A Common Sense Approach to Account Based Analytics

June 16, 2020

In a recent webinar for DemandGen Club, Arun Sivashankaran (Founder & CEO of FunnelEnvy) shared the good, bad, and ugly from his years of experience with B2B demand generation teams tackling account based analytics & personalization.

It’s tough to know where to start when it comes to understanding how your website experience fits within an Account Based Marketing strategy. Account-based marketing and personalization might seem like a natural fit, but how do you do it in a way that improves your customers’ experience and your KPIs?

In a recent webinar for DemandGen Club, Arun Sivashankaran (Founder & CEO of FunnelEnvy) shared the good, bad, and ugly from his years of experience with B2B demand generation teams tackling account based analytics & personalization. Instead of ineffective “vanity experiences” that consume time and resources, he shared a framework and tools to develop a data-driven understanding of your customers’ revenue journey. Finally, he addressed how to accelerate that journey through common-sense campaigns that actually help your customers and deliver revenue. You can watch the full webinar at this link.

 

Intro to Account-Based Analytics and Personalization

 

account based marketing analytics

First off, how do you move from a strategy mindset to an execution mindset? So you’ve gotten through the ABM basics: you have researched target accounts and identified personas and characteristics, developed account segments, and aligned with Sales on pipeline and revenue KPIs. Your team will then move into a more “execution” mindset by looking at the various channels you have available, and developing playbooks by channel to better reach target accounts.

 

How do you apply this “account based” context by channel to develop your playbook? Not all marketing channels were created equal - first you have your website, which is a static channel offering the same experience for everyone. However, with channels like Direct Mail, Outbound, Email Nurture, and even Paid Media, marketing campaigns are relevant to both accounts and individuals. This means that you are using your research, data, and context to understand the persona and deliver them highly targeted, personalized experiences.

 

Why can’t we personalize our website the same way we do with emails and ads?

 

Unfortunately, according to Arun, marketers often end up creating “vanity experiences” when trying to personalize their websites. By this he means personalizations such as using the visitors’ account names in their site’s headline. He calls these “vanity experiences” because they look cool, but don’t help the customer solve their problems, and don’t factor context about where the customer is in the journey. Unless you’re selling the ability to do these kinds of customizations, these campaigns are unlikely to be effective.

 

abm personalization
Example - Vanity Experience

 

abm personalization
Example - Vanity Experience

 

 

 

 

 

 

 

 

 

 

 

What does work?

 

So, what does work? When you personalize the offer that a user sees based on their profile, stage, and behavior, you can then use this knowledge to serve them the next best offer. This type of personalization can help your customers and increase conversion and revenue. Here are some examples:

abm personalization analytics

 

 

“How it Works” - Awareness Stage

 

 

 

 

 

abm personalization analytics

 

 

 

“Try it Free” - Consideration Stage (SMB)

 

 

 

 

 

 

 

“Book a Demo” - Consideration Stage (Enterprise)

 

 

 

A Common Sense Framework

 

Arun presents a common sense framework to help customers progress on their revenue journey. A simple definition of “common sense” - sound and prudent judgement based on a simple perception of the situation or facts. By “simple” he does not mean “simplistic”, he means easily explainable and interpretable. This idea involves using data to develop a better understanding of the revenue journey based on who that customer is, and what they’ve done. Ultimately, you will use this quantitative evidence to deliver more relevant experiences based on context about the prospect’s journey.

 

The Revenue Journey

account based marketing journey

 

Start from a model that breaks down the stages of your customer’s journey, as well as the different touchpoints within each stage. As a note, your stage names may be different, but should present the same idea. Because we are talking about B2B and ABM, it’s common for the revenue journeys to look different for each of your segments. For example, a small business might have more of a self-service experience where they go to your site, look around a bit, and then convert by requesting a demo. A mid-market company might visit a landing page, start a free trial, and then lead to a paid conversion. An enterprise customer would likely have the longest journey, starting with something like a LinkedIn content download, then they might reply to a sales email, then finally take a meeting to discuss their complex needs and eventually become an opportunity.

b2b abm journey

 

So, how do you quantify the revenue journey? The complexity of the B2B journey presents challenges, but you will ultimately want to be able to measure accounts by stage and segment, evaluate progression through journey over time, and use data to develop hypotheses to help customers through journey. 

 

Google Analytics for B2B

 

Google Analytics (GA) wasn’t designed for B2B/ABM. Some limitations we’ve seen is that the platform was built for individuals, not accounts. It emphasizes more linear, transactional purchase funnels (like in B2C), and is not good for tracking offline goals and touchpoints (such as events, meetings with Sales, etc). To be able to close these gaps, we have to add that additional context to GA. 

