A/B Testing Archives - Page 2 of 2

4 Key Metrics for Your B2B Sales Pipeline

There’s a lot for marketers today to consider when it comes to tactics for bringing in new business, but your sales pipeline is what ultimately defines the success of all your funnels, marketing campaigns, and other efforts to add new clients and revenue consistently. But considering the huge array of software tools and data available to modern B2B marketers, it can feel overwhelming to measure all the metrics in a pipeline.

We’ve identified four of the most important metrics for B2B marketers looking to understand their pipeline better. Every company is different, but in our experience, an organization can get a great sense of the overall health of its pipeline by paying attention to the four metrics below.

Velocity

Sales velocity is defined by HubSpot as the measurement of how quickly a deal moves through a pipeline and turns into actual revenue. Velocity is an important metric because it helps you understand and identify obstacles or bottlenecks in your sales process. It doesn’t matter how great your landing pages or nurture sequences are – if there’s an issue with your pipeline velocity, it’ll constrain your sales.

It doesn’t matter how great your landing pages or nurture sequences are – if there’s an issue with your pipeline velocity, it’ll constrain your sales. Share on X

The widely-accepted formula for calculating pipeline velocity is to multiply the number of opportunities by the average deal value, then times your team’s average close rate, and divide that number by the length of your sales cycle. This formula is a great starting place to closely analyze each part of your pipeline to determine any obstacles.

For example, perhaps when evaluating the sales cycle length, you realize that your sales team is taking too long to follow up after the initial appointment with a prospect. Armed with this knowledge, you can adjust your sales process and inform your reps to follow up immediately after an appointment to keep the prospect moving through the sales journey.

Deal Size

The size of your deals doesn’t require any kind of complex formula to calculate, but it can still be difficult to identify issues in this area without some intentional analysis. For example, if your company has the same number of opportunities with the same sales cycle length as last year, but your revenue is down, it’s a sign that you may not be pursuing large enough deals.

Addressing a deal size issue is solely a matter of prospecting. Here are a few tips for finding higher-value prospects:

Use account-based marketing (or ABM). ABM defines a process by which you identify a handful of high-value accounts and create customized sales and marketing collateral explicitly designed for those accounts. According to Gartner, by the end of 2020, over 70% of marketers at midsize and large B2B organizations will be using or testing ABM.
Invest in thought leadership or content that brands give for free to gain trust and credibility by helping its intended audience. In a study published by LinkedIn and Edelman last year, 60% of B2B buyers said thought leadership builds credibility when a brand enters a new category, and 54% said they purchased a new product or service they had not previously considered. Thought leadership can help attract a more discerning type of buyer.
Get social. Participate in events, post on social media, and engage with your prospects in their communities. Remember to be genuine – it’s easy for people to identify someone who’s only interacting with them for the sake of a sale. Let your natural sense of curiosity and desire to help people guide your interactions.

Close Rate

Another straightforward yet critical metric for your sales funnel, a close rate identifies the number of prospects that become paying clients relative to the overall number of leads generated by your marketing efforts. Every company will have a slightly different close rate depending on the nature of their business. A more specialized company with a niche audience might be fine with a close rate between 5% and 7%, while others might be aiming closer to double digits or beyond.

While it’s possible to find general data online about close rates, a better approach is to track your own company’s close rate and assess where it needs to be for sufficient revenue growth. If there are problems with your close rate, it’s generally a sign to evaluate your sales team, the specifics of your client offer, or both. Solicit feedback from prospects whenever possible to better understand which elements are slowing down your close rate.

Sales to Support Ratio

This ratio may not be specifically related to the performance of your sales or marketing, but it’s still vital to understand how your sales pipeline affects the rest of your operations. It’s sometimes expressed as “sales staff to support staff,” but it isn’t necessarily just the number of people working at the organization in each department. For smaller companies with employees or contractors who handle multiple organizational tasks, it isn’t a simple “this to that” ratio.

