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.

Identify, Track, and Serve Custom Experiences to Leads

You’ve decided to improve on your one-size-fits-all website content by serving personalized content to leads. You figure that a free trial user will literally never click “Start Free Trial” … but they very well might click “Buy Now.” Especially if you give them clear reasons to do so.

Great! So, how will you target these visitors?

It’s a straightforward process of identifying “leads only” behavior, then ensuring you’re able to activate this data on your site.

What do leads do?

The answer is unique to your product, but it’s not a trick question.

Here are visitor behaviors you can use to identify leads:

  • Sign up for a trial
  • Opt in for a lead magnet
  • Click through on an email message sent to leads only
  • Trigger a domain or company match to an account that’s in the pipeline
  • Click “Log In” on the homepage

If you’re only looking to segment out leads in your analytics reporting, this might give you everything you need.

Your “Leads” segment is the set of all visitors who carried out any of the above actions. Even if you’re not tracking “Log In” clicks or using a firmographic data provider, you’ve got pageviews on /app, or /dashboard, or /whitepaper-download-thank-you. That’s enough to define a segment.

But to take the next logical step of serving a more relevant experience to these visitors, you’ll have to have this data available not just in your reports, but on the frontend of your website.

How to activate experiences for leads

Once you’ve narrowed down the list of  actions that define “leads only ” behavior on your site, you’ll need to attach some sort of identifiable metadata to the user across your website.

If you can spare a few developer cycles, setting a first-party cookie is a good option. Whenever a visitor starts a trial, or signs up for a webinar, set a cookie you can use to identify that they’re a lead. All your dev needs to know is the exact trigger (or triggers), the name and value you want to use for the cookie, and when it should expire.

Once this cookie is set in the visitor’s browser, you can use it to activate personalization campaigns, experiments, customized lead magnet offers, and whatever else you think might get leads to convert.

First party cookie targeting with Google Optimize

If you’re using Marketo, you already have a source of truth for a visitor’s status in the sales process, along with useful metadata about their site behavior, lead score, and more. All packaged up into a cookie that’s already on your site.

In that case, the easiest path forward is to use FunnelEnvy for Marketo to activate this data, which you can then integrate with Google Analytics, Google Optimize, Optimizely, Drift, and whatever else you’re using. No custom code required.

What to do next

You can start scoping this project right now. Write down the actions that identify visitors as leads in your pipeline. Forward this along to your dev team, and ask them what it will entail to set a custom cookie for visitors who complete these actions.

Or skip the back-and-forth by signing up for FunnelEnvy for Marketo. We’ll solve analytics, targeting, and activation. You can move on to designing a higher-converting experience,

By |2020-07-08T10:41:56-07:00July 7th, 2020|Digital Marketing, Analytics, B2B|0 Comments

Real-Time Personalization with Segment and FunnelEnvy

Segment is a very popular CDP (customer data platform) that specializes in unifying data sources in real-time across your digital touchpoints and with your ever growing marketing technology stack.

Segment is an especially popular choice with product and engineering teams because it can serve as the organization’s centralized hub for data collection, integration, and syndication. And what really makes Segment so powerful is its 300+ API integrations with top marketing technology platforms.

This has resulted in Segment becoming the foundational data platform for thousands of organizations that now use this unified data to power a variety of business use cases throughout their entire marketing stack and across their digital touchpoints.

Today we are going to do a deep dive on delivering site personalization using Segment data. This can be accomplished through the FunnelEnvy’s Segment integration which makes it easy for Segment customers to test, target, and personalize user journeys using all of that rich user and event data in real-time.

Activating Segment Data with FunnelEnvy

Segment is one of the out of the box integrations that come with the FunnelEnvy Platform.

The first step to activate the integration is to select Segment as a Data Source within the FunnelEnvy Platform.

Next, copy the API Key displayed in the integration details, check the “Active” checkbox and save the integration.

The Segment integration will be listed as active and FunnelEnvy will now start tracking identify calls and associated traits from Segment.

The last step is to configure FunnelEnvy as a new Destination within the Segment Platform. In Segment search for and add FunnelEnvy as a destination.

In the settings you’ll need to add the API key that you copied from the FunnelEnvy platform when you activated Segment integration.

Save the destination and you’re done. Identify calls and track events and associated traits/properties will start flowing from Segment to FunnelEnvy.

That’s it! The FunnelEnvy and Segment integration is now ready to go. If you want to also set up FunnelEnvy as a data source for Segment (Segment as a FunnelEnvy destination) you can follow the steps outlined at the bottom of this help guide. This step is useful for syncing FunnelEnvy audiences with Segment and tracking which FunnelEnvy campaigns and experiences a user is associated with.

Now that we are up and running and sending our Segment data to FunnelEnvy, let’s explore a few popular personalization use cases.

Popular Personalization Use Cases With Segment Data

The most exciting use cases with using Segment data for personalization is the ability to deliver targeted 1:1 experiences based on the user’s relationship with you and serving the most relevant offers for them.

