To prepare for today’s unpredictable markets and future-proof business, brands must focus on existing and new customers, drive efficient growth at lower costs, build brand loyalty and increase customer lifetime value.

The key to all of that? A richer and fuller understanding of customer data and lifecycles with the brand. This understanding allows companies to discover and act on customer insights and orchestrate personalized, impactful experiences at scale.

Justin DeBrabant, senior vice president of product at ActionIQ, said that with the departure of third-party cookies, brands need to ensure they have solid zero-party, first-party and second-party data strategies in place. “This will help them ensure their customer data is authentic, scalable, fresh and accessible to the business users in a privacy-conscious, secure way.”

And brands can make that happen with a CX “hub” serving as the axis around which all CX operations evolve. This hub unifies and resolves different identity frameworks into a single customer or account profile.

DeBrabant said many brands increasingly use models and predictive analytics to drive an optimal customer experience.

“However,” he added, “models are only as good as the data put into them; in fact, data is often considered more valuable than the specifics of how the model is built or configured.”

Related Article: The Future of Personalization and 1st Party Data

Putting Your Data House in Order

DeBrabant said businesses need to start by getting their first-party data in order, as this is the most important and differentiated data set they have. “They need to ensure it’s clean, accessible, accurate and scalable.”

“Next,” he said, “companies can add privacy-centric third-party data sources that don’t rely on anonymous cookies to enrich their first-party data.”

Finally, companies can look to clean room integrations to add to their data sets’ validated second-party data, which is data from their retailer, brand and/or publishing partners.

Mike Anderson, founder and CTO of Tealium, said for data enrichment tools, teams should assess three key areas: data organization, technology stack and talent.

“Understanding where your data lives and how it needs to be organized for optimal business outcomes is essential,” he said. “There are solutions that can help collect and connect that, as well as maximize the existing tools within a company’s tech stack.”

Having the right people in the right places in the organization is also essential to successfully implementing data enrichment tools, said Anderson.

“Organizations should think with the business value and outcomes in mind and then stack those across the customer journey over time,” he said. “This will ensure the creation of a successful roadmap with a purpose.”

Enrichment Leads to Personalization

Jodi Alperstein, vice president and general manager of Twilio Segment, said that for many companies, data enrichment means using data from third parties — companies that don't have a direct relationship with their consumers.

“This goes against the current shift to first-party data and attention to consumer privacy, including the elimination of cookies,” she said. “Legacy systems simply aren’t built to scale and support the modern customer’s journey or privacy expectations.”

She said that by massaging and enriching first-party data that streams into your system, data teams can gather intent signals — for example, which products a website visitor clicked on — and use them to personalize experiences in each channel.

“Over time,” said Alperstein, this behavioral data can be used to inform machine learning models that can further personalize experiences for audiences with more accurate recommendations, suggestions and journeys.”

Dan Adams, SVP of data strategy and operations at Precisely, recommended starting with defining what you’re trying to achieve, ensuring you should know where data resides, that you can access that data and design workflows from the analytics or actions you want to achieve.

Learning Opportunities

“Most importantly, select a data enrichment supplier that understands this is what is critical for your business operations,” he said. “The right provider can provide data and software designed to do those things over time. Unless you are working on a one-time project, applying updates to data and software are an important design step.”

Related Article: Is It Possible to Have Both Privacy and Personalization?

Data Enrichment in an Age of Complexity

DeBrabant pointed out that with the deprecation of third-party data, gathering customer data, extracting insights and acting on them becomes much more complex, as well as sensitive. Why? Because that data must be shared in a secure, privacy-conscious way.

This means brands must be able to identify and analyze customer signals — the ones that speak to preferences, brand affinity and churn risk.

“More importantly, they must be able to do so at scale and across all relevant channels,” DeBrabant said. “By gathering and centralizing this information for customer-facing teams — as opposed to keeping it locked up behind IT and analytics teams — brands will help the people responsible for CX truly know their customers and fine-tune their strategies accordingly.”

Alperstein said that even after building customer profiles with first-party data, many companies still rely on third parties to enrich their profiles.

“We've seen a trend of our most advanced customers rejecting this common practice and taking their approach to first-party data a step further,” she added.

These organizations enrich their customer profiles with object data from their own CX business systems, which often comes from cloud applications — CRMs, help desk software, ERPs, transaction processing systems, etc. However, it can also come from application databases and data warehouses.

Then, data teams use these more complete customer profiles to produce nuanced and accurate audiences.

For example, a company could use identity-resolved customer profiles to combine event data from a website with object data from a CRM in a data warehouse. It could then use that data to model a “likely to convert” audience, and create a unique experience for that audience on websites or mobile apps.

Anderson said automation and machine learning (ML) are key tools that will help organizations do more with less time, calling automation “fundamental” to future success and understanding customers more efficiently and effectively.

“In a market where data is an organization’s most valuable asset, even the best data goes to waste if you can’t effectively put it to use,” he said. “It’s no longer enough to just integrate data, you must figure out how to use it at scale, across an increasingly complicated customer journey.”