About Arun Sivashankaran

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

Optimizing Campaigns & Websites by Integrating Offline Conversions into Google Analytics

Integrating offline conversions into Google Analytics can help you optimize your campaigns and website experiences. This is something that we commonly do for our customers when we’re optimizing their inbound funnels. In this post I’m going to go into some of the details about how it actually works.

So, what do I mean by offline conversions? If you’re doing demand or lead generation you’re capturing those leads onsite, typically through a form or  a chatbot. As you know that’s only a small part of the much larger funnel, most of which happens offline. Your Marketing Qualified Leads, Sales Qualified Leads, opportunity stages, closed won revenue. Those are all captured offline – and can think of them as offline goal conversions.

In addition to being offline, you also have the distinction, if you’re in B2B, of having individual offline conversions, like qualified leads versus account or company level conversions, like opportunities or revenue.

demand generation funnel

This can also exist in the e-commerce world, especially if you’re in B2B. You might be selling products on your site but also have volume based quote functionality where larger buyers are initiating quotes online and the quote generation and order capture is actually taking place offline.

The biggest optimization mistake that we have seen over the years is using the wrong success metric. And if you’re only measuring online goals, clicks and leads, that can lead to inefficient spend in your paid media campaigns and wasted activity and low quality leads from your website experimentation and optimization.

A solution on the paid media side is ultimately being able to adjust your bids based on those offline conversions. And for your website, assessing the results of experiments and making decisions based on those down funnel conversions.

channel optimization

Google Analytics is often the source of this analysis and decision making. By default Google Analytics is only tracking individual onsite activity. How traffic gets to the site, the bounce rates, page views, etc. Even the goal conversions typically stop at the form completion (on-site).

Google Analytics is great at digesting paid media campaign information website activity, and linking to ad platforms like Google Ads. But the majority of your offline conversion information is happening in different systems. Marketing automation platforms, CRM, or maybe a quote and order management solution.

To solve this we need to integrate those directly into Google Analytics. By doing so we can actually see those offline conversions, like marketing, qualified leads, opportunities, revenue as goals directly in Google Analytics and even evaluate the performance of campaigns, channels, landing pages, everything else in Google Analytics by down funnel goals like closed won revenue.

Backend integration to GA

offline goals in GA 2022-02-07

So here are the main requirements to accomplish this integration:

  1. Understand the important identifiers that Google Analytics uses and store them in your backend platforms.
  2. Capture those offline conversions from those backend systems.
  3. Translating them into the identities and events that Google Analytics expects.
  4. Send  hits to Google Analytics via the offline API known as the Measurement Protocol.

full GA integration

Let’s start by understanding the identifiers that Google Analytics uses.

Here, I’m going focus on Universal Analytics. GA4 (Google Analytics 4) is still being rolled out as at the time of this post. A lot of the concepts in GA4 are very different. So we’re still very focused on Universal Analytics.

First off, Universal Analytics has a Tracking ID. This identifies the account and property that you’re going to be sending hits to. If you log into the Google Analytics interface, you’ll be able to see that Tracking ID. You can also retrieve it using JavaScript from the browser.

The second identifier that we care about is the Client ID. This identifies a device or browser. It’s automatically set by the Google Analytics script, persisted in a cookie on the browser and has a predefined structure.

GA tracking id client id

The final identifier is the User ID. This is less commonly used in these use cases. But it is important to understand because this lets you identify an individual user on the site. Unlike the prior two IDs, this is actually set by a site owner, typically post authentication, where the user might be logging in with multiple devices, to identify an individual user and as such it has no predefined structure.

Importantly, when you send offline hits to Google Analytics, you need to include the Tracking ID and either the Client ID or the User ID. So the approach that we’re going to take is to store these identifiers alongside the lead in your backend solution.

And really, there are two options here. If you only care about individual goals and you only care about the Google Analytics tracking, you can store that Google Analytics Tracking ID and Client ID directly in your backend platform.

The second option, which we use at FunnelEnvy and gives us a little bit more flexibility across different implementations and other identifiers that we have to track is to actually capture those GA identifiers, the Tracking ID and Client ID in the browser, and associate them with another Visitor ID that we set as a cookie. Then we store that single ID, that Visitor ID in the backend platform and translate it when we’re setting those hits to GA.

To actually send them to the backend platform, you can populate the IDs that you’re going to use as hidden input fields on the form. When the lead submits that form, the GA identifiers will be saved in your backend platform.

This is relatively straight forward to do in platforms like Salesforce, Marketo or HubSpot, in which you can create custom fields or properties in that backend platform. And then populate with hidden fields in the forms using JavaScript. We commonly do this using Google Tag Manager.

storing GA identifiers in backend

The second step then is capturing the offline conversions that are happening in those backend platforms. The first thing you have to do is identify the offline goals that you care about. If you’re doing demand generation, it’s typically marketing, qualified leads or sales qualified leads at an individual level.

New opportunities closed won revenue, are very common, and if you’re generating offline orders, the generation of the quote or the order itself.

The most common way that we actually capture these offline conversions is triggering a webhook from the source platform for each offline conversion. A webhook is simply an HTTP request that’s made to our system.

Again, these webhooks will have to include the Google Analytics IDs that you’re storing on the backend, or the visitor ID, if you’re taking that approach. And importantly, where you do have an amount, like an opportunity value, a closed won value, an order value, you want to include that because you’ll be able to send that as well to Google Analytics in either the event or order that you’ll be submitting.

Now you can configure webhooks very simply in a variety of these solutions. HubSpot has workflows with webhooks, Marketo natively supports webhooks. In Salesforce, you can code webhooks in Apex or use an application like the Hooked app to visually create workflows and webhooks. Now for some reason you can’t create a webhook as an alternative you can poll through the API or directly from a database.

webhook options hubspot, marketo, salesforce

Once you’ve captured the offline conversions, you’re going toto translate those into the format and the identities that Google Analytics understands. If you’re storing the Google Analytics Tracking ID and Client ID directly in the backend, and you only care about those individual goal conversions, that you don’t really need to do any identity translation. The identity is encapsulated within that Tracking ID and Client ID.

