Every year, the arrival of spring brings open windows and blossoming trees. For many, it’s also time for spring cleaning. Whether you love or hate the spring clean, it’s an important ritual in ushering in the warmer seasons.
This year, we recommend extending your spring cleaning ritual to your company’s customer data for consumer personalization programs. Clean, accurate, and organized customer data is the fuel that powers effective personalization efforts. Organizations that don’t regularly shore up their customer data risk annoying customers with irrelevant and invaluable interactions. But that’s no small feat; according to Forrester’s Q1 2026 CMO Pulse Survey, B2C marketing and advertising decision-makers experience multiple data challenges when executing consumer personalization programs — particularly in the areas of macro privacy regulations, increased consumer privacy behaviors, and difficulties accessing data within the organization.
Whether organizations are struggling to break down data siloes, manage poor data quality, or identify missing data attributes, a unified approach to mapping consumer and customer data will elevate organizations’ personalization efforts. To help with customer data spring cleaning, we’re excited to share two Forrester reports that help organize that data and assess its readiness for consumer personalization programs:
A Data Primer For Consumer Personalization
Use our newly updated report, A Data Primer For Consumer Personalization, to categorize your consumer and customer data into six dimensions and run through a checklist of important data questions:
Data category. Data category buckets data into high-level groups based on business purpose or system of origin and articulates which business domains the data typically supports (but not what the data looks like).
Data type. Data type defines a specific kind of data with a detailed description of the data’s unique attributes and what information it captures.
Data level. Data level, or resolution level, orders the data from individual to group.
Data frequency. Data frequency refers to how quickly the consumer or customer changes their data.
Data structure. Data structure distinguishes between whether the data is structured or unstructured.
Data source. Data source identifies ownership and where the data is stored within an organization’s many tech systems and platforms.
A Consumer Personalization Data Inventory Tool
Use our adapted tool, A Consumer Personalization Data Inventory Tool, to inventory your consumer and customer data for personalization tactics and check for data readiness against six evaluation criteria:
Accessibility. The data currently exists, and you’re able to access it in the format and speed you require.
Relevance. The data is directly aligned with the specific personalization program or tactic.
Quality. The data is accurate, complete, and consistent.
Compliance. The data meets legal, regulatory, and organizational privacy/security requirements.
Matching. The data can be precisely linked across sources using appropriate identifiers (e.g., customer ID, email).
Timeliness. The data is refreshed at a rate that’s suitable for my personalization program or tactic.
Forrester clients can set up a guidance session or inquiry for hands-on support in preparing your customer data for impactful consumer personalization efforts. In the meantime, happy spring cleaning!