However you quantify it, this metric is vital to understanding how much of your capacity the company uses for each client. You can also track this number based on deal size, client longevity, and other measures to get a sense of which types of clients require the most attention from your support team. You can use this data in your sales and marketing efforts going forward, helping you focus on the best types of buyers for your offering.

In a well-balanced organization, sales and support can handle a sufficient volume of responsibility that allows the company to stay on track with its goals. In assessing this metric at your own company, you may need to either increase your sales activity or add additional capacity for support, depending on how much your company’s product requires.

Final Word on Key Metrics for Your Pipeline

It’s essential to customize your pipeline metrics like any sales or marketing data. Every company has its own needs, meaning it might not make sense to track the same metrics as an organization in a different industry.

For best results with these pipeline metrics, track them for as long as possible and establish a performance baseline in each area. Note where the numbers are when things at the company are going well and vice versa when you hit a slow period. Doing this allows you to gain insight into where each of your metrics should be, giving you better context when you analyze your pipeline going forward.

If you’re unsure about what steps you need to take or which metrics to track to help improve the performance of your organization’s sales and marketing performance, our team is ready to help. FunnelEnvy has several years of combined experience working with B2B clients to identify gaps in their funnels, tweak critical elements like forms and landing pages, and optimize other aspects of the lead nurturing process to maximize your conversion rate.

Click here to take a short quiz and learn more about our pricing structure.

By Raj C|2025-05-12T04:37:01-07:00October 31st, 2022|A/B Testing, Attribution Modeling|0 Comments

Finding the Right Analytics Operator for Your Marketing

Software is essential for successful B2B marketing campaigns, but it’s only half the battle. You can have the most sophisticated software deployed on top-of-the-line hardware – but if you don’t have the right people running it, you won’t maximize your (likely significant) investment into these resources. It’s like racing with a souped-up car driven by someone who’s never been behind the wheel.

You need your marketing tools and the people using them to be well-aligned so that your organization can take full advantage of today’s technology. Whether you’re running complicated data analytics platforms driven by AI and machine learning or a simple email marketing automation platform doesn’t matter. The people responsible for them need to be well-suited for the role and equipped with everything they need to be successful.

In this article, we’ll go over a few different ways you can find the right operator for your analytics, including information about the pros and cons of each method. Finally, we’ll offer some general tips on how to set up whoever you choose to operate your marketing analytics to do the best job possible.

Internal Assignment

The quickest way to find an operator for your marketing analytics platforms is to choose someone on your existing team to take the role. Even if your organization already has a well-defined marketing department that manages its own tools, this step can come with some challenges. What if it’s a new system with which no one has training? If they have the expertise, does your internal marketing team have the bandwidth to take on the responsibility of another platform?

This path gets even trickier for early-stage companies that don’t have someone designated to oversee these types of tools. These super-lean organizations typically have to assign the role to someone who already has a lot on their plate, which brings up the potential for errors or incomplete data.

If you plan to go this route, ensure the internal team member has the necessary availability and knowledge. Otherwise, this option should be a short-term choice that you transition out of immediately – for example, having a marketing manager run an analytics platform until you can transition the responsibilities into a more-fitting candidate.

Hiring a New Team Member

This is ideal if all circumstances allow it. Adding someone to your team specifically to manage one or more analytics platforms is an excellent way to have a dedicated resource on this task, ensuring that it never slips to the bottom of the list of an employee with more generalized skills.

Of course, the challenge with this method is it requires the largest investment of time and money. Giving a task to an existing team member can be done instantly, and you can quickly start most external marketing resources if there’s an urgent need. Hiring a new person, though will take weeks, if not months, from start to finish. Even when you’ve completed the hiring process, there’s still a ramp-up time while the employee gets comfortable and fully acclimates to the new responsibilities.

On the other hand, if you don’t need someone immediately and have the capital available to support a dedicated team member, this might be the best choice. This is especially true if you’re looking for someone to manage a marketing system you use frequently. Choosing this option also gives you the most control over how you operate your marketing analytics.

