6 Ways to Set Up Funnels in Google Analytics

Analyzing the customer journey is pivotal to conversion optimization. But how do you track user journeys in a way that is digestible, visual, and useful? With funnels, of course! Funnel tracking in Google Analytics is one of the best ways to identify—in detail—where you’re going wrong. I’ll show you six funnel features in Google Analytics […]

The post 6 Ways to Set Up Funnels in Google Analytics appeared first on CXL.

Analyzing the customer journey is pivotal to conversion optimization. But how do you track user journeys in a way that is digestible, visual, and useful?

With funnels, of course! Funnel tracking in Google Analytics is one of the best ways to identify—in detail—where you’re going wrong.

I’ll show you six funnel features in Google Analytics to boost your conversions by understanding where prospects falter in their journey.

But first, let’s define a Google Analytics funnel and explain why it matters.

What are Google Analytics funnels, and why are they important?

Website users take specific paths from start to finish, and every site has a goal for its visitors. Google Analytics funnels track this journey so that you can optimize your website and ensure visitors hit your goals.

For example, when prospects land on your homepage, you may want them to:

  1. Navigate to the category page.
  2. Visit a specific product page.
  3. Add an item to their cart.
  4. View their cart.
  5. Make a purchase.
  6. See the confirmation page.

By analyzing how visitors browse your site, you can optimize their experience. For example, a funnel analysis that shows a high exit rate on product category pages suggests that visitors aren’t finding what they want, which could be because product filtering is clunky or unhelpful.

Ultimately, your goal is to increase conversions. Analytics funnels help you home in on the exact stage in the journey that’s causing the most dropouts.

Before we proceed to the types of Google Analytics funnels, we need to understand the difference between strict and flexible funnels.

Strict funnels vs. flexible funnels

In a strict funnel, a user follows an exact sequence of linear steps—they cannot skip or add steps. An example of a strict funnel is:

Homepage  > Category Page  > Cart > Checkout

However, a strict funnel is useful mainly as a model to highlight likely drop-off points in an idealized journey. In the real world, the user path inevitably varies. (Harvard Business Review has talked about the “death of the linear funnel.”) To account for this reality, you can use a flexible funnel model.

In a flexible funnel, the customer journey is fluid. Not everyone follows the same path before they become a lead or purchase a product. Some users may find the cheapest product they can and immediately place an order; others may review multiple product pages or the About page before purchasing.

Flexible funnels account for these variations. Users aren’t restricted to specific pages or a specific order. In that sense, flexible funnels are better equipped for the real-world user journey.

A visitor may still satisfy a flexible funnel’s criteria in their journey as long as they hit defined pages on the site. For example, consider this path:

Homepage  >  Story Page  > Product Page  > Category Page  > Product Page  >  Cart  > Checkout

At some point in their journey, users must visit the steps in bold, but they can still fulfill funnel requirements no matter which pages they visit in between.

When should you use a strict or flexible funnel?

Prospects at the top of the marketing funnel are just learning about you. Don’t worry if they fail to follow a specific path. After all, you can’t expect each person to visit the same pages (in the same order) during an initial research phase.

But once a prospect has decided to buy—when they near the bottom of the funnel—you can expect them to follow a more specific sequence of steps to completion.

If they’re visiting a miscellaneous page when they’ve already started the checkout process, you should consider it a dropout (even if they end up purchasing). A page or other site element is likely distracting the prospect from the end goal.

Identifying the drop-out points lets you start work on solutions. A funnel won’t give you the “why” behind the dropout, but you can get that answer from polls, surveys, and other qualitative analyses.

You may find that people are more likely to buy after reading the Brand page, so you’ll incorporate that content into the funnel. Or you may find that a miscellaneous Instagram link distracts users from taking the desired action.

Google Analytics funnel visualization reports

We’ve covered what Analytics funnels are, why they matter, and strict versus flexible funnels. Now, I’ll introduce six Google Analytics funnel features that track prospects’ journeys to show how to improve conversion optimization.

1. Goal funnels

  • Why choose this funnel type? This funnel feature is great for beginners who want an accurate report that they can expand to make more granular.

To use a Goal funnel, you must set up a goal in Google Analytics and specify the funnel path.

goal funnel visualization

To do so, follow these short steps:

  1. Go to Admin  > Goals > +New Goal  > Choose a Goal (e.g. Place an order).
  2. Select “Destination” Goal  > Goal Details.
  3. Turn on the “Funnel” switch.
  4. Name each step of the funnel and add a URL. You can also specify whether a step is optional (flexible) or required (strict).

goal funnel setup

Once you enter the necessary information, you’ll see the results under “Conversions” in Google Analytics. Under the “Goals” section, you can access many reports to learn about user behavior, like “Goal Flow.”

There’s one major limitation: You cannot apply segments to Goal funnel reports. Goal funnels include all site visits from that view. If you want to measure performance by traffic source, device, or any other segment, you’ll need to create a custom horizontal funnel (detailed below).

2. Reverse Goal Path funnels

  • Why choose this funnel type? This funnel is a unique way to reverse engineer conversion problems and opportunities.

Simply put, reverse goal funnels trace a user’s path backward through your site—from conversion back to entrance. This unique pathway identifies common steps to conversion and highlights undesired steps along the way.

Once you have at least one Goal set up, go to:

Conversions  > Goals > Reverse Goal Path

You’ll see a count of Goal Completions and the pages that users visited leading up to that Goal.

reverse goal funnels

Currently, Reverse Goal Path lets you go back only three steps. You can export the data as a CSV and use a pivot table to find common paths or dissect the data in other ways.

