How Data Cloud Segmentation Can Transform Your Marketing

Understanding and targeting your customers effectively is crucial in today’s data-driven world. According to a recent study by McKinsey, companies that leverage customer behavior data to generate insights outperform peers by 85% in sales growth and more than 25% in gross margin. Data Cloud Segmentation offers a powerful approach to achieve this precision. 

This blog will delve into the intricacies of Data Cloud Segmentation based on the topics covered in our recent master class. Let’s explore the segmentation process, benefits, and best practices for creating and managing segments.

What is Data Cloud Segmentation?

Data Cloud Segmentation is the process of categorizing profiles that share common attributes or behaviors. This method allows businesses to create distinct buckets for targeted analysis and communication, ensuring that the right message reaches the right audience at the right time.

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Key Concepts of Segmentation

1. Customer Attributes: These are specific details related to customers, including:
   • Profile Attributes: Name, Country, Interest, Zip Code, Age, Loyalty Points.
   • Behavioral Attributes: Email Opens, Clicks, Bounces, Sent Emails, Unique Clicks, Unique Opens.
2. Segmentation Criteria: Using filters to target individuals based on their attributes. For instance, segmenting customers above the age of 40, residing in a specific zip code, with high loyalty points, who have also interacted with recent emails.

Understanding Segmentation

Segmentation helps businesses comprehend and analyze customer data more effectively by joining profile and behavioral attributes. For example, you might create a segment of customers over 40 years old, live in zip code 10001, have accrued over 200 loyalty points, and have recently opened and clicked on emails. This approach ensures that marketing efforts are precisely targeted.

How Segmentation Works

Unified Individual

Segmentation often works on the concept of a Unified Individual, combining multiple data points into a single profile. Consider a scenario where an individual has multiple profiles across different databases. Unifying these profiles creates a comprehensive view of the customer, enabling more accurate segmentation.

Segment Creation Process

Creating a segment involves several steps:

1. Define Segment Criteria: Determine the attributes and behaviors that define your segment.
2. Attribute Library: Utilize the attribute library, which includes all possible attributes that can be used for segmentation.
3. Segment Population: Populate your segment with profiles that match the defined criteria.

Publish Types

After creating a segment, it can be published using different methods. However, once a segment is published as ‘standard,’ it cannot be changed to ‘rapid publish.’

Segment Creation Process: Step-by-Step

Creating effective customer segments is a structured process that involves several detailed steps. Each step ensures that the segments are accurate, relevant, and actionable. Here’s a detailed breakdown of the segment creation process:

1. Select Data Model Object (DMO)

The first step in creating a segment is selecting the Data Model Object (DMO) on which your segment will be based. This is crucial as it determines the type of data you will work with and the attributes available for segmentation.

• Unified Individual: It is often recommended that Unified Individual profiles be used because they combine data from multiple sources, providing a comprehensive view of each customer.
• Profile Type Objects: Ensure that the DMO you select is of the profile type, as this allows for a broader range of attributes to be included in your segment.

2. Choose Attributes

Once you have selected the DMO, the next step is to choose the attributes that will define your segment. Attributes are the specific data points that describe your customers.

• Attribute Library: The attribute library allows you to select from a wide range of available attributes, including profile and behavioral attributes.
• Profile Attributes: These might include demographic information such as age, gender, location, and loyalty points.
• Behavioral Attributes: These include data on customer interactions such as email opens, clicks, purchases, and website visits.

3. Define Segment Criteria

With your attributes selected, the next step is to define the criteria for determining which customers will be included in your segment.

• Filters and Conditions: Use filters to narrow the customer pool. For example, create a filter for customers aged 30-40 who have made purchases in the last month.
• Compound Criteria: Combine multiple criteria to create more refined segments. For instance, customers over 30 who live in a specific region have high engagement scores.

4. Create Nested Segments

Nested segments allow for more complex and detailed segmentation by incorporating related attributes with different relationships to the segment target.

• Related Attributes: Attributes with an N:1 relationship with the segment target allow you to include broader criteria.
• Direct Attributes: Attributes that have a 1:1 relationship with the segment target, enabling precise targeting.
• Inner Segments: Develop inner segments within a larger segment for highly granular targeting. For example, within a segment of high-value customers, create an inner segment of those who have not purchased in the last three months.

Also Read – Salesforce Data Cloud Implementation Approach

5. Segment Population

After defining your criteria and creating nested segments, the next step is to populate your segment with profiles that meet the criteria.

• Data Validation: Ensure that the data used to populate your segment is accurate and up-to-date. This may involve cleaning and standardizing data.
• Data Sources: To populate your segment, utilize the most relevant and current data sources. These could include CRM data, marketing automation platforms, and transactional databases.

6. Segment Validation

Before finalizing your segment, validating it accurately to reflect the intended customer profiles is crucial.