Even when you’ve completed the hiring process, there’s still a ramp-up time while the employee gets comfortable and fully acclimates to the new responsibilities. Share on X

Using an External Resource

This choice typically involves initiating a working relationship with an agency or contractor (or both, depending on the complexity of your needs). In the best cases, an external resource should be a middle ground between assigning marketing management roles to poorly-qualified or overworked existing team members and hiring someone new.

This option still has a process that requires screening, and you may interview contractors or agencies the same way you might interview a full-time team member. The big difference here is cost – except for the most high-end, premier operators in the field, you can usually bring on an external resource for a fraction of the cost of hiring a new team member.

It’s also a quicker process to get them started, and there’s no long-term commitment required when hiring a dedicated team. Additionally, it’s much easier to scale workloads up and down when you use an external resource. This is great for seasonal businesses that may need a lot of work for a few months of the year but don’t have the demand for marketing analytics management to sustain a full-time team member year-round.

The drawback of using an external resource is that you’ll still need to devote time to managing and directing them, especially at the beginning stages. You’ll also have less control over how they work – in fact, legal standards dictate that you cannot provide specific requirements for when, where, and how work gets done when you hire a contractor. Some agencies or contractors spread particularly thin may not communicate the way you’d prefer.

Setting Up Any Type of Marketing Analytics Operator for Success

None of these three options is the right or wrong answer. Many companies have used all three approaches for marketing operators – some larger companies may even need to apply all three simultaneously.

Whichever source you decide on for your marketing analytics operator, you can do a few things to help them do the best possible job they can:

Be descriptive. This applies to everything from the initial job description you use to hire to the ongoing instructions you provide on new projects. Use quantitative, specific language when discussing skills, responsibilities, project timelines, and everything else you discuss with your analytics operator.
Communicate. In the era of remote work, it’s imperative to ensure the lines of communication between you and your team members stay open. You should be proactive about getting in touch and asking if they have any questions or obstacles – particularly when they’re new in the role and still getting settled.
Allow them to have input. Very few people want to be in a position with completely rigid instructions and no room for personalization. Autonomy is particularly important for employees of your company, who will want to incorporate their own unique skills and interests into their day-to-day role

The Last Word on Finding a Skilled Marketing Analytics Operator

It doesn’t matter how many resources you invest in the right tools for marketing analytics. If you don’t have the right person or people at the helm of the operation, you will eventually be disappointed in your ROI. On the other hand, bringing on the right talent – even if it’s a freelancer or someone you already have on the team – can help you maximize your return on investment in marketing analytics, even if your tools are limited.

Interested in working with our team of B2B marketing funnel specialists? Just fill out this short quiz to see if you’d be a good fit for FunnelEnvy. We can help you optimize how you approach marketing analytics so that it’s much easier for anyone – individual or agency – to achieve the desired results.

By Raj C|2025-05-12T04:37:00-07:00September 19th, 2022|A/B Testing, Attribution Modeling|0 Comments

The Top 4 A/B Tests That Will Drive Revenue

Experimentation is at the heart of digital marketing success in almost any area, from paid advertising to content production. Marketers are constantly pushing the envelope to innovate in a way that brings measurable results to their organizations.

Modern marketing technology allows us to run experiments on many areas, from buying behaviors and customer preferences to information delivery techniques and specific advertising messages. A/B testing, or split testing, has been used for decades to improve customer satisfaction in B2C businesses. In the last few decades, technology has allowed B2B marketers to run powerful, sophisticated experiments leveraging analytics and machine learning. By some measures, more than half of all B2B marketers today use A/B testing as their primary conversion rate optimization (CRO) method.

On a basic level, many organizations could improve their websites and drive more customers to their products and services by using A/B testing to identify what works and doesn’t. However, with so many tests available to run, knowing which ones will give you the best return on investment (ROI) can be difficult.

We’ve compiled a list of the top four A/B tests for B2B marketers that can directly impact ROI through increased conversion rates.