The exciting promise of CDP’s like Segment is they contain your best data view of your users and customers. Now by integrating Segment with FunnelEnvy we can target your users in a more personalized way and present them the best offers and experiences to drive better business outcomes.

Let’s look at how to set up a few of these personalization use cases in FunnelEnvy.

Personalizing for Existing Customers

The majority of websites are static and prioritize the site experience for that new visitor or prospect. This is the right strategy for growth but it results in a suboptimal user experience for our most valuable audience, existing users and customers.

Common sense tells you that if you can, you should personalize the site experience for your existing customers that may be coming back to your site frequently to login and use your products and services.

In this client example, we have a SaaS business where users must login to the website to access their services. Because this client tracks active logins in Segment we can easily personalize the site for this user audience.

Once the Segment integration is activated you can create new conditions, audiences, or goals based on any combination of Segment data. To begin, just create a new condition and select Segment as your data source.

In the below screenshot we will create a new condition in FunnelEnvy based on the user being an active user based on Segment data. In the drop down I can pick from any and all Segment data being passed to the FunnelEnvy Platform. In this case, I select the “Is Active User?” attribute and specify it must be true.

Once created, this new condition can be used to build targeted audiences that I can then use to personalize content to existing customers. My newly created Active User audience can now be easily applied to any of our A/B, predictive, and rules based campaigns, like in this example below:

I can now target active users with personalized customer content the next time they return to my website to login.

We can create simple audiences like in the example above or we can create more advanced audiences that combine multiple attributes from Segment and other data sources. In this example below, I am creating an audience based on the user being an active user AND has viewed our resource content section based on their onsite browsing behavior.

And the best part is that I can set the audience to automatically sync with Segment as one of FunnelEnvy’s data source destinations.

Use Next Best Offers to Convert More Prospects into Customers

Another great use case with personalization is the idea of serving your prospects the next best offer based on where they are in their buyer journey and based on what offers they have already activated.

While this is a very straightforward campaign in your email nurture campaigns, it’s often hard to execute on your website in real-time. Because of this challenge, most sites continue to show the same offer over and over again to prospects even if they already signed up for that offer. The end result is another missed opportunity to provide a more engaging experience to another high value audience, active prospects.

Luckily, this challenge is easily solved, and is just another straightforward site personalization use case when you pair Segment data with the FunnelEnvy Platform.

For this client, a product demo is the most popular entry offer for new prospects, but once they watch a demo there are a number of other offers that can help move the deal forward. They have webinars, calculators, and case studies that all work well as offers in the middle and bottom of the buyer journey.

In this example below we want to target active prospects who have completed a demo. Our client tracks in Segment which users complete a demo based on a Registered for Demo attribute. That allows us to easily create conditions and audiences based on the user being in the demo stage, like in the condition screen below.

With a few clicks we have created an audience of all site visitors that have completed a demo. We can now target our homepage banner to run a personalization campaign that will predict the next best offer for that active prospect from a collection of their top offers.

This is another example where instead of presenting an irrelevant offer, we can provide a more relevant experience and personalize the next best offer for our valuable active prospect audience.

Make Better Real-time Personalization and Attribution Decisions

The first two personalization use cases I shared focused on creating very specific audiences based on user stage data coming from Segment. Our last use case is focused on leveraging all of that rich Segment data to make better personalization decisions and to measure the attribution impact on revenue.

If your Segment implementation is typical of most of our clients, you likely have dozens if not hundreds of data attributes about your users coming from your customer databases, transaction systems, and various other marketing platforms. Typically, Segment has the richest and most accurate real-time data profile for your users, especially when it comes to revenue data.

For example, did that trial user successfully convert into a paid user 30 days later and generate revenue? How much revenue has that user generated over their customer lifetime?

Going back to our previous SaaS client example, they have a service that you typically start as a trial, and they can generate revenue from that user from both a monthly subscription fee and from specific actions they take in their account. In this case, the client tracks all revenue activities for a user with a Segment event named Generated Revenue.

Because all Generated Revenue for a user is tracked within Segment we can easily build a goal in the FunnelEnvy platform that will update whenever that Segment event fires and populate with the actual revenue generated from that event.

Since Segment track events become FunnelEnvy events, they can be used for goal tracking like any other event in our platform.

In the example below, we are setting up a new goal in FunnelEnvy that fires each time the Segment track event named Generated Revenue fires.

In real-time, every time that Segment track event fires we are now tracking that revenue and associating it back to the user and personalized experience they saw.

With this data flowing back to use in real-time we can now unlock two powerful capabilities.

Predicting Better Experiences to Maximize Revenue

Because we can track the total revenue a user has generated we can now start using that data to predict which personalized experience generates the most revenue for the business, not just the most trials or initial orders.

The FunnelEnvy personalization platform uses machine learning to predict which experience will perform best for an individual based on that user’s profile and on the history of how similar users converted on those same experiences.

