Digital Marketing Analytics – Introduction (Part 1 of 3)

This is Part 1 of 3 in a series of digital marketing analytics. This series is more of a brain-dump of my thoughts when thinking about the topic.

What is Digital Marketing Analytics?

The number 1 SERP from Google of “Digital Marketing Analytics” is from Hubspot: “the translation of customer behavior into actionable business data.

I agree with the definition at a high-level.

To take it one step deeper, Digital Marketing Analytics is “driving data-driven decisions (with a focus on calculating return on investment) to provide insights on obtaining a maximal return on every dollar invested across all marketing channels or campaigns”.

If you have a good foundation of digital marketing analytics, there is always more your business can do.

An example of next level would be using the data to forecast and predict return with data modeling.

What is “return” in B2B digital marketing?

If getting return is one of the objectives of digital analytics, we need to define what is return.

Return can be calculated on a number of metrics in B2B – it could be revenue, pipeline and leads.

Why?

Given the whole point of B2B digital marketing is to fuel the business, the metrics on which you measure return should match the sales/marketing funnel progression that the business is built upon.

This is the most intuitive way for digital marketers and their work to form a direct relationship with revenue and other revenue-generating teams.

Once you have those output metrics established. You can join it against cost data to calculate cost per revenue, cost per pipeline and cost per lead. These become your efficiency metrics.

If efficiency is ‘good’ (i.e., falls within your threshold of what’s acceptable), it makes sense to continue to invest with positive returns.

What about other metrics?

Many other metrics closer to the top of funnel – for instance impressions, clicks, web visits, conversions – have a less direct relationship to revenue.

They are typically have a many-to-many relationship, which makes their impact very difficult to measure accurately.

However, there is a temptation to anchor on these as they are typically available out-of-the-box (via ad and campaign platforms) and require less computation.

Definitions are important

We touched on the definition of “return” above, but there are many more business metrics, in terms of inputs and outputs, that are critical to unpack the drivers of any business.

To start with Sales, the inputs are often headcount — assuming each headcount performs as expected to hit 100% attainment (the efficiency), the business will get predictable revenue (i.e, outputs).

Many may say that the unit economics of sales staffing is science, the management of sales selling is art.

For marketing, the marketing activities that drive the outputs are the art, and the science is what we are trying to unpack with digital analytics.

To set a baseline, let’s align on the following digital marketing metrics, which may give more context later:

  • Top of the Funnel (TOFU): A web visit
  • Middle of the Funnel (MOFU): A lead (or conversion event, e.g., email capture)
  • Bottom of the Funnel (BOFU): A stage X opportunity accepted by AE

The Ultimate Challenge

The real challenge of digital marketing analytics is to understand the relationships between pre-TOFU and MOFU in a cross-device, multi-touch environment.

From MOFU to BOFU, most companies can do it pretty well with a Marketing Automation Platform (MAP) (i.e, Marketo, Pardot, Hubspot..etc) and Customer Relationship Management software (CRM) (i.e, Salesforce)

To understand the relationships between pre-TOFU and MOFU by factual data analysis, you will need to collect the data.

And it is difficult, because of 2 reasons.

  1. While Lead>Account>Opportunity is a 1-to-1 relationship, impressions>web visits>leads are many-to-many relationships
  2. The sources of impressions and web visits are complex — it could be search, paid, direct, referral
    • Compliance regulation like GDPR, ad-blocking technologies, browsers removing cookies and privacy mode make it even more difficult to attribute the sources

It is worth noting that if the business has no data, there is no science to be done.

Now, we should be on the same page on business understanding in terms of digital analytics and the marketing funnel.

Let’s dive deeper into data/digital analytics with inputs and outputs.

Inputs and Outputs in Digital Analytics

The framework of inputs and outputs are important to structure what is need to produce certain outcome, that includes both technology and human effort.

In broad stroke, a digital analytics process cycle can be split into 5 steps:

  1. [Input] Data collection – the process to collect raw data
  2. [Input] Data piping – the process to manipulate raw data to a structured format
  3. [Output] Data analysis and insights – the process to extract insights from structured data
  4. [Output] Visualization – the process to surface structured data to be digested easily
  5. [Input & Output] Optimization – the process to translate the insights to execution literally

Depending on the maturity of the business, the implementation of the 5 steps could look very different.

For a startup, a general analytics tool like Google Analytics makes step 1 – 4 easy and step 5 becomes the iterative process that your team is executing.

If your business has grown out of Google Analytics from an analysis standpoint, you could consider expanding the analytical capability by extending the number of data sources available and performing the analysis out of a data warehouse directly.

Conclusion

Business stakeholders at each organization need to identify the requirements and subsequence actions that would be performed with the insights in order to make the investment of data analytics worthwhile.

As the business continues to grow, it may be necessary to specialize in web analytics and advertising analytics. I will share more details in part 2 and part 3.