Reverse Goal Path isn’t the best tool to identify common drop-offs. But it will help you check if the most common paths are the desired ones.

You may find, for example, that most visitors arrive at a goal through a long-neglected page. You can then identify a strategy to get more traffic to that page.

3. Ecommerce Shopping Behavior Report

  • Why choose this funnel type? This funnel type delivers specialized data for ecommerce sites.

This funnel is only for ecommerce and requires you to turn on Enhanced Ecommerce. To see the data from the funnel, go to:

Conversions  > Ecommerce >  Shopping Behavior

This Google Analytics feature counts the number of user sessions for each step in the funnel. It also gives a visual display of the percentage of visitors who arrived at the current step from the previous one.

You can also drill down to specific metrics or pages. To illustrate, you can see how many sessions turned into transactions by clicking:

All sessions  > Product Views  > Add to cart > Check-Out  > Transactions

google analytics shopping behavior

Focus on optimizing the page with the highest percentage drop-off. One fashion accessory client of ours had a huge drop off between the homepage and a product page.

With this insight, we found a great opportunity to improve their navigation menu. The navigation menu was too small and tucked away; it didn’t showcase the products and product categories we had to offer, especially on mobile.

The Ecommerce Shopping Behavior report is great for analyzing your funnel’s performance at a macro level. Shopping Behavior shows how many people view each product and indicates which pages are least persuasive—a great starting point for optimization efforts.

4. Checkout Behavior

  • Why choose this funnel type? This funnel delivers granular, sophisticated data for checkout form fields.

This Google funnel visualization feature is a funnel within a funnel. (Funnelception!)

Also within the Ecommerce section, Checkout Behavior shows where users drop off within a checkout process, grouped by form field (e.g. email, phone, address, credit card number). You can figure out which field causes the most friction.

For instance, a user may start the checkout process and enter their email (which usually isn’t a drop-off point) but abandon the page on the payment info fields (which usually is a common drop-off point).

If that’s the case, you can explore more convenient alternatives, like adding a Paypal button or a one-click purchase button.

google analytics checkout behavior funnel
(Image source)

5. Horizontal funnels via custom reports

  • Why choose this funnel type? This funnel allows you to apply advanced segments to compare conversion paths for different types of visitors.

Horizontal funnels are a great way to compare drop-off points by segment. As the name suggests, funnel steps are visualized horizontally instead of vertically. The funnel tells you the abandonment rate between funnel steps (rather than the completion rate, like Goal funnels) and the number of visits for each step.

Horizontal funnels are also more accurate than Goal funnels because they don’t backfill steps. As Google explains, a Goal funnel visualization “backfills any skipped steps between the step at which the user entered the funnel and the step at which the user exited the funnel.”

To create a horizontal funnel, set each funnel step as a Goal (e.g. a product page visit). For every Goal you create after the first Goal, turn on the Funnel option and add the destination URL of the previous Goal as a single funnel step.

horizontal funnel first step

Once you create your Goals, select Custom Reports under the Customization section of Google Analytics and click +New Custom Report.

horizontal funnel step two

Add each Goal Completion to the Metric Groups section in chronological order, with the Abandonment Rate metric between each Goal Completion:

  1. Goal 1 Completions
  2. Goal 2 Abandonment Rate
  3. Goal 2 Completions
  4. Goal 3 Abandonment Rate
  5. Goal 3 Completions…

You can sort your data by any custom dimension (Landing page, City, Browser, etc.) by adding dimensions to the Dimension Drilldowns section when building your Custom Report:

horizontal funnel step three

Once you’ve created the report, you can add multiple segments to the same report to see how different visitors interact with parts of your funnel, which a standard Goal funnel doesn’t allow.

Importantly, you’ll be able to identify segments that behave the same except for one drop-off point. That’s how you identify key opportunities to improve the user journey. We recommend looking at prospect, returning customers, and cart abandonment segments.

The drawback to Horizontal funnels is that they can consume many of the 20 Goal slots that Google Analytics offers.

6. Custom Funnels in Google Analytics 360

  • Why choose this funnel type? This funnel offers robust customization to splice data by almost any variable.

Available only for Google Analytics 360 users, Custom Funnels let you create a funnel for any trackable user action or behavior. For instance, you can use pageviews and events as stages of a funnel—the possibilities are endless.

To create a Custom Funnel, go to:

Customization  > Custom Reports  > +New Custom Report

Then, select the “Funnel” option in the “Type” section. Below that is a “Funnel Rules” section where you can define funnel stages by Google Analytics Dimensions, including custom and ecommerce dimensions.

The beauty of this feature is that it allows you to track funnels based on specific events, like filling out form fields, which you can’t do with other funnel reports that depend on URLs. You can define funnel stages by Event Label, Action, and/or Category.

The report also lets you decide if users:

  • Can enter at any stage.
  • Must enter at a certain stage.
  • Complete the funnel in one session.
  • Complete the funnel in multiple sessions.

The Custom Funnels report also lets you use remarketing to engage users who drop off during a specific step. (You can also create an advanced segment for that same audience.)

Using custom segments to get more granular with your funnels

Add custom segments to any funnel to splice data even further. There are endless ways to divide the data, including by geography, gender, browser, and landing page.

For instance, you can view funnel data filtered by mobile traffic only or compare mobile data side-by-side with desktop data:

segmented funnels

Those insights can help you prioritize areas of your site for optimization. For example, if the mobile version of your site is doing poorly, you can identify the most frustrating parts of the user experience.

