Conversion Rate Optimization Archives - Page 3 of 9

How Marketing Teams Should Optimize Website for Conversions that Align with B2B Sales

You might have heard before that marketing and sales can sometimes experience the business version of a sibling rivalry, but it’s not quite what you think.

Within business-to-business (B2B) organizations, marketing’s focus is on generating leads, while sales focuses on getting those leads to close. A disconnect happens when your marketing team (with good intentions) focuses on volume over quality, therefore resulting in passing over a high volume of leads to sales that just won’t close.

In this article, we’ll walk through a framework for how to categorize leads that come in through your website, how to build website messaging and landing pages that are consistent and relevant for each type of category, and how to optimize for intent as you go.

A General Framework for Categorizing Website Leads

It might seem obvious that your marketing team should focus on quality over quantity, or ideally both at the same time, but in practice the two can get a bit muddled.

We recommend generally categorizing leads into three different buckets:

High volume, high intent. These leads should be sent to sales and prioritized.
High quality, Low intent. These leads should be sent to a nurture funnel where they continue to be educated and engaged.
Low quality. These should get filtered out altogether, or directed to a different offer.

Ultimately, we’re talking about being more efficient with qualification by allowing your website to do a lot of the work for you.

This includes building consistent messaging for each lead category, building and presenting relevant landing pages for those people, and optimizing for intent as you go.

Create Messaging that’s Consistent and Relevant

In order to qualify each website visitor as a member of one of the lead categories above, you’ll need to be able to automatically consider two things before displaying website content:

How that person got to your website. The messaging on the page they visit should be consistent with the email, ad, social posts, blog post content, or search result that preceded it.
Their business demographic. Use marketing automation, CRM and / or 3rd party data to ensure that messaging is also relevant to their business size or industry. Focus on the industries and business sizes that have an expected value for your sales teams, and send all others into the low quality bucket.

One effective way to do so is to display case studies from relevant industry competitors, if you have them available.

For example, if someone from Wells Fargo visits your fintech website, they’ll likely respond more positively to a landing page with logos or success stories from Chase or Bank of America then from Investopedia or Stripe. If that’s not in the cards for you, focus on business size first. Before showing a Wells Fargo visitor logos from a fintech startup, show a success story from Macy’s, Delta, or another enterprise business.

This example from Shopify that’s optimized to attract businesses in e-commerce fashion. The logos and success stories listed on the page include e-commerce fashion brands, like AdoreMe, Cee Cee’s Closet and Coco and Breezy, immediately signaling to other fashion e-commerce companies that Shopify’s solution might be a good fit for them.

This example from Shopify that’s optimized to attract businesses in e-commerce fashion.

FunnelEnvy offers reverse IP, or account matching, and real time data integration to help marketers surface insights that allow them to display industry-specific webpages like these.

We also help companies display pages based on other types of data, like funnel stage, company size, and more.

This example from a large call center showcases how experimenting with personalized offers on their website by buyer segment led to an increase in qualified leads.

In fact, MQLs increased by 10X between March and June of 2020.

Graph showing MQLs increased by 10X

Landing pages that set the right expectations

Your landing pages essentially start the sales process by presenting your products to people for the first time. For them to be effective, they need to accomplish two things:

Mimic your sales people. This should be true for every lead category. Once a person converts through your website and makes it to the stage where they speak to sales, they shouldn’t receive an entirely different message than what led them to convert in the first place.
Clearly communicate what each site visitor should expect next. This will change depending on the lead category. If your site visitor is categorized as “high quality, high intent” and on their way to talking to a sales person, tell them that. If they’re getting redirected to a different offer or getting more information sent to their inbox, tell them that instead.

One common mistake we see companies make is sending leads to a discovery meeting with a sales development representative (SDR) after they register for a demo. They’re expecting to see the product, when in fact, they end up in a frustrating meeting where they’re asked a lot of questions, afterwards which the real demo is scheduled depending on how they’ve qualified.

One way to rectify this is to make the discovery process part of the inbound flow, like we do at FunnelEnvy.

Our quick questionnaire helps us to categorize site visitors that convert so that we can set expectations for what will happen next, once they’ve completed the form.

Take a moment to fill out this questionnaire

Here’s another example that qualifies leads using company size and sales strategy:

Example that qualifies leads using company size and sales strategy

Optimizing for intent as you go

You’ve created messaging for each lead category and set up your landing pages so that the right expectations are set. Now it’s time to take it a step further by putting in place a mechanism to filter out low-quality leads or show them a different offer.

If a website visitor that’s not a highly valuable lead for your sales team comes along, you’ll want to be able to identify them with data that reveals their business size, industry, title, or any other identifying signal that makes a difference for you.

If someone comes along that doesn’t fall into any of the buckets you’ve identified as high value, consider sending them to your self-service solution (if one exists) or including a message upfront that right now, you’re just not the right fit for one another.

