Credera’s MarTech Reference Architecture part 7: Advanced analytics & marketing measurement

Matthew Roberts and Olin Moran

Credera’s MarTech Reference Architecture part 7: Advanced analytics & marketing measurement

Throughout this series, we have walked through each layer of our MarTech reference architecture and how to successfully drive digital marketing maturity through a thoughtful strategy and technology solution. In our final article of the series, we'll discuss how to use advanced analytics to effectively measure success and ensure ongoing optimizations to engage with customers in increasingly meaningful ways.

What is advanced marketing analytics?

Marketing expectations have undergone a dramatic transformation. Many organizations are shifting the way they view their marketing investment—seeing it less as an expense and more as a legitimate driver of revenue and growth. Chief marketing officers (CMOs) are tasked with proving out the value of these investments. Customer expectations have similarly shifted. With growing technologies and channels available for engagement, customers expect organizations to deliver personalized and relevant content that align to their individual preferences.

Without accurate and understandable analytics, marketers are effectively flying blind and unable to evaluate true marketing performance, much less satisfy customer expectations of personalized, relevant content.

Advanced analytics is the key that unlocks deep insights and optimization opportunities within your marketing organization. The chief goals of this analytics environment are to allow your organization to measure past success, pinpoint ongoing issues, and proactively identify opportunities to increase engagement moving forward.

The road to analytics maturity begins by first assessing your current data and technology and articulating a clear vision based on business priorities. Once you've gained an understanding of your organization's current state, you can craft a focused analytics strategy that will define and prioritize your efforts. With your analytics North Star in sight, you can then effectively develop, visualize, and integrate measurement models with the rest of your marketing processes to fully operationalize your insights.

Why marketing analytics matter

The marketing landscape is constantly shifting. Changing laws and regulations are impacting all industries, new technologies and tools seem to spring up every year, and businesses are branching out to countless channels to engage their customers where they digitally reside. With this added complexity, it can be challenging to measure your marketing performance, much less to navigate the road ahead. A clear measurement strategy and advanced analytics can cut through this complexity and deliver confidence through data in all your marketing decisions.

Below are a few of the benefits of investing in an advanced analytics environment:

Improve customer affinity by identifying marketing initiatives that are meeting (or not meeting) customer expectations.
Identify areas of investment that will deliver real business value by attributing customer engagements to downstream conversion events.
Secure marketing budgets by combining these real opportunities with measurable ROI to form strongly articulated business cases.

Top marketing analytics technologies & players

With numerous players in the analytics and measurement space, selecting the right technologies can be a daunting task. We’ve provided an overview of several critical technologies and key players below to help you lay a strong foundation and drive further analytics maturity.

Keys for driving advanced marketing analytics maturity

1. Craft a marketing return on investment (MROI) strategy and align your organization

As with many decisions involving technology adoption, it is critical to first define success as it pertains to your business. Marketing KPIs should be measured and aligned with the set of business priorities that occur at different levels of your organization. Credera’s proven methodology can help you find and follow this measurement North Star.

2. Enable omnichannel analysis through a central data warehouse

With so many channels through which customers can engage with your brand, a lack of data can leave you blind to potential issues or changes in customer behavior. Still, tracking alone may be less than useful if that data is not accessible within the context of the rest of your business. Breaking down these walled gardens and ingesting data into a central customer data warehouse establishes a single source of truth and enables more accurate and holistic analysis of your customers.

3. Employ data modeling and visualization to streamline analysis

Data arriving from many different channels can quickly become intractable if care is not taken to properly structure and organize this data. Data modeling efforts provide the order for this intractability, ensuring the integrity of your data and simplifying analysis. Still, analytics data, no matter how well modeled, must be made meaningful to business users through clear and purposeful presentation. Visualization tools allow data to tell its story clearly and save valuable time when performing analysis.

4. Automate critical measurement activities

Your business insights may be less than useful if delivered too late or not at all. Automation ensures critical reporting metrics and alerts are delivered to the right people at the right time. Actionable insights can similarly be delivered to the data activation layers of your MarTech architecture to continue driving business value and optimization with little to no human involvement.

Current marketing analytics trends & considerations

Growing impact of data privacy

Shifting data privacy laws have left many organizations scrambling to be compliant. Beyond legalities, a concern for data privacy is a critical factor in establishing consumer trust in an organization.

Different platforms are taking different approaches to data privacy, and with customer data arriving from many different sources these approaches can have correspondingly different implications for measurement and analysis.

No matter the approach—whether it be anonymization through hashing, data aggregation, or differential privacy—organizations must prepare for these restrictions to maintain high quality measurement and analysis while guaranteeing customer data privacy.

Concern for signal resiliency

A natural byproduct of an increase in data privacy policies is a corresponding concern for signal resiliency. Long-standing methods of data collection and identification are disappearing —most modern browsers have eliminated third-party cookies and Apple is all but eliminating the IDFA with iOS 14.

To combat this, organizations must turn to more resilient identifiers and methods of collection. Encrypted user identifiers, such as hashed email addresses, provide quality signals across many different platforms while maintaining user privacy. Server-side data integrations are similarly resilient to changing platform policies. Combined with a customer data platform these integrations can be guarded by the appropriate processes for user consent and data governance.

Take your next step

We hope this series has equipped and empowered you to drive forward strategic digital marketing conversations and craft a long-term plan to realize transformation. If you're interested in learning more about our approach to marketing measurement, or MarTech in general, please reach out to us.

Overview of articles in the series: