How to Calculate B2B Goal Values in Google Analytics

How to Calculate B2B Goal Values in Google Analytics.jpg

Setting goals and goal values is one of the most important things you can do with Google Analytics, second only to actually installing it properly. With goals and goal values, you can infer the economic value of nearly any activity on your website. Without goals and goal values, you’re relegated to vanity metrics.

Today, let’s look at how to calculate a B2B goal value, or the value of a complex sale. I say this because B2B vs. B2C is largely a meaningless distinction; complexity of the sale matters more.

In the complex sale, customers typically pass through the following stages:

  • Audience/visitor
  • Prospect: someone interested in interacting, but no commercial intent. Examples would be a newsletter subscriber, white paper downloader, or webinar attendee.
  • Marketing Qualified Lead: someone who has expressed commercial intent. Example would be someone asking for a demo of our product or for someone to contact them. This is typically what we’d call a goal in Google Analytics.
  • Sales Qualified Lead: someone who is a qualified buyer; they have purchase intent, budget, and authority to make a decision.
  • Opportunity/Deal: someone in active negotiations to buy from us; we have made our sales pitch and we are one of possibly several brands the buyer is courting.
  • Closed Won: someone who has signed, sealed, and delivered a contract or made a purchase.

Note that while this does fit B2B, it also equally describes complex B2C sales such as automotive and real estate sales.

How do we calculate a Google Analytics goal value? We work backwards from the bottom of this structure to arrive at an inferred goal value.

Let’s start with the customer. What’s the value of a customer to you? For example, if you’re a SaaS business, the customer’s value is their monthly subscription value multiplied by how long the average customer stays subscribed to you. The same is true of a services business, from public relations to housekeeping services. This is customer lifetime value, or CLTV.

What does it cost you to acquire a customer? From advertising to marketing to sales staffing, how much in total does each customer cost to obtain? This includes the costs of trade shows, marketing software, CRM software, the hours and commissions paid to sales professionals, etc. This is the customer acquisition cost, or CAC.

Our net customer value (NCV) is CLTV – CAC.

CLTV – CAC =NCV

Let’s say a customer’s CLTV is $100,000 but our CAC is $10,000.

CLTV – CAC = NCV
$100,000 – $10,000 = $90,000 = NCV

That’s the true value of a Closed Won deal.

Next, how effective is our sales team? What’s our sales closing rate (SCR) between Deal and Closed Won? If our salespeople close 1 out of 4 deals they’re given, then the effective net deal value (NDV) is the NCV multiplied by 25%. Why? Because for every 1 deal they win (NCV), they lose 3, so the value of the one win is spread over four deals.

NCV x SCR = NDV
$90,000 x 0.25 = $22,500 = NDV

How many sales qualified leads become deals? After all, just because someone is qualified doesn’t mean they’ll buy from us. We may have had an input call and prepared for a deal, but then our sales lead chose another company before we ever had a chance to pitch. If we lose 1 out of 4 deals between qualification and pitching, we multiply our NDV by this deal closing rate (DCR) to find our sales qualified lead value (SQLV).

NDV x DCR = SQLV
$22,500 x 0.75 = $16,875 = SQLV

How many marketing qualified leads are truly qualified?

theleadsareweak.jpg

If you remember in Glengarry Glen Ross, Jack Lemmon’s character Shelley Levene protests at one point, “The leads are weak!”, summarizing the often antagonistic relationship between sales and marketing. Suppose only 1 out of 4 marketing qualified leads were actually sales qualified (our qualification rate, or QR), meaning they had budget, authority, and need for our product or service. That’d be our marketing qualified lead value (MQLV).

SQLV x QR = MQLV
$16,875 x 0.25 = $4,219 = MQLV

For some of our Google Analytics goals, like people asking us to contact them or requesting a demo, we would use MQLV as our goal value. People did what we wanted them to do, which was to ask us to reach out.

We still have other digital activities, like newsletter subscribers, white paper downloads, etc. that we know have some value. Suppose 1 out of 100 email newsletter subscribers eventually asks us to contact them. That’s essentially our prospect qualification rate (PQR) leading to a prospect value (PV).

MQLV x PQR = PV
$4,219 x 0.01 = $42 = PV

Thus, in the scenario above, even a prospect has value, and we can set the appropriate value of that prospect as a goal and goal value in Google Analytics.

Do this exercise in accordance with your sales and marketing processes; some companies will have even more stages in their pipeline. Others will have fewer. The goal is to identify which digital activities have value, then calculate with reasonable accuracy what those values are.


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How much do marketing tools matter?

How much do marketing tools matter? I’m asked this question in one form or another nearly every week, by coworkers, clients, friends, and colleagues. The question is often coached in terms of specific products. Is Marketo better than Pardot? Is Hubspot better than Infusionsoft? Is Buffer better than Hootsuite? Is Sysomos better than Meltwater?

The answer to the question is relatively straightforward. Marketing tools are like spatulas.

latkespatula.png

Have you ever tried to cook a dish like steak or pancakes without a spatula? It’s awful. You either end up improvising with an assortment of tools that were not meant to do the job, or you ruin the food. Try flipping a pancake with chopsticks if you don’t know what I mean. You can do it, but your rate of success is significantly lower without a spatula.

Any spatula, even a mediocre one, is better than no spatula. When someone asks about marketing automation, the answer is that any marketing automation system is better than none at all.

