Defining and implementing advanced segmentation and personalization strategies is essential to achieving the best customer experience. As consumer expectations continue to rise, businesses need to know their customers well enough to anticipate their needs and deliver relevant interactions at the right time to meet their desires.
This is where Calculated Insights in Salesforce Data Cloud play a key role. These advanced metrics enable the transformation of scattered data into actionable information. This gives brands a better understanding of customer behavior, allowing for more precise segmentation, real-time campaign optimization, and personalized user experiences at every point of interaction.
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What Are Calculated Insights in Data Cloud?
Calculated Insights (CI) are real-time calculated metrics within Data Cloud that enrich customer profiles without the need to preprocess data in external systems. These calculations can be applied to any unified entity within Data Cloud and, being dynamically updated, provide key insights to deploy unique marketing strategies.
If we had to highlight three key benefits of this functionality, they would be:
- Real-time processing: Always reflects the latest data updates, ensuring decisions are based on the most current information.
- Zero-copy integration: Insights can be generated on data stored in Snowflake, Amazon Redshift, or other connected environments without duplicating or moving data, thanks to Data Cloud’s zero-copy integration.
- Immediate activation: CIs can be used in Journey Builder, Paid Media, Agentforce, and any other platform connected to Data Cloud as additional information.
Use Cases: Calculated Insights to Boost Your Marketing Strategy
Calculated Insights enable the creation of advanced segmentation and personalization strategies. Below, we explore two of the most relevant use cases, leveraging Journey Builder in Marketing Cloud and Paid Media, activating insights directly from Data Cloud:
RFM Metrics for Each Customer
The RFM model (Recency, Frequency, Monetary) helps classify customers based on their purchasing behavior:
- Recency (R): When was the last time a customer made a purchase or interacted with the brand?
- Frequency (F): How often has a customer purchased within a specific period?
- Monetary (M): How much has a customer spent in total within a given timeframe?
This classification helps segment customers based on their value to the company and their level of engagement. With customers segmented by these new attributes, we can develop strategies to increase R, F, and M for each of them, providing experiences that respond appropriately to their current stage:
- High-Value Customers (High R, High F, High M)
- Loyalty programs with exclusive benefits
- Sending premium content or priority access to new products
- Loyalty rewards, such as discounts or extra points in reward programs
- Dormant Customers (Low R, High F, High M)
- Reactivation strategies with personalized offers
- Automated reminders in Journey Builder to recapture their interest with product recommendations similar to their previous purchases
- Limited-time offers to create urgency
- High-Potential Customers (High R, Low F, High M)
- Cross-selling and up-selling strategies based on higher-value products
- Testimonial campaigns or success stories to reinforce the brand relationship
- At-Risk Customers (Low R, Low F, Low M)
- Surveys to understand low engagement
- Exclusive recovery promotions
Activation of Lifetime Value (LTV)
Lifetime Value (LTV) represents the total value a customer has generated throughout their relationship with the brand. It is a key metric for determining how much to invest in customer acquisition and retention. Therefore, it helps make strategic marketing decisions by optimizing efforts toward customers with the greatest potential to generate revenue.
Some strategies for building around LTV with Marketing Cloud and Data Cloud could include:
- High LTV Customers
- VIP programs with exclusive benefits to retain these customers.
- Re-engagement campaigns with premium content and recommendations based on their historical purchases.
- Special offers to encourage repeat purchases.
- Medium LTV Customers
- Personalizing experiences based on their interests detected through web browsing or interactions with Agentforce.
- Cross-selling campaigns with value-added products.
- Subscription offers to maximize long-term engagement.
- Low LTV Customers
- Paid media segmentation to adjust advertising spend and prioritize customers with higher potential.
- Retargeting strategies on social media with incentives to increase purchase frequency.
- Reducing marketing efforts for customers with low conversion probability.
- Excluding low-value audiences from high-budget campaigns, focusing instead on lower-cost strategies like email remarketing
Both RFM and LTV are essential metrics for maximizing the impact of marketing strategies. Thanks to Data Cloud and Journey Builder in Marketing Cloud, we can activate these metrics in real time to ensure that each customer receives the right communication at the right moment. This way, we can make something as valuable in CXM as ensuring the customer feels that we are addressing their need or desire at the ideal moment.
But Calculated Insights are not just RFM and LTV; they allow us to go much further. Without needing to write code and with the help of Data Cloud Flows, we can perform many other calculations that form the basis for additional layers in our marketing strategy. Some examples include:
- “Spend by Customer“, which measures a customer’s total spending over a specific period. This allows us to personalize campaigns in Journey Builder based on purchase value, while also excluding low-value customers from Paid Media campaigns to optimize advertising spend.
- “Spend by Customer and Product“, which focuses on specific categories or products and guides us in cross-selling strategies in Journey Builder based on products previously purchased.
- To identify the preferred contact channel for each customer, as well as for specific products/offers, we can rely on “Count of Emails Opened”, allowing us to personalize sending frequency in Journey Builder and improve engagement rates.
- If we focus on our customers’ affinity toward certain products or categories, we can work around “Purchase Insights” to classify customers based on purchasing trends and patterns, enabling us to track their evolution across different tiers and influence their behavior with various strategies.
- “Customer Rank by Spend” is another Calculated Insight that ranks customers based on their total spending compared to other customers. We can use this to create levels such as VIP, offering the most valuable customers exclusive benefits and prioritizing actions across all channels, including Paid Media for high-value audiences.
A Key Tool in Next-Generation CXM
This is how Calculated Insights provide a key competitive advantage in Customer Experience Management (CXM). By integrating with tools like Journey Builder and being activated across all channels, including Paid Media, they enable the launch of data-driven strategies that go beyond the usual, improving conversion, customer loyalty, and profitability.
In this context, companies that successfully structure and leverage these data efficiently are able to enhance their business results and build stronger, long-lasting relationships with their customers.
Are you ready to boost your marketing strategy with Data Cloud and Calculated Insights? At Omega CRM, A Merkle Company, we assist you in implementing Data Cloud and designing and activating data-driven strategies with the most advanced Salesforce solutions. Let’s talk!
