Choosing the right solution for your business
10 essential considerations for assessing if a CDP is right for you
These are ten essential considerations of assessing if a CDP is the right fit for your business.
Integrations: The process of establishing an account and the data necessary to execute marketing campaigns.
After a one-year contract comes up for renewal, you might have found that the world’s best point solution doesn’t fit your use cases. Flexible iteration of your stack is essential to remaining not only competitive but relevant. Easy, seamless integration is critical to keeping pace.
Migrations: Changing the data in an account, whether by adding, removing, or updating data.
Change is constant for any company. If you’re undertaking a digital transformation, change is even more central. When the business evolves, so too must the data, so choosing technology that enables seamless, continuous migration is crucial.
Operability: A property of the overall system, how often it is available functioning correctly versus malfunctioning or offline.
Your marketing operations depend on your martech solutions’ uptime. Suppose you pick a vendor with poor operations. You’ll quickly witness the ripple effect of broken data to service downtime to your marketing team’s inefficiencies to missed opportunities in your marketing program. Exceptional organizations know when traffic spikes are coming — like Black Friday or Cyber Monday — and preemptively scale their systems accordingly.
This also includes quality assurance measures to ensure that data is accurate and complete and indicating to someone when it is not.
Quality assurance for marketing data comes down to two things:
- Using the most authoritative and accurate data available to power campaigns
- Robust safeguards for detection and remediation for when data isn’t accurate to prevent negative effects downstream
Just as data migration is a continuous process, so must be the quality assurance of that data. Martech that “gets” data has multi-layered data interaction built-in so marketers can understand what’s going on with their data, build intuition around patterns and cadence of change, and dig in to fix issues when they inevitably arise.
Modeling & Metadata: Describes the internal structure of data after ingestion for storage and downstream use.
Choose your data models carefully — they must serve a purpose toward a desired downstream outcome. The model needs to help with ingesting data, segmentation, targeting, content, reporting, etc. The data items must be consistently available to you across the stack.
There will be times when you must help the solution understand the semantics behind your data. Providing this as metadata annotating your underlying data model allows general-purpose marketing tools to be brought to bear on your bespoke data model.
Customer targeting & segmentation ownership: Manipulating and combining data on the way in to produce enriched, derived, or aggregated data for downstream use then building audiences for targeting marketing campaigns.
Great customer experiences require detailed personalization, which comes from fine-grained data. Take, for example, an ecommerce campaign that segments first purchases by category. If the orders table doesn’t include this information, it must be appended from the product catalog.
The exploration and understanding of target audiences cannot be a function of your data engineering teams. It has to go into the marketers’ hands. After all, marketers will ultimately define and execute campaigns over the segments. The key is to enable data engineers to quickly and easily get data into the system while empowering marketers to build intuition and experiment with slicing and dicing audiences.
If marketers wait 2 weeks for the data team to come back with a new segment they’re interested in, only to find it contains 5 users, you are not effectively unlocking the potential of your data.
Content & personalization: The use of personalized attributes of contacts, and potentially joining in additional data, like that from a product catalog, to produce content and copy for marketing messaging.
The days of spray and pray are over. Every time one company improves its personalization, the bar raises for every company. Many things fall under the umbrella of “personalization.” The first thing that pops into your mind is probably product recommendations. Personalization can also include triggered content flows, automatically adjusting email cadence to suit average open rates, and geo-located promotional offers.
Experimentation: Running controlled tests that can measure the lift for marketing variants across dimensions, including timing, channels, content, etc.
Marketing is about influencing behaviors. Experimentation is the cleanest way of measuring attribution and, ultimately, causality. Experimentation solutions must work across all communication channels if you are going to test the full customer experience holistically.
Insights & reporting: Data analysis in the form of standing reports that indicate marketing campaign operations and performance and exploratory tools for understanding factors driving those outcomes.
You cannot manage what you cannot measure. The truism has never been more valid than in business technology, with so much money on the line. There’s little to no point in testing if results cannot be easily interpreted, ingested, and incorporated into the never-ending quest for optimization.
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Table of Contents
- Chapter 1 Note to buyers
- Chapter 2 What is a customer data platform?
- Chapter 3 Why is it necessary to understand customer data?
- Chapter 4 What should a CDP do?
- Chapter 5 How do CDPs differ from other marketing tools?
- Chapter 6 What outcomes should a CDP enable?
- Chapter 7 Are there different types of CDP?
- Chapter 8 Choosing the right solution for your business
- Chapter 9 How does a CDP in my future look?
- Chapter 10 How to talk about a CDP within your organization
- Chapter 11 Additional resources