This allows us to take advantage of all the data we have as the FunnelEnvy platform will predict and promote those experiences that maximize generated revenue for each user.

So in this case Segment data helps us convert more visitors to higher revenue generating experiences, all without the manual hassle of pre-defining segments or personalization rules. We let machine learning crunch all the data and predict the best experience for each user, all in real-time.

Better Attribute Incremental Revenue to Personalization

While generating more revenue is always priority one, as marketers we also need to demonstrate that our programs and campaigns are working and providing strong ROI.

In our example with the Generated Revenue event, in addition to making better predictions, we can also clearly demonstrate the impact on the revenue we are creating through our personalization initiatives.

With our integration with Segment, we are able to credit and report on how much revenue our personalization activities are generating and which personalization experiences covert best. We can then show incremental revenue by running a controlled A/B test that serves half the audience the control experience and the other half the predicted experiences. We then measure the difference between the two groups and report on uplift in conversion and value per visitor and calculate statistical significance.

With the Segment integration, it is now much easier to measure and demonstrate the incremental value of personalization. Not just on the initial order, lead, or trial but on customer lifetime value (CLTV).

This can all be done in real-time right out of the FunnelEnvy Reporting UI without any ongoing support from your IT or data team and without having to wait for delayed manual reporting.

As a marketer, you can finally focus on optimizing your personalization strategy without all the data and operational headaches. Wouldn’t that be nice.

Getting Started

As you can see, integrating Segment data into your personalization program is very straightforward and can unlock some very valuable capabilities and use cases.

If you’re not yet using FunnelEnvy but are interested in personalizing your website using Segment data we’d love to hear from you! You can contact us here: https://www.funnelenvy.com/contact/

10 Tips for Running Effective Predictive Personalization Campaigns

When it comes to personalization there is a growing trend with using machine learning to predict and optimize for the user experience at a 1:1 level.

We call the collective use of these machine learning techniques as predictive personalization campaigns.

Predictive personalization refers to a type of campaign where a machine learning model is used to predict what is the best experience to serve a visitor based on current/historical performance and the user’s individual data profile (contextual bandit). Decisions are made in real-time and at a 1:1 level and the model makes use of all the data available about that user and also takes in the context about the location, content, and other factors that go into that experience.

The predictive campaign will send the majority of traffic to the experiences that the model predicts will perform best, exploiting those insights in real-time, but continue to hold out some traffic to continue learning and exploring performance trends for the other experiences.

Just like with an A/B test, you can test predicted campaigns vs. a control to determine if a statistically significant uplift is achieved. But unlike an A/B test, the expectation with predictive campaigns is that it is “always on” and that it’s constantly adjusting traffic to the right experience at a 1:1 level.

The adoption of predictive personalization campaigns is still in its early days for our industry. For many programs, experience and maturity with these techniques are still low but there is a growing interest, especially as the solutions on the market continue to grow.

In this article we discuss best practices we have acquired from years of running predictive campaigns across a range of mid-market and enterprise clients using the FunnelEnvy platform.

What Makes For More Effective Predictive Personalization Campaigns

Below is a list of our observations after running hundreds of personalization campaigns over the years on the optimal conditions that contribute to successful predictive personalization campaigns.

  1. A variety of user intent. With a predictive campaign the more varied in intent and behavior that exists with users the more valuable predictive models will be in detecting and predicting better user experiences. The more varied the signal the more value predictive decisions can play a role in deciding and predicting the best outcome for a range of user intentions.

A good example of this is with predictive campaigns on the homepage, where user intent typically varies across the board.

  1. A variety of goals to predict for. A variety of possible user outcomes to predict for is another valuable ingredient for a successful predictive campaign. Like with user intent, we want to capitalize on the strengths of predictive models, and the more varied outcomes to predict for usually results in better business outcomes.

Good example of this is with a campaign that rotates a variety of goals or user journeys as the primary offer and call to action (CTA). Like in B2B, where you may have 4-5 different offers like free trial, request demo, request pricing, book a Drift meeting, download whitepaper, or register for a webinar. Having multiple offers to present or predict usually results in better performance of the predictive models.

  1. A variety of goal values to predict for. Multiple goals are a good thing, and having multiple goals with different values is even more helpful to your predictive campaigns. Once again we are seeking out scenarios where we have multiple goal options, all with a range of perceived value. This becomes important because we want to give the model more detailed feedback on what is working and what is more valuable to the business. If like in the previous example, you had 5-6 B2B goals but all of them were valued the same, (say $100 per goal completion), all goals would be perceived as equal, providing less signal back to the model. However, if your goals vary widely in range from $50 to $1000 per lead, then the predictive model has far more data and data points to work with.

An example of goal variety we typically see with SaaS clients is that goal values will fall into 2 tiers, lower value content engagement goals (content download, webinars, video views), and higher value sales intent goals (request demo, request pricing, contact sales, free trial).