The spatula analogy extends further. Amazon lists 8,127 spatulas for sale, from the Global GS-25 spatula for $70 to the Rite Lite Menorah Shaped Hanukkah Latke Spatula for $1.35. Is the GS-25 51x better a spatula than the Rite-Lite? Can you cook 51x more food or make food that tastes 51x better with it? Probably not. The difference between the two is largely aesthetic. They fulfill the same function.

Once you have a spatula of any functional use, what matters more is the skill with which you use it. If your pancake batter recipe is made of solely flour and water (yuck), then no spatula is going to make those pancakes taste better. You have to fix the recipe first.

Likewise, the gap, the difference between a Marketo and a Pardot or a Buffer and a Hootsuite is significantly smaller than the difference between a Marketo and nothing, or a Buffer and nothing. Once you have a marketing tool, your ability to be productive, profitable, or powerful with it is far more dependent on your skills and ingredients than the tool.

Buy the spatula, to be sure. But don’t get so caught up in spatula upgrades that you fail to actually cook something good.


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How To Check Every Page On Your Site For Google Analytics / Tag Manager Tags

Does this sound familiar? Maybe you or someone you work with made a change to your website and suddenly, your site traffic is down considerably.

One of the most common reasons for Google Analytics to show a decline in site traffic is someone removing the tracking code. If you, like me, question whether every page on your website is tagged properly – especially when you use marketing automation software – then you’ve probably wished for a tool that checks every page for Google Analytics tags.

You, like me, have Googled for such tools and found them available, but at prices that seem a little steep, especially for a task that you shouldn’t need to do more than a few times a year at most.

So what can we do?

The good news is, we have access to open source tools which let us build these tools for free. Because they’re open source, they’re also much more flexible and adaptable. Let’s walk through the steps of setting up our Google Analytics / Google Tag Manager Power Tag Checker.

Pre-requisites

  • An operating system environment that supports Python 2.7+
  • Basic working knowledge of Python
  • A text editor
  • Optional: a cloud-based server so you can set it and forget it
  • The Scrapy Python library

Step 1: Scrapy Installation

If you don’t already have Python installed, you should install Python. Consult Python.org for specific instructions for your computer/operating system.

From the command line/terminal, type:

pip install scrapy

Allow Python’s installer, pip, to set the library up.

Step 2: Start a Scrapy project

From the command line/terminal, navigate to part of your hard drive or server where you keep documents and type:

scrapy startproject TagChecker

Your computer should say something that resembles this:

New Scrapy project ‘TagChecker’, using template directory ‘/usr/local/lib/python2.7/dist-packages/scrapy/templates/project’, created in: /home/cspenn/scrapers/TagChecker
You can start your first spider with:
cd TagChecker
scrapy genspider example example.com

This will create a folder named tagchecker, and inside that folder will be a whole bunch of files. Don’t worry about them just yet. Follow the instructions from the startproject script to navigate down into the tagchecker folder in the command line/terminal.

Step 3: Create a Tag Spider

From the command line/terminal, type:

scrapy genspider TagSpider www.YOURSITEHERE.com

For my site, I typed:

scrapy genspider TagSpider www.christopherspenn.com

Your computer should say something like:

Created spider ‘TagSpider’ using template ‘basic’ in module:
TagChecker.spiders.TagSpider

Step 4: Configure the Spider’s Item Collection

If you’re doing this on a server, open up your SFTP/FTP client. If you’re doing this on your desktop computer, navigate to the folder and subfolders.

Find the items.py file.

tagcheckercontents.jpg

Open it in your text editor of choice. Edit it to look like this:

(you can copy and paste this right into your file, unchanged)

This is telling the spider what items we want to collect – URLs and three kinds of tags. Note that these are entirely arbitrary; you could configure this spider to look for Marketo tags, Pardot tags, Adobe Omniture tags, etc. We’ll use Google Analytics and Tag Manager because that’s what most websites use.

Save and close the items.py file.

Step 5: Configure the Spider’s Tag Detector

Next, find and open the TagSpider.py file in your text editor. Edit it to look like this, but don’t obviously copy my website URL. Change it to yours!

What this script does, simply put, is crawl our entire website and check for three items – the old, outdated Google Analytics classic tracking code, the Universal Analytics tracking code, and the Google Tag Manager tracking code. If it finds any one of those three, it changes an output variable to 1; otherwise, output variables are 0.

Step 6: Run the Spider!

From the command line/terminal, run the following command:

scrapy crawl -o giveyourexcelfileaclevername.csv TagSpider

This will create a CSV file which you can open in Excel. Your command line/terminal window at this point should fill with text scrolling by at an astonishing speed as the spider does its work.

Step 7: Analyze Your Site!

Find the CSV file that the spider created in its folder.

tagcheckeroutput.jpg

Open it in Excel. What you’ll see is something like this:

rawtagcheckerexcel.png

As you can see, I use Tag Manager on my site, so the first two columns after the URL – Classic and Universal – are zeroes. Let’s apply some conditional formatting to the Tag Manager column, and suddenly everything will become clear:

tagmanagerexcelconditional.png

In the case of my blog, I’m okay with not having the tracking code on my admin login page. However, if I saw this on any other page, I’d know I had tags missing – and what pages those tags were missing on. I could then go in and fix them.

Conclusion

Install these tools and use them to check your site for missing tags. As mentioned earlier, when you dig into the script, you’ll see how it detects different tags. If you’d like to track other systems like Pardot, Mautic, Marketo, etc. in addition to Google Analytics, just add the appropriate lines.

Disclaimer: The gists published in this post are released under the GNU General Public License. Absolutely no warranty or support is provided.


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mautic is open source marketing automation