If, however, you are storing the visitor ID then you’ll translate that into the relevant Google Analytics IDs. And if you are tracking account level goals, like opportunities, deals, or revenue at an account level, then you’re going to need to translate your account to all of the contacts that you have available. To do this you’re probably going to need to pull some kind of account to contact mapping from your CRM.

The following diagram illustrates this. For an individual goal, like MQL, you can web hook that directly from the CRM into Google Analytics. Just get the Tracking ID and Client ID and send that on a one-to-one basis to Google Analytics. But if you are sending an account level goal, there’s another step involved of pulling the account to contact or individual mapping, then translating account identifiers into contact IDs, and then into Google Analytics Tracking IDs so that you can send those offline hits to Google Analytics.

individual vs account goals

What you’re really going to want to do is identify all of those offline conversions and then map those to the types of hits that you’ll be sending to Google Analytics through the Measurement Protocol. You’ll want to identify the type, whether that’s account or individual.

Typically for demand generation type of scenarios we use events as the hit type whereas for e-commerce we use orders. As part of this mapping you can identify the relevant fields in those hits and make sure you’re accounting for all of them, including the value, which can be either an actual value coming from the offline conversion or predicted value if you can do that.

map offline conversions

The final step is actually sending those hits to Google Analytics. So for this again, we’re going to use the Google Analytics Measurement Protocol. This is an offline API to be able to send hits directly to the GA servers over HTTP. Now Google Analytics includes a pretty helpful tool that allows you to build and test those offline Measurement Protocol hits.

measurement protocol hit builder examples

So here you can see an example of an event and the event structure that’s sent to Google Analytics. As well as an enhanced e-commerce purchase example that you might use for an offline e-commerce order.

If you are sending event hits, then you’ll want to create a goal of type event so that the goal is triggered on the offline event hit that you’re sending to GA. And if you are sending an event value along with that measurement protocol hit, you’ll want to set the goal value as the event value.

GA goal setup

Unfortunately the measurement protocol has been abused over the years by bots. In the view settings Google Analytics has the ability to block bots. We’ve seen that if you leave the IP address blank, and don’t set an IP address on that Measurement Protocol hit you’re more likely to get that traffic blocked as bot traffic. So we typically recommend setting the IP address of the hit to the same visitor IP that initiated the prior session (that submitted the form), and send that along with your Measurement Protocol hit.

The other thing that you can do that’s really helpful is set a custom dimension along with those measurement protocol hits. One example is to send the buyer stage. So if the visitor is an MQL, if they have an active opportunity, send that as along as a dimension to Google Analytics so you can further segment that traffic and understand what they’re doing on site.

And then finally you can backdate hits, but only up to four hours reliably using the queue time parameter. What that practically means is you can’t expect to send hits days or weeks in the past, you need to be pretty on top of it and this process needs to be running relatively frequently because you can really only backdate those hits by four hours.

I want to spend a little bit of time talking about attribution. By default, the measurement protocol hits that are going to be sent are, will attributed to the direct channel in a new session. The default reporting in Google Analytics uses the last click non-direct attribution model for reporting.

Practically, what that means is that when that conversion comes over offline from the Measurement Protocol, it’s going to be in a new session. But the conversion will actually be attributed to the prior non-direct channel.

So to visualize this that let’s say we have a visitor coming along, hitting the site, viewing a few pages and then submitting a form, and that visitor came in from a paid search ad. When we send over the offline conversion, that’s going to generate a new session which by default it will be attributed to the direct channel. So the conversion is actually going to be attributed to the prior session’s (paid search).

offline conversion attribution in GA

I want to wrap this up with some final thoughts. To be able to track these offline conversions and associate them to marketing campaigns, as well as website experience, you don’t need an entirely new tech stack. You can use Google Analytics in the same way that you have. You just have to integrate the data into it.

As I mentioned the number one mistake that we see when doing optimization of any kind is picking the wrong success metric. And without measuring these meaningful outcomes, optimization experimentation, things like segmentation and personalization are really just vanity exercises.

With these offline conversions, however, you can optimize your bidding through your ad platform, for example, by linking Google Analytics and Google Ads and triggering goal conversions that occur from offline events. And you can optimize your experiences, most importantly, your offers and your form experiences that are most likely to have an impact on those down funnel KPIs.

Finally, if you do have a long sales cycle and a relatively low volume of revenue conversions, you can take this a step further. You can actually take a more frequent, upstream conversion and use a predictive model to predict revenue and optimize faster. As we see from the table below, more frequent conversions (a higher number of conversions in a certain time period), in Google Ads will actually allow the algorithm to optimize faster. So sending along a predicted revenue value with your MQLs can allow that algorithm to optimize traffic much faster, while those down-funnel actual closed won revenue conversions are coming in.

google ads impact of conversion volume

By |2022-02-07T15:05:30-08:00February 7th, 2022|Analytics, The Funnel, B2B|0 Comments

Optimizing the Account Based Revenue Funnel

Transcript

Let’s assume that you’re targeting B2B companies and you built out your account list. And if you have a heavy ABM strategy, those accounts probably represent the vast majority of your potential revenue. So how do you go about converting that opportunity into conversations and qualified pipeline? Generally it all boils down to some combination of inbound and outbound funnels; but what if you’re not getting them to convert?

Just because you’re doing ABM doesn’t necessarily mean you’ll magically alleviate the common B2B funnel problems, such as low conversion rates, qualified pipeline, high customer acquisition costs and low quality leads.

When you’re facing this, it’s very easy to be critical of the factors that are under your control, like messaging, campaigns or list building. But it’s also very often the case that there are factors outside of your control. Most notably buyer behavior. It’s relatively well known that the buyer journey in B2B is long, especially for net new customers. And it’s only getting longer.

long buyer journey

There are a lot of frameworks that try to describe buyer behavior, and they all look something like this.

buying stages

This is what we and the experts out there think buyers are doing when they’re going through a purchase decision.

I think that’s a somewhat limited view. Let’s zoom out a little bit to understand our buyers over a longer period of time, not just when they happen to be looking for products or services. The reality is that outside of this relatively short buying window, they’re not thinking about you at all. In fact, they’re likely doing other stuff like executing projects, managing their teams, or, after they’ve actually made a purchase decision, implementing the solutions that they selected with no immediate need to find another one.

