You’re already leveraging Google Analytics to drive website strategy. But you’re missing crucial context about the single most relevant factor for predicting and influencing visitor behavior: their stage in the buying process.

You use this context in your marketing automation campaigns – different messaging and different offers based on content consumed and lead score. You exclude existing customers from paid acquisition campaigns. But when you look at website data in Google Analytics, all of those visitors are lumped together.

The revenue opportunity for an existing customer is different from a new visitor. And the content and offers that are relevant to a new visitor are redundant for a lead in your marketing or sales pipeline.

Let’s take a look at how you can get data on leads into your Google Analytics reporting views, and what to look for once you’ve got it.

What’s a lead?

Leads are visitors who have identified themselves, but haven’t paid you money. They might be on a free trial, or they might have filled out a form to access gated content.

Getting lead data into Google Analytics

First, create a Custom Dimension to hold data about visitor stage.

Open up your Google Analytics account and click on the admin section.

Next, go the “Property” panel and click on “Custom Definitions” then “Custom Dimensions.”

(more…)

I’m writing this because it’s the year 2020 and we’re still having trouble attributing onsite campaigns/testing to Marketo MQLs/Opportunities/Revenue.

It is meant primarily for technical practitioners as a quick-start guide, and is so simple you’ll probably be able to get it hooked up today.

As a result, this guide is very tactical in nature, with the end goal of helping you answer the question ‘What impact is my onsite campaigns/tests having on revenue?”.

What solution is for you

If you happen to already be using FunnelEnvy, then enabling our Marketo Integration is all you need to do. This not only enables attribution, but unlocks additional functionality, including the ability to target onsite users based on their Marketo classifications. It’s a paradigm shift in how you think about marketing automation and I encourage you to read our recent article to find out more.

If on the other hand you’re still using a web-based testing tool like Optimizely or Adobe Target to run your onsite campaigns then read on, as this guide is primarily for you. But first, a cautionary tale:

Always optimize for downfunnel outcomes, not onsite vanity conversions

Recently, FunnelEnvy ran a very visible experiment via Adobe Target for a well known SaaS company. Early results were trending downward and web analytic data showed a sharp downtrend in incremental conversions/leads. Talks were had about ending the test early as this was potential a huge economic impact.

Luckily, the attribution module you are about to see had already been installed, allowing us to query Marketo directly, telling a different story. We were able to calculate that the test was actually responsible for a significant increase in annual recurring revenue! Without this additional layer of information, we likely would have moved on, but.

But back to our discussion:

The Solution

We’re going to break this guide up into two main parts:

Part 1: Create/maintain a running list of campaigns/tests a user has seen

Part 2: Pipe this list into Marketo

Create/maintain a running list of campaigns a user has seen.

In order to automate this as much as possible, we’re going to create a centralized module that fires on every page. This allows us to just install it once (via GTM, Launch etc.) and not have to make any edits when new campaigns/tests are launched.

You’ll need to take advantage of Adobe’s Response Tokens or Optimizely’s client side object if you want to go this route. Users of other platforms like Google Optimize will need to follow a more manual approach, adding the module to every new campaign/test. The principles will stay the same, it just requires a bit more to upkeep.

The Code
[javascript]try{
(function () {
// Callback for Adobe Target response tokens
document.addEventListener(adobe.target.event.REQUEST_SUCCEEDED, function(e) {
var tokens = e.detail.responseTokens;

if (isEmpty(tokens)) {
return;
}

var uniqueTokens = distinct(tokens);
})();
} catch(err) {
console.log(err);
[/javascript]
The start of our module – An Adobe Response Token listener
[javascript] //Cycles through each token
uniqueTokens.forEach(function(token) {

var cookieName = token["activity.name"] + ‘ ‘ + token["experience.name"];

// Slugify the cookie name.
cookieName = cookieName.toLowerCase().replace(/\\((evar.*?)\\)|\\[(.*?)\\]/g, ”).trim().replace(/[^a-z0-9]+/g, ‘-‘);
});
[/javascript]
Next we cycle through each response (there is 1 per active campaign) slugifying the campaign/variation name.
[javascript]/*
Find the existing cookie if it exists. Adds new campaign values to the front of the cookie
*/
var existingCookie = _satellite.cookie.get(‘marketoCookie’) || ”;

if (existingCookie.indexOf(cookieName) === -1) {
var newCookie = cookieName + ‘|’ + existingCookie;

_satellite.cookie.set(‘marketoCookie’, newCookie, {expires: 30});
}
[/javascript]
Since this module runs on every page load, we also need handle duplicates in the cookie name
[javascript]var checkLength = function checkLength() {
if (newCookie.length > 2000) {
newCookie = newCookie.split(‘|’);
newCookie.pop();
newCookie = newCookie.join(‘|’);

checkLength();
}
};
checkLength();
[/javascript]
Finally, Marketo inputs (you’ll set this up soon) have a character limit of 2000 characters, so we truncate older campaigns.