 

To do this, you should first use your goals to track all successful outcomes and touchpoints in the revenue journey, and track how much each touchpoint contributes to revenue. You should end up with something that looks like this:

 

abm google analytics b2b

 

So, how do you get your offline goals from your marketing automation platform into GA? Google Analytics automatically sets a “client id” cookie for each device it is tracking. Marketo, or another automation tool of your choice, can send offline conversions as events to Google Analytics via the measurement protocol. By passing the client id, GA can associate the conversion with the same device. 

 

Step 1: Create a custom field in Marketo to store the GA client id

Step 2: Pass the client id value through a hidden form field

Step 3: Trigger a webhook in Marketo to POST an event to Google Analytics via the MP endpoint. Pass the custom client id field as a query parameter

Step 4: Configure a goal on the event in GA

 

Configure Custom Dimensions and Segments

First, you want to configure custom dimensions in Google Analytics to add additional context for your segments. Custom dimensions let you add additional context about visitors - are they a known lead, customer, which account they are from, etc. These can be determined in real time and used for targeting. Segments, on the other hand, are basically a subset of your analytics data. These are set up with rules in GA, and used for analysis. Some examples include:

 

abm google analytics b2b

There are various ways you can populate those dimensions and segments. Behavioral segmentation, the most common, looks at what the visitor has done and creates segments that way. Behavioral segmentation is high volume, but is not always deterministic (meaning you can’t always determine with 100% confidence that a visitor is in a particular segment based on what they’ve done). You can use this kind of segmentation to create stage based GA segments based on goal conversions or high probability actions. You can also create other segments relevant to your strategy (by industry, persona, etc), which can be inferred based on engagement (probabilistically) based on content, chat, or self-selection. For example, visitors who visit the login page are usually customers. Depending on which type of content they engage with, you might be able to infer what industry they are in. 

 

Identifying Accounts via Reverse IP

 

Another common way to identify account characteristics is through reverse IP. Reverse IP providers determine firmographic (account level) attributes based on a visitor’s IP address. While this will work for “anonymous” visitors, the match rates are quite low at ~10-30% (work better for larger organizations). Finding providers range from some DIY options, to some very expensive solutions with higher match rates. Rather than evaluating their total match rate, look at the match rate within your target account list or tiers. 

 

Identifying Visitors via Marketing Automation Platform

 

Using Marketo as the example again, you are also able to identify known leads and contacts through your marketing automation platform. Marketo, like most marketing automation platforms, cookies every visitor hitting the site and are then able to match the cookie to a known lead/contact record in Marketo. This requires an integration between the marketing automation platform and Google Analytics, and real-time lookups of exported data.

 

With better context you can answer more relevant questions

 

As B2B marketers, we want to know what visitors are doing and how they’re engaging with your site through the customer journey. A report like this will allow you to look at each stage and segment and ask: What are the best performing goals? Which goals are underperforming? Are visitors seeing relevant offers on the pages they’re landing on? Are visitors converting on the offers? How effectively are visitors transitioning between stages? 

 

abm google analytics b2b

 

Personalizing the Revenue Journey

 

Arun listed a few tactics that don’t work: vanity personalizations that don’t help the customers or impact your KPIs, and superficial UI changes (B2B buyers don’t really care about your button color). Instead of these, use the common sense approach to focus on personalizing the offer. Use evidence based hypothesis from your analytics to deliver the next best offer based on what segment they are in, what they’ve done (touchpoints & stages), and what they’re likely to want to do next. 

 

Some common sense violations to avoid: too many offers (leaves them with the paradox of choice, you want to guide them based on where they are in their journey), offers that are irrelevant to the customer stage or segment, and offers for goals that have already been completed.

 

An example for mapping offers to stages:

 

abm analytics b2b

 

 

Summary

b2b abm analytics

 

More of the B2B buying journey is happening digitally, and that’s only going to increase as time goes on. Some key tips to remember from Arun’s presentation: 

1) Always map your customers’ revenue journey by stage, touchpoint, and key segments. 

2) Try to add additional B2B and ABM context to your analytics - start by tracking all successful touchpoints (both online and offline), and segment everyone who engages with your site based on behavior. 

3) Develop common sense, personalized offers based on evidence-driven hypotheses - what would a visitor in X stage and Y segment want to do?

 

 

 

 


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