Conclusion

Patching the holes in your user journey offers a huge opportunity to increase sales. But to patch those holes, you need to know where they are. A strict funnel is an outline you can use to create a flexible funnel—the type that users actually follow.

The six Google Analytics funnels covered in this post identify drop-off points at a macro and micro level.  Finding the right one for your site depends on the type of site you manage (e.g. ecommerce vs. lead gen) and the level of detail you want in your reports (e.g. segmented vs. not).

Drop-off points help you identify which pages or page elements merit testing to improve performance. That testing, in turn, reveals why potential customers are dropping off—and what to do about it.

The post 6 Ways to Set Up Funnels in Google Analytics appeared first on CXL.

B2B Marketing Attribution: Models, Tools, and Processes

Talk to any B2B marketer about attribution and they’ll either roll their eyes or rant about how it’s important but hard to get right—long lead cycles, multiple contacts from a single organization, etc. Some might stare blankly and ask what you mean. As a group, we’ve gotten a firmer grasp on top-of-the-funnel metrics. We can […]

The post B2B Marketing Attribution: Models, Tools, and Processes appeared first on CXL.

Talk to any B2B marketer about attribution and they’ll either roll their eyes or rant about how it’s important but hard to get right—long lead cycles, multiple contacts from a single organization, etc.

Some might stare blankly and ask what you mean.

As a group, we’ve gotten a firmer grasp on top-of-the-funnel metrics. We can prove that social media and SEO drive traffic and top-line conversions, but we still struggle to show the bottom-line impact of marketing.

To get marketing a seat at the table and prove that it can drive revenue and pipeline, we’ve become borderline obsessed with numbers. The accuracy of those numbers, however, depends largely on the quality of our attribution model.

What is attribution?

Attribution takes marketing analytics a level—or a couple levels—deeper. If we look at the hierarchy of marketing data, it goes something like this:

    1. Acquisition (e.g. site visits)
    2. Engagement (e.g. bounce rate)
    3. Conversions (e.g. form fills)
    4. [Black box]
    5. Revenue

As marketing moved online, we got decent at tracking link clicks and website behavior, but we still struggle to connect the dots between someone visiting a website, taking an action, and becoming a customer.

To reframe marketing as an investment rather than a cost center, we must connect all the dots to revenue—but it’s hard to do without the right tools and models to get started.

No marketing attribution model is perfect. The prospect-to-customer journey is not a linear series of events; it’s hard to predict and measure every engagement. However, some visibility is better than none at all.

Why do I say that? Too often, in my in-house and now consulting days, I talk to clients and executives who want to know how much each dollar spent brings back in revenue and which channels and activities generate the largest return.

That’s the dream—but it’s nearly impossible to do with 100% accuracy.

The non-linear user journey

Marketers like to work with frameworks. The traditional marketing/sales funnel (soon to be replaced by Hubspot’s Flywheel Model) is a fantastic framework to put things into perspective.

But it wrongly assumes that people go through the funnel in a straight line. In fact, they take all kinds of detours. Since the journey isn’t linear, it’s impossible to determine exactly which touchpoint generates revenue for each client account.

Here’s what I mean:

  • Prospect A sees a Facebook Ad for your newest blog post and clicks through. She reads the post but doesn’t opt in to your newsletter, free trial, or any other offers. She might have gotten up to go to lunch. (Attribution: Paid social)
  • Three days later, she sees someone share the same blog post via Twitter and goes back to it through the Twitter link. (Organic social)
  • She becomes a lead a week later after seeing your display ad on NYTimes.com, but as she types your URL into her browser, the URL auto-completes. (Direct)
  • After she gets a series of lead nurturing emails and sales/marketing communications, she signs up for a demo and converts into a paying customer. (Email)

Which touchpoint generated the highest return? Was it the Facebook Ad, which got her interest in the first place? The Twitter post? Or a whole bunch of other things she might have seen in the meantime? How do you justify the Facebook Ad spend or social media manager if you can’t tie their work to closed deals?

This challenge is why you need some form of attribution modeling. Otherwise, you’re just looking at top-of-funnel traffic and conversions—not how those channels intersect to generate pipeline and revenue.

A case study on the need for attribution

One of the companies I consult with spent a lot of money sponsoring conferences. One in particular was highly regarded in the industry—it had the right titles and personas in attendance, so this client spent $50,000 on an annual sponsorship. It was a significant chunk of the marketing budget and came at the expense of other channels.

They measured success by the number of attendees, which they counted as net new prospects. One of the first exercises I did with them was to create an attribution model to measure trade show success. Using Salesforce (which I dive into below), we created a report on how the event drove net new pipeline and whether it moved existing opportunities toward closed sales.

After looking at the data across 3–6 months (the average sales cycle), it became clear that—even though the campaign was a “success” based on the number attendees—it drove a relatively low percentage of pipeline revenue.

Attribution took a top-of-funnel metric (net new prospects) and dove deeper to determine how much revenue we actually drove, which proved to be less than the investment.

As a result, the team changed how it approached the event. It was still important for the brand and getting face time with customers and prospects, but they reduced their sponsorship amount.

The same argument can be made for any marketing activity:

  • Maybe Twitter is bringing in a lot of traffic, but it’s actually Twitter combined with a brand search that drives conversions.
  • Targeting someone with ads leading up to a conference may make them more likely to come by your booth and chat with a team member.

Without attribution, you would wrongly assign all the credit to Twitter or the trade show.

