Predictions Objectives

Using predictions, marketers can achieve the following objectives:

Improve customer retention and overall conversions

You can identify loyal against churn customers based on user propensity. Selectively you can target customers with personalized campaigns having offers or promotions to retain them. Similarly, identify loyal customers and suitably reward them to improve over engagement.

Increase the customer lifetime value

Predict future customer preferences and their overall interactions with the app and further with certain products or categories. Identify the likelihood of a user purchasing a certain product or product category. Based on the available data, create targeted campaigns, and understand user preferences and brand alignments.

Optimize marketing spend through customer interactions

Based on user propensity, target users needing attention