If you think about this in terms of actual purchase intent of the buyer, it’s usually quite low for anything that has a significant price tag associated with it.

buying window

Usually we only see it shift dramatically within that buying window. This doesn’t mean that you completely ignore accounts that are outside of the buying window, but it does suggest that you’re more likely to see buying motions within it when they have higher purchase intent.

Of course, an important point here is that unlike account selection, campaigns offers purchase intent is largely outside of your control. It starts when buyers become aware of a problem or solution and it progresses from there. So your goal is to engage them in a conversation as early in that process as possible.

What’s the value of the purchase intent within the buying window? Let’s do some funnel math to solve for it. The expected value of an account at any point in time can be modeled as the revenue expected from that account, either on an LTV or annual contract value basis multiplied by the probability of conversion at that point in time.

From our experience the probability of conversion to opportunity is largely determined by the customer’s purchase intent and the offer that you put in front of them. What if you could allocate more budget and therefore create higher impact offers to those high in 10 accounts that are within the buying window?

expected value and targeting

There are several ways to model this, but here’s one approach. In it we assume some percentage of the total accounts that you’re targeting are in the buying window at any point in time, that you have a fixed budget for acquisition and you’ve established an average cost to generate an opportunity from your accounts.

high intent targeting model

If you’re distributing that budget evenly across all of your target accounts, irrespective of their intent (known as the spray and pray model), then you have less total budget available for those high in 10 accounts. If however, you’re able to focus and optimize, so that 80% of your budget is focused on the high intent accounts and 20% on the rest, then you’re able to actually generate eight times more opportunities from accounts that are in that buying window because of the higher spend.

Of course this assumes that there’s a correlation between the budget you can spend per account and the conversion rate, which in this case is a sales conversation. Now, typically that involves higher touch and more expensive plays like high touch outreach, personalization, increased PPC bids or direct mail.

conversion rate optimized

But if you are confident that you can generate more conversions at high intent accounts by focusing your budget and spend, the next step is being able to identify when an account starts showing intent. To understand this, we can think about all the various activities buyers go through when they go through your purchase stages. That could be everything from internal activities, like recruiting and fundraising and coming to your website, engaging with your content ads and outbound campaigns and actually filling out forms and piloting solutions.

buying signals

All of these are signals that can be captured and tracked by different sources from third parties to analytics and marketing automation platforms. And although intent data is often discussed as something you buy exclusively from a third party, recognize that most of these are first party data sources often siloed away. General intent is useful, but it’s actually more important to establish an intent for your specific product or service.

signal sources

Our recommendation is before you run out and buy expensive third party intent data, make sure you’re fully taking advantage of your first party data sources that can help you try and triangulate buyer intent. The final point here is even if we’re running ABM campaigns, the traditional conversion points, when a customer fills out a form on your website or schedules meeting from an outbound campaign, can be way too late in the journey. Any sales rep is going to tell you that the earlier they can engage that account, who’s entering the buying window, the better they can control the conversation. And if you don’t do that, your competitors sure will.

So if we’re not waiting for prospects to fill out forms, how do we identify them? Well, your website is probably your most important source of intelligence and you own it. Knowing who’s coming to it, how they got there and what content they’re consuming can tell you a lot about their purchase intent. So one way you can identify so-called anonymous visitors before they fill out a form is through what we call reverse IP.

Let’s walk through how it works. Every website visitor coming to your site requests pages from the site, and with that sends their IP address. That’s the key. Now various services, including AccountMatch by FunnelEnvy can turn that IP address into an account record. And that account record contains various firmographic attributes like the company website, industry number of employees, revenue ranges, and potentially even the technologies that they’re using.

reverse ip with account match

AccountMatch isn’t the only solution out there. There are plenty of other services that also provide the same capability. The important point is that record can be pushed back to your webpage and also to various analytics tools, your CRMs, Zapier or anyone else that needs to go. So again, account match determines firmographic or account level attributes based on that visitor’s IP address. Here’s a couple of things you should know about them. First off, as I mentioned, they work for anonymous visitors, and don’t rely on someone filling out a form on your site.

They will not match a hundred percent of traffic. Match rates typically vary around 10 to 30%. It can vary significantly based on the nature of the traffic coming to your site. The good news is that for larger organizations with known IP blocks, you tend to see much higher, effective match rates. That’s good because most of the time in our high value target accounts, tend to be comprised of larger organizations. Finally, some of the providers tend to be better at real time responses and it allows for not just intelligence, but also targeting and personalization on your website.

So one of our goals then is to be able to quantify the intent of any account by generating an intent score for each of those accounts. This obviously starts with our target account list or our TAL, which includes within it, the expected revenue for each account. To score these, what we need to be able to do is factor in the various different buying signals we reviewed on the earlier slide. We can model all of these as events, each of which has a source, a type and a score value associated with it.

event types

As you can see a source like the website can have different event types, with different scores associated with them. For example, filling out a blog page generally indicates less intent than viewing a pricing page or filling out a form. Similarly, you can model out all the various different types of events from multiple sources that are relevant to your buying journey. As far as the scores themselves, you can start by taking educated guess at them, using your analytics and attribution models as well as your intuition to guide you. If you do have submission volume, you can also take predictive machine learning, one to one approaches to generate scores as well. Once you are capturing events from your sources and you can tie them back to accounts, you sum those over a period of time to get an overall view and then normalize those across accounts to get a relative account score from zero to one.

