A single, consistent view of customer data is powerful for any organization. A single customer view (SCV) is an opportunity to maximize your brand’s customer experience and boost revenue. However, it’s hard to achieve the full potential of your customer data if it’s riddled with errors.
If your goal is to implement a thoughtful SCV strategy, these six steps will help you prepare your data for a more complete customer view.
Your objective for an SCV is to strive for trustworthy data that is unified, validated, and complete. However, your customer data may be scattered across different systems and sources. Extract the data and pull it together in a centralized location like a CRM, marketing automation platform, or a data lake. Bringing data together in one location gives stakeholders across the organization a holistic view of customer data.
Once you’ve pulled the data together, use data profiling to evaluate its accuracy and completeness. Data profiling helps you discover relationships and trends within your data, understand what problems may exist, and identify the actions to take to remedy the issues. Make sure each customer record is unique, complete, and accurate by focusing on uncovering duplicates, incomplete values, and anomalies.
By uncovering the inaccuracies with your data, you will have the ability to see errors like invalid, duplicated, and incomplete records. Transform your data and fix these inaccuracies by standardizing inconsistent formats, validating emails, addresses, and phone numbers; and appending additional information to incomplete records. Ensuring that your data is high-quality with consistent formatting is a critical step in creating a customer view that is cohesive and accurate.
Ensuring that your data is high-quality with consistent formatting is a critical step in creating a customer view that is cohesive and accurate.
Another way to transform your data is by incorporating the matching and harmonizing steps in your single customer view efforts. These steps are like joining puzzle pieces together. Techniques like “fuzzy matching” help you find connections between data elements across different channels despite keyboard errors, missing words, or name variations. Additionally, you should “harmonize” the data. Harmonization is a deduplication process that takes clusters of duplicate records and, with the right technology, allows you to apply sophisticated business rules to create a single resulting record for each cluster. Matching and harmonization help you filter out duplicates and create the most accurate records for your SCV initiatives.
You’ve standardized your data and removed duplicates, but still notice missing entries. A solution like a data append service is helpful for adding missing bits of customer information to your existing records. For example, a bulk data append could provide emails for customers you’ve only been sending “snail mail.” Another path to a more complete customer view is data enrichment. Enrichment will enhance your contact records with consumer attributes like demographics or buying preferences. This helps you develop a fuller, more detailed picture of your customer.
After doing all the work to build a Single Customer View, you’ll want to continually monitor your data to make sure it’s accurate and fit for purpose. The right platform can show its present state with real-time dashboards, flag any issues with automated alerts, and export reports easily. An SCV is a continuous process, so it’s important to update and maintain your data regularly.
A Single Customer View gives your organization a single source of truth about your customer. It is a way to streamline and improve customer data to achieve a holistic view of the customer. The more you can arm yourself with consistent, accurate information about your customers, the better you’ll be able to serve them. Maximizing the potential of your customer data can help you grow your revenue and surpass your competition.