  1. Goals are aligned to business revenue. While goals can vary widely, it is important that your goals are either revenue goals or events that correlate closely with revenue. Ultimately, predictive campaigns do best when they predict which experience is the most valuable.Therefore it’s important that you set up your campaigns to predict high value outcomes like purchases, trial to paid conversions, MQL/SQL, and closed/won deals. Where possible avoid micro-conversion and vanity metrics as your primary KPIs whenever possible. Examples of vanity metrics would be clicks, page views, video plays, etc. Whenever your KPIs are not tied to revenue/business value the harder it will be to have predictive campaigns be effective for you.
  2. There is some version of a revenue journey that you can track and optimize for. The more diverse and complex your buyer journey is the more you will benefit from using a predictive campaign that can take in all the data and outcomes and predict for better outcomes for your users.

If anyone has the same exact journey, there is less to predict for. However, if your a typical SaaS business and you have a variety of SMB to enterprise offerings, and you offer self service and enterprise sales scenarios, or you have a longer sales funnel that includes MQLs/SQLs/Opportunity Stages, then identifying the valuable trends across multiple goals in that revenue journey is a specific challenge that machine learning models do better at predicting for.

  1. Testing of high impact placements on the page. This recommendation is not unique to predictive campaigns and holds true for all types of campaigns, including A/B tests. If you are going to run a predictive campaign, you need to run it in a high impact location for it to be most effective.So examples of good placements would be primary offer/CTA on page sections above the fold. We have seen predictive campaigns run on small content strips or on content sections below the fold, and while they may contribute value, it’s just not going to generate a significant impact compared to continually optimizing for the high impact locations.
  2. Higher volume of traffic and conversions. Another no brainer but worth calling out. You want to run campaigns on highly trafficked pages that correlate well to conversions and revenue (think product and pricing pages vs. community or blog pages).

You should be prioritizing opportunities where you can move the needle and truly impact revenue for your site.

  1. More available data attributes for predictions. Predictive models need data to provide relevant signals. In many of the above recommendations I call out that the more variety of intent and goal outcomes, the more data points the model has to work with for its predictions. The objective here is to generate more contextually relevant data attributes that can feed into the model. The strategy here is to instrument more meaningful data events and attributes to feed the predictive model, often referred to as feature engineering.

Examples here would be setting up more targeted audience segments (flagging prospects and customers for example), or integrating a new data source like a firmographics API from Demandbase or Clearbit, or integrating with your CDP, CRM or Marketing automation platform, or defining content and product affinities based on user behavior and pages visited. In each of these examples we are feeding additional relevant data around user intent, behavior, or data we have about our users. All of which can help a model identify additional experience/user matches that correlate better to revenue.

  1. Stakeholders understand and appreciate the differences between A/B testing and predictive campaigns. This is an organizational consideration, but when you run predictive campaigns, there are certain differences that you and the larger organization needs to be aware of and comfortable with. Unlike an A/B test, your experiences may not be sticky to a user over the life of the campaign (optional). Secondly, predictive campaigns are designed to maximize revenue, you may not have a clear winner or a straightforward outcome or winner after running the campaign. In predictive campaigns there is often no one winner. You find that different experiences perform better for different user segments. Some organizations are very comfortable with this reality, others struggle and prefer the simpler situations and outcomes that come with traditional A/B tests where you have a clear learning and you can full scale a single winner for the entire population.This is where you need to educate your organization on the pros and cons of running predictive campaigns.
  2. Stakeholders value and are responsible for revenue. While everyone in the company will say they value revenue, not everyone is responsible for generating revenue on behalf of your business. And that is ok. But, when it comes to predictive campaigns the best outcomes are usually achieved when the primary stakeholder is aligned to revenue targets and takes the responsibility to grow those revenues as best they can with proactive initiatives like site personalization. This is where we often see the most success with our clients. Being motivated by revenue vs. just learnings, will result in a more focused approach to revenue optimization, and in many cases a predictive campaign is more effective than an A/B test in maximizing revenue for your site and user experience.

Please keep in mind that while these are 10 preferred conditions we believe improve the likelihood of your predictive campaigns being successful, this full list should not serve a prerequisite or requirements before you launch your first or next predictive campaign. Typically, if you can align on 2-3 of these conditions you are often in good shape to see success.

The goal of this list was to share with you lessons learned and what to look out for as you continue to grow and expand your personalization programs and are ideally incorporating predictive campaigns more often into your portfolio of marketing and personalization initiatives

Getting Started

Now that you know what to look out for when designing and prioritizing your predictive campaigns, let us know how we can help you maximize your personalization initiatives.

We’d love to hear from you! You can contact us here: https://www.funnelenvy.com/contact/

When to A/B Test and When to Use Predictive Bandits

For the majority of marketers, when you talk about CRO and optimization programs, people immediately jump to A/B testing and assume that is the primary tactic and is the only tactic to demonstrate success for their programs and initiatives.

While A/B testing is a critical tool in your optimization program it shouldn’t be the only option on the table. While A/B testing is a valuable solution it is not a one size fits all solution for all your business challenges.

