems is the fact that as humans we tend to more easily comprehend simplistic linear systems and cause-effect relationships. Much like rewarding people for killing cobras should result in less cobras, investing in more data-driven tools should produce better decisions and outcomes!

Unfortunately, it’s very likely that the data-driven approaches being increasingly adopted by marketing organizations are producing their own cobra effect and paradoxically reducing the quality of decisions and resulting outcomes.

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By |2018-10-20T22:54:43-07:00September 24th, 2018|Digital Marketing, Strategy, B2B|0 Comments

A Culture of Optimization Eats Experimentation and Personalization for Breakfast

 

As marketers we could learn a lot from ants.

They don’t attend conferences, have multi-million dollar budgets or get pitched by the latest AI-based tech vendors. Yet over millennia they’ve figured out a radically efficient solution to an important and complex problem – how best to find food to sustain the colony.

This is no easy task. The first ant leaving the colony walks around in a random pattern. It’s likely he (foraging ants are always male) doesn’t find food, so he’ll return back to the colony exhausted. It’s not a completely wasted effort however, he (and every other ant behind him) will leave behind a pheromone trail that attracts other ants.

Over the course of time and thousands of individual ant voyages, food will (likely) be found. Ants that do find food will return immediately back to the colony. Other ants will follow this trail and, because pheromone trails evaporate over time, they’re most likely to follow the shortest, most traveled (highest density) path.

This approach ensures that the colony as a whole will find an optimal path to a food source. Pheromone evaporation also helps ensure that if the current source runs out, or a closer one is found, the colony will continue to evolve to the globally optimal solution.

It’s a classic optimization solution that maximizes a critical outcome as efficiently as possible, and one that has been studied by entomologists, computer engineers and data scientists. In the current B2B marketing environment it can illuminate where we’re spending our time and money.

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FunnelEnvy Personalization Without Rules

Dictionary.com 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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