To actually integrate those events and set things up correctly, you’ll need a database of some kind in the middle. This can be a traditional relational database, a customer data platform, or even your CRM if it’s flexible enough to accommodate this. You’re going to start by feeding it your TAM spreadsheet, which includes your accounts and expected revenue or a fit score. And then we’ll start integrating your website data. For this, we usually sync that reverse IP data as account data into Google analytics, and then send the relevant events over to the database. And then depending on your event sources, you can integrate all of them from outbound to your ads, third party data and your marketing automation platform into the same database. Finally, from that, you’d be able to run the queries, generate an account, prioritize report, showing both fit expected revenue as well as intent.

data architecture

What do we actually get from this report? So we take our expected revenue from our target account list and multiply that by the intent score from the events that we came up with, to arrive at a present day expected value. That is the value in today based on all of the events and the intent that that account is shown. If you establish a customer acquisition cost ratio target, maybe a third of the ACV, or if you can come up with one, then you can establish a present day budget that you can expand for each account for acquisition.

expected value and budget

So this is what that report could look like. Now, instead of a list of accounts that you spray and pray against you have. So what do we get from this report? Well, if we take our expected revenue from our account list and multiply that by the intent score, that score from zero to one, we can arrive at a present day, expected value per account.

accounts prioritized by value and budget

That’s the value of each account in the present day based on the behaviors and the intent that they’re showing. If you established a customer acquisition cost ratio target (or if you can come up with one) then you can establish a present day budget that you can spend for acquisition per account. So this is what that report could look like. So now instead of a list of accounts that you spray and pray against, you actually have a prioritized list that has both expected value and the budget that you can use to focus your SDRs and other high cost plays at your most valuable accounts, which means the one that have both high value and are demonstrating higher intent to purchase. So sort descending by budget or expected value and prioritize accordingly.

So with that only give us some takeaways. Account based revenue funnel optimization is largely an exercise in intent based prioritization. You want to adjust your goals for low intent, inbound and outbound impression. So if an account is not showing intent, you may not want to sell them right away. Instead, you may want to build brand awareness, positioning and use it as a vehicle to evaluate intent through content. For this to work, you obviously have to be able to develop higher touch plays, both outbound and inbound to take advantage of that increased budget. But don’t silo your channels, recognize that outbound, inbound impressions whether ads, your website or outbound on emails are all an impression that can be counted towards the overall intent score. And finally, you might come up with one score, but certainly you’re going to need to be able to test, measure and iterate on the model over time.

By |2021-09-29T22:47:42-07:00September 29th, 2021|Digital Marketing, The Funnel, B2B|0 Comments

Optimizing Conversational Marketing: A Data-Driven Deep Dive

Transcription

Hey everyone. I’m Arun from FunnelEnvy. I’m sure all of you are aware of conversational marketing and probably many of you have deployed chatbots on your own sites for lead capture and conversion. Now, I’ve been digging into some of the data around them myself and I wanted to share some more that I learned along the way along with some hypotheses on how you might be able to improve conversational marketing performance on your own funnel. So let’s get started. I wanted to establish some shared context first. Now, web chat on B2B and demand generation sites used to be used primarily for customer support, but increasingly whether it’s because of actual results or FOMO, you see the chat widgets all over the place as part of the revenue funnel.

chatbot

Let’s start with some shared context. Webchat on b2b & demand gen sites used to primarily be for customer support. Increasingly, whether it’s because of actual results or FOMO you see the chat widgets all over as part of the revenue funnel. Conversational marketing presents an alternative to the website funnel & forms to engage visitors in what is supposed to be a more human, interactive medium.

Does conversational marketing actually work?

The Google featured snippet here tells us that it’s the fastest and most effective way to guide prospects through the sales funnel and that it provides an authentic experience and real value for your customers. This is despite the fact that 99% of the time it’s a bot on the other side (and everyone knows it). 

Let’s start with the first customer, who uses Drift on their site. 

Customer Engagement with Drift

First up we’ve got a customer who uses Drift on their site. Now, we’re going to be looking at the engagement and conversion metrics based on the number of new visitors coming to the site over a certain time period and using the metrics that each platform sends to Google Analytics. Now, as you can see here, this site isn’t doing too well when it comes to chatbot engagement, less than half a percent of visitors are actually engaging with the Drift bot, but it’s doing even worse when you look at the email and phone number capture rates, less than a 10th of a percent. When you compare that to the form baseline, contact us form in this case, it’s certainly outperforming the chat experience.

Conversion with Qualified. 

When we look at another customer who happens to have Qualified, we see pretty similar results. In this case, again, we’re seeing a small fraction of visitors engage with the chatbot and of those that do, we see a pretty small percentage actually proceed to book a meeting or give their email address. Again, when we look at the form conversion rates, in this case, it’s a request a demo form, it significantly outperforms the qualified chatbot.

But of course, I’ve just been showing you top of the funnel numbers and this is demand generation. So it would be a mistake to only focus on the top of the funnel leads. So in the third example, let’s fit a customer that is Intercom and take a few steps further down the funnel. Now, the top of the phone numbers looks pretty similar. The baseline contacts us form outperforms the chatbot for lead conversion. However, in this case, we segment it by known leads and try to evaluate the effect of engagement with Intercom on the conversion to pipeline or opportunity. And what’s interesting here is we get about 10% of the 4,600 or so known leads engage with Intercom.

down funnel with intercom

So a small number of engaging with Intercom, but those that do convert to pipeline at a much higher rate than the leads that did not have any Intercom interaction. So what can we observe from all of this? Well, despite the fact that Drift used to tell us that forms are dead, from this data here, they clearly are. In the data that we looked at, the top of the funnel engagement with chat is relatively low on an absolute basis and the forms on the site, static web forms outperform chat for lead conversion. But both from the example that I showed you as well as conversations that I’ve had with other marketers, chatbots can have a significant impact lower in the funnel on lower-funnel leads and accounts and their conversion to the pipeline.

Observations

So how do we think about this? Well, like everything on your site experience is all about the friction for the visitor and the value that you’re providing. It’s important to recognize that conversational marketing and chat are a form of interruption marketing. The question you need to ask yourself, of course, is, is that interruption adding or removing friction from the buyer’s journey? So some ways to think about that in the context of your visitors are, what is the context of buying stage and intent of that visitor? What are they actually trying to accomplish in that session on your site? How does that impact and affect their behavior? And finally, what value will interrupt that experience with chat have on their experience and their objectives?

So we can make this a little bit more concrete by considering visitors at various stages of your funnel or buyer journey. Starting with that top-of-the-funnel visitor, they’re typically in an awareness stage with relatively low commercial intent. Now in this stage, visitors are first trying to figure out if what you are selling on your site is even relevant to them and then maybe educating themselves and seeing if they can trust you for a future commercial decision. Now, this type of behavior is characterized by passive content consumption, and introducing or interrupting that experience with chat is likely to increase their friction in that experience.

