At its core, data may be strings of ones and zeroes, but not all data are the same. Customer data is different because it’s personal. Behind customer data is an actual customer, and every customer has a story that’s worth knowing. Worthwhile, that is, for the enterprise that stands to gain revenue from deciphering what the data reveals about a customer. Understanding a customer’s desires, intent, behaviors, preferences and relationships both at a household and business level is important for driving a relevant customer experience (CX), as well as smart AI.
Because customer data is so different, with underlying complexities and unique traits, maximizing the value of your customer data depends on understanding the complexities, and how to solve for its unique challenges. Here are the five hidden challenges you can’t ignore:
- Identities Are Complex and Ever-Changing
Unlike product SKUs or vendor IDs, customers are people. They move, change phone numbers, update emails, and even change names. They use multiple identifiers across channels such as social handles, loyalty IDs, different nicknames, and more. They generate a constant stream of dynamic, contextual behavioral signals like clicks, purchases, and engagement patterns. They identify as individuals, as household members or perhaps as employees of a business. This variability makes identity resolution a constant challenge. A single customer might appear as five different records, and merging them incorrectly can lead to embarrassing and costly mistakes.
Stakes are even higher because of the real time, dynamic nature of a customer journey. A relevant customer experience (CX) at the moment of interaction requires real-time decisioning, which demands that identities are resolved continuously and in real time as new data enters the system.
- Data are Scattered Across Silos
Customer data rarely lives in one neat system. It’s fragmented across CRMs, marketing platforms, websites, billing systems, and support tools. Each system captures different attributes, often with inconsistent formats.
The result? Duplicates, gaps, conflicting records, data quality issues, and ineffective use. One recent survey shows that while 87 percent of companies actively gather data, just 25 percent say they use it effectively. Data siloes are a huge culprit responsible for the massive gap, and the organizations on the winning side of the gap are those that have largely eliminated data siloes.
- Real People = Higher Stakes
A wrong merge or misclassification isn’t just an operational hiccup. Because there are real people involved, mistakes can damage trust, ruin personalization, and even trigger compliance violations. Imagine sending a high-value customer a discount meant for new buyers, or failing to honor an opt-out request. Unlike inventory data, mistakes in customer data have direct human consequences that impact your brand reputation.
- AI Authenticity Counts
Your customers enjoy the convenience of AI but authenticity matters. In a Dynata survey, 73 percent of respondents said that AI can positively impact customer experience (CX), but by the same token 76 percent say that they are less likely to trust and continue engaging with a brand if they sense disjointed communications across channels.
Your customer data has to be “AI ready” which is different than having your data ready for analytics. AI-ready data is contextually complete, continuously updated, and unified across touchpoints.
- Privacy and Consent Are Non-Negotiable
Customer data is subject to strict regulations like GDPR and CCPA. Brands have to track consent, honor opt-outs, and manage “right-to-be-forgotten” requests. Other entity data rarely carries this legal weight. Treating customer data casually can lead to fines, lawsuits, and reputational harm. Compliance isn’t optional, it’s foundational, and this is particularly true for companies in regulated industries with a greater risk exposure.
Solve for the Hidden Challenges with Enterprise Data Readiness
Solving for these challenges is where data readiness comes into play. Data readiness treats customer data as an enterprise asset. As such, it focuses on making sure that data is right and fit for purpose for every conceivable use case. Data readiness focuses not on generic data quality, but data quality suitable for how the business intends to use customer data. It is about trust, timeliness, and context.
If you recognize any of the hidden challenges outlined above, the chances are that your customer data is holding you back from next-level CX and effective AI.
By re-examining your customer data foundations and making data readiness an enterprise discipline, you will be one step closer to joining those organizations who have already experienced the payoff that comes from making data readiness a priority: smarter AI, insights tailored to specific use cases, and more human customer experiences.
To learn more about how Redpoint can help you develop a solid data readiness foundation – using your own data – click here.
