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Includes a Free Marketing Data Strategy & Audit Template by Steinert Analytics
Introduction
In my experience, marketing data remains largely untapped in organizations of all sizes.
Due to marketing’s subjective nature, prioritizing marketing analytics gets pushed to the backburner. Often, data teams like focusing on departments with more absolutes such as finance and operations.
In her book Here We Grow by Marcia Barnes, she states that typically only 70-75% of marketing data can be tracked. Even in companies with state of the art infrastructure, at the most it’s only 85-90% tracked.
A marketing team needs to be “certain enough” about what is driving revenue. A return on marketing spend (ROMS) is not able to signal everything a team could be focusing on to increase sales. Some things remain hidden.
Ultimately, ROMS is a fancy metric to understand the impact of marketing attribution. Where are you putting your marketing dollars that leads to more sales? This is a classic use case, and boils down to a company having a handle on their customer journey.
Surprisingly, I’ve seen companies from $3M to $2B in revenue struggle with understanding their digital customer journey. My goal is to provide you with a data strategy framework to increase the likelihood of success for tackling this problem.
Start With Questions
Since the start of my data career, I’ve learned that it starts with questions. This not only applies to building a report or dashboard, but to crafting a data strategy. In fact, it might even be more important to ask these questions for a data strategy than a single report. Any reporting built needs to answer questions from the strategy and overall goals of the organization.
In an impactful episode of The Joe Reis Show with Dylan Anderson (below), Dylan defines data strategy as what businesses do with data and why it’s important to them. Beginning with questions at the start of a data strategy roadmap helps to answer how a company uses data and why it’s important to them.
In the context of a digital marketing data strategy, here’s a few examples of business questions to ask:
How are blog pages performing on Google?
How do users behave after landing on high performing SEO pages?
What behaviors lead to a user converting to a lead?
Which digital pages drive opportunities and revenue?
These particular questions derived from a real client use case at Steinert Analytics. This SaaS start-up was struggling to understand their digital customer journey, essentially what I alluded to in the introduction. After several discovery consultation sessions, we nailed down these high priority questions they needed answers to.
These questions provided the framework for the business objectives to accomplish. Confirming these questions made for a seamless transition into the next step - selecting KPIs to track against these questions.
Ask yourself - which metrics and/or KPIs will help to answer the questions above?
Furthermore, the goal for our client was to present these KPIs in a “single pane of glass” dashboard to be used efficiently and quickly by the VP of Marketing.
Selecting the Right KPIs
Let me begin with the goal of KPIs in a data strategy. They’re meant to illustrate how your business is progressing towards the objectives that lead to successful business outcomes (ie. We see X% increase in organic page traffic which is leading to Y% increase in leads and Z% revenue increase to our pipeline).
One might assume I’m about to cover an elaborate process for selecting KPIs that help status check my questions above. In a perfect world, I’d tell you a consultant holds expert knowledge and is able to craft a list of KPIs for a specific process flawlessly.
Given how horizontal and broad data consulting can be, that isn’t always the case. Leverage your resources, and lean on the customer to uncover KPIs/metrics they’ve historically tracked.
In fact, anyone with a decent grasp on marketing will probably succeed at the KPI selection for a digital customer journey. I realize this is one of an infinite number of processes to track in a business. Prompt ChatGPT or Google Gemini to guide you in selecting the KPIs relevant to your process. Pair that with the conversations with your end user.
Here are the KPIs we landed on to include in our “single pane of glass” dashboard:
SEO
Organic Traffic by Page
Keyword Rankings (Top Pages)
User Engagement
Average Time on Page
Bounce Rate (%)
Behavior to Conversions
Chat Engagement Rate (%)
Chat to Form Submission Rate (%)
Sales Attribution
Lead Count
Close Rate
Revenue by Lead Source
Each KPI presented here is fairly high level by design. The scope of this particular marketing data strategy caters towards a minimum viable product. Keeping it high level was a strategic move on our part. This brings up a critical point - start with summary metrics when gaining traction at an organization. Reason being, it unlocks early slam dunks that are paramount for building trust with your stakeholders.
Conducting a Comprehensive Data Audit
Admittedly, when I started the first data audit I ever did I was blind on a framework. The term “data audit” is thrown around in technical jargon speak, but what does it really entail? Based on my past experience in data management, we formulated our own structure for data audits at Steinert Analytics.
First, lay out the intent of each source system in a table. What is the purpose of each? Tables help when presenting concise slides in a roadmap deck.
With the client I’m describing, we focused on 3 of their marketing & sales source systems:
Once they are defined, conduct the data audit one source system at a time. Always tie the source system back to the defined KPIs. Map the source system’s functionality to the correct KPIs.
Stick to the C-G-S framework as follows:
* to be used one source system at a time
Current State
Study the relevant data & reports used.
List out the current KPIs available and map back to your list of defined goal KPIs.