With advances in new data capabilities, marketing technologies, and real-time computing power in recent years, there are now more ways to solve for the same optimization problem.

For this discussion, we want to focus on two different techniques.

A/B test – A controlled experiment where traffic is split across 2 or more experiences and visitors are randomly assigned an experience and they stay in that experience for the duration of the test. At the end of the test, you determine a single winner based on which experience generated the best outcome for your primary KPI based on a predefined sample size/test duration and ideally reaching a specific statistically significant threshold. You then stop the test and full-scale the single winner for all traffic either directly in the testing platform or hard-coded into your CMS/platform.

Predictive Bandit – An experiment where traffic splits are not equal and where visitors are not randomly assigned. In these campaigns, a machine learning model predicts what is the best experience to serve a user either based on current + historical performance (multi-arm bandit) or based on current/historical performance + the user’s profile (contextual bandit). Like an A/B test, you can run the predicted experience vs. control to determine if a statistically significant uplift is achieved. But unlike an A/B test, the expectation with bandits is that it can run ongoing.

Now that we better understand the two techniques let’s explore reasons why both have a place in your optimization strategy.

Pros and Cons of A/B Tests

A/B testing is embraced in the analytics community as the smart and scientific way to measure the impact of change. Controlled experiments are used in testing the effectiveness of new medicines, academic/scientific studies, and of course in marketing. A/B testing is not new to marketing. It’s been done for decades in the direct mail/direct response world. And has become the recommended way to test changes on your website and mobile app assuming you can convince your stakeholders that testing is needed within your organization (it’s 2020 but still some organizations resist).

PROs:

  1. Quantify the impact of changes on your site. Don’t leave change to chance, measure and quantify the positive and negative impact of changes in experience with confidence.
  2. A/B tests are well understood in our industry. And for the most part well understood across an organization. Statistics may not be, but in general most folks understand the approach. In the end, if everything goes according to plan you have a clear outcome; either control won, or one of the challengers is declared the winner.
  3. Clear learnings. Related to the 2nd benefit of being well understood, benefits of an A/B testing is not just impact on business results, it’s shared learnings of what may or may not work for your site/business. With A/B tests you ideally gain a better understanding of your visitors and customers.
  4. At scale, you can create an organizational culture of experimentation. This change in culture leads to more creativity, risk taking, and better data driven decision making. Typically organizations that test more, make better and more informed decisions for their organization.

CONs:

  1. Success rates will vary, but are often low. Industry averages place the average success rate of A/B tests at 30-40%. So you have to expect that the majority of the time you are not going to end up with a new winner. Even within the winners, even fewer are high impact wins. The one hidden benefit to this, is that it makes the argument of why A/B testing is so important. If we didn’t test, the majority of the time those great ideas we think will perform better, actually perform worse or have no impact.
  2. One size fits all and the winner takes all. When you analyze your typical A/B test you will often see at a segment level, different groups convert very differently across the variants. At the end, you pick the variant that was best on average across all your traffic and full scale that one. But in doing so you do leave some money on the table, as the winner will not be the winner of all segments.
  3. Results can and do change over time. If you look at performance trends over the life of the test, it’s often the case where test results trend up and down over time. Guess what, that data reality continues to occur even after you hard code and full scale the winner. You can lock in the experience but you can’t lock in the results going forward. Results will continue to change, and a decent percentage of the time, as tests continue over time you experience a regression to the mean, where results start to flatten out. After all, if you run the same test twice, you will seldom get the same results. This is why, for many programs when you full scale that winner you usually don’t experience that lift ongoing. It still is a better way to make decisions, but as we know audiences and behaviors change. Seasonality can also be a factor.
  4. You sacrifice business value for concrete learnings. By design, an A/B test is designed first and foremost to generate a solid learning. Usually you are willing to sacrifice short term uplift if a variant does better than the rest, and willing to suffer through some short term downside if a variant clearly underperforms. You are willing to accept a sub optimal impact on revenue during the duration of the test because you are prioritizing a clear test result for short term business benefits.
  5. You need traffic. Not every organization can run A/B tests. Sufficient traffic and conversions are required to reach a statistically significant outcome. Not all sites have that, especially in B2B for example.
  6. Operational costs can run high. From selecting a testing platform, bringing on web developers, additional creative, and data analysis, the tool and people costs can be meaningful. Plus, there are the operational costs of introducing more time and resources to launch something, and the occasional negative costs from broken experiences or flawed tests. All marketing teams and programs incur people, tools, and operational costs, and testing is no different and often carries more cost overhead.

While I go into more detail on the cons with A/B tests, I do some because some of the cons are less understood. Still, when done right, A/B test is worth the effort and the benefits will far outweigh the cons.

But as I hopefully outlined above, A/B tests do have different strengths and weaknesses.

Pros and Cons of Predictive Bandits

Bandits have gained more popularity in marketing in recent years as computing power has advanced to the point where real-time machine learning predictions can be applied in more and more marketing technologies and for more use cases.