• Preview and Analysis: Use tools to preview the segment and analyze the profiles included. Ensure that the segment criteria are correctly applied and that the segment population is as expected.
• Testing: Run initial tests on a small portion of your segment to see how it performs. This can help identify any issues or adjustments needed before a full rollout.

7. Publish the Segment

Once your segment is created and validated, the final step is to publish it.

• Standard Publish: This method publishes the segment to be scheduled and used across different marketing campaigns and analytics.
• Rapid Publish: Rapid publish is an option if you need to deploy a segment for immediate use quickly. However, once a segment is published as standard, it cannot be changed to rapid publish.

8. Monitor and Refine

After publishing your segment, ongoing monitoring and refinement are essential to maintain its effectiveness.

• Performance Tracking: Continuously track the performance of your segments using metrics like engagement rates, conversion rates, and ROI.
• Iterative Improvements: Regularly revisit and refine your segments based on performance data and changes in customer behavior or business objectives.

Practical Example

To illustrate the segment creation process, let’s walk through an example:

1. Select DMO: Choose Unified Individual profiles.
2. Choose Attributes: Select attributes such as Age, Location, Purchase History, and Email Engagement.
3. Define Criteria: Create a segment for customers aged 30-50 who have purchased in the last six months and have opened an email in the past month.
4. Nested Segments: Within this segment, create an inner segment for customers who have high loyalty points but haven’t purchased in the last month.
5. Populate Segment: Validate data from your CRM and marketing automation platform.
6. Validation: Preview the segment to ensure it includes the right profiles and run a small test campaign.
7. Publish: Publish the segment using the standard method for upcoming marketing campaigns.
8. Monitor: Track the segment’s performance and make necessary adjustments.

Also Read – Data Cloud Strategy and Topology: A Comprehensive Guide

Hands-on Exercises

To solidify your understanding, here are some practical exercises:

1. Create a Segment: ‘Customers parked at EV charging stations.’
2. High Loyalty Customers: ‘High Loyalty Customers who are not actively purchasing.’
3. Reward Program: ‘Reward Program Customers with Low CSAT Score.’
4. Parking System Only: ‘Customers who are only in the parking system, not the reward system.’

Prerequisites and Considerations for Segmentation

Before creating segments, consider the following questions:

• Are you using a standard data model with predefined data bundles?
• What attributes are available for segmentation in these bundles?
• Do you need data standardization (e.g., ‘United Kingdom’ to ‘UK’)?
• Which data sources provide the most up-to-date information?
• How frequently are data sources refreshed?

Segment Considerations

• Refresh vs. Publish: When a segment runs, its eligible profiles are refreshed, not published. Publishing is a separate action.
• Membership Analysis: Analyze current and historical membership segments using tools like Tableau CRMA or Data Explorer.
• System Limits: Segmentation referencing engagement data is limited to a two-year look-back window.
• Credit Consumption: Running segments consume credits, so plan your segmentation strategy carefully.

Enhancing Your Testing Strategy

Testing your segments is crucial to ensure they perform as expected. Here are some specific steps to enhance your testing strategy:

1. A/B Testing: Conduct A/B tests to compare different segment criteria. For instance, test two segments with slight variations in age or interest to see which one yields better engagement.
2. Historical Analysis: Use historical data to simulate how your segments would have performed in the past. This can help you predict future performance and adjust your criteria accordingly.
3. Feedback Loop: Establish a feedback loop with your sales and marketing teams. Gather insights on how the segments perform in real time and make necessary adjustments.
4. Segmentation Tools: Leverage advanced segmentation tools and platforms like Tableau CRMA or Data Explorer. These tools can provide deeper insights and more granular control over your segments.
5. Performance Metrics: Define clear performance metrics for your segments. Track metrics such as conversion rate, click-through rate, and customer lifetime value to assess their effectiveness.
6. Iterative Refinement: Segmentation is not a one-time task. Continuously refine your segments based on performance data and evolving customer behaviors. This iterative process ensures that your segments remain relevant and effective.

Explore our Salesforce Data Cloud series on YouTube for expert guidance and tips on leveraging Salesforce Data Cloud effectively.

Conclusion

Data Cloud Segmentation is a robust tool enabling businesses to target and analyze customers effectively. By understanding and implementing the abovementioned processes, you can ensure that your marketing efforts are precise and impactful, leading to better customer engagement and higher conversion rates.

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Frequently Asked Questions (FAQs)

1. What is Data Cloud Segmentation? 

It’s the process of categorizing profiles with common attributes for targeted analysis and communication.

2. What are customer attributes in segmentation? 

Attributes include profile details (name, age) and behavioral data (email opens, clicks).

3. How does segmentation work? 

By combining profile and behavioral data to create comprehensive customer segments.

4. What is a Unified Individual? 

A unified profile combining multiple data points to give a complete view of a customer.

5. What is the segment creation process? 

Define criteria, use the attribute library, and populate the segment with matching profiles.