The Marketing Data Stack - Part 2: A Framework to Assess Current State vs Aspiration for your Modern Marketing Capabilities

In part 1 of this series on the Marketing Data Stack, we introduced the Marketing Data Stack, what it does, and how it can be a critical missing piece in your modern marketing approach to more purposefully connect your data, customer channels, and platforms to your business and marketing objectives in order to push through the the barriers we all typically face as our businesses scale.

In this second part of this series, we’ll introduce the Marketing Data Stack framework that we use to evaluate your brand’s current state versus your aspirational state and how to identify the missing capabilities needed to push through the barriers that may be holding you back from achieving your desired outcomes.

A Quick Recap of the Marketing Data Stack

The Marketing Data Stack is the strategy, technology, and processes that turn raw data into valuable outcomes for your business. It is how data is captured, activated, and applied throughout your marketing platforms and customer/marketing engagement channels. It supports your martech stack to ensure that all people and platforms are aligned toward your business objectives.

A full Marketing Data Stack is made of five elements and your business will need some degree of these at various stages of your growth:

1. Strategy

2. Data collection

3. Integration & automation

4. Analysis & visualization

5. Activation & optimization 

By focusing on your Marketing Data Stack, you will have greater success in activating your customer and marketing data across your marketing platforms to ensure they are working in concert to effectively achieve your marketing and business objectives.

A Framework to Master Your Marketing Data Stack

The first step in mastering the Marketing Data Stack is to understand where you are today and create a vision for where you want to go. We do this through a framework to review current capabilities, identify gaps, and plan for future growth in a manageable and bite-sized approach.

At the highest level, we’re focusing on 3 key elements of your marketing & customer ecosystem:

1. Customer engagement channels - First and foremost, this is where we believe everything must start and end. Website, mobile, apps, advertising, sales, call center, email, retail/pos, online commerce, out of home, etc. This is a critical connection between your brand and your customers that we ultimately seek to impact.

2. Data Flow & Transformation - How your critical data flows through these data activation capabilities, integrates to become a unified view of your customer audiences, measures channel performance, reveals new insights about your customers, and presents opportunities to form deeper engagement with them.

3. Activation - How the unified view of your customer interactions and insights are applied back into your engagement channels to provide the brand experience your customers deserve from your brand.

To do this, we use a framework to map out and assess your Marketing Data Stack in terms of supporting your marketing & customer ecosystem.

This framework reviews the five components:

1. Strategy & Governance - Clearly defined goals to ensure everyone knows when an initiative is successful.

2. Data Collection - A process for collecting accurate, reliable, and usable data.

3. Integration & Automation - Applications for integrating and modeling data across sources.

4. Analysis & Visualization - A sandbox for analyzing data, setting goals, validating performance, and testing hypotheses.

5. Activation & Optimization - The ability to deploy targeted, personalized, and optimized experiences to customers across channels based on the attributes we know about them.

Assessing Your Marketing Data Stack

First, there is no right or wrong level of maturity or aspiration in our view. The maturity of your Marketing Data Stack is simply establishing a perspective of your current vs. aspirational capabilities across the set of items we defined above to understand where you are relative to your customer activation goals.  

Today vs. Future

Assessing the maturity of a marketing data stack involves understanding the current state and envisioning the desired future state based on your business objectives. This includes evaluating existing capabilities, identifying areas for improvement, and setting goals for future enhancements.

Considerations

Industry: Different industries have varying requirements and priorities when it comes to customer data and marketing tactics. For example, transactional, e-commerce businesses may prioritize real-time journey analytics, awareness, and offer decisioning, while B2B companies may need to focus on longer-cycle account-based marketing capabilities. Healthcare businesses must prioritize HIPAA compliance while financial services businesses are often focused on prospecting and third-party audience augmentation.

Business Size: The appropriate maturity of a marketing data stack can also depend on the size of the business. Small businesses may have simpler needs and fewer resources but may want to have a robust foundation in place for anticipated growth, whereas large enterprises require more complex solutions and infrastructure that may not yet fully take advantage of the advanced capabilities they already have in place.

Internal Capacity to Support/Govern: A key factor in achieving and maintaining a mature marketing data stack is the internal capacity to support and govern it. This includes having the right skills, processes, and governance in place to manage data effectively and ensure its quality, security, and effective activation. The maturity framework helps with planning to stay ahead of your internal capabilities and skills needed to maximize the business impact of your marketing data stack.

