3 Innovative Ways to Segment Your CRM campaigns with Real-life success stories

In my last blog, we spoke about how it's important to build a strong Segmentation framework, in this one we will look at 3 innovative ways for brands to enhance their segmentation techniques, using live examples from brands that are already capitalizing on these strategies.

?1. Segmenting Based on Customer Lifetime Value (LTV)

Segmenting customers based on Lifetime Value (LTV) enables brands to identify their most profitable customers - often the top 20-30% and distinguish them from others with medium or low LTV. This approach lets brands focus on high-value customers and optimize interactions with other segments.

For High LTV Customers

For customers with high LTV, brands can use targeted, low-frequency messaging to avoid overwhelming them and risking blocks, unsubscribed, or uninstalls. Loyalty can be further reinforced with exclusive benefits, such as:

• Early Access to Exclusive Offers: For instance, Amazon Prime gives members early access to special sales and exclusive discounts, fostering a sense of exclusivity among high-LTV customers.
• Loyalty Program Incentives: Sephora automatically upgrades its top-spending customers to “VIB” and “Rouge” tiers in its loyalty program, offering additional perks, including free shipping and birthday gifts.
• Personalized Celebrations: Some luxury brands even send cakes or small gifts to their VIP customers on special occasions like birthdays, a memorable gesture that adds to their sense of value.

By employing these strategies, brands can maintain strong relationships with their top customers and enhance retention, leading to long-term profitability.

2. Segmenting Based on Customer Affinities

Another powerful segmentation layer is affinity-based segmentation. This involves using machine learning models to predict customer preferences based on categories, brands, or even price sensitivity. By understanding customer affinities, brands can create highly relevant offers that align with individual customer interests.

Nike leverages AI-powered models to predict customers’ affinities for specific product categories, such as running shoes, athleisure wear, or training gear. On the Nike app, customers receive personalized app notifications and emails, which drive higher conversion rates by showing products basis the user's category affinity.        

All this is done using implicit data from browsing and purchase behavior without requiring customers to state their preferences explicitly.

3. Propensity-Based Predictions for Product Purchases

Predicting customer propensity—that is, forecasting which products customers are likely to buy and when—has become a valuable tool in segmentation. With predictive models, brands can segment customers based on their likelihood of purchasing a specific product within a set time frame, enabling well-timed, relevant promotions.?

Spotify uses propensity models to predict the types of music a user is likely to enjoy at specific times, creating curated playlists that match their preferences and listening patterns. This approach ensures that users receive fresh, personalized playlists regularly, leading to a 15-20% increase in App retention.        

Brands embracing LTV-based segmentation, affinity insights, and propensity predictions are seeing substantial improvements in customer engagement, retention, and loyalty.?

We’d love to hear from you! Share in the comments any innovative segmentation strategies you've tried or plan to experiment with—your insights could inspire others to take their customer engagement to the next level!

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