And let’s contrast that with a lower-funnel visitor that is in the consideration or decision stage that has greater commercial intent. Typically, they’re seeking answers to very specific questions on your site. And unlike passive consumption, they’re actively trying to answer that question, and then they’re doing some focused research. And if they can’t find it on your site, that introduces friction. So in this case, reaching out to chat can actually add value by alleviating that friction of not being able to find the answer to the question on your site and connect them either with a bot or with a salesperson.

Conversations shouldn’t be limited to chatbots

So how do we think about this to improve our conversational experiences? Well, first off, when we think about the top of the funnel, recognize that the concepts of conversational marketing don’t have to be limited to chatbots. They become synonymous with chatbots, but conversations are how we as humans engage with other humans and the traditional static website form which asks for all sorts of personal information upfront is daunting. And that’s why we see a lot of conversion drop-off. But we can adapt that form to be more conversational in nature, more interactive. And when we do that through our multi-step forms at FunnelEnvy and we’ve tested these, we average about 53% improvement in lead conversion.

Strategic Interruptions

This obviously is the added benefit of keeping the visitor on the site experience on the page and presenting less of an interruption than the chatbot. If you’re going to try this yourselves, we recommend leading with some easy-to-answer questions to establish both intent and conversion momentum. Typically two to three questions, initially not asking for personal information, making them very relevant to the visitor and also setting proper expectations along the way, both in terms of the steps that they have to go through and the outcome when they fill out that form.

Strategic Interruptions

Now, when you do choose to interrupt with chat, do it strategically, don’t settle for the standard out-of-the-box transcripts that you get, welcome to our site, do you have any questions? You can be very effective with the chat by understanding and answering and handling common or, and specific questions or objections from that visitor. If you can identify visitors with specific intent and a common example is if they’re on the pricing page lingering there, they usually have a specific question and are close to making a decision. You can very effectively intercept them, interrupt them with chat and answer those questions, or even connect them to a sales person to get them over the line.

Of course, they don’t have to be on a specific page. You can use data to identify known leads, target accounts, even use real-time predictive scoring to identify those high-value, high intent visitors and answer their questions and get them in front of a salesperson faster. One thing to think about might be to reduce the interruption by have them opt into that chat experience through an online and onsite CTA.

Key Takeaways

First off, know your own numbers. I presented some examples from what I observed, but certainly, you should know your own chat and conversational marketing engagement and conversion rates, not just at the top of the funnel, but all the way through by buyer stage and to opportunity and revenue.

Secondly, recognize that forms aren’t dead. And you can apply those same interactive conversational marketing principles outside of your chatbot and to your onsite experience. When you do choose to interrupt, do it strategically, check, and be very effective to handle common questions and intercept those high-value, lower-funnel leads and accounts.

And finally, recognize that not everyone is going to engage with chat even further down the funnel. So there are ways to use the investment in chat to improve your onsite experience. You can do that by studying your chat transcripts, understanding the common questions, objections that your visitors have, and testing different site experiences to better meet those.

If you’re going to go to the trouble of targeting lower funnel visitors with chat, you can also use that to personalize the site experience and change your offers and content to better meet the expectations and needs of your lower funnel visitors. So with that, I want to thank you for listening today.

Customer Journey Analytics Optimizes Demand Generation Marketing

To do it successfully, you need visibility into every step of your customers’ journeys and the means to track and analyze their data to understand what motivates them now and in the future. But how do you do that? Fortunately, customer journey analytics provides valuable insights into your visitors’ behavioral patterns and preferences throughout their entire customer journey. These insights allow you to create enhanced customer experiences that motivate visitors to reach the endpoint in your sales funnel.

What exactly is customer journey analytics, and how can you use it to optimize your lead generation marketing strategies? Let’s find out.

What Is Customer Journey Analytics?

As the name suggests, customer journey analytics is an application that explicitly analyzes customer journeys. This application involves tracking and analyzing the way your customers use various channels to interact with your brand. It analyzes all channels — used currently and in the future — that your customers touch directly.

These channels could include:

  • Channels with human interaction, such as call centers
  • Two-way interaction channels, such as display advertising
  • Channels that are fully automated, such as mobile devices or websites
  • Third-party operated channels, such as independent retail stores
  • Channels that offer live customer assistance, such as joint site navigation or live chat

Why Do I Need Customer Journey Analytics?

Even as customers’ journeys have grown increasingly complex, today’s customers expect their business interactions with your brand — across multiple channels — to be on par with CX leaders such as Amazon and Google. If your customers’ journeys aren’t seamless every step of the way, they will become dissatisfied and quickly move on to a competitor. Conversely, studies show that positive customer experiences drive revenue growth.

Studies also show that investment in customer feedback management isn’t enough to improve your CX levels. This failure is attributed to the fact that feedback is generally only requested at points along the journey. Unfortunately, this means that only some of the customer journey is captured, misrepresenting your customers’ overall experiences.

This incomplete data reduces your ability to gain a complete picture and accurate insights into your marketing strategy performance. It also leaves you at a disadvantage for enhancing customers’ experiences and tying customer experiences to tangible business outcomes.

Customer journey analytics is the bridge between your customers’ behaviors and your business outcomes. A customer journey analytics program enables your business to track, measure and improve customer experiences across several touchpoints and time periods, encompassing the entire customer journey.

Leveraging customer journey analytics enables demand generation marketing leaders to answer complex questions, such as:

  1. What causes our customers’ behaviors?
  2. What past interactions or journeys have our customers undertaken that led them here?
  3. What paths do our customers take on their journeys?
  4. What are the most likely outcomes for each customer or journey?
  5. How will these journeys and outcomes impact our business outcomes?
  6. What are our customers’ goals?
  7. How do their goals align with our business goals?
  8. How do we add value for each customer and enhance their customer experience?

What Are the Benefits of Customer Journey Analytics?

Customer journey analytics is a vital ingredient in an effective customer journey management program. It is the piece that analyzes comprehensive data and generates actionable insights. The insights gained from this type of customer management program are valuable for both customers and businesses alike. Here’s how.

Optimized Customer Experiences

The insights gained through effective analyses of your customers’ journeys enable you to optimize each step along the way for a seamless overall experience.