While it might seem scary to direct some leads away from sales, it can actually improve your sales team’s productivity and have a positive impact on revenue.

Working with FunnelEnvy, one startup increased their monthly marketing qualified leads (MQLs) by 30%, and grew revenue from closed or won deals by 250% the following quarter. Here’s what that success looks look over time:

One startup increased their monthly marketing qualified leads (MQLs) by 30%

This success came from optimizing their website to align with their B2B sales strategy, and by only surfacing high quality leads to their sales teams that were ready to buy.

Bonus: treat your high quality leads like gold

Those leads that are high quality and have the appropriate purchase intent should be treated like gold.

To ensure that your sales team is successful, make sure there’s an established service-level agreement (SLA) on when and how sales is following up on those leads. For example, Marketo’s sales team commits to a 24-hour SLA.

If a tight 24-hour turnaround isn’t in the cards for you, automate your follow-up process with marketing automation or your customer resource management (CRM) software.

End the infamous sibling rivalry

The infamous sibling rivalry amongst marketing and sales isn’t actually a sibling rivalry at all — in fact, it only exists when these teams try to help one another in the wrong way.

Your website can do most of the heavy lifting to close this gap and help to qualify leads that are sent to sales automatically.

If you’re looking for a custom solution to help personalize your website content for leads of different types, FunnelEnvy can help — contact us.

By Megan Morreale|2021-04-19T15:40:48-07:00April 15th, 2021|Experimentation, B2B, Analytics, Digital Marketing, Conversion Rate Optimization, Uncategorized|0 Comments

Solving the Revenue Funnel Data Challenge

Transcript:

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

By Arun Sivashankaran|2021-06-18T05:48:19-07:00March 10th, 2021|B2B, Digital Marketing, Conversion Rate Optimization, Uncategorized|0 Comments

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

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

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

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

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

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

First-party cookies vs. third-party cookies

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

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

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

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

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

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

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

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

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

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

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

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

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

We bet your answer is the former.

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

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

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

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

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

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

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

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

Optimizing the demand gen funnel with first-party data

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

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

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

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

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

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

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

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

Conclusion: the death of the cookie is nothing to lament

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

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

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

By Megan Morreale|2021-03-02T11:52:02-08:00March 2nd, 2021|Strategy, Digital Marketing, Conversion Rate Optimization, Uncategorized|0 Comments

The Secret to Making Your ABM Personalization Campaign a Success

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

visual screenshots of abm personalized accounts

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

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

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

Identify targeted accounts
Target those accounts in real time with personalized campaigns
Measure the effects of those campaigns

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

pyramid-three-steps-abm-personalization

Choosing the Right ABM Measurement Strategy

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

homepage-optimizely-cro-website-optimization

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

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

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

graphs-target-image

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

What to Include when Measuring your ABM Personalization Campaign

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

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

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

simple attribution solution

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

The Right Attribution Model for ABM Personalization

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

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

bizible full attribution model path

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

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

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

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

abm personalization touch points

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

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

The Better the ABM Attribution, the Better Your Metrics

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

graph of results over time

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

personalization by intent chart

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

testing variation chart funnelenvy

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

Don’t Forget About Segmentation

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

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

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

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

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

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

By Arun Sivashankaran|2021-06-18T05:47:31-07:00February 18th, 2021|B2B, Digital Marketing, Conversion Rate Optimization, Uncategorized|0 Comments

Optimize your Revenue Funnel by Focusing on the Offers

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

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

Online Experimentation

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

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

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

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

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

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

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

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

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

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

Optimizing Offers

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

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

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

Examples

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

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

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

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

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

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

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

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

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

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

Final Thoughts

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

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

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

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

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

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

By Arun Sivashankaran|2021-02-18T11:31:20-08:00February 17th, 2021|Conversion Rate Optimization, B2B, SaaS, Strategy, Analytics, Digital Marketing|0 Comments

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.

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.

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.

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):

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.

By Arun Sivashankaran|2021-02-11T09:56:50-08:00February 11th, 2021|Uncategorized, Conversion Rate Optimization, B2B, Digital Marketing|0 Comments

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.

By Arun Sivashankaran|2021-06-18T05:45:27-07:00February 10th, 2021|B2B, Digital Marketing, Conversion Rate Optimization, Uncategorized|0 Comments

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.

By Arun Sivashankaran|2021-06-18T05:46:59-07:00February 10th, 2021|Digital Marketing, B2B, Conversion Rate Optimization, Uncategorized|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/

By James Niehaus|2020-03-17T07:50:35-07:00March 17th, 2020|Strategy, Digital Marketing, Conversion Rate Optimization|0 Comments

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.

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.

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.

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).

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.
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.

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.
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.

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.

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.
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/

By James Niehaus|2020-03-17T07:40:19-07:00March 17th, 2020|Conversion Rate Optimization, Strategy, Digital Marketing|0 Comments

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