We have seen the trend emerge in digital advertising where bid and creative recommendations are often driven by machine learning decisions.

However, when it comes to site optimization the adoption and acceptance of bandits as a proven technique is still in the early days. For many programs, experience and maturity with these techniques are still low. Even though some of the most popular testing platforms have included those capabilities for a number of years. Let’s discuss why.

PROs:

  1. Bandits are by design biased toward business outcomes. Unlike A/B tests which are designed to maximize time to a clear learning. Bandits are typically designed to maximize the business outcomes at the expense of clear and precise learnings. The algorithms typically send more traffic to experiences that perform best, and route traffic away from experiences that underperform as a whole or for a specific audience segment.
  2. Bandits use all the data to your business advantage. In A/B tests you may use data to inform and drive your test hypothesis, but when it comes time to setup your test, you typically set it a controlled randomized experiment where traffic is split evenly and your visitors are randomly assigned to one of the variants during the duration of the test. Again, this is absolutely the right way to run a controlled experiment to generate your best chance at a concrete learning. However, this also means you are ignoring the data and trends that live within a test and the micro trends of which traits and segments perform best for each variant. With Bandits, machine learning is consuming and using all available data on the variant performance and the user to determine the best possible user experience for that user to drive the optimal business outcome. It’s far from perfect, but you are not leaving the outcome to chance. You are using all the data you know about the user and situation to make the experience decision to maximize your business outcomes.
  3. Bandits adjust to changing trends and behaviors. With Bandits, the intention is for it to be always on and constantly adjusting to the latest performance trends of the campaign. Unlike an A/B test where you pick one winner and lock it in, a Bandit can adjust as results change over time, and minimize any loss in performance and capitalize on any shifts in winning experiences.
  4. Bandits can work with less traffic. Because you are optimizing for revenue instead of learnings, you can still benefit from bandits even though you have less than optimal traffic to run a clean test.
  5. Bandits work well in time sensitive situations. Popular content and Black Friday sales are good examples. By the time an A/B test gives you the right answer the opportunity may have passed you by. With Bandits, it reacts in real-time to the trends and that allows you to take advantage of short term and seasonable situations.

CONs:

  1. Bandits are hard to interpret, understand and communicate. While A/B tests are well understood, bandits are not. The fact that decisions are controlled in real-time by a machine learning model vs. set A/B splits, means users do not have certainty about why an experience was shown or control over the traffic rotation.
  2. Bandits offer limited learnings. A/B tests by their design as controlled experiments are designed to produce learnings. Bandits will typically sacrifice clarity of learnings when it comes to which experience works best. You can often be informed about what segment or what feature influenced the bandit model decision, but it’s not as clear cut on what is the final absolute winner. Often you are A/B testing the technique, do bandits outperform control or an A/B test for this page/site. But with bandits it’s harder to generate clear learnings on winning experiences. In the end you are optimizing for revenue and other business outcomes at the expense of a clear isolated learning. As an organization you need to be aware and accept this reality and your stakeholders need to accept this reality too.
  3. Bandits can provide an inconsistent user experience. As we called out earlier, to maximize business outcomes, bandits by design do not make experiences sticky to the user, and will often serve a different experience to the same user if the data suggests it will result in a better outcome. While this maximizes revenue, it can lead to experiences being more dynamic and changing for a given user. While dynamic websites are generally considered a positive, because it is not very common in 2020 (surprisingly), this dynamic approach to site experience can be a drawback for some users.
  4. Bandits require more thoughtfulness on experience design. While you can A/B test most things from layout, to copy, to color, to offers, the same isn’t always true for predictive bandits. Bandits work best where you have varied user intent and ideally varied offers and outcomes. Bandits are more effective if they can predict for more outcomes for a larger range of user intent. If you are running CTA color and size changes you are better off running a traditional A/B test. There is likely less signal in the data in terms of user preference of a button color/design and the results will likely hold true on average for most users. That is why bandits work better when intent varies across the visitors and potential offers displayed can also vary.

When to Use A/B Tests and When to Use Predictive Bandits

Now that you know the PROs and Cons for A/B tests and predictive bandits let’s talk about some practical applications of each and when you should one over the other.

When A/B Tests are Recommended

  1. When you need a clear learning and more certainty on the final decision. Examples here would be a pricing or homepage test.
  2. When you want to lock in a specific design or experience for all. Examples here are things like a new homepage design, a new form layout, or say our site wide CTA treatments. Here there is more operational value in locking in that specific winner and then optimizing further on that.
  3. When intent and offer options are narrow in scope. When intent for all users is similar the offer is the same for all, then A/B testing usually works better than predictive bandits. An example would be optimizing for the final cart checkout page. Everyone there is ordering (or not) and the offer is a checkout (or not).