Ongoing Measurable Performance Results

In addition, ongoing analytics allow you to continuously measure demand generation marketing initiatives’ performance across multiple channels and define the appropriate KPIs to measure each journey.

Data Analyses From Numerous Channels and Timeframes

When you look at customers’ journeys across several channels and timeframes, authentic pain points become evident. Identifying these pain points enables you to take action and positively impact your customers’ journeys.

How Can I Optimize Customer Journey Analytics?

Customer journey analytics is typically optimized by leaders in customer service, analytics, marketing and CX. These leaders adopt customer journey analytics platforms to improve their demand generation marketing strategies and performance measurement capabilities.

These teams optimize customer journey analytics to:

  • Amass customer journey data
  • Resolve multi-channel customer identities
  • Analyze innumerable interactions throughout countless cross-channel journeys
  • Identify CX pain points and their root causes
  • Verify potential customer journey enhancements
  • Quantify CX investments’ ROI

Customer Journey Mapping vs. Customer Journey Analytics

As a demand generation marketer, you may already implement customer journey mapping and feel that it provides the same insights as customer journey analytics. Unfortunately, this is not the case. While journey mapping focuses on qualitative insights, customer journey analytics is more quantitative and incorporates a much larger scope.

Static Snapshots vs. Continuous Detail

Journey mapping provides only static snapshots of some of your customers’ journeys and lacks the required detail to represent the multitude of your customers and their unique behaviors.

Static vs. Time-Based Data

Customer journey analytics is driven by time-based data, allowing you to see how customers’ journeys change over time. The ability to continuously measure complex multi-channel customer journeys and touchpoints along journeys help marketers predict customer journey successes.

Trial and Error vs. Real-Time Testing

Without visibility to up-to-date data on each interaction along the customer journey, businesses are left to experiment with new enhancements on the entire customer journey. Not only does this potentially waste time and resources, it means marketers will be waiting for aggregated results that don’t pinpoint where the issues are.

Customer journey analytics provides marketers with the visibility to see how customers respond to improvements along several touchpoints and time periods. In addition, this application enables marketers to test and track the success of customer experience interaction improvements in real-time.

Powered by machine learning and AI, customer journey analytics enables marketers to identify pain points along the entire customer journey that negatively impact CX. These insights allow data-driven businesses to prioritize opportunities for customer journey optimization and drive revenue growth.

Revamp Your B2B Landing Page: 5 Things to Consider

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

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

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

5 things to consider when revamping your B2B landing page

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

1. Have a clear value proposition

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

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

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

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

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

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

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

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

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

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

4. Showcase your testimonials and partnerships on your landing page

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

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

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

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

5. Create a video to engage with the visitor

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

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

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

Boost your B2B landing page performance with FunnelEnvy

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

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

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

How To Improve Your Site Experience In A User-Centric World (And Still Generate Leads)

The algorithmic world of web optimization is rapidly moving towards a user-centric paradigm. To keep up, brands will need to prioritize their site experience like never before. 

In this post, we’re going to cover why site experience is key to your success and give you some actionable tips you can use to improve your site experience today. 

Let’s get into it!

Your site experience is key to generating leads

Website experience, or UX, encompasses everything your users encounter when they visit your website. It’s the visuals, the ease with which they can uncover information, and the process they go through to make a purchase. 

In a world that is becoming more user-centric, focusing on user privacy and moving away from keyword-based SEO, lead generation is going to become increasingly entangled with the performance of your website. The better your site experience, the more leads you’ll generate. And, with that in mind, here are our top ways to improve your site experience…

5 ways to improve your site experience

1. A great site experience starts with pages that load quickly

The first thing that anyone is going to notice when they visit your website is how quickly it loads. Or, ideally, they won’t notice this at all.

The goal for any website should be for any page on your site to load in less than three seconds on an average WiFi download speed. If your website takes longer than this to load, you have some work to do. When it comes to mobile, things need to be even faster. Less than two seconds is ideal for reducing your bounce rate. 

One of the first things you should do to reduce a web page’s load time is to compress all of the images on your website. There are free resources online (like TinyPNG) that will quickly compress images for you. 

Plus, white space is also important. Not only is it important to a pleasing design, but blank space loads faster than content-filled space. 

And, finally, ensure that the most important and visible elements of your webpage load first. Typically, buttons and navigation bars should appear first, then your text, then images and media. 

2. Simple and efficient registration and sign-up forms are crucial

Another crucial component of a great site experience is simple registration and sign-up forms. These are the forms on your website that visitors use to sign up for your newsletter, subscribe to your service, and register an account on your website. 

If these forms aren’t easy to use, then visitors aren’t going to use them. When you ask your visitors to complete a task, you aren’t just asking for their attention span, but also their mental effort. Seamlessness is key. 

A simple form process uses as few fields as possible, doesn’t needlessly violate the person’s privacy, and keeps everything on one page if possible. The more information a visitor has to give and the more pages that have to load for them to give, the more likely they are to bounce.

3. Have clear CTAs –– and not too many of them!

Everyone working in the marketing industry knows that CTAs dramatically increase engagement. Direct CTAs, such as “Order this product today!” as well as indirect CTAs like colorful buttons both count. They give a webpage purpose, guide visitors down a path, and most importantly, close sales!

Despite the overt marketing at work here, visitors like CTAs. They’re on your website because they are curious about your business. CTAs make their journey simple. Click here for this information, go here to buy this, and subscribe in these three steps. 

On the other hand, you can overdo your CTAs. Try to keep it to just one CTA per page, and don’t have every CTA be aimed at landing a sale. Maybe have a CTA to your newsletter on your blog instead of a CTA for a product you sell. Or, work on personalized CTAs (or smart CTAs) tailored to different audiences and their specific needs. 

And use clever design and logical flow when placing your CTAs. You don’t need a big red arrow telling visitors to look at your CTA if you place the CTA where they’re already looking. 

4. Follow conventions creatively over creatively ignoring them

A common pitfall that businesses run into is the idea that everything they do needs to be unique to them. So they overcompensate when doing something simple (like crafting a great site experience) and try to stand out by breaking convention.

This more often than not will scare visitors off. Design conventions exist for a reason –– because they work! And because conventions are popular practice, they’re what users expect when they visit your website. 

Moving the navigation bar to the bottom of the screen adjusting all of your text to the right, and having images of your product flash around the screen will help you stand out –– but probably not in the way that you hope for. 

Instead of trying to be quirky, stick to tried-and-true web design conventions. Then, put your personality into them! Follow traditional functional practices while adding unique and personalized aesthetics to your website. 

A pleasing color scheme and clever animation in an otherwise standard website will take you much farther than an obtuse (albeit original) site experience.

5. Save your writing for the blog

Our last tip is pretty simple. Save your writing for your blog! Articles and content marketing perform great there, but they’re not going to perform as well on your landing pages. 

Instead, try to replace text content on your home and landing pages with graphics, blurbs, and bullet-point lists. Video content performs particularly well on landing pages (not so much on your home page). Use text in short sentences to give clarity, flow, and concise information. For everything else, stick to visuals!

Eager to keep learning about how to improve your site experience?

The tips listed above are just a few of the ways that you can improve your site experience. To become a lead generating pro, you can check out the rest of the posts on the FunnelEnvy blog.

And if you’re ready to take your marketing and site performance to the next level, reach out to the FunnelEnvy team for expert advice, guidance, and optimization. 

By |2021-06-04T00:02:20-07:00June 2nd, 2021|Uncategorized, Conversion Rate Optimization, Analytics, Strategy, B2B, Experimentation|Comments Off on How To Improve Your Site Experience In A User-Centric World (And Still Generate Leads)

Revenue attribution: Everything You Need to Know to Ramp Up Your Marketing ROI

Revenue is a top priority for any business, no matter how big, no matter how small. It’s fundamental: without money coming in, you’ll have nothing to cover overheads or invest back into the company. 

We all know that a hard-working sales team is key for bringing in new business and increasing revenue. But revenue is increasingly a priority for marketing teams too. 

Many marketers turn to ROI (return on investment) to determine the profitability of a promotional campaign. In fact, more than 40% of marketers claim their main priority in 2021 is to “better measure the ROI of [their] demand generation initiatives”. 

It makes sense: effective marketing should achieve a healthy return on investment (ROI) and generate new revenue. A portion of this can then be invested back into marketing campaigns to keep bringing in more money, and so on. It’s a cycle of profitability that can help businesses grow and grow. 

And revenue attribution can help you create more effective, successful marketing campaigns. But what does it mean and involve? 

In this article, we’ll explore everything you need to know about revenue attribution and how it relates to improving marketing ROI. 

What is revenue attribution? 

Revenue attribution (also known as marketing attribution) is a reporting process that involves matching revenue brought in, to a specific marketing output. 

For example, you might utilize revenue attribution techniques to monitor the impact that a particular piece of thought leadership content made on sales within two months of its publication. Or you may prefer to track the effect that a new series of videos made on revenue over a shorter or longer period. 

Businesses have more channels — and more opportunities — to reach consumers than ever with targeted marketing campaigns. But it’s unbelievably competitive and marketing teams must take advantage of real creativity to make an impact, especially in the most crowded sectors or niches. 

Employing revenue attribution techniques empowers marketers to hone in on their most effective work and understand how they can keep refining their techniques over time. 

Why is revenue attribution important and how can it help?

Revenue attribution is crucial for marketing teams who want to gain a clear insight into their strategies’ value and learn how they affect customer engagement. Fortunately, there’s a wealth of data available online to help marketers build an accurate overview of campaign performance and ROI. 

Identifying how specific campaigns and strategies have been received by audiences (target and/or general) enables you to make more informed, calculated decisions on future campaigns. 

You’ll have a tighter grasp on what works, what doesn’t, and what elements should be combined to cultivate the most impactful marketing campaigns. You’ll be able to capture more leads, close more sales, and improve ROI thanks to continued analysis. 

Another key benefit is that revenue attribution helps businesses (particularly those in their infancy or experiencing financial challenges) get more out of their marketing spends while still streamlining their budget. 

Essentially, it can make your money go further. You’re not throwing ideas at the wall to see what sticks — you’re basing your decisions on provable facts. 

You can jettison those marketing techniques and campaigns that fail to bring in satisfactory ROI. All resources usually dedicated to those will be put to better use on more effective options instead. 

How can you use a revenue attribution model to measure and ramp up your marketing ROI?

We understand what revenue attribution is and why it matters. But how do you put a revenue attribution model to work and start improving your marketing ROI?

While it can appear complicated for newcomers, and more than a little daunting, it will seem far simpler when we take a deeper look. In this section, we’ll cover how to use this model to both track and measure ROI — and improve it. 

What types of revenue and marketing attribution models are available?

First-touch attribution 

The first-touch (or first-click) attribution is one of two single-source models (along with last-touch attribution, below). 

In this model, the first channel with which a converted user engages receives all credit for generating revenue. This could be an in-depth whitepaper, a blog post, a video, or any other piece of marketing content that captures the lead’s interest enough to drive a conversion. 

For example, around half of marketers describe webinars as the top-of-the-funnel format generating the most high-quality leads. 

While a spectacular piece of content can be enough to push users towards a sale, the first-touch model may have a blindspot — a failure to take other interactions following this first one into consideration.

As a result, you may not have an accurate insight into how effective other channels are in swaying users’ decisions. 

Last-touch attribution 

Last-touch (or last-click) attribution is regarded as another easy model. Why? Because it involves looking at the final touchpoint before the sale is completed, which is usually simple to find.

The last touch could be something as straightforward as a well-researched sales call or a webinar that whets the lead’s appetite and inspires them to commit to a purchase. 

However, the last-touch attribution model may overlook previous interactions with a user. These could include a visit to your website or hearing an ad for your business on a podcast. And, again, this could cause you to overlook the value of other channels 

Multi-source attribution

As you can probably assume, the multi-source (or multi-touch) attribution model focuses on all channels that lead to a conversion. Multiple touchpoints will be attributed instead of just one. 

Still, while the multi-source attribution model is more of a holistic approach to measuring marketing success, there’s a crucial factor to consider: it doesn’t provide an accurate reflection of a specific touchpoint’s actual contribution to a conversion. It could lead to a false representation of certain channels’ role in the customer journey. 