Form Names and Text

You’ll likely use forms in conversion elements at every point in a funnel. The names of your forms and the text within them can impact conversions more than you may think. A/B testing can help you determine which form of words and text are most effective in getting people to convert. Try testing different fonts and header placements to see if any are more effective than others. You could also switch up the order of fields to see if visitors prefer to fill out one before the other.

This small change can make a big difference in your conversion rate. Remember not to change any form fields for existing clients or customers so drastically that people who already have an account with you are redirected back to fill out their information again. Also, remember that less is more: asking too many questions might scare away potential customers. On the other hand, adding new elements to a form may show visitors that you really understand their particular issues, pushing them closer to converting.

Element Colors

Different hues can evoke different emotions in your website visitors, which impacts how users interact with your website or app. Try different combinations of colors and see which ones result in the most conversions. According to researchers, blue is often associated with calming safety and trustworthiness, while red is associated with urgency and excitement. Green is typically associated with nature and growth.

You want your funnel pages to be visually appealing to potential customers, but you don’t want to go overboard. You can turn them away with too many bright colors or combinations that clash. A/B testing can help you find the perfect balance of colors for your website. A few specific areas to test different color combinations include background, text, button, and form field colors.

Also, remember not to go overboard on changes in a single experiment. Try to test just one color against another (e.g., red vs. green) by changing just one element at a time to measure each change’s effect on your conversion rates.

CTA Language

Asking your visitors to take action correctly can differentiate between a successful conversion and a bounced visitor. But what words should you use in your call-to-action? That’s where A/B testing comes in. Marketers can try out different phrases and see which ones get the best results.

Generally, you shouldn’t overuse generic terms like “Click here!” or “Go.” Visitors are more likely to convert when they know exactly what you’re asking them to do. Ensure your CTAs include clear directions about what will be on the other side to avoid confusion and frustration over unmet expectations.

Emphasizing the benefits of your product or service in your CTA can be another powerful way to increase conversions. People are always more likely to take action if they know what’s in it for them. For example, if you’re promoting a case study that shows how your accounting software streamlines a firm’s operations, instead of using “Download the case study,” try something like “See how [Client A] saved 30% on staffing costs.”

Images

Though it’s something of a cliche by now, when it comes to conversion rate optimization, a good picture really can be worth a thousand words. A/B testing can help to find the perfect image for your funnel. One way to A/B test images is to divide them into categories, then test pictures in each category against one another.

For example, you might choose categories like:

People
Faces
Landscapes
Abstract

After that decision, you can test images of the same category, then compare how images in different categories perform. This is just one option – the specific level of detail you want to A/B test with your images will depend on how many visuals you have and the nature of its placement in the funnel. In other words: you may not need to A/B test every image you send out in your weekly newsletter, but you might want to be more thorough when it comes to testing the one image you include on a critical landing page in your funnel.

One way to A/B test images is to divide them into categories, then test pictures in each category against one another. Share on X

A Final Note on A/B Testing

Though A/B testing is undoubtedly popular and can be effective, it comes with its own drawbacks and dangers. As we’ve covered previously, you shouldn’t consider A/B testing as a panacea that will fix all the issues with your campaign. We’ve worked with many clients who gathered little to no valuable data from A/B tests, despite waiting many weeks or months to collect data. A/B testing can also be difficult for newer ventures that haven’t yet had enough time to build up a sufficient traffic baseline to be statistically significant.

That’s why we suggest using A/B testing strategically as a supplement to your CRO efforts. We believe that for modern B2B marketers to achieve the greatest success from A/B tests, it’s essential to move past “experimentation 1.0.” Marketers should consider their customer’s holistic journey and optimize each stage in concert with one another, using personalized insights and data about their preferences and habits whenever possible.

