How Data Standardization Leads to Better Marketing Insights & Decisions
Non-standardized, inconsistent, and unreliable marketing data prevent organizations from thriving in an increasingly digital world where customer experience is paramount.
Yet many organizations haven’t established data standards or a set of data components that uniformly describe data in a manner that meets all data consumers’ expectations.
And their customers’ experience isn’t the only thing that suffers — they also contend with poor ROI on infrastructure and SaaS investments, productivity issues, the consequences of bad decisions, and lackluster marketing campaign performance.
We will explain why data standardization is important, how to do it, and how it supports better marketing insights and decisions. We’ll also throw in some data standardization examples along the way.
But first, let’s start with a fundamental question: why standardize data?
Why Standardize Data?
The effectiveness of your marketing efforts hinges on the usefulness of your underlying data. And unless it is in a consistent format that addresses the needs of all users, you can’t effectively analyze customer behavior, predict trends, nurture, target, and drive conversions.
Standardizing data is becoming increasingly urgent as the drop-dead date for third-party cookies approaches. It’s the only way to make sense of the scores of first-party data organizations will be collecting. Standards also make campaign tracking possible, enabling you to determine the optimal mix of cookieless marketing tactics (i.e., cohort marketing, marketing mix modeling (MMM), contextual marketing, and more).
Unless you take steps to implement data standards before Google Chrome finally ends support of third-party cookies, you risk measurement blackout.
How to Standardize Data in 6 Steps
Of course, you’ll want to avoid that. So here’s how to standardize data.
1. Audit data sources
First, you need to audit all data sources (the location from which data is created or digitized) that you rely on for marketing analysis.
Common types of data sources include:
- Traditional databases like Oracle, MySQL, and MS Access
- Transactional databases such as HRM, ERP, and CRM
- Flat files like a comma-separated values (CSV) file
- Campaign execution systems like Facebook, Google, Amazon and more
- Scraped web data, such as product information that’s exported into an API or spreadsheet
- Static and streaming data services, such as social media feeds, real-time inventory and management systems
- Physical devices like smartphones’ and smartwatches’ location and measurement data
Note: these are not exhaustive, just examples. Other places may have valuable data you need to consider.
During this process, you’ll need to meet with all data stakeholders — not just sales and marketing — to discuss the strategic importance of standardized data and how it contributes to overall marketing effectiveness. You’ll also need to learn how each team uses data to accomplish their business objectives and preferred data formats.
2. Collect and review existing taxonomies and data dictionaries
Different teams and systems may have their own taxonomies or a way to describe data assets already in place. Teams may also have individual data dictionaries describing their terminology, data elements, and formats. Review these and any existing metadata management practices to see what you can use going forward.
The end goal is to create a unified, enterprise-level marketing taxonomy to ensure data consistency across your organization so marketers can better optimize campaigns and customer experiences.
3. Define your data standards
Once you’ve taken stock of all your data sources and collected data users’ requirements, you can develop common data standards. These will define elements like referenceable fields, powered lists, patterns, and taxonomy and data models describing how these elements should work together.
Creating this centrally managed data language will allow you to codify consistency, serve the needs of every team, and ensure interoperability between systems.
Unfortunately, big data can throw a wrench in things if you don’t plan accordingly. So while you may not be managing massive amounts of marketing data today, your data standards must accommodate future needs. Choosing standards that strike the right balance between precision and comprehensiveness will help you manage data quickly and at scale.
4. Apply your data standards across teams
Once there’s organizational buy-in on your proposed data standards, it’s time to implement them across your teams and workflows. Embedding your common data language into the way people work and collaborate can mark the end of silos and reduce inconsistencies, errors, and poorly informed decisions. But you must train your teams effectively to reap the benefits, a process that involves:
- Delegating ownership and access to teams’ data standards to ensure data integrity
- Providing a single, accessible portal to the data standards
- Establishing a standardized data management framework or protocols for searching, filtering, validating, editing, reviewing, and creating data
Once you ensure all teams work from the same data blueprint, it helps create a connected, comprehensive view of your customer across your key digital channels and assets.
5. Connect your data standards to the MarTech+ stack
Unfortunately, applying your data standards to all of your tools and systems isn’t as easy as flipping a switch — after all, they’ve been used to doing their own thing. Enabling interoperability requires that you tap into them at the point of data creation. While you can do this manually, it’s far easier and more effective to use a data standards platform. They offer pre-built integrations that automate and integrate your new standards with each technology’s requirements, allowing you to:
- Connect in and out of your applications and digital marketing channels
- Understand and make adjustments to any required system formats
- Transition and coordinate between platforms with ease
- Respond to technology changes proactively
Connecting your standards will maximize your investment in technology because your systems will be able to respond in real time as more reliable, consistent data flows in. It will help you meet essential MarkOps KPIs too.
6. Address 3rd party data sources
You probably rely on other data sources outside of what you collect from 1P cookies and 2P data partnerships. When you apply organizational data standards, you’ll be able to extract maximum value from your third-party data sources, such as:
- Data from third-party cookies or data related to consumers’ interests and behavior and personal information like gender, age, and location
- Datasets purchased from marketplaces or data exchanges
- Data pulled from clean rooms or platforms that aggregate and anonymize first-party data
Once you’ve established data standards, all data becomes a potentially valuable asset regardless of the source. It can provide a more comprehensive view of the consumer, accelerate data-driven decisions, and increase your ability to adapt to an increasingly competitive market.
Data Standardization Examples
Implementing data standards can have a profound impact on your organization. Once teams can standardize, connect, and control data collaboratively across the enterprise, it can unlock better-informed decisions, stickier consumer experiences, and increased ROI.
As you pursue data standards for your organization, here’s a little inspiration:
A Fortune 500 hospitality company that lacked visibility into marketing effectiveness implemented data standards to support better decision-making with their marketing spend. Once they established a common data language and automated tracking strategy, they achieved true, cross-channel insights for their 5K+ annual campaigns and reduced the amount of time spent on data cleaning by 60%.
Data standards provided the basis for analytics tools and attribution models to optimize marketing spend across the global enterprise, resulting in nearly a 2% YOY increase in revenue per available room (RevPAR) within the first year. Read more of the story here.
A Fortune 50 technology company lacked insight into the performance of their $2 billion investment in marketing, costing millions of dollars in wasted ad spend and lost productivity. With a complex technology stack numbering 20-plus systems and no taxonomy or metadata management for asset tagging, information was fragmented across technology, teams, and marketing activities. As a result, they couldn’t effectively create and manage content or assess, measure, or adjust campaigns in real time.
By applying metadata to their content and paid media campaigns and automating cross-platform data standard enforcement, they could reclaim over $10 million per quarter in lost productivity and wasted ad spend. Read more of the story here.
A Fortune 100 retailer specializing in sportswear was about to embark on a massive World Cup campaign involving thousands of ads on multiple digital channels across the globe. However, there were no data standards or governance to validate that classifications were implemented and flowing correctly to their analytics tools before execution. This meant leaders had limited visibility into tracking and campaign effectiveness.
By creating a central taxonomy and enabling metadata enrichment, they dramatically increased paid media tracking by 65% and proved the value of their media efforts for this important campaign. Read more of the story here.
The End Result: Better marketing insights
The sheer volume of data that’s being generated by consumer behavior can easily overwhelm an organization. But if you adopt data standards, you’ll be able to manage whatever big data throws at you while extracting meaningful marketing insights to fuel better decision making.
Ready to learn how to future-proof your marketing and ensure your marketing stack lives up to its potential? Contact Claravine to talk through your specific challenges. And for more information about data standardization, check out our deep dive on data standards.