Advanced Segmentation Techniques for Composable CDPs

Going Beyond Basic Demographics

In the age of personalised marketing, basic demographic segmentation no longer cuts it. Customers expect brands to understand their individual needs and preferences. A Composable CDP like Wondaris empowers marketers to leverage a wealth of data for advanced segmentation, creating highly targeted and effective campaigns.

Here's how you can move beyond basic demographics:

1. Behavioural Data:

• What it is: This includes data on how customers interact with your brand across various touchpoints:

  • Website interactions: Page views, clicks, dwell time, search queries.

  • App usage: Features used, session duration, in-app purchases.

  • Purchase history: Products purchased, order frequency, average order value.

  • Email engagement: Opens, clicks, forwards, unsubscribes.

  • Social media interactions: Likes, shares, comments, follows.

• Example: Segmenting users who have added items to their cart but haven't completed the purchase. This allows you to target them with abandoned cart emails or special offers to incentivise them to complete the purchase.

• Tips:

  • Track a wide range of behavioural data points to get a comprehensive view of customer interactions.

  • Use behavioural data to identify patterns and trends in customer behaviour.

  • Combine behavioural data with other data sources for even more granular segmentation.

2. Contextual Data:

• What it is: This refers to real-time information about the customer's current situation:

  • Location: Country, city, region.

  • Time of day: Morning, afternoon, evening.

  • Weather: Sunny, rainy, cold.

  • Device type: Mobile, desktop, tablet.

  • Website or app being used.

• Example: A travel company could show different offers to users based on their location. Users in cold climates might see ads for tropical vacations, while users in warm climates might see ads for ski resorts.

• Tips:

  • Use contextual data to deliver highly relevant and timely messages.

  • Consider the customer's immediate needs and interests when using contextual data.

  • Combine contextual data with behavioral data to create even more personalized experiences.

3. Predictive Data:

• What it is: This involves using machine learning to forecast future customer behavior:

  • Likelihood to purchase: Identifying customers who are most likely to buy a specific product or service.

  • Churn risk: Predicting which customers are at risk of canceling their subscriptions or discontinuing their use of a product.

  • Predicted lifetime value (pLTV): Estimating the total revenue a customer is expected to generate over their relationship with your brand.

• Example: An e-commerce company could target users with a high predicted likelihood to purchase a specific product with personalised product recommendations and special offers.

• Tips:

  • Use Wondaris' out of the box enrichments or work with your data scientists to curate our advanced machine learning enrichment into accurate predictive models.

  • Use Wondaris to centralise the data required for predictive modelling - if you are using On Your Data mode, your existing data can be exposed to Wondaris' enrichments to train & evaluate machine learning models directly on your data where it lies.

  • Continuously monitor and refine your predictive models to ensure accuracy - Wondaris will do this automatically over time with it’s enrichment capabilities!

Creating Dynamic Segments

Wondaris enables marketers to create dynamic segments that update in real-time based on customer behaviour. This allows for timely and relevant messaging.

• How to set up rules and triggers: Define specific criteria that automatically add or remove customers from a segment. For example, a rule could be "add users to the 'VIP Customers' segment if they have made more than 5 purchases in the last month."

• Benefits of real-time segmentation:

  • Increased relevance: Deliver messages that are tailored to the customer's current behaviour and needs.

  • Improved engagement: Capture customer attention with timely and personalised offers.

  • Greater efficiency: Automate segmentation and targeting, freeing up marketers to focus on strategy.

• Use cases:

  • Personalising media messaging based on current user behaviour: Show different product recommendations or promotions based on the pages a user has browsed.

  • Sending triggered emails based on recent actions: Send a follow-up email after a customer makes a purchase, or send a reminder email to users who have abandoned their cart.

Improving Targeting and Campaign Performance

Advanced segmentation is crucial for improving targeting and campaign performance.

• Aligning segmentation strategies with marketing objectives:

  • Clearly define your marketing goals (e.g., increase sales, improve customer retention, drive brand awareness).

  • Develop segmentation strategies that support those goals.

  • Example: If your goal is to increase sales, you might segment customers based on their purchase history and product preferences to deliver targeted offers.

• Personalising messaging and offers:

  • Use your segments to create tailored messaging that resonates with each audience.

  • Offer relevant products, services, or promotions to each segment.

  • Example: Send a different email campaign to new customers than you send to loyal customers.

• Measuring the impact of segmentation:

  • Track key metrics such as conversion rates, click-through rates, and engagement for each segment.

  • Compare the performance of different segments to identify which ones are most responsive to your campaigns.

  • Use this data to refine your segmentation strategies and improve your overall marketing ROI.

By leveraging the power of Wondaris for advanced segmentation, marketers can create highly targeted campaigns that deliver exceptional results.