6 Tips for Future-Proofing Your Data to Enhance Personalization: A Quick Guide to Formatting and Storage

Personalized customer experiences aren't just accepted — they're expected, according to 80% of consumers in BCG's recent 23,000-person survey. From customized greetings to curated recommendations based on browsing history, brands are benefiting from more relevant, more emotional interactions with customers, and AI is providing more opportunities for hyper-personalization than ever. Yet this opportunity comes with a critical caveat: customer personalization efforts can backfire spectacularly if your data isn't up-to-date, uniformly collected, and properly monitored.

Picture this seemingly straightforward marketing task: you want to target customers in Georgia. Your query of "State = Georgia or GA" should work flawlessly — but it doesn't. Why? Your database is a maze of variations: "Ga.," "Georgia (US)," and typos like, "goergia." What appears as a simple problem often reveals more systemic data quality issues.

If this tip-of-the-iceberg scenario sounds painfully familiar, don't panic. There's plenty you can do to correct practices around formatting and storage so you can solve immediate problems and future-proof your data collection. In this article, we’ll provide a quick overview of how to organize your messy data so you can pursue game-changing personalization at scale.

Why Cleaning Up Your Data is Worth the Effort

Future-proofing your data offers these top benefits that impact every aspect of your marketing efforts:

• Efficiency: Standardized data collection streamlines work processes and simplifies queries.
• Reliability: Consistent data leads to trustworthy insights.
• Scalability: Standardized collection methods facilitate easy growth.
• Cost-effectiveness: Less time is spent updating and correcting existing data.

Six Considerations When Future-Proofing Data to Enhance Customer Experiences

As you begin the task of evaluating your data, you'll need a systematic approach that addresses both current pain points and future scalability — here are six fundamental considerations that will help you build a robust framework for assessment and implementation:

1. Relevance

Determine what data is essential for your goals and priorities in your marketing efforts. For instance, do you really need to store customer birthdates? Remember that data requires storage space and quality maintenance, which should scale with your evolving customer base. Prioritize data that directly impacts customer experience and drives personalization. For example, purchase history and browsing behavior are often more valuable for tailoring experiences than demographic data alone.

2. Storage and access

Decide where data will be stored, how it will be formatted, and who should have access. Common storage solutions include Customer Relationship Management (CRM) systems and cloud storage. Work internally to determine what data should be stored where, considering that sensitive data is typically kept in on-premises CRM systems.

3. System compatibility

To support automation and other personalization efforts, ensure that your systems can transmit and read data between platforms. This is where understanding data types and file formats becomes critical. Consider implementing APIs and middleware solutions to ensure seamless data flow between systems. This integration is useful for real-time personalization and creating a unified customer view across touchpoints.

4. Data types

Choose appropriate formats for storing different kinds of information. Examples include:

• Integer: Whole numbers (e.g., customer ID numbers)
• Floating point: Numbers with decimals (e.g., price points)
• Character: Single letters or symbols (e.g., grade levels)
• String: Text of any length (e.g., names, addresses)
• Boolean: True/false values (e.g., subscription status)
• Array: Lists of values (e.g., multiple phone numbers)
• Date: Calendar dates (e.g., birthdates)
• Time: Clock times (e.g., transaction times)

Understanding these data types is crucial for effective segmentation and personalization. For instance, combining string data (like product preferences) with Boolean data (such as newsletter subscription status) can create powerful, targeted marketing campaigns.

5. Data storage formats

Consider how data will be retrieved and analyzed.

• Excel (.xls): Spreadsheets that allow complex calculations and formatting
• CSV (comma-separated values): Simplified spreadsheets without formatting
• JSON: Text-based format commonly used for API data exchange
• Apache: Parquet, ORC, and Avro

The choice of format not only impacts storage efficiency but also query speed and analytics capabilities. For real-time personalization, consider formats that support quick data retrieval and processing, such as columnar storage formats like Parquet.

6. Update frequency

Your marketing strategies are bound to evolve. Determine how often data will be updated, considering what should be overwritten and what should be stored cumulatively or non-cumulatively.

Implement a robust data governance framework to manage updates effectively. This ensures that your personalization efforts always use the most current and relevant data, enhancing customer experiences while maintaining data integrity.

Ensure Success with a Team Approach

Regardless of your chosen approach, involve all relevant stakeholders in the decision-making process. This collaboration ensures alignment on organizational and customer needs, purpose, and goals across your organization.

By error- and future-proofing your data, your brand can more accurately forecast important metrics and query data with confidence, minimizing the impact of outliers. This robust approach to data management empowers your team to make informed decisions, adapt effectively to future challenges, and harness the potential of AI-powered tools to maximize customer interactions.