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Having worked with a handful of leading B2B marketers, we’ve noticed there are some that absolutely dominate their competitors – in terms of both lead volume and ROI. We call them Exponential Advertisers. Exponential Advertisers are characterized by a few common traits, including:
- Religiously testing everything
- Having a deep understanding of what the world looks like through the eyes of their customers
- Adopting an iterative, agile cycle of continuous improvement
- Establishing key metrics that provide a holistic view of performance
- Knowing what improvements will provide exponential returns
- Applying technology to drive sustaining improvement cycles
- Most importantly, believing that exponential returns are possible and not settling for incremental returns
For B2B marketers to launch into the realm of being an Exponential Advertiser, in-depth granular tracking on lead quality is imperative. It’s no longer okay to simply use the engine-provided pixels to measure a conversion; you must go deeper into your pipeline to uncover learnings that will truly drive your campaign forward and give you that all-important competitive edge.
In this article, we’ll walk you through setting up the tracking to get the needed insights from your pipeline, how to blend the back-end and front-end data to enable full-funnel analytics and, finally, how to analyze the data once you have it pulled together.
Setting up Tracking For Your B2B Campaign
It may sound obvious but you can’t optimize against something you can’t measure. Qualitative insights from the sales team that “lead quality isn’t good” won’t cut it.
Step 1: Identify the Right Metrics
The first step is to identify the one stage in the pipeline that will be used as the goal stage. Many stages can be measured and reported on, but ONE stage must be agreed on as the right stage for optimization – at least to start. This metric must:
- Be meaningful and have value to all parts of the organization (IT, marketing, sales, executive level)
- Provide enough data to make statistically significant optimization decisions down to the ad group level (measuring opportunities and revenue down to the ad group level often does not yield enough data)
- Be trackable down to the ad group level across ALL marketing channels
Some good examples that our clients use are MQLs, SQLs, Signups, Entry of Payment Details, etc. Essentially, we’re looking for a metric that signals an intent to act or a well-qualified prospect. Typically this is one step further than a lead conversion tracked by a pixel but before a revenue event for B2B companies.
Revenue events are too far and few between to optimize to the ad group level, but raw leads are not qualified enough to be meaningful. So think through your metrics but this can be any metric that satisfies the principles above.
Step 2: Configuring Your CRM or Marketing Automation System
Note: If you’re ONLY using Google AdWords and Salesforce Sales Cloud then Google has made this easy – simply follow the directions here for the built-in integration.
Data will need to be passed from the front-end ad platforms (such as Google, LinkedIn or Facebook) to the back-end CRM systems (such as Marketo, Salesforce, Eloqua etc) in order to properly track down to the adgroup level. This can be done through:
- Hidden fields in a lead form which pull parameters from a URL (see parameter list in next step)
- Hidden fields in a lead form which pull parameters from the cookie (see parameter list in next step)
We do NOT recommend creating separate landing pages per adgroup as this is not a scalable solution.
Step 3: Configure Your Designation URLs
Now that you’ve identified the right metric, you’ll need to make sure it’s trackable down to at least the adroup level if not keyword and ad level. This will take some work as the destination URLs will need to be modified across your campaigns. You’ll have a couple of options depending on your marketing and platform mix.
If Your Only Channel is Google AdWords:
- Option 1: Set up your CRM solution to accept Google’s GCLID from the URL. The GCLID can be automatically retrieved from the URL and embedded into a hidden field on your lead form.
There are plusses and minuses to this approach. The conversion information attached to the GCLID can be imported directly into the AdWords interface to give you all the same data that you would normally get from the pixel conversion tracking. However, outside of the Google interface, it’s in an unreadable form. So if you like to review data within your CRM system, this may not be a good solution for you.
- Option 2: Use Google’s valuetrack parameters to capture campaign ID, adgroup ID, keyword and match type at a minimum. Device type and ad network might also be good options if applicable for your strategy. These valuetrack parameters are:
Depending on the advertiser and our targeting strategies, we will often pull in additional parameters for device type and ad network.
If You’re Using Multiple Channels
If you’re using multiple channels, then the approach is similar to Option 2 above but without the use of valuetrack parameters. Simply append parameters to the back of the destination URL with the following details embedded:
- Ad platform – Google, Yahoo, Bing, Facebook, LinkedIn
- Campaign – Campaign Name*
- Adgroup – Adgroup Name*
- Keyword – Google, Bing and Yahoo all support the valuetrack parameter so we recommend using valuetrack parameters to track to the keyword level.
- Match Type – Google, Bing and Yahoo all support the valuetrack parameter so we recommend using valuetrack parameters to track to the keyword level.
Now that all this is set up, don’t forget to test and confirm that it’s working and all the data is coming through.
*Make sure this matches exactly as it’s found in the interface.
Connecting the Dots
Now that tracking is all set up, you’ll start to get data into your CRM system with the parameters above. While this data is helpful on its own, Exponential Advertisers know the power of blending back-end and front-end data together.
We recommend pulling a daily report from your CRM system with the date, ad platform, campaign, ad group, keyword, and match type (plus any other parameters.) Then we recommend pulling a keyword report also segmented by day. Using software such as Tableau will make it easy to blend the reports together using each of the common fields (ie; date, campaign etc.) This can also be done using Excel although much more manipulation is needed.
Once the files are blended, it’s time to analyze the results. We recommend starting with that key metric you identified in Step 1 above and analyzing the ‘cost per’ over time. In this example, we’ve analyzed cost per MQL over time and layered it on top of MQLs to show the improvements to efficiency and growth.
Next, we can use the campaign blending to get a much better understanding of which campaigns are performing best for MQLs as opposed to pure leads. The first image here, shows campaign performance using cost per lead (blue is a low CPL and red represents a high CPL). This might lead you to believe that campaign #1 and #12 are performing really well.
However, when we review it against back end data for cost per sale, we get an entirely different understanding.
Campaign #1 and campaign #12 are actually poor performers and need to be pulled back. These types of insights are invaluable and make the difference between an average advertiser and an Exponential Advertiser.
There are an endless number of ways you can now slice and dice this data to uncover unique insights into your B2B campaigns. Incorporating these unique insights will propel your campaigns to the next level making you the next Exponential Advertiser.