What to focus on for marketing attribution

There are different ways to look at attribution based on how you slice and model the data. The critical touchpoints in B2B are the transition stages in the customer journey:

  • First Touch. Where did the first anonymous visit come from? This gives you an idea of which channels generate top-of-funnel traffic and brand awareness.
  • Lead Creation. A visitor submits some kind of form with their email, name, or other information to become a known lead.
  • Opportunity Creation. When you add an opportunity to your CRM (i.e. an account executive thinks the prospect is a good fit and likely to buy).
  • Closed-Won. When an opportunity closes and becomes a customer.

(Note: I borrowed some terminology above from Bizible)

You can track other stages, but the point is not to track every stage; otherwise, you’ll have too much noise. Focus on the stages you care about most and what drives the stage change.

How to model attribution

Your entire customer journey is worth 100%. Different models assign different percentages to each touchpoint.

For example, if you have five touchpoints, a Linear Model will assign credit equally across all touchpoints—each engagement is credited with 20%. You can also break this down by revenue: If a deal is worth $1,000, then each touchpoint or engagement is worth $200.

Here’s an overview of the different models:

  • First Touch. How did the visitor first come to your site?
  • Last Touch. What was the last touchpoint before Lead Creation?
  • Last Non-Direct Click. This excludes direct traffic as an attribution source.
  • Last Google Ad Click. This is specific to Google Analytics.
  • Linear. Equal credit is given to all touchpoints.
  • Time Decay. The most recent source to Lead Creation gets the most credit.
  • Position Based. Sources for First Touch and Lead Creation get the most credit; the rest is evenly distributed.
  • W-Shaped. Specific to B2B and looks at only three engagements: First Touch, Lead Creation, and Opportunity Creation.

(CXL has a fantastic article explaining the different attribution models in detail.)

No model is the “right” one. It depends on the context of your business and how you prefer to measure. And remember: While not perfect, each model is better than not having any framework in place.

Tools to measure marketing attribution

Salesforce and Salesforce Campaigns

salesforce b2b attribution
For B2B businesses, managing individual contacts within an account is crucial for attribution.

By far, Salesforce is the most widely used CRM for B2B businesses. But one of its limitations is relying solely on the lead source for attribution.

Here are the issues that can arise:

  1. If the lead-source assignment is a manual process, you leave it to someone’s judgment. And if you measure only the channel that created the lead, you don’t capture what drives that lead to become an opportunity.
  2. Lead sources may get “dropped” when a lead is converted into an opportunity. Account executives may overwrite lead sources when they create an opportunity.
  3. In B2B, you’re measuring attribution at the prospect level and the account level. Selling to companies adds complexity to the Lead Source method. In Salesforce, you can report on objects (Contacts/Accounts, Opportunities, Leads) but not across objects. So should you track top-of-funnel lead sources then use account sources to show how the company ended up buying? One method is to add a primary contact(s) source in the “Account Source” field. But this relies on a contact hierarchy in the account object to determine which one is the primary.
  4. Reporting from a single field misses all the touchpoints in the middle that resulted in the opportunity. You’re limited to reporting on a single object at a time; the sales process doesn’t match that.
field mapping salesforce
Field mapping between Objects in Salesforce.

Thankfully, Salesforce Campaigns solve these issues brilliantly, allowing for:

  1. Cross-object reporting. Report on Salesforce Campaigns across Leads, Contacts, or Opportunities to see how many of each (as well as, ultimately, revenue) each campaign generated.
  2. Campaign Influence reporting. Break down all the campaign touchpoints associated with an Opportunity. (A Campaign object is not a 1:1 mapping but a 1:many mapping.) This lets you determine that to close Company X, prospects touched Adwords, branded search, attended events, and interacted with Social Ad campaigns.
  3. Parent and Child Campaigns. Roll up reporting for “’All Marketing Campaigns” and break it down by each campaign. You can create campaigns for Sales/Marketing types and set up hierarchies that make sense for your process.

salesforce campaigns attribution

salesforce chart campaign attribution

For accurate reporting, you need to capture every touchpoint and assign it to a Salesforce Campaign. But you also want to avoid setting up and managing hundreds of campaigns.

To make the process simpler, you consolidate touchpoints. For example, paid media (Google Ads, LinkedIn Ads, etc.), events and tradeshows, and webinars are all important to track independently. But maybe you can bundle newsletter and marketing emails (outside of nurtures) into a single campaign.

It’s also important to use relevant campaign statuses. So if it’s a webinar, set up custom statuses to track how many registered, attended, or were no-shows (versus relying on the binary, out-of-the-box “sent/responded’ statuses). To make this process more efficient, I set up campaign templates to easily replicate statuses in the future.

Hubspot, Pardot, and Marketo Campaigns

You can set up attribution for marketing automation tools, too, and connect them with Salesforce to trigger campaign assignments.

HubSpot

Hubspot lets you create attribution models in a couple of ways: Original Source and HubSpot Campaigns.

Original Source. Original Source tracking tells you which channel a prospect came from before becoming a known contact. It relies on a cookie that’s added to a prospect’s browser when they first come to a web property with a HubSpot tracking code.

When they fill out a form, HubSpot ties the cookie to a known contact in its system to give you a complete picture of how the user first came to your site, how long it took them to convert, and where they converted.

You can take this a step further and attach UTM parameters to all links for all channels. Those parameters show up on form submissions, effectively offering First Touch and Lead Creation attribution to show, for example, that a contact came from Organic Search but then converted from Google Ads.

How does this look in practice? Suppose I came to your website through Twitter, and a week later I converted through a Google Ads campaign. Reporting from those two fields gives you two touchpoints—what first caught my attention and what led me to convert. Without tracking both, we’d mistakenly give 100% of the credit to Twitter or Google Ads.