Scoring Maturity vs Aspiration

Let’s look at a high-level assessment that provides a broad view of where you are vs where you need to be and then we’ll delve into an in-depth assessment approach.

The High-level Evaluation

A high-level assessment looks at your current capabilities and compares them to your midterm customer channel aspirations along the five main categories of the Marketing Data Stack framework:

1. Strategy & Governance: Do we have clearly defined goals that ensure everyone knows how we are measuring success and what’s needed to do this? Are we ahead of privacy compliance and respecting our customer’s information, and do we have the appropriate level of governance in place to manage this ecosystem?

Dimension/Scoring	None	Basic	Advanced	Leading
Measurement Strategy	We don't have one.	We have some reporting and some KPIs defined but do them inconsistently.	We have channel reporting and KPIs defined for most channels and use them to make decisions and optimize performance.	We have advanced reporting and KPIs defined, use them across all channels, and align them to business goals.
Privacy Compliance	We don't have any.	We have basic consent on our website and opt out forms as needed for our geography/industry.	We have a data governance model in place across our channels and data systems that control access to sensitive data.	We have tightly defined data access policies and processes with automated tools that control access to sensitive data.

2. Data Collection: Are we collecting accurate and trustworthy data across customer, sales, and marketing channels?

Dimension/Scoring	None	Basic	Advanced	Leading
Channel Engagement	We don't capture any data on customers.	We have basic tools in place for web analytics, sales, advertising but not customized beyond out of the box features.	We have robust reporting across web, call-center, and commerce that we can use to measure performance and some audience level information.	We have advanced data collection across our customer channels capturing interactions, attributes, and filtering of fraudulent/non-human interactions.
Advertising	We don't capture ad data or do significant advertising.	We have access to the default ad performance reports from channels and review them individually.	We import ad performance data into channel management tools to assess performance across campaigns and audiences.	We aggregate ad performance data centrally and are able to run media mix model or attribution models to optimize our media plans.
Sales & BD	We don't use centralized tools to track sales & BD activities.	We use a CRM to track sales & BD pipeline and activities but only do basic reporting.	We capture interactions with sales & BD channels in our CRM tool and may cross reference with customer and prospect segments.	We augment CRM records from additional sources to provide sales teams with information to support the sale, perform propensity modeling, and are able to use this information outside of the sales process.

3. Integration: Do we have a single view of the customer and their journey across channels, are we missing opportunities to further expand our view into them, and is all of this usable to drive better insights and activations?

Dimension/Scoring	None	Basic	Advanced	Leading
Cross-Channel Analytics	We capture channel analytics in one or none channels.	We capture channel analytics in more than one channel but do not cross connect them.	We have the ability to aggregate and compare channel analytics across channels and do some cross-channel analysis (e.g. attribution of campaign source to conversion)	We are able to identify customers across channels and see a holistic view of their journey.
Customer Profile 360	We don't capture customer profiles.	We just capture customer profiles in commerce, CRM, or loyalty platforms but they are not connected.	We have a customer or audience platform that allows us to do some segmentation or personalization within channels.	We have all of our key customer engagement channels hydrating a central customer profile that builds an identify graph for known and pseudonymous customers that is available to use to personalize back into other channels.
Data Enrichment	We do not currently augment customer data with internal or external sources.	We are able to augment customer data captured in channels with internal sources of data from other systems.	We leverage DSP or partnerships to augment our own customer data with other attributes we can use to expand our profile view beyond what we capture ourselves.	We use data clean rooms to exchange 2nd and 3rd party data without exchanging personally identifiable information.

4. Analysis & Visualization: Are we able to measure our performance against the goals defined in our strategy? Can we think creatively about the problems that stand in the way of our success, and conduct analysis and run experiments to drive the marketing outcoming in which we seek? How well do we know our customers, how they interact with our brand, and what types of engagement they need from our brand?