Here’s the reusable function in it’s entirety:
[javascript]try{
(function () {
// Callback for Adobe Target response tokens
document.addEventListener(adobe.target.event.REQUEST_SUCCEEDED, function(e) {
var tokens = e.detail.responseTokens;

if (isEmpty(tokens)) {
return;
}

var uniqueTokens = distinct(tokens);

//Cycle through each token
uniqueTokens.forEach(function(token) {

var cookieName = token["activity.name"] + ‘ ‘ + token["experience.name"];

// Slugify the cookie name.
cookieName = cookieName.toLowerCase().replace(/\\((evar.*?)\\)|\\[(.*?)\\]/g, ”).trim().replace(/[^a-z0-9]+/g, ‘-‘);

/*
Find the existing cookie if it exists.
Adds new campaign values to the front of the cookie
*/
var existingCookie = _satellite.cookie.get(‘marketoCookie’) || ”;

if (existingCookie.indexOf(cookieName) === -1) {
var newCookie = cookieName + ‘|’ + existingCookie;
/*
If above the 2000 Marketo input character limit,
truncate old campaign values
*/
var checkLength = function checkLength() {
if (newCookie.length > 2000) {
newCookie = newCookie.split(‘|’);
newCookie.pop();
newCookie = newCookie.join(‘|’);

checkLength();
}
};
checkLength();
_satellite.cookie.set(‘marketoCookie’, newCookie, {expires: 30});
}

});
});

function isEmpty(val) {
return (val === undefined || val == null || val.length <= 0) ? true : false;
}

function key(obj) {
return Object.keys(obj)
.map(function(k) { return k + "" + obj[k]; })
.join("");
}

function distinct(arr) {
var result = arr.reduce(function(acc, e) {
acc[key(e)] = e;
return acc;
}, {});

return Object.keys(result)
.map(function(k) { return result[k]; });
}
})();
} catch(err) {
console.log(‘Error in Target Marketo Cookie’);
}
[/javascript]
A simple solution – copy and pasteable.

Create a hidden input field on all Marketo forms

Lastly, we need a way to get the running list into Marketo.

Marketo has out of the box functionality to create an input to ingest a cookies value. All you need to do is add this input to your forms, specify the cookie name (in our case, marketoCookie) and the rest happens by default.

You’ll now have a historical list of campaigns/tests an individual user saw whenever they submit a Marketo form.

Easy peasy.

A/B testing suffers from a “winner take all” problem whereby it optimizes a single experience across an entire population and does not take context into account.

A better solution for optimizing experiences across the wide variety of visitor context is predictive optimization. It works by using bringing together all of the data about the visitor into a Unified Customer Profile (UCP), continuously optimizing with each impression and using a predictive model to identify the experience that’s most likely to result in onsite and down-funnel outcomes on a 1:1 basis.

Homepage Personalization

The homepage is often one of the most highly trafficked pages, usually with a high volume of direct and organic (branded) search traffic. As a result, it generally has pretty generic top of the funnel content and often serves as a “traffic cop” – funneling visitors to the sections of the site with more specific content.

Hero with no context about customer intent

What if instead of the headline, copy and CTA we could replace it with something that better reflected the visitor’s intent?

Intent: Learn about Localytics for IOS

Intent: Try Discover Platform

Intent: Interested in Localytics CRM

Content Personalization

Localytics also has an extensive resource collection of case studies, ebooks, whitepapers, and webinars. The featured content in the slider is prime real estate to showcase personalized content.

Intent: Looking for Social Proof

Intent: Decision Maker Learning for Media Industry Stats

The important part to remember here is we are matching the content to the customer context.

Down-Funnel Personalization

After a visitor is identified as in the target market and has shown some commercial intent, marketers must continue personalizing. In B2B that often means that they’ve returned to the site and engaged with more commercially oriented content, and likely filled out a gated content form. That could also mean that multiple visitors have come to the site from the same account.

We want to continue to provide these visitors with relevant content that continues to engage them, but also give them on-ramps to take the next step.

In Localytics’s case, this “next best action” is either starting the free trial or talking to sales. Since we may also have information about the visitor’s account and role we can incorporate that into the experience and call to action. For example, we may want Marketing Decision Makers to Talk to Sales.

Intent: Ready to Engage with Sales

If the visitor is an engaged decision maker we can present them with more specific content and a CTA that takes them directly to a Contact Sales form.

Intent: Named account which has expressed interest in joining partner program

Localytics has an opportunity to showcase partners based on what they know about the account and the specific opportunity being discussed.

Strong Commercial Intent

Visitors in here have shown strong commercial intent. This goes beyond filling out a form for a piece of content, they’ve demonstrated an interest in engaging in the sales process. Traditionally this is where marketing would have taken a “hands off” approach (it’s a sales problem now!) but that’s no longer sufficient.

For a product like Localytics, the prospect will likely be asking certain questions depending on their role:

• What support options are available relative to what I need?
• What have effective implementations at similar companies looked like?
• How much and what kind of training will our developers require?
• What professional services or partner resources are available for implementation?