Are you looking for some assistance with integrating A/B testing into your funnel? Maybe you’ve already been running A/B tests for a while and haven’t seen the definitive data you were hoping for to direct your campaigns going forward. FunnelEnvy specializes in helping all types of B2B clients make A/B testing and other CRO experiments much more effective through the use of personalized solutions custom-built for a specific audience of sophisticated decision-makers.

Want to learn more about our services? Click here to fill out a short quiz that will help determine if we’d be a good fit to work together.

By Raj C|2025-05-12T04:36:59-07:00August 22nd, 2022|A/B Testing, Attribution Modeling|0 Comments

Revamp Your B2B Landing Page: 5 Things to Consider

Today, we’re going to talk about what might be the most important page on your website. No, it’s not your home page, or your contact page, or that snazzy blog post that got lots of clicks. It’s your landing page.

Landing pages are the pages that leads land on right before they convert. This is the page that should sell your product or service best. If you don’t get your landing page right, your sales are going to be undercut.

If your current landing page isn’t getting it done, then it’s time for a refresh!

5 things to consider when revamping your B2B landing page

Below are some of the most critical things to keep in mind when revamping your B2B landing page for maximum effectiveness.

1. Have a clear value proposition

First things first, you need to have a clear value proposition. As soon as your lead starts scanning the page, they should be getting an idea of what your product can do for them.

This is especially important when you’re offering an unfamiliar product or service. Everyone already knows the value of a cloud storage service, but not everyone will understand why they need NAS drives in their office at first glance.

That said, familiarity doesn’t translate to a value proposition. If you’re selling in a popular market, then your value proposition is going to be what differentiates you. If everyone already knows what Slack, Zoom, Skype, and email are, then what unique selling point do you have to offer, and what’s the fastest way to showcase it on your landing page?

2. Make sure the journey from your marketing campaign to your landing page is cohesive

Next, you need to view your B2B landing page as your user’s end point in a marketing campaign journey. From the first time someone hears about your product to the moment they’re about to make a purchase, they are on a journey with your brand.

Visuals are a great way to tie this journey together. Using colors, images, logos, and keywords throughout your marketing campaign to your landing page will help solidify the landing page’s purpose for your leads. Conversely, changing up your visual narrative and tone on the landing page can dissociate the customer’s previous experiences from your landing page, breaking the customer journey at the last moment. Essentially, it’s crucial to stay cohesive with your language, messaging, visuals and call to action.

3. Have an obvious CTA front and center, and reduce navigation elements

Another key component of a cohesive B2B landing page is a clear call to action (CTA). CTAs are proven methods of pushing engagement, despite how naggy they may seem on the surface.

Not only do they work, but they help leads make their decision, too. If someone visits your landing page and either A) Doesn’t know what the page is for, or B) Can’t find the CTA, then they’re probably going to scroll around and then click away.

Don’t let this happen to you! Whether your CTA is a “Buy Now!” button, a sign-up form, or a choice between payment plans, make sure it’s the first or second thing that your visitors see.

4. Showcase your testimonials and partnerships on your landing page

For our last two tips, we’re going to tie everything together with actionable steps. The first of which is to establish trust quickly.

In B2C, entry trust (i.e., before the customer becomes a repeat customer) comes from reviews and word of mouth. Consumers want to hear that a product is great from their peers before they hear you say it.

B2B works much the same way, except that your customers’ peers are going to be other businesses. This means you’re going to want to rely on testimonials and partnerships rather than reviews.

Having familiar logos on your landing page as well as kind words will quickly ingratiate you with your leads. If they recognize brands that you’ve partnered with or see their needs and issues reflected in your positive testimonials, they’ll trust your product before they’ve made it to the checkout page.

5. Create a video to engage with the visitor

Our second actionable tip is to place a video on your landing page. It might sound crazy, especially if you’ve never invested in video content before. But in the same way that blog content drives leads, video content drives sales. In fact, it often does it better.

A landing page video should be a concise pitch of your product, about 2-3 minutes at the most. You should quickly explain what your product does, what problem it solves, how its features solve that problem, and if you have time, include a story from someone who has had success with your product.