Follow this structure:
Contains: Defined KPI > KPI currently available (name in source), logic
Does Not Contain: Defined KPI > logic needed
Gaps
Note gaps due to source integration issues (ie. Google Tag Manager not set up correctly for given event or Salesforce 3rd party digital attribution tracking)
Possible Solutions
List ways to optimize/improve source integration limits
Identify new integration/reporting tools to help with difficult KPI tracking
Repeat this for each source system and respective KPIs. The intent of this audit is to bring tangible analysis and actionable solutions to the strategy.
This raises transparency from the consultant to the stakeholder, and helps set expectations for what is possible in the scope.
List Out What is Practical to Accomplish Now
This section is designed to list out the benefits of the data product itself. What will the end user and company gain from this product and initiative in the near term?
Here are some example bullets:
Consolidated Customer Experience Journey
More accurate attribution reporting with GA4
By page metrics/KPIs
Stop analysis paralysis, know where and what to look for in one place
Low risk, cost effective dashboard using tools we already have available
Define the Business Outcomes
One of the most critical components to a successful data strategy & audit - tying the Objectives & KPIs to real business outcomes.
How does the data product and KPIs increase revenue potential?
How does it increase time savings and reduce costs?
At the end of the day, a solid marketing data strategy understands how a company currently uses its marketing data, maps out how to better enable the business with it, and ultimately how that enablement drives revenue, time savings and profit increases.
Here are some of the outcomes we noted with our client’s marketing data strategy & audit:
Tie SEO and user behavior/engagement more accurately to understand which pages are converting to revenue
Time Savings & Reduced analysis paralysis with consolidated dashboard
Time savings = cost savings
Uncover opportunities to improve digital integrations
Setting a Timeline
The timeline shown during your presentation depends on the project’s scope. I know some of you are thinking “well, duh that’s obvious.”
What do I really mean by that?...
Not all marketing data strategy & audit roadmaps are built the same. Some capture the entirety of a customer’s data lifecycle journey. From immature digital and data architecture all the way to the future state of robust data infrastructure, enterprise reporting and AI capabilities.
This kind of roadmap is ideal for larger companies with a huge budget.
For small to medium sized companies and start-ups? Pitching the above may only overwhelm and have stakeholders scratching their head as to the need. Given that our client example is a SaaS start-up on a tight budget, the roadmap we built them focused on a near term scope.
Therefore, the timeline presented during our pitch only encompasses the near term deliverables.
It goes without saying, leverage your past experience to gauge timelines. It’s okay if your estimations aren’t perfect, but be transparent with your stakeholders about it.
Here it is (*dates shown are for example only):
Send contract & sign (2/21/2025)
We begin Looker Studio & Digital Integrations build aligned to Gantt Chart
2 week sprint (2/24/2025 - 3/10/2025)
Check in with our stakeholder after first week (2/28/2025)
Deliver MVP dashboard (3/7/2025)
Feedback and iteration refinement (further integration work & dashboard metrics)
We find that this approach works well because it keeps the scope limited, and reduces the risk of overblowing our clients’ budgets. A fundamental learning after years working under senior data engineers & architects stems from software engineering: always break large projects down into sizable chunks.
That’s exactly what we do here, especially when budgets are incredibly tight.
Smaller scope, less risk, less expectation, higher chance to gain traction early and over deliver.
Current State Data Architecture
Visuals work wonders here. A data architecture diagram of the current state serves to show the stakeholder the misalignment from best practices.
When presenting, explain some of the limitations of the current state. I like to write these bullets in the notes section of the slide.
Here are a few examples:
Can’t track history, dependent on source APIs
Limited curation for custom metrics
Lack of data governance / cataloguing / documentation
Combining data from all systems to unified data model impossible
Cannot use for predictive advanced analytics & AI long term
Future State Data Architecture
Now show the future state data architecture diagram.
Speak on some of the advantages/benefits of this architecture vs. the current state:
One unified marketing data model / data mart
Self-service analytics readily available
Data documentation/cataloguing that stays with the company after talent leaves
Easier talent onboarding
Unlimited analytics & AI use cases unlocked
Long term - AI in BI (speak/ask question and get an answer immediately)
Scalable data compute/processing
Keep speed top of mind while balancing costs
Conclusion
There you have it. A basic framework to execute a comprehensive marketing data strategy & audit roadmap when you kick-off a client project. We’ve received positive feedback on the structure of this valuable data strategy asset from the clients we work with.
Get your free marketing data strategy audit & roadmap template here.
We realize that not every marketing project will be the same process and scope. However, we tried to formulate this guide to resonate with all marketing data professionals through this common customer journey use case.
Furthermore, like we said in the timeline section, the scope can vary considerably depending on the size and budget of the company. You’ll need to pay close attention to those variables during the discovery process with your client. Always ask yourself - given their budget and time, what will deliver ROI so they can begin realizing the value of their data?
If budgets are tight, you need to narrow the scope to realize ROI fast. Those with deeper pockets may understand the long term investment of robust data infrastructure and future state architectures.
If you have anything to add, or questions on anything we covered here, we’d love to hear your feedback. If you’re interested in discussing your marketing data strategy with me, feel free to set up an initial consultation either here or steinertanalytics.com/managed-analytics-steinert
We wish you the best in driving marketing analytics successfully in your organization or for the clients you serve! Until next time, take care!
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