When Predictive Bandits are Recommended

  1. When you want to maximize revenue. If you have aggressive revenue goals then bandits are a better tactic to get you there as you are using all the data available to make the optimal revenue decision. A good example here is presenting the right offer on a homepage hero or promoting a specific price/package on the pricing page. In those situations, the general site is the same but you are predicting the best offer and experience to spotlight to maximize revenue.
  2. When intent and offer options vary broadly. Predictions work better when there’s a wider range of users and intent and a wider range of offers and outcomes to present. This is where the value of machine learning and crunching dozens and hundreds of data attributes in real-time is helpful. Good examples here can be homepage, brand landing pages, and pricing/plan pages. In these scenarios intent and options/offers presented can vary.

Now that you know the strengths and benefits of both tactics I think you will agree that both tactics should be part of your optimization and personalization toolkit.

Running A/B Testing and Predictive Campaigns in FunnelEnvy

With the FunnelEnvy platform we give you the ability to run both A/B and predictive campaigns and apply the right tool for the job. Unlike other platforms, both tactics are available as part of our standard license.

To get started you just create a new campaign and then select the preferred template option between Predictive and A/B testing.

If you selected an A/B testing then the “Campaign Decision” section will default the settings typical of an A/B test.

As you can see A/B testing is the decision type defaulted, and the “Persistence Variations Decisions” feature is checked so the same experience is served to the user across sessions. Lastly, you can adjust the traffic allocation to determine what percentage of traffic enters into the test. By default and typically it’s set to 100%.

If you were to select a Predictive campaign template, the decision type would instead default to Predictive as seen in the screen below.

In addition, the “Persist Variation Decisions” feature is not selected by default. As mentioned earlier, we typically see improved revenue performance when the campaign can rotate different offers over time to the same user. That makes sense, as a user may not respond to an initial offer, but maybe convert when presented another. But we do recognize their legitimate reasons to also make predictive decisions sticky, especially when running in locations like your pricing and plans page. And like with the A/B template, you also can set traffic allocation from 0-100%.

What is specific to Predictive campaigns is the predictive experiment options. Here you have two choices to make. The first choice is what percentage of traffic should be included in the predicted group vs holdback. The experiment section is where you can test the incremental value of a predictive campaign by holding back a portion of your traffic as the control.

In the screen below we have set the holdback to 50%. This will result in 50% of the traffic being assigned to the predictive experience and the remaining 50% gets served a control. This allows you to test the incremental value of running a predictive campaign on your site.

Typically, we recommend the client start a new campaign as a 50/50 campaign and as they see the predictive campaign outperform the control we then recommend full scaling the predictive campaign to 100%.

The second option you have with a predictive campaign is to determine the composition of your holdback group. You can choose between assigning the variation a specific variation, like baseline control, or you can set holdback to random in which case the holdback will run as an A/B test for that traffic across all the available variants. The “Random” option is useful if you want to determine the incremental uplift in running a predictive campaign vs. an A/B test.

As you can see, our predictive campaign setup still allows you to isolate and measure the incremental impact of a predictive bandit approach by running it as a controlled experiment.

Our typical client will often run both types of campaigns simultaneously on different areas of their site and targeted for different audiences. Rather than having to choose one tactic or another, FunnelEnvy clients have the best of both worlds.


Getting Started

As you can see, A/B testing and predictive bandits are both valuable tactics in your optimization/personalization programs. Like any tactic you need to pick the right tool for the job.

If you’re not yet using FunnelEnvy but are interested in personalizing your website using a combination of A/B tests and predictive campaigns we’d love to hear from you! You can contact us here: https://www.funnelenvy.com/contact/

 

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/

 

Real-Time Personalization with Marketo and FunnelEnvy

For many organizations, Marketo serves as the real-time customer database for marketing. Unfortunately, for most organizations today this rich intelligence living in Marketo is not being leveraged to drive personalized user experiences across your site which is one of the most valuable opportunities with this data.

The good news is that when it comes to personalizing with Marketo, you don’t have to be limited to just personalizing your emails and Marketo forms. You can actually use all that valuable customer centric Marketo data to drive your website personalization programs.

Why might you want to do this? Instead of showing everyone the same lead capture experience, you could show prospects who have already filled it out more product content. Or show existing customers opportunities to expand. Maybe even segment your experiences and customer journey by company size or industry.

With FunnelEnvy’s Marketo integration you can use your rich Marketo data in real-time to deliver personalized experiences across your site.

Setting up the Marketo Integration in FunnelEnvy

Within the FunnelEnvy user interface you can activate and configure the Marketo integration. FunnelEnvy fetches Smart Lists periodically from Marketo and automatically keeps these updated with Marketo. Configuring the integration also lets you setup offsite goals triggered by Marketo webhooks such as Marketing Qualified Leads (MQLs).

The Data Filtering interface lets you choose which fields to import, and exclude PII or other data based on your compliance policies.

Typically these four steps are done by the Marketing Ops team that manages the Marketo instance:

  1. Activate the Marketo data source.
  2. Authorize FunnelEnvy to access Marketo
  3. (Optional) Configuring Data Filtering
  4. Selecting Smart Lists to Import

Step 1: Find and activate Marketo under the Integrations settings. You should see it as an activated Data Source.