In case you haven’t put it together, that’s your entire landing page in one engaging pitch. Except for your CTA, which should be sitting right next to the video.

Boost your B2B landing page performance with FunnelEnvy

While the five revamping tips listed above are a great way to get started, it’s far from everything you need to craft an engaging B2B landing page. And if you don’t have a lot of experience in this area, it can be tough to know how to even implement the above suggestions.

To supplement your experience, you can partner with FunnelEnvy. In case you couldn’t tell, we’re lead-generating experts, and we have a solid grasp on how to turn your landing page into a conversion machine. We offer services like Lead Gen Experiences, which will help you turn your traffic investment into a moneymaker, and Account Match, which will identify the most high-value accounts for your business and help you target them.

Reach out to the FunnelEnvy team today and start growing your business like never before.

By Arun Sivashankaran|2025-05-12T04:36:56-07:00July 7th, 2021|Conversion Rate Optimization, Full-Funnel Optimization, A/B Testing|0 Comments

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:

Activate the Marketo data source.
Authorize FunnelEnvy to access Marketo
(Optional) Configuring Data Filtering
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/

By James Niehaus|2025-05-12T04:36:48-07:00March 4th, 2020|A/B Testing, Full-Funnel Optimization, Revenue Funnel Optimization|0 Comments

Why B2B Marketers Should Stop A/B Testing

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

It does not optimize the KPIs that matter well. – Experimentation does not easily accommodate down-funnel outcomes (revenue pipeline, LTV) or the complexity of B2B traffic and customer journey.

It is resource-intensive to do right. – Ensuring that you are generating long-term and meaningful business impact from experimentation requires more than just the ability to build and start tests.

It takes a long time to get results. – Traffic limitations, achieving statistical significance and a linear testing process makes getting results from experimentation a long process.

I. KPIs That Matter

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

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

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

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

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

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

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

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

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

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

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

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

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

III. Experimentation Takes a Long Time

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

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

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

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

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

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

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

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

Conclusion

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

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

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

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

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

By James Niehaus|2025-05-12T04:36:47-07:00January 16th, 2020|A/B Testing, Full-Funnel Optimization|0 Comments

B2B Marketers Should Stop A/B Testing in 2018

Several years ago I was hired to help fix some serious website conversion issues for a B2B SaaS client.

A year earlier the client had redesigned their website pricing page and quickly noticed a significant drop in conversions.

They estimated the redesign cost them approximately $100,000 per month.

The pricing page itself was quite standard; there were several graduating plan tiers with some self-service options and a sales contact form for the enterprise plans.

Pricing Page

Before coming to us, they accelerated A/B testing on the page. After investing a considerable amount in tools and new team members they had several experiments that showed an increase in the tested goals. Unfortunately, they were not able to find any evidence that these experiments had generated long-term pipeline or revenue impact.

I advised them of our conversion optimization approach – the types of A/B tests that we could run and plan to recapture their lost conversions. The marketing team asked lots of questions, but the VP of Marketing stayed silent. He finally turned to me and said:

“I’m on the hook to increase enterprise pipeline and self-sign up customers by 30% this quarter. What I really want, is a website that speaks to each customer and only shows the unique features, benefits and plans that’s best for them. How do we do that?”

He wanted an order of magnitude better solution. He recognized that his company had to get closer to their customers to win and he wanted a web experience that helped them get there.

I did not think they were ready for that. I told him that we should start with better A/B testing because what he wanted to do would be very complicated, risky and expensive.

Though that felt like the right answer at the time, it is definitely not the right answer today.

Why Website Experimentation Isn’t Enough for B2B

At a time when the web is vital to almost all businesses, rigorous online experiments should be standard operating procedure.

The often cited Harvard Business review quote from the widely distributed article The Surprising Power of Online Experiments supports years of evidence from digital leaders suggesting that high velocity testing is one of the keys to business growth.

