About You
Your Needs
Finish

Schedule a demo to learn more.

Please provide your first name
Please provide your last name?
Please provide your company's name?
What is the size of your company?
What is your country/permanent residence?
By signing up, I agree with Iterable’s privacy policy. I understand I can unsubscribe at any time.
What channels are you currently using? (Optional)
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Thank you !

Thanks for contacting us, we’ll be in touch shortly.

prioritizing customer data quality

The Value of Prioritizing Customer Data Quality

Your customer database is the foundation for the work that you do. Whether you’re building cross-channel customer journeys, A/B testing email messaging, or using AI-based insights to power decisions, you’re likely leveraging customer attributes, insights, and engagement history to power your workflows.

Although customer data has become more available, many growth teams still struggle with aspects of data quality management such as data consistency across systems, identity resolution, and data planning, often relying on engineering for support. In fact, a 2018 Harvard Business Review study found that only 3% of surveyed companies had acceptable data quality standards.

Growth teams with sound data quality management processes, on the other hand, are able to make data-driven decisions with more confidence and launch campaigns with greater efficiency. This post will walk through why customer data quality matters, the components of customer data quality you should be focusing on, and how a Customer Data Platform (CDP) can help you simplify data quality management.

Why Data Quality Matters

In today’s customer-driven era, personalized customer experiences have become the customers’ expectation. With the releases of Apple’s App Tracking Transparency and legislations such as GDPR/CCPA, customers have also become more aware of the value they are gaining from exchanging their data for personalized experiences. For marketers, this introduces a challenge — a mistargeted message can lead customers not only to disengage but also to lose trust in your brand.

As customer data is the foundational asset with which you make targeting decisions, a lack of data quality at the start of the data pipeline can result in inaccurate messaging throughout your campaigns. Additionally, if you’re leveraging ML predictive intelligence to drive your targeting, the quality of recommendations generated will only be as good as the customer database that your models are trained on.

Even when data quality issues are caught, engineering teams often need to be called upon to remedy errors with one-off transformations, consistency checks across tools, and implementation updates, pulling them away from core development.

With a system in place for data quality management, however, you’re able to deliver targeted campaigns faster and with more confidence. For any paid campaigns, the increased targeting accuracy will make your campaigns more cost-efficient.

What does a high-quality customer database look like?

When managing customer data quality, here are a few characteristics to focus on:

Consistency, Accuracy, and Compliance

As customer events, attributes, and insights are collected from different digital touchpoints and cloud feeds, data points are often formatted differently. For example, a tool may be implemented to collect a user’s first name from your iOS app as `firstName`, but implemented to collect the first name from Android as `First_name`. Such inconsistencies can make it difficult to build audience segments and activate campaigns within that tool.

To maintain customer data quality, it’s important to ensure that customer data points are implemented consistently across channels.

Accuracy and completeness of data is also critical. If the data points attached to each customer profile are not correct or up-to-date, there will be a detrimental impact on downstream campaigns, analysis, and modeling.

Furthermore, you need to make sure the data you’ve collected is done so in compliance with all regulations. mParticle and Iterable have tools to help maintain compliance like Consent Management and SMS Opt-Out, respectively.

Accessibility

Teams across the organization from marketing, product, analytics, engineering, to support, all need to leverage customer data to make decisions. Even the most robust customer database is not worth much if key stakeholders cannot access it when they need to. When access to data is democratized, multiple teams are able to work off the same, high-quality data set, and make strategic decisions.

It’s also important that growth teams are enabled to make updates to their data pipeline, such as changing how audiences are built or which events are available in each tool, without having to request engineering support. This data independence allows you to get campaigns to market much faster and allows engineering to stay focused on core development.

Single View of the Customer

Growth teams need insights on customer interests and engagement history to inform strategic decisions. Without cross-channel data unified to single customer profiles, you’re left stitching data across systems and marrying known and anonymous profiles manually—a laboursome and inefficient process.

Often, teams struggle to build high-quality customer profiles for two reasons: data is inaccessible or exists in silos across various systems, and/or events and key actions such as cart abandonment can’t be reconciled to individual user profiles.

To activate your customer data for marketing initiatives and support compliance with GDPR and CCPA, it’s important to have a system in place that allows you to unify cross-channel data to single customer profiles, control how known and anonymous profiles are merged, and enrich those profiles with engagement events.

Using mParticle’s IDSync in conjunction with Iterable’s nested data structure and segmentation capabilities keeps your customer profiles organized for greater impact in your personalization strategies.

Data Schema Validation

As your customer data set grows in complexity (more events, more channels), it can be extremely difficult to catch mis-logged events as they’re collected in real time. Although your team may have established a data plan that catalogs the data points you are expecting to collect, data can easily be implemented inconsistently across channels or ‘fat-fingered’ when being logged.

Simplify data quality management by having a monitoring system, like mParticle’s Data Master, in place that enables you to flag data plan violations as your data is collected. As errors arise, you’ll be able to inspect the issue and work with your engineers to get it resolved.

Connecting With Iterable

For any customer-first organization, the point of collecting data is using it to deliver better customer experiences. While the systems used to ingest and validate customer data are critical, equally important are the tools used to activate that data and deliver engaging experiences.

mParticle’s Event and Audience integrations with Iterable enable you to send high-quality customer data to Iterable via a user-friendly connection, where it can then be used to power personalized experiences across channels. Additionally, mParticle’s Iterable Feed integration allows you to forward engagement events across email, in-app message, sms, and push channels from Iterable back into mParticle, where they’re tied to centralized mParticle customer profiles to keep your data consistent, organized, and actionable.

The limiting factor of any data-driven campaign will be the quality of data used to power that campaign. With mParticle and Iterable, you’re able to automate data quality protection, control how data is forwarded to Iterable without developer support, and use your data to deliver better experiences to your customers.

For more, dive deeper into the Iterable and mParticle integration.

Search Posts