Dimension/Scoring	None	Basic	Advanced	Leading
Customer Data Analysis	We don't actively perform analysis or exploration from our customer data.	We use the default reporting tools within our channel tools to do some reporting and exploration. We may have basic executive dashboards.	We aggregate customer analytics data into a centralized reporting and exploration tool to do comprehensive analysis into audience identification, behavior, and performance.	We are able to explore customers across channels and see a holistic view of their journey, identify new audiences, and identify new engagement opportunities through a cross-channel view.
Experimentation & Optimization	We don't currently have an experimentation program in place.	We do some light testing of campaign versions or variations in the web channel in pursuit of increased conversion.	We run a medium-to-high volume of tests across channels and have a well defined experimentation process that governs this capability.	We actively groom a hypothesis library and run advanced testing across our channels leveraging cross-channel data and customer data.
Customer Modeling	We don't do any modeling.	We do some predictive scoring and forecasting within specific channels.	We have a data science capability that models customer behavior and propensities to help us identify new marketing, audience, or product opportunities.	We have established models that we actively manage (typically by a data science team) to proactively optimize our media channels, make offer recommendations to micro audiences, and facilitate personalization.

5. Activation & Optimization: How well are we activating this valuable information back into our customer engagement channels to provide the best possible brand experience, growth, and loyalty as a result?

Dimension/Scoring	None	Basic	Advanced	Leading
Journey Personalization	We don't personalize within our channels.	We do some limited personalization or offer-decisioning based on defined audiences	We are able to personalize channels based on centralized profile views of known and unknown customers using data from agent channels.	We are able to perform real-time, 1:1 personalization within channels based on event triggers in channels and specific customer attributes.
Cross-Channel Marketing Automation	We don't currently automate any marketing channel engagement.	We have basic marketing automation with one or more channel (e.g. email delivery for a website purchase or account registration or promotional emails to campaign audiences).	We have activity-based channel automation based on events, customer profile attributes across two-more more customer engagement channels.	We have well integrated, cross-channel marketing automation that incorporates our highest value customer engagement channels that is able to update audience or profile information from one channel into another.
Media Channel Optimization	We only have limited media spend or optimize media allocated based on available budget (we potentially underspend due to budget constraints).	We regularly monitor channel performance and directionally adjust spend allocations.	We conduct attribution analysis across some channels and/or leverage programmatic allocation approaches to optimize.	We utilize goal based cross-channel models (e.g. Media Mix Modeling) through custom modeling or SaaS solutions to holistically optimize all-in and per channel spend allocations across our entire media investment and channels.

The In-Depth Evaluation

The in-depth assessment framework dives deeper into specific capabilities across the five categories (governance, data collection, integration and automation, modeling and analysis, and channel activation) and related subcategories to determine the specific and relevant functional capabilities needed to move you to your aspirational maturity level based on your based on your goals, the industry your brand sits within, and the expected growth of your business.

This in-depth evaluation also prioritizes these capabilities based on business return, availability of existing platforms, business and technology dependencies, and customer goals. Ultimately, it becomes a living capabilities inventory to help drive initiative priority to establish, activate, and optimize those capabilities.

How do we Benchmark?

One common question asked of us is to compare a client’s marketing data stack maturity to competitive or industry benchmarks. We don’t. First, these benchmarks don’t exist and second, if they did, they’re not meaningful to focus on. The only benchmark we’re interested in is whether you’ve improved channel engagement relative to your goals and whether it’s had the positive return on your investment that you anticipated.

Connecting the Marketing Data Stack Approach to Platforms

Once we have a prioritized view of how we want our data marketing stack to function based on the framework, now we can start assessing capability gaps vs platform solutions and how well we’re activating data across them. You may find existing solutions have longer-term reach than expected. Perhaps a more compostable platform stack makes sense based on your mid-to-long-term aspirations. In other cases, the marketing data stack can help build greater confidence in that enterprise solution purchase that you’re currently evaluating. Again, form follows function.

Wrap Up

The Marketing Data Stack is a critical underpinning of any modern marketing ecosystem today. There is no one-size-fits-all solution or level of maturity but what is critical is that you have a solid view where you are today relative to your marketing strategy and what data capabilities your strategy needs to have in place for your customer and marketing programs to scale with your business.

By understanding the current maturity level and identifying areas for enhancement, businesses can create a robust and scalable marketing data stack vision that drives engagement, supports data-driven decision making, ensures compliance, and better prepares the organization for growth. Marketers, data practitioners, and martech platform owners must work together in order to achieve these goals and deliver the best possible customer experience across all digital engagement channels. Your Marketing Data Stack is the bridge.

If you have questions about how well your Marketing Data Stack supports your marketing ecosystem and business objectives, we’d love to chat with you.