To track which source drove a contact to become an Opportunity or a Closed-Won, we can create an automation workflow that stores the UTM value in a field called “Lead Creation Touch.” When a stage changes from, for example, Lead to Opportunity, we can write the value of the UTM parameter into “Opportunity Creation Touch” and determine the three touchpoints—Original Source, Lead Creation, and Opportunity Creation.

Reporting all of these will give you a more complete picture of the journey from anonymous visitor to customer.

Hubspot Campaigns. Hubspot has recently expanded reporting to break down First Touch, Influenced, and Customers sources by the campaign, enabling reporting on overall campaign performance:

hubspot influenced attribution

Pardot

Similarly, in Pardot, you can assign Pardot Campaigns based on Source Tracking or Custom Redirects. You can use either to track contact touchpoints from First Touch to Lead Creation and, ultimately, Closed-Won.

At present, Pardot doesn’t offer an Original Source tie as Hubspot does for anonymous visitors. But there’s a workaround: Create Custom Redirects for high-value campaigns and write in the Pardot Campaign and Source values for anyone who clicks on that link. The Pardot cookie can tie that Custom Redirect Source back to the contact after they become a known lead.

For a client who uses Pardot, I connected Google Analytics and Google Ads to Pardot to add UTM and GLICD parameters. After capturing UTM values, we set up automation rules to write the source values on the contact based on the UTM value combinations and assigned them to specific Salesforce campaigns based on that data.

I write the values into a source field rather than reporting directly from UTMs because:

  • We want to use a system and naming convention for sources.
  • UTM values get overwritten when a prospect clicks/converts through a new tagged link. The source value fields can be locked to preserve every touchpoint.

With Pardot, you can also track keyword-level performance through their Google Ads Connector.

Marketo

Marketo offers two methods. One is Programs, which are similar to tracking statuses in Salesforce Campaigns. You can use tracking parameters to set Program memberships and statuses to determine how your programs perform over time.

However, Marketo Programs don’t offer a multi-touch report like Salesforce Campaigns. You can use a bit of JavaScript to write source values into the Marketo cookie (Munchkin tracking) for the specific browser and tie it back to the contact when it’s created.

The power of using Marketing Automation and Salesforce campaigns becomes apparent when you use your marketing automation tool to capture and assign Salesforce campaigns based on source/referral/medium values.

Google Analytics

Google Analytics (GA) is perhaps the most widely used yet underutilized tool for web analytics.

Ninety percent of the time I audit client GA setups during onboarding, they’re missing Goals. Goals tell GA what’s important to your business and let you track things like form submissions or trial starts, allowing you to see, for example, how many people from Twitter actually convert.

The other reason Google Analytics is so powerful is that once you set up Goals, you can create Multi-Channel Funnels to view which interactions (based on channel, parameter, etc.) lead to conversions.

Here’s what a Multi-Channel Funnel report looks like:

google analytics multichannel funnel

You can see that Direct traffic assisted a lot of conversions (expected), but if we look a level deeper, it shows that Organic Search, Paid Search, and other channels also assisted conversions.

As I mentioned, a customer’s path to purchase is not a straight line. What this tells us is that—of all the conversions during this time—prospects engaged across these channels most often.

So, a prospect might have:

  1. Seen a Display Ad.
  2. Clicked on a Referral Link (from content on a third-party site).
  3. Seen a Google Search Ad.
  4. Ultimately converted via Direct traffic (which includes any visit without source data).

This gives is a clearer picture of all the paths a prospect took before converting. You can slice this data by segments, parameters, device, location, landing page etc.

A note on Direct traffic

This is a great opportunity to understand what Direct traffic means and if there’s a spillover effect from other campaigns. Most marketers curse Direct traffic since it doesn’t show you where it came from or what caused it. However, if you combine Direct traffic data with GA Multi-Channel Funnel reporting, you can see some insights.

For example, a recurring trend I’ve seen is an uptick in Direct traffic when Facebook Ads are running. A hypothesis (which was validated by a correlation analysis) confirmed that Facebook Ads indeed spiked Direct traffic.

This could be because prospects who see ads on Facebook are more likely to go to a site directly or via an untagged URL, or that there were higher-than-reported conversions from Facebook Ads due to session expiration.

Even more opportunities in GA

This is what makes GA conversion paths so powerful. With marketing automation tools or Salesforce, you cannot layer in additional data because it’s simply not available. But in GA, you can add more data to segment your channel report and really understand the user journey toward conversion.

The Top Conversion Paths report shows you the sequence of channel interactions prior to conversion. Additionally, the Path Length report gives you an idea of, on average, how many engagements a prospect has before converting. (The answer may surprise you.).

A third powerful tool in GA (though one that’s not directly related to attribution) is the Time Lag report. If you run a lot of retargeting ads (which you should be running), the Time Lag report shows you how long it takes for someone to convert.

In one of my consulting engagements, a client ran tons of retargeting ads on prospects who had visited the site in the past two days. However, looking at the Time Lag report, we saw that 30% of people converted 5 days after coming to the site. Changing the strategy from a 2-day to a 3–7 day window increased our remarketing ads’ performance by 25%.

Note: By default, the “look-back window” or “attribution window” in GA is 30 days, so GA will look at a 30-day window to create the first-visit-to-conversion path.

Google Attribution 360

google attribution 360 model explorer
A report showing weighted attribution in Google Attribution 360’s Model Explorer.

At $150k per year, Google Analytics 360 doesn’t make sense for most businesses. For those it does, however, it offers vastly improved attribution modeling.