The traditional process of website experimentation involves:

Gathering Evidence. – “Let’s look at the data to see why we’re losing conversions on this page.

Forming Hypotheses. – “If we moved the plans higher up on the page we would see more conversions because visitors are not scrolling down.”

Building and Running Experiments. – “Let’s test a version with the plans higher up on the page.”

Evaluating Results to Inform the Hypothesis. – “Moving the plans up raised sign ups by 5% but didn’t increase enterprise leads. What if we reworded the benefits of that plan?”

Every conversion optimization practitioner follows some flavor of this methodology.

Typically, within these experiments, traffic is randomly allocated to one or more variations, as well as the control experience. Tests conclude when there is either a statistically significant change in an onsite conversion goal or the test is deemed inconclusive (which happens frequently).

If you have strong, evidence-based hypotheses and are able to experiment quickly, this will work well enough in some cases. Over the years we have applied this approach over thousands of experiments and many clients to generate millions of dollars in return.

However, this is not enough for B2B. Seeking statistically significant outcomes on onsite metrics often means that traditional website experimentation becomes a traffic-based exercise, not necessarily a value-based one. While it may still be good enough for B2C sites (e.g. retail ecommerce, travel), where traffic and revenue are highly correlated, it falls apart in many B2B scenarios.

The Biggest Challenges with B2B Website Experimentation

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

It does not optimize the KPIs that matter well. – Experimentation does not easily accommodate down-funnel outcomes (revenue pipeline, LTV) or the complexity of B2B traffic and customer journey.

It is resource-intensive to do right. – Ensuring that you are generating long-term and meaningful business impact from experimentation requires more than just the ability to build and start tests.

It takes a long time to get results. – Traffic limitations, achieving statistical significance and a linear testing process makes getting results from experimentation a long process.

I. KPIs That Matter

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

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

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

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

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

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

Traffic Complexity and Visitor Context

Unlike most B2C, B2B websites have to contend with all sorts of different visitors across multiple dimensions and often with a long and varied customer journey. This customer differentiation results in significantly different motivations, expectations and approaches. Small business end-users might expect a free trial and low priced plan. Enterprise customers often want security and support and expect to speak to sales. Existing customers or free trial users want to know why they should upgrade or purchase a complementary product.

An added source of complexity (especially if you are targeting enterprise), is the need to market and deliver experiences to both accounts and individuals. With over 6 decision makers involved in an enterprise deal, you must be able to speak to both the motivations of the persona/role as well as their account.

One of the easiest ways to come face-to-face with these challenges is to look at the common SaaS pricing page.

Despite my assertions several years ago, the benefits of A/B testing are going to be limited here. You can change the names or colors of the plans or move them up the page, but ultimately, you are going to be stuck optimizing at the margin – testing hypotheses with low potential impact.

As the VP of Marketing wanted to do with us years ago, we would be better off showing the best plan, benefits and next steps to individual visitors based on their role, company and prior history. That requires optimization based on visitor context, commonly known as website personalization.

Rules-based Website Personalization

The current standard for personalization is “rules-based” – marketers define fixed criteria (rules) for audiences and create targeted experiences for these them. B2B audiences are often account, or individual based, such as target industries, accounts, existing customers or job functions.

Unfortunately, website personalization suffers from a lack of adoption and success in the B2B market. 67% of B2B marketers do not use website personalization technology, and only 21% of those that do are satisfied with results (vs 53% for B2C).

Looking at websites that have a major marketing automation platform and reasonably high traffic, you can see the discrepancy between those using commercial A/B testing vs Personalization:

The much higher percentage of sites that use A/B testing vs personalization, suggests that although the value of experimentation is relatively well understood, marketers have not been able to see the same value from personalization.

What accounts for this?

Marketers who support experimentation subscribe to the idea of gathering evidence to establish causality between website experiences and business improvement. Unfortunately, rules-based personalization makes the resource-investment and time to value challenges involved with doing this even harder.

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

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

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

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

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

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

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

III. Experimentation Takes a Long Time

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

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

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

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

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

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

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

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

Is there Better Way to Improve B2B Website Conversions?

The short answer? Yes.

At FunnelEnvy, we believe that with context about the visitor and an understanding of prior outcomes, we can make better decisions than with the randomized testing that websites are using today. We can use algorithms that are learning and improving every decision tree, continuously, to achieve better results with less manual effort from our clients.

Our “experimentation 2.0” solution leverages a real-time prediction model. Predictive models use the past to predict future outcomes based on available signals. If you have ever used predictive lead scoring or been on a travel site and seen “there is an 80% chance this fare will increase in the next 7 days,” then you have seen prediction models in action.

In this case, what we are predicting, is the best website visitor experience that will lead to an optimal outcome. Rather than testing populations in aggregate, we are making experience predictions on a 1:1 basis based on all of the available context and historical outcomes. Our variation scores take into account expected conversion value as well as conversion probability, and we continuously learn from actual outcomes to improve our next predictions.

Ultimately, the quality of these predictions is based on the quality of the signals that we provide the model and the outcomes that we are tracking. By bringing together behavioral, 1st party and 3rd party data we are building a Unified Customer Profile (UCP) for each visitor and letting the algorithm determine which attributes are relevant signals. To ensure that our predictive model is optimizing for the most important outcomes, we incorporate Full Funnel Goal Tracking for individual (MQL, SQL) and account (opportunities, revenue, LTV) outcomes.

Example: Box’s Homepage Experience

To see what a predictive optimization approach can do, let’s look at a hypothetical example:

Box.com has an above the fold Call to Action (CTA) that takes you to their pricing page. This is a sensible approach when you do not have a lot of context about the visitor because from the pricing page, you can navigate to the right plan and option that is most relevant.

Of course, they are putting a lot of burden on the visitor to make a decision. There are a total of 9 plans and 11 CTAs on that pricing page alone, and not every visitor is ready to select one – many still need to be educated on the solution. We could almost certainly increase conversions if we made that above the fold experience more relevant to a visitor’s motivations.

SMB visitors might be ready to start the free trial once they have seen the demo, the enterprise infosec team might be interested in learning about Box’s security features first, customers who are not ready to speak to sales or sign up might benefit from the online demo, and decision makers at enterprise accounts and who are engaged might be ready to fill out the sales form.

Modifying the homepage sub-headline and CTA to accommodate these experiences could look something like the image below. Note that they take you down completely different visitor journeys, something you would never do with a traditional A/B test.

If we had context about the visitor and historical data we could predict the highest probability experience that would lead to both onsite conversion as well as down funnel success. The prediction would be made on a 1:1 basis as the model determines which attributes are relevant signals.

Finally, because we are automating the learning and prediction model, this would be no more difficult than adding variations to an A/B test, and far simpler and with higher precision than rules-based personalization. The team would be alleviated from having to do the analytical heavy lifting, new variations could be added over time and changing conditions would automatically be incorporated into the model.

Conclusion

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

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

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

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

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

This post originally appeared on LinkedIn.

By Arun Sivashankaran|2025-05-12T04:36:43-07:00January 19th, 2018|A/B Testing, Full-Funnel Optimization|0 Comments

The B2B Marketers Guide to Thinking Beyond A/B Testing

Deliver more conversions with less effort with ‘The B2B Marketers Guide to Thinking Beyond A/B Testing’.

Download the guide and be inspired by optimization strategies that blow A/B testing away.

If you’re striving for experimentation excellence, there’s no better teacher than learning from the best of the best. Whether you are looking for ways to increase on-site conversions, marketing attributable revenue, or testing velocity, the B2B Marketers guide to thinking beyond A/B testing will equip you to succeed.

Download this ebook to discover:

Why traditional A/B testing is not effective for B2B, and what you can do about it.
How to optimize for on-site and down funnel (pipeline & revenue) outcomes.
How to re-think your on-site optimization to drive more predictable revenue.

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