Regardless of your past investments, marketing reporting is often broken for large-scale digital marketing organizations. It may be time to explore a new approach.

Your marketing analytics team spent years and significant resources improving marketing reporting, then creating systems and training marketers on how to use them. With so much invested, it's painful even to consider: what if your approach isn't the right solution anymore?

If you've read any of our content, you're probably familiar with our perspective on why this approach doesn't work for enterprise marketing reporting. We have pioneered a new approach and architecture centered around automating data ingestion, processing, and harmonization, while enabling effective self-service for marketers. Since its introduction, the marketing reporting as a product approach has taken the industry by storm and has fundamentally changed how leading organizations like HelloFresh, Uber, and Delivery Hero use data to steer marketing.

This article outlines some common shortfalls associated with the traditional approach. If you're experiencing these failures, there's a good chance your company could better accomplish its goals by leaving your traditional approach behind and exploring the marketing reporting as-a-product approach - no matter how much time and money you've invested into it.

Your marketing reporting doesn't deliver the results needed

The first category of challenges revolves around enabling actionable insights at the speed of business. Chances are, you've built many data models, dashboards, metrics, dimensions, etc. to ultimately deliver insights to marketers. While these dashboards are great for monitoring metrics, we often hear from our customers a few key areas where the traditional approach falls short - and where we can better serve them with the marketing reporting as a product approach.

1) Long turnaround times for marketing requests

What good is a great dashboard or insight if it's too late?

We've heard from many marketers that they often have to wait weeks or months for a new dashboard, metric, or dimension. And many requests are deprioritized or postponed.

If you have to wait for your data team to get insights, your marketing reporting isn't flexible and agile enough. The product-based approach, however, automates the data preparation process and enables true self-service. Therefore, it accelerates time to insight, making data more actionable.

Marketing Analytics workflows: Traditional vs. Product-driven Approaches

Key questions to ask:

• What's the average turnaround time for marketing data requests?

• How many requests are deprioritized or postponed due to limited analytics bandwidth?

2) Limited data exploration

What good is a nice dashboard or structured data model if it's not used?

Uncovering actionable insights requires data exploration. The same applies for diagnosing performance changes and understanding what's working. We've heard from many marketers how they struggle to explore data on static, inflexible dashboards that often lack granular data. Thus, answering follow-up questions is typically impossible. Marketers either jump into spreadsheets or have to resort to SQL adventures. There's no real self-service.

The product-based approach enables true self-service and data exploration - delivering on flexibility, granularity, and usability.

Key questions to ask:

• To what extent can marketers explore data and answer follow-up questions to uncover actionable insights?

• What's the granularity level of your marketing reporting (campaign, ad group, ad)?

3) Shadow reporting - Spreadsheets and Looker Studio

Ask around the marketing team: what are they using for daily/weekly analysis?

The promise of marketing reporting is to make data and insights accessible to marketers. However, inflexible BI dashboards that don't enable exploration, missing data granularity, and long waiting times for data support lead to marketers building workarounds - spreadsheets and shadow reporting.

We often hear paid social teams running reporting on tools like Smartly, paid search teams using Looker Studio, and marketers managing spreadsheets for budget pacing and channel deep dives. All these systems slow decision-making and iteration cycles, lack data governance, create silos, and consume valuable time.

We've seen consistently high adoption from marketers with the product-driven approach because of its flexibility, underlying data model, data granularity, and ease of use. It covers all use cases and needs.

Key questions to ask:

• Where and how are marketing steering analyses being made? Most common:

  • Budget pacing and performance diagnosis

  • Ad-hoc data exploration to answer "Why" questions

  • Weekly business reviews

  • Asset level analysis (i.e., keyword- or creative-level deep dives)

4) Decisions based on incomplete data

Driving marketing decisions requires looking at the right metrics. Backend data metrics - such as internal attribution, CLV, COGS, Contribution Margins - are critical for the quality of decision making. To what extent are marketers actually using these metrics for daily and weekly steering?

We often hear marketers lack backend data - such as COGS or granular attribution data - joined with channel data and available for daily/weekly decisions. This means marketers either don't use it (and take decisions based on channel data) or have to manually join data on spreadsheets.

Key questions to ask:

• To what extent are your backend metrics (Attribution, CLV, COGS, CM, etc) operationalized and available to use for daily/weekly decisions (including all relevant breakdowns and granularity)?

Operating costs are too high

The second major category of failures stems from another fundamental problem: serving insights usually gets very expensive (labor and tools). Marketing analytics teams often keep hiring more people (data engineers, analytics engineers, and analysts) to keep up with demands. In addition, enabling granular insights (ad or ad group level) often means skyrocketing data warehouse (storage + compute) and ETL tools costs.

This ultimately leads to marketing reporting costing millions of dollars per year (check yourself here).

Generally, the product approach is far more affordable – it's designed to provide asset-level reporting at a fraction of the cost while enabling self-service and quick iterations without "throwing" more resources at the problem.

5) Never-ending cycle of marketing requests

Marketing analytics teams should be focused on marketing measurement (MTA, MMM, Incrementality) and AI to maximize their business impact. However, we've heard from many analytics professionals that they spend most of their time on ad-hoc maintenance work - from "quick questions" to marketing tickets. This "on-call" work creates stress and takes time away from priorities.

The Marketing Reporting as a product approach delivers real self-service, saving significant time for the analytics team.

Data Teams: trapped in the cycle of reactive marketing reporting maintenance

Key questions to ask:

• How much time does your marketing analytics team spend on strategic proactive work vs. reactive, ad-hoc maintenance?

• How does this affect the core priorities and projects (MTA, MMM, AI, Incrementality, etc)?

6) Data warehouse and ETL costs increase

The volume of marketing data skyrockets with increasing granularity (down to ad level). This increases the total cost of ownership - both in tools and labor. More specialized data resources are needed to build and maintain such granular level of reporting. Data warehouse (storage and especially compute) plus ETL tools costs skyrocket.

Many analytics teams compromise by aggregating data, resulting in missed insights.

The product-driven approach has a proprietary design and architecture to deliver asset-level reporting at a fraction of the cost. It features automatic data ingestion, processing, and harmonization, including data exports to the data warehouse for a single source of truth. These data exports are typically at campaign level, providing the granularity required for marketing measurement and other applications.

Marketing reporting as a product is a much more affordable approach

Key questions to ask:

• What's your total tools cost for marketing reporting (Data warehouse + ETL)?

• What are you compromising on data granularity and what are its implications (missed insights)?

Exploring an alternative approach - marketing reporting as a product

Missed optimization opportunities - late insights, hidden insights, and optimization based on incomplete data - combined with high operating costs lead to your marketing reporting system costing millions of dollars per year.

This is how the reality has changed for large-scale digital marketing teams, such as Uber, HelloFresh, On, About You, Udemy, Delivery Hero, etc.

Data teams

Before vs After for data teams

Feedback from leading brands

Marketing teams

Before vs After for marketing teams

Migration made easy

I know that even considering a switch in marketing reporting tools sounds scary! You've spent years building and improving your current system, and you've come a long way. But take a moment to ask yourself - does your setup truly serve you, or are you constantly working around its limitations? Now imagine your day-to-day without endless tickets and quick questions. Marketing reporting that frees up your time and lets you focus on MMM, Incrementality, AI, and other initiatives to drive real business impact.

The good news? Making the switch is easier than you think. Spoiler: We've helped some of the largest, most complex teams transition smoothly in just weeks. Learn more about the process here.