Google Attribution 360, the attribution system within Google Analytics 360, uses machine learning to create a custom attribution model based on data from all connected accounts: Google Ads, Google Display Network, Campaign Manager, etc.

Attribution 360 also folds in offline data, like the impact of television advertisements; allows you to upload spend data from non-Google accounts; and extends the look-back window beyond 90 days, more than three times past the standard in Google Analytics.

The Model Comparison Tool compares three potential models to see how each affects the valuation of marketing channels. You can also create custom rules for attribution models, then reallocate marketing spend to test the validity of the proposed model.

A comparison between a Linear attribution model and one in which Paid Advertising receives additional credit.

Google Attribution is a free, lightweight, but not-yet-released version of Google Attribution 360. According to plans, it will connect data from Google Analytics, Google Ads, and Campaign Manager accounts—without additional tagging.

Google intends to roll out the free version, in part, to help advertisers better understand the impact of their ads (and, presumably, encourage them to spend more by showing the role of events like ad impressions in driving conversions).

Bizible and other enterprise tools

If you want to take attribution a step further and have the budget to do so (about $25–40K annually), you can add Full Circle Insights or Bizible to your existing suite of tools.

Bizible adds a layer of extra reporting and custom objects into your Salesforce data. But note that Bizible is built on top of Salesforce campaigns, so if you don’t have Salesforce or are not using Salesforce Campaigns, then Bizible isn’t for you.

Bizible creates touchpoints for each Salesforce campaign and maps them along the attribution journey (First Touch, Opportunity Creation, etc.). You can create different models or even your own model (with higher-tiered plans).

Bizible shines with its native Google Ads integration, which pulls in cost and revenue data from Salesforce to give you a true return-on-spend visualization:

bizible attribution

You can also set up matching rules to add UTM parameters to custom touchpoints (like campaigns that turn leads into marketing-qualified leads). Since all this is native to Salesforce, you can create dashboards and reports based on campaign/channels to gauge performance accurately.

However, Bizible is an enterprise tool—not every business needs it to create a functional attribution model. As with any system, make sure that the data inputs are clean, or you’ll end up with a “garbage in, garbage out” scenario.

Also, Bizible lacks native Facebook Ads integration. Facebook has been a strong channel in a lot of B2B consulting work I’ve done, so, for now, you’ll have to add the costs and rules manually.

Attribution windows: Google Analytics and Facebook Ads

I mentioned attribution windows briefly. What are they? Attribution windows are the timeframes in which, if a conversion occurs, the credit will be given to the channel(s) involved.

For Google Analytics, it’s the timeframe (30 days) in which Google Analytics traces back to the First Touch from the date of conversion.

To understand attribution windows, let’s look at a Facebook Campaign. If a prospect:

  1. Clicks on one of your Facebook Ads;
  2. Doesn’t convert right away;
  3. Then comes back to your website and converts within a 30-day window;

Facebook will record it as a conversion in the Facebook Dashboard and optimize around the event.

Let’s break it down a little more: If a prospect clicks on an ad and then converts anytime in the next 30 days, Facebook will record it as a conversion. Why? Because Facebook influenced the conversion (and because Facebook, as a platform, has an interest in taking credit for that event).

You can customize attribution windows for Facebook Ads, Google Ads, Adroll, and other paid media channels, but this is why Facebook often shows one number for conversions but your data shows another—the attribution windows between your two platforms don’t align.

Another reason is the difference between view-through conversions (VTC) and click-through conversions (CTC). Most ad platforms count people who saw (VTC) or clicked (CTC) an ad within a certain timeframe as converters. Adroll breaks it down, but some don’t—keep that in mind when you’re attributing paid media spend back to pipeline revenue.

Where to Start

Start somewhere. Anywhere. If you aren’t properly tagging and capturing UTM or other parameters, start there:

  • Have a consistent naming convention
  • Be strict about what goes into each field and what it means.
  • Get every team (social, demand, creative) to tag links
  • Never share “naked” links, which pollute your data with Direct traffic.

Establish a single source of truth for your data. Most of my clients ask why a number in one tool doesn’t match another. There are a lot of reasons; different systems measure things differently.

One example is Facebook conversions versus prospects created. Another is how Hubspot measures website traffic versus GA’s approach. (GA doesn’t filter for internal Hubspot pages and previews.)

  • Pick a tool to report from and stick with it.

If you’re going to report new leads from social ads via your CRM, ignore the Facebook reports (but be aware that some Direct traffic might be Facebook-influenced).

Accept the limits so that you don’t spend your time trying to find the right data but instead spend it gathering insights and making decisions.

  • Decide on terminology, like what “influenced” versus “created” means for your attribution model.

It’s easy to get lost in the details—but it’s never worth it.

Most importantly, remember that a prospect’s journey from visitor to paying customer isn’t linear. Plenty of “assist” and “influence” channels don’t get captured by tracking codes.

Branded search is a classic example. If a rise in branded search correlates with a marketing campaign, there’s a high probability that you’re generating a lot of word of mouth and, as a result, branded search. If you look at campaign results in isolation, you may think it underperformed.

Conclusion

Increasingly, as marketers, we’ve become “data-obsessed,” but sometimes we lose sight of the big picture. Data and tracking are meaningless without context, and not every action can be measured—no attribution model is perfect.

If you’re not currently connecting marketing spend with results, start somewhere. You don’t need enterprise-level tools. Google Analytics is a great place to start and make a case to your organization for how marketing drives bottom-line results.