Six multi-source attribution models are available:

  • Linear: This is the easier model to implement, providing all touchpoints with the same weight, though it can be hard to determine which were most important (as mentioned above). 
  • Time decay: Touchpoints will be separated by bigger and bigger gaps in long sales cycles. With the time decay model, you’ll apply greater credit to those in the later stages than those in the earlier period. They might not have been as valuable to the eventual outcome, and in particularly long sales cycles, the buyer might have totally forgotten about their initial interactions with your business anyway. 
  • U-shaped: A U-shaped revenue attribution model applies the credit to two main touchpoints, with fixed percentages. These are the initial touchpoint and the last, as well as any between those points. The first and last touchpoint receive 40% of the credit each. The 20% remaining is split between those touchpoints taking place in between. 
  • W-shaped: A W-shaped model is similar to the one above, but it adds an extra touchpoint: when a prospect is converted into a lead. So, this covers the first touchpoint, the last touchpoint, and the occurrence falling somewhere between them. These receive 30% of the credit each, while the remaining 10% is shared among other touchpoints that may be detected between them. 
  • Full path: The majority of the credit is assigned to the key steps in the customer journey and the rest goes to those touchpoints between. Unlike the other models explored so far, this includes follow-up chats between the customer and the sales team. 
  • Custom: Teams can come up with their own weighting shares according to the channels used, customer behaviors, etc. For example, you may decide that a user who subscribed to your newsletter should have more weight than someone who clicked on an ad. 

Weighted multi-source attribution 

Weighted multi-source attribution involves accounting for every interaction during the sales cycle and assigning weight to the most important touchpoints. This model can lead to the most reliable views of a customer’s journey. 

However, it’s one of the most challenging to put into effect, as weight must be applied to a potentially large number of touchpoints. 

Why is it so important for marketing and sales teams to work in partnership?

Traditionally, businesses tend to keep sales and marketing activities separate. They consider marketing teams’ role to create leads and sales teams’ to transform them into paying customers. That’s simple enough to understand — but it could be a big mistake. 

Why? 

Because overhauling and refining your marketing to achieve an increase in leads won’t guarantee a rise in high-quality leads. 

Yes, marketing teams can drive clicks and interest, but a large proportion of leads could be of a lower quality than expected. 

The aim should be to bring in leads more likely to evolve into conversions, based on carefully targeted marketing with specific demographics in mind. 

By uniting your marketing and sales teams, you can start to develop a clearer understanding of which marketing efforts bring in the most valuable leads and, ultimately, conversions. Those that consistently generate the weakest leads and harm ROI should be replaced. 

What are the key benefits of using these revenue and marketing attribution models?

Here are five key benefits of using revenue and marketing attribution models:

  • Improved ROI
    Effective revenue attribution provides businesses with an accurate insight into how much return they gain on their marketing investments. Over time, you can start to cultivate a better awareness of those techniques and strategies that engage your target audience best.

    And you’ll keep reaching the right people with the most appealing messaging. This will increase the number of conversions you can expect to achieve and, eventually, the ROI you earn.

  • Save money on ineffective marketing
    Attribution models reveal the most important touchpoints throughout sales cycles and show how marketing money is best invested. Fewer funds will be wasted on dead-end marketing.

    That may free up money to channel into better marketing or other areas of your business, including sales or post-purchase support.

  • Hone your audience targeting
    Audience targeting is one of the top methods through which advertisers increase demand. And studying attribution data reveals which types of content, messaging, and channels engage your ideal customers best.

    Marketing teams can keep sharpening their material to consistently engage your target demographic(s) and minimize the risk of missteps.

  • Learn how to make products or services better
    Marketers can get a better understanding of target customers through attribution data analysis.

    Over time, this can open your eyes to ways in which you can improve products or services to suit your audience better. For example, the response to a blog post covering specific software features could inspire future releases.

The power of Revenue Funnel Optimization 

Hopefully, you’re now in a place where you can see the key benefits of revenue and marketing attribution to your business. But, one of the most important aspects of attribution strategy is acting on attribution insights. And, that’s where we come in…

We’ve designed our Revenue Funnel Optimization strategy so you can get the most out of your revenue insights. 

FunnelEnvy enables you to generate revenue insights by updating analytics to measure the complete end-to-end customer journey. You can pinpoint the most valuable funnels, offers, and other factors that drive revenue. 

Revenue funnels comprise strategy sets focused on maximizing your website’s revenue generation through targeting the most effective offers to the priority buyer segments in your top conversion funnels. 

Funnels can also be personalized by the user’s stage in the customer journey to maximize revenue further. You also can run campaigns and experiments on your most important funnels. Use direct response best practices to optimize offers, messaging, and more. 

With Revenue Funnel Optimization, your decisions are driven by data and genuine insights into the buyer journey. 

You’ll make stronger choices for your marketing and sales teams — and your business as a whole — by studying the facts. 

Many companies are already achieving success with Revenue Funnel Optimization, with up to 250% growth in revenue and a 10x increase in Marketing Qualified Leads (MQLs)

Want to try Revenue Funnel Optimization? Start using FunnelEnvy and drive real revenue growth for your business! 

By |2021-06-04T00:02:29-07:00June 2nd, 2021|Uncategorized, Conversion Rate Optimization, Digital Marketing, The Funnel, Strategy, B2B|Comments Off on Revenue attribution: Everything You Need to Know to Ramp Up Your Marketing ROI

Website Analytics & Attribution in the Privacy Era

If you’re wondering how the new privacy landscape will affect your ability to track visitors and customers you’re not alone. In this conversation, we dig deep into the governmental, technology, and other considerations that marketers need to be aware of. Hopefully, we’ll clear up some confusion and misconceptions along the way!

 

 

By |2021-06-01T09:51:21-07:00May 27th, 2021|Uncategorized, Analytics, Testing, B2B|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.

 

The Secret to Making Your ABM Personalization Campaign a Success

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

visual screenshots of abm personalized accounts

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

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

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

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

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

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 qualityWe should ask ourselves, are these assumptions consistent and compatible with our ABM strategy?

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

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. 

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