Pros & Cons of Salesforce Marketing Cloud Segmentation Tools

Salesforce Marketing Cloud offers a range of segmentation tools across its products: Account Engagement, Engagement, and Advanced Edition. Each tool comes with strengths and limitations, and choosing the right one depends on your team's expertise, your data structure, and the complexity of your segmentation needs.

With so many segmentation methods available, let’s highlight their advantages and potential drawbacks, to help you determine the best fit for your business and marketing needs.

Data Filters

Data Filters are a simple yet powerful way to create audience segments within Marketing Cloud Engagement. Using a drag-and-drop interface, marketers can define rules to filter audiences based on specific attributes, such as demographic data or behavioural engagement.

Pros:

• No coding knowledge needed - making it accessible to all marketers.

• Quick to implement for simple segmentation needs.

• Useful for one-time or frequently updated audience lists.

Cons:

• Limited flexibility compared to more advanced segmentation tools.

• Not suitable for complex audience segmentation.

SQL queries

For more advanced segmentation, Structured Query Language (SQL) provides a customisable way to extract audience data. This method requires SQL skills! Those comfortable with coding can create detailed segmentation rules based on data stored in Data Extensions.

Pros:

• High flexibility for complex segmentation needs.

• Enables cross-data extension queries, improving targeting accuracy.

• Ideal for advanced marketers with technical expertise.

Cons:

• Requires knowledge of SQL, which may not be available in all marketing teams.

• Can be time-consuming to create, test, and refine queries.

• Prone to errors if not managed carefully.

Contact Builder

Contact Builder is a standalone module in Marketing Cloud Engagement, allowing marketers to manage prospect data across multiple data sources, and making it easier to build segmentation strategies with a unified customer view. It connects various Data Extensions and enables advanced audience filtering.

Pros:

• Segmentation across multiple data sources.

• Provides a more holistic view of the customer journey.

• Offers advanced data management features.

Cons:

• Can be complex to set up, especially for those unfamiliar with data relationships.

• Requires a well-structured data model to work effectively.

• May involve additional training and support.

Scoring and grading

Account Engagement (formerly Pardot) provides a robust scoring and grading system to segment leads based on engagement levels and profile attributes. This system helps prioritise leads that are most likely to convert but can also be used to define segments for inclusion/exclusion in marketing campaigns.

Pros:

• Helps marketing and sales teams align on lead quality.

• Automates lead prioritisation based on predefined rules.

• Enables dynamic lead segmentation for targeted nurturing.

Cons:

• Requires regular refinement to ensure accuracy and relevance.

• Can become complex when incorporating multiple scoring and grading criteria.

• May not always reflect real-time lead behaviour.

Dynamic lists

Dynamic lists automatically update based on predefined segmentation criteria. Unlike static lists, which require manual updates, these lists adjust in real time as prospects enter the system and data changes.

Pros:

• Ensures audience lists remain up to date without manual intervention.

• Reduces administrative workload for marketers.

• Useful for ongoing nurture campaigns and automated journeys.

Cons:

• Limited by the available filter options in Account Engagement.

• More complex segmentation may require additional tools.

• Large datasets may slow down processing time.

Marketing Cloud Account Engagement customers can access Advanced Edition features within their existing accounts. 

Einstein Segmentation

Einstein Segmentation leverages artificial intelligence (AI) to analyse customer behaviour and predict audience segments. This feature helps marketers target customers more effectively without requiring manual segmentation rules.

Pros:

• Identifies patterns that may not be immediately obvious to marketers.

• Continuously learns and improves segmentation accuracy.

• Enhances personalisation efforts at scale.

Cons:

• Relies on high-quality data to deliver accurate insights.

Data Cloud enables segmentation across multiple data sources, offering a complete view of each customer. By consolidating data from various platforms, it provides marketers with deeper insights into customer behaviour. Salesforce customers using Data Cloud alongside Marketing Cloud are at an advantage when it comes to intelligent segmentation and marketing.

Pros:

• Creates unified customer profiles for more precise targeting.

• Integrates data from multiple systems, improving segmentation depth.

• Supports complex, multi-dimensional audience segmentation.

Cons:

• Requires integration with multiple data sources, which can be complex.

• Best suited for organisations with robust data infrastructure.

• May involve additional time and investment for setup and maintenance.

How marketing AI agents improve segmentation

Salesforce Agentforce plays a crucial role in segmentation by connecting marketing teams with sophisticated automation and AI-driven insights. By leveraging Salesforce AI marketing agents, businesses can enhance accuracy and streamline the process of audience targeting.

Pros:

• Automates audience segmentation, reducing manual workload.

• Uses AI to refine customer segmentation based on real-time interactions.

• Enhances collaboration between marketing and sales teams.

Cons:

• Requires integration with existing Salesforce systems.

• Effectiveness depends on the quality and volume of customer data.

• Should be used with clear brand and communications guidelines.

Assess team expertise

Some segmentation tools require technical expertise, such as SQL, while others, like Data Filters, are more user-friendly. Evaluate your team's skills before committing to a specific approach.

Complexity of segmentation

For simple segmentation, Data Filters and Dynamic Lists may be sufficient. However, if your segmentation needs involve multiple data sources and predictive analytics, more advanced tools like Contact Builder or Einstein/Agentforce may be necessary.

Data structure and quality

The effectiveness of any segmentation depends on the quality of your underlying data. Ensure your data is well-structured and regularly maintained to maximise segmentation accuracy.

Budget considerations

While native Salesforce Marketing Cloud tools cover a wide range of segmentation needs, third-party solutions like DESelect may offer additional benefits. Weigh the cost against the potential efficiency gains.

Lean on the experts

By carefully assessing your segmentation requirements and understanding the strengths and limitations of each tool, you can create more effective campaigns that drive engagement. But a lack of time, resources and know-how to go it alone can be a bottleneck to success.

For a faster route to Salesforce marketing ROI, work with a certified Salesforce Partner skilled in each of the segmentation methods we’ve discussed. Request a call with MarCloud today.