Alternatively, you may already be doing it but know you have blind spots—every company does. Find out what you don’t know. Just don’t forget to improve the value of what you do know.

The post B2B Marketing Attribution: Models, Tools, and Processes appeared first on CXL.

Five Strategies for Slaying the Data Puking Dragon.

If you bring sharp focus, you increase chances of attention being diverted to the right places. That in turn will drive smarter questions, which will elicit thoughtful answers from available data. The result will be data-influenced actions that result in a long-term strategic advantage. It all starts with sharp focus. Consider these three scenarios… Your […]

The post Five Strategies for Slaying the Data Puking Dragon. appeared first on Occam’s Razor by Avinash Kaushik.

If you bring sharp focus, you increase chances of attention being diverted to the right places. That in turn will drive smarter questions, which will elicit thoughtful answers from available data. The result will be data-influenced actions that result in a long-term strategic advantage.

It all starts with sharp focus.

Consider these three scenarios…

Your boss is waiting for you to present results on quarterly marketing performance, and you have 75 dense slides. In your heart you know this is crazy; she won’t understand a fraction of it. What do you do?

Your recent audit of the output of your analytics organization found that 160 analytics reports are delivered every month. You know this is way too many, way too often. How do you cull?

Your digital performance dashboard has 16 metrics along 9 dimensions, and you know that the font-size 6 text and sparkline sized charts make them incomprehensible. What's the way forward?

If you find yourself in any of these scenarios, and your inner analysis ninja feels more like a reporting squirrel, it is ok. The first step is realizing that data is being used only to resolve the fear that not enough data is available. It’s not being selected strategically for the most meaningful and actionable insights.

As you accumulate more experience in your career, you’ll discover there are a cluster of simple strategies you can follow to pretty ruthlessly eliminate the riffraff and focus on the critical view. Here are are five that I tend to use a lot, they are easy to internalize, take sustained passion to execute, but always yield delightful results…

1. Focus only on KPIs, eliminate metrics.

Here are the definitions you'll find in my books:

Metric: A metric is a number.

KPI: A key performance indicator (KPI) is a metric most closely tied to overall business success.

Time on Page is a metric. As is Impressions. So are Followers and Footsteps, Reach and Awareness, and Clicks and Gross Ratings Points.

Each hits the bar of being “interesting,” in a tactical oh that’s what’s happening in that silo soft of way. None, passes the simple closely tied to overall business success standard. In fact, hold on to your hats, a movement up or down 25% in any of those metrics may or may not have any impact on your core business outcomes.

Profit is obviously a KPI, as is Likelihood to Recommend. So too are Installs and Monthly Active Users, Orders and Loyalty, Assisted Conversions and Call Center Revenue.

Each KPI is of value in a strategic oh so that is why we are not making money or oh so that is why we had a fabulous quarter sort of way. A 25% movement in any of those KPIs could be the difference between everyone up and down getting a bonus or a part of the company facing layoffs. Often, even a 5% movement might be immensely material. What metric can say that?

When you find yourself experiencing data overload, don an assassin's garb, identify the metrics and kill them. They are not tied to business success, and no senior leader will miss them. On the ground, people will use metrics as micro diagnostic instruments, but they already do that.

A sharp focus on KPIs requires concentrating on what matters most. Every business will have approximately six KPIs for a CEO. Those six will tie to another six supplied to the CMO.

After you go through the assassin’s garb process above, if it turns out that you have 28 KPIs… You need help. Hire a super-smart consultant immediately!

2. Focus only on KPIs that have pre-assigned targets.

This is a clever strategy, I think you are going to love it.

Targets are numerical values you have pre-determined as indicators success or failure.

Turns out, creating targets is insanely hard.

You have to be great at forecasting, competitive intelligence, investment planning, understanding past performance, organization changes and magic pixie dust (trust me on that one).

Hence, most companies will establish targets only for the KPIs deemed worthy of that hard work.

Guess what you should do with your time? Focus on analysis that is worth your hard work!

Start by looking at your slides/report/dashboard and identify the KPIs with established targets. Kill the rest.

Sure, there will be howls of protest. It'll be John. Tell him that without targets you can’t identify if the performance is good or bad, a view every CEO deserves.

John will go away and do one of two things:

1. He will agree with you and focus on the KPIs that matter.

2. He will figure out how to get targets for all 32 metrics along all 18 dimensions.

You win either way. :)

An added benefit will be that with this sharp focus on targets, your company will get better at forecasting, competitive intelligence, investment planning, org changes, magic pixie dust and all the other things that over time become key assets. Oh, your Finance team will love you!

Special caution: Don't ever forget your common sense, and strive for the Global Maxima. It is not uncommon for people to sandbag targets to ensure they earn a higher bonus. If your common sense suggests that the targets are far too low, show industry benchmarks. For example, the quarterly target may be 400,000 units sold. Common sense (and company love) tell you this seems low, so you check actuals to find that in the second month, units sold are already 380,000. Suspicion confirmed. You then check industry benchmarks: It is 1,800,000. WTH! In your CMO dashboard, report Actuals, Target and Benchmark. Let him or her reach an independent, more informed, conclusion about the company’s performance.

3. Focus on the outliers.

Turns out, you are the analyst for a multi-billion dollar corporation, with 98 truly justifiable KPIs (you are right: I'm struggling to breathe on hearing that justification, but let's keep going). How do you focus on what matters most?

Focus your dashboards only on the KPIs where performance for that time period is three standard deviations away from the mean.

A small statistics detour.

If a data distribution is approximately normal then about 68 percent of the data values are within one standard deviation of the mean, about 95 percent are within two standard deviations, and about 99.7 percent lie within three standard deviations. [Wikipedia]

By saying focus on only reporting on KPIs whose performance is three standard deviations from the mean, I’m saying ignore the normal and the expected. Instead, focus on the non-normal and the unexpected.

If your performance does not vary much, consider two standard deviations away from the mean. If the variation is quite significant, use six (only partly kidding!).

The point is, if performance is in the territory you expect, how important is it to tell our leaders: The performance is as it always is.

Look for the outliers, deeply analyze the causal factors that lead to them, and take that to the executives. They will give you a giant hug (and more importantly, a raise).

There are many ways to do approach this. Take this image from my January 2007 post: Analytics Tip #9: Leverage Statistical Control Limits

Having an upper control limit and a lower control limit makes it easy to identify when performance is worth digger deeper into. When you should freak out, and when you should chill.

Look for outliers. If you find them, dig deeper. If not, move on permanently, or at least for the current reporting cycle.

Use whichever statistical strategies you prefer to find your outliers. Focus sharply.

4. Cascade the analysis and responsibility for data.

In some instances you won't be able to convince the senior leader to allow you to narrow your focus. He or she will still want tons of data, perhaps because you are new or you are still earning credibility. Maybe it is just who they are. Or they lack trust in their own organization. No problem.

Take the 32 metrics and KPIs that are going to the CMO. Pick six critical KPIs for the senior leader.

Cluster the remaining 26 metrics.

You'll ask this question:

Which of these remaining 26 metrics have a direct line of sight to the CMO’s six, and might be KPIs for the VPs who report to the CMO?

You might end up with eight for the VPs. Great.

Now ask this question:

Which of these remaining 18 metrics have a direct line of sight to the eight being reported to the VPs, and might be KPIs for the directors who report to the VPs?

You might end up with 14 for the directors.

Awesome.

Repeat it for managers, then marketers.

Typically, you'll have none remaining for the Marketers.

Here's your accomplishment: You've taken the 32 metrics that were being puked on the CMO and distributed them across the organization by level of responsibility. Furthermore, you've ensured everyone's rowing in the same direction by creating a direct line of sight to the CMO’s six KPIs.

Pat yourself on the back. This is hard to do. Mom is proud!

Print the cascading map (CMO: 6 > VPs: 8 > Directors: 14 > Managers: 4), show it to the CMO to earn her or his confidence that you are not throwing away any data. You've simply ensured that each layer reporting to the CMO is focused on its most appropriate best sub-set, thus facilitating optimal accountability (and data snacking).

I’ll admit, this is hard to do.

You have to be deeply analytically savvy. You have to have acquired a rich understanding of the layers of the organization and what makes them tick. You have to be a persuasive communicator. And, be able to execute this in a way that demonstrates to the company that there’s real value in this cascade, that you are freeing up strategic thinking time.

You’ll recognize the overlap between the qualities I mention above and skills that drive fantastic data careers. That’s not a coincidence.

Carpe diem!

5. Get them hooked on text (out-of-sights).

If everything else fails, try this one. It is the hardest one because it'll demand that you are truly an analysis ninja.

No senior executive wants data. It hurts me to write that, but it is true.

Every senior executive wants to be influenced by data and focus on solving problems that advance the business forward. The latter also happens to be their core competence, not the former.

Therefore, in the next iteration of the dashboard, add two more pieces of text for each metric:

1. Why did the metric perform this way?

Explain causal factors that influenced shifts. Basically, the out-of-sights (see TMAI #66 if you are a subscriber to my newsletter). Identifying the four attributes of an out-of-sight will require you to be an analysis ninja.

2. What actions should be taken?

Explain, based on causal factors, the recommended next step (or steps). This will require you to have deep relationships with the organization, and a solid understanding of its business strategy.

When you do this, you'll begin to showcase multiple factors.

For the pointless metrics, neither the Why nor the What will have impact. The CMO will kill these in the first meeting.

For the decent metrics, it might take a meeting or three, but she'll eventually acknowledge their lack of value and ask you to cascade them or kill them.

From those remaining, a handful will come to dominate the discussion, causing loads of arguments, and resulting in productive action. You'll have known these are your KPIs, but it might take the CMO and her team a little while to get there.

After a few months, you'll see that the data pukes have vanished. If you've done a really good job with the out-of-sights and actions, you'll notice notice that the focus has shifted from the numbers to the text.

Massive. Yuge. Victory.

If more examples will be of value, I have two posts with illuminating examples that dive deeper into this strategy…

Strategic Dashboards: Best Practices, Tips, Examples | Smart Dashboard Modules: Insightful Dimensions And Best Metrics

You don't want to be a reporting squirrel, because over time, that job will sap your soul.

If you find yourself in that spot, try one of the strategies above. If you are desperate, try them all. Some will be easier in your situation, while others might be a bit harder. Regardless, if you give them a shot, you'll turn the tide slowly. Even one month in, you’ll feel the warm glow in your heart that analysis ninjas feel all the time.

Oh, and your company will be data-influenced — and a lot more successful. Let's consider that a nice side effect. :)

Knock 'em dead!

As always, it is your turn now.

Have you used any of the above mentioned strategies in your analytics practice? What other strategies have been effective in your company? What is the hardest metric to get rid of, and the hardest KPI to compute for your clients? Why do you think companies keep hanging on to 28 metric dashboards?

Please share your ideas, wild theories, practical tips and examples via comments.

Thank you.

The post Five Strategies for Slaying the Data Puking Dragon. appeared first on Occam's Razor by Avinash Kaushik.