         

Step 2: Authorize FunnelEnvy to access the Marketo REST API with API keys.

Step 3: Optionally configure data filtering rules. When fetching lists FunnelEnvy will only import lead attributes that are selected.

Step 4: Select Smart Lists for Import. Assuming your API credentials in Step 2 were correct, you should see a list of Smart Lists available for import. Note that it may take up to an hour for this list to reflect any recently added Smart Lists.

Once you’ve configured the Smart Lists for import you’re done! FunnelEnvy will refresh the lists every few hours, retrieving leads and refreshing the local copy of Marketo data, which is then available immediately for audiences, predictive campaigns and offline Marketo-triggered goals.

More details on setting up the integration can be found in our knowledge base article.

Using Marketo for Site Personalization in FunnelEnvy 

Once you’ve configured the Marketo Data source you open up a number of valuable personalization use cases. Below are three ways you can use FunnelEnvy and Marketo together to better target, personalize, and measure your personalization initiatives.

Target Experiences and Offers using Lead Attributes and  Smart Lists

Stop serving a static one size fits all website experience to all your visitors. Want to personalize your site experience only for prospects, or to specific accounts, or members of specific campaigns? 

With FunnelEnvy you can create very rich audiences that can be built off Marketo data and that can also be used as part of more advanced audience segments that combine Marketo data with firmographics and/or real-time user behavior as well.

In the condition builder interface you have access to all of the Marketo lead fields that were imported, and can define logical conditions based on them.

These conditions can also be combined with other data sources. In the audience screenshot below we’re combining a Marketo condition with a user’s behavior (but this could also be Demandbase, Clearbit or any of the sources we support). 

And just like any of the FunnelEnvy audiences, these can be used for targeting within predictive campaigns or A/B Tests:

This flexibility allows you to setup a dynamic “always on” personalization strategy that targets the right user segments in real-time based on that visitor’s stage and their relationship with you.

Personalize Experiences at a 1:1 Level with Marketo Data

While targeting is a powerful first step in executing your personalization strategy, the more powerful opportunity is to use all that rich user data to predict the best experience to serve each visitor. 

Choosing in real-time which experience to serve each user based on their full user profile truly allows for 1:1 marketing. That is where the personalization magic really happens.

FunnelEnvy uses machine learning to predict which experience will likely convert best based on all the data we see for that user, including their Marketo data and based on the history of how similar users converted over time.

And unlike A/B tests where a specific experience is randomly assigned, or rules based personalization where you fix a specific experience to an audience, FunnelEnvy allows you to take advantage of all the data you have on that user and serve the experience mostly likely to convert for that user.

This allows you to avoid the manual analytics effort of trying to identify and capitalize on all the possible experience and segment combinations that perform best. As a marketer you can stay focused on the message and offer and allow the algorithms to optimize the segment/experience matches.

As the report below shows, we are scoring/weighing the effectiveness of every attribute we see for every user by experience.

Here, Marketo audience data along with all the other behavioral and firmographics data is used to predict the best possible outcome for each and every user and experience combination.

This allows us to use all the data to our advantage and serve the right experience that will most likely result in revenue. 

The best part is that there’s no additional setup required here. Once we have the Marketo data within our profiles we’ll use it as long as the decision mode on your campaign is set to “Predictive”.

Measure and Attribute Personalization Campaigns by Revenue (not Form Fills)

With personalization, one of the bigger challenges is being able to measure the program’s contribution to revenue and business outcomes. 

It can be done, but often requires integrating data sets or pulling reports from multiple systems and generating manual reports after the fact.

WIth FunnelEnvy, once you set up your important online, MQL, and any other revenue goals you then start tracking and attributing success to each personalized experience. Below is an example where we created a MQL goal based on a Marketo List and assigned a specific MQL value to it.

To setup this, ensure that the Marketo Data Source is activated and configured and create a new individual goal. Under “API Triggering” you’ll should see an option for Marketo. Once selected, this is the URL that your Marketo instance will hit via a webhook to trigger the goal conversion. More details on setting up these webhooks is available in our knowledge base article.

Once that’s done the Marketo goal will shows in real-time in our campaign reporting dashboards.

It now becomes much easier to tell the story of how specific tests or personalized experiences are driving down funnel goals like MQLs, SQLs, opportunities, and deals won in addition to top level goals like trial signups, demo requests, or engagement.

This makes it much easier to attribute the positive impact personalization has on the organization’s revenue outcomes. Now instead of talking about form completes you can talk the language of sales which is revenue.

Getting Started

As you can see, integrating Marketo into your personalization program is very straightforward and can unlock some very valuable use cases and capabilities. The best part with this approach is that there is no custom development or IT involvement to get this up and running. You can setup the integration and be live with your first campaign on the same day.

If you’re not yet using FunnelEnvy but are interested in personalizing your website to Marketo Leads and Contacts we’d love to hear from you! You can contact us here: 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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