What does account mapping mean?

Account mapping is the process of linking or mapping customer accounts from different systems together. It enables companies to get a complete view of a customer across all applications, databases and touchpoints.

Why is account mapping important?

Account mapping is critical for many reasons:

• It provides a 360-degree customer view – By linking accounts together, you can build a single source of truth for customer data across sales, marketing, support and other teams. This gives a complete picture of customer interactions.
• It improves personalization – With connected data, you can better understand customer needs and preferences. This allows you to deliver personalized experiences across channels.
• It enables accurate reporting – Account mapping avoids duplicate or overlapping customer data which can skew reports. Accurate reports inform better decision making.
• It reduces operational costs – By eliminating redundant data entry and manual processes, account mapping streamlines operations and reduces costs.
• It improves customer satisfaction – With better personalization and seamless experiences, customer satisfaction typically improves.

In summary, account mapping provides the foundation for gaining a unified customer view which is critical for delivering excellent customer experiences.

How does account mapping work?

There are four key steps involved in account mapping:

1. Data Collection – First, customer data is extracted from all relevant source systems. This may include databases like CRM, email, ecommerce, marketing automation, support desks and more.
2. Matching – Next, algorithms and/or data scientists match accounts across systems. This identifies accounts which belong to the same customer. Probabilistic and deterministic matching approaches are common.
3. Mapping – The matched accounts are then mapped together via a common unique identifier. This links the separate accounts into one golden customer record.
4. Data Updates – As customer data changes in the source systems, the mappings are updated in real-time or via batch processes. This keeps the unified view current.

Additional data transformation and enrichment may be required along the way to normalize data formats across systems. The end result is a single identifiable and consistent record per customer.

What are the methods for account mapping?

There are a variety of techniques used for the matching process in account mapping. Common methods include:

Rules-Based Matching

This uses business rules to match accounts. Some common rules may check:

• If the name and address fields match exactly
• If the email addresses match
• If phone numbers match

Rules are easy to understand but less flexible. They may fail to identify matches with minor differences.

Statistical Models

Statistical models are more advanced. They use machine learning algorithms to calculate match probability based on multiple account attributes. For example, minor differences in names or addresses with an email match may still result in a high probability match.

External Data

Reference data like mailing addresses can be used to normalize fields before matching. This data helps identify matches where source system fields differ.

Human Review

For uncertain matches, human review can finalize the mapping. This overcomes the limitations of automated approaches. However, it doesn’t scale as well.

Unique Identifiers

When available, identifiers like account numbers, user IDs or loyalty numbers can deterministically map accounts.

Often a hybrid approach works best. Machine learning provides the foundation, with supplemental data and human review to refine matches.

What are some common challenges with account mapping?

While account mapping provides significant benefits, it’s not without its difficulties. Common mapping challenges include:

• Incomplete customer data – Missing or blank attributes like names, addresses or dates of birth make accurate matching difficult.
• Data quality issues – Data errors, duplication or formatting differences can prevent matches.
• Technology constraints – Disparate source systems can make data extraction complex.
• Organizational silos – When departments don’t share data, a complete view of customers gets harder.
• Regulatory compliance – Data privacy regulations may restrict how customer data can be used.
• Mergers & acquisitions – Uniting customer accounts from acquired businesses is often challenging.
• Matching speed at scale – Matching algorithms need to provide rapid, accurate results across huge customer volumes.

Organizations need clear standards and quality checks at every phase to overcome these barriers. Ongoing data governance is critical for successful account mapping initiatives.

What are some best practices for account mapping?

Follow these best practices for superior account mapping results:

• Consolidate customer data into a master marketing database for easier matching.
• Standardize data formats, structures and taxonomy across source systems.
• Cleanse, validate and enrich customer data prior to the mapping process.
• Build a complete view of customers by connecting all relevant data sources.
• Define distinct, reliable matching rules based on your data types.
• Employ machine learning to overcome limitations of rules-based matching.
• Leverage external reference data to validate mapped results.
• Review uncertain matches manually to confirm mapping decisions.
• Update mappings in real-time to keep pace with new data.
• Monitor mapping quality through accuracy metrics and auditing.

By following these tips, you can maximize the accuracy of account mappings while minimizing manual efforts.

What tools can you use for account mapping?

Many customer data platform and data integration tools provide account mapping capabilities. Here are some top options:

With any tool, allow time to customize the approach for your specific customer data landscape.

How can you enrich data during account mapping?

Account mapping presents a great opportunity to enrich your customer data along the way. Useful enrichment techniques include:

• Appending demographic attributes like age, gender, income level, etc.
• Adding behavioral insights like purchase history, channel preferences, etc.
• Attaching location data such as geography, distance to store, etc.
• Scoring customer value tier based on transaction history.
• Applying industry-specific segment labels.
• Generating a persistent unique ID per customer.
• Hashing or encrypting identifiers as needed for security.

Enriching the data improves the downstream utility while the sources are already being unified. But take care not to compromise processing performance.

How do you maintain accurate account mapping?

Maintenance is key to keeping account mappings current. Strategies for ongoing accuracy include:

• Re-run the matching process periodically to detect new duplicate records.
• Update mappings whenever source systems are updated.
• Fix outdated or incorrect mappings based on data quality checks.
• Refresh external reference data on a regular basis.
• Monitor mapping coverage to identify missing source systems.
• Continuously optimize matching rules and machine learning models.
• Automate processes via scripts and jobs vs. manual updates.
• Document policy and procedures for update frequency.

Make maintenance an integral part of your account mapping plan. Measure ongoing accuracy to prove the value.

What are some use cases for account mapping?

Here are a few examples of how companies leverage account mapping:

Marketing Personalization

A retailer links web, mobile app, email, in-store and support data to tailor promotions across channels for each customer.

Loyalty Programs

A hotel chain maps guest data across reservations, dining, spa visits and other sources to build a 360-degree view to enhance experiences.

Sales Acceleration

A technology vendor correlates sales interactions, service tickets and web visits to identify and prioritize high-value sales prospects.

Fraud Prevention

A bank links credit card, deposit account and loan information to quickly detect suspicious, fraudulent customer activity.

Ad Targeting

An online travel site connects web browsing, booking and social media data to define audience segments and serve relevant ads.

These examples illustrate the diverse applications of account mapping across industries. The key is linking all the right data sources for your specific business goals.

What risks are associated with account mapping?

While delivering clear benefits, account mapping also poses some risks to consider:

• Inaccurate mappings – Linking data to the wrong customer detriments experiences and decisions.
• Security vulnerabilities – Centralizing data also creates risks of breaches or misuse.
• Regulatory non-compliance – Usage, retention and sharing of mapped data must adhere to policies like GDPR.
• User mistrust – Customers may question the combination of their data across sources.
• System complexity – Introducing many-to-many mappings across systems adds architectural complexity.
• Vendor dependence – Reliance on third-party matching tools may create vendor lock-in.

Organizations must strategically balance the risks and rewards of account mapping for each initiative.

Conclusion

Account mapping is the essential process of unifying disparate customer data across systems to enable a 3600 view. Mastering mapping delivers personalized experiences, informed decisions and operational efficiency. However, it takes considerable effort to do properly. Invest in quality data governance, matching technology and ongoing maintenance to realize the full benefits while avoiding pitfalls.

Overall, when implemented strategically, account mapping provides the data foundation that makes true customer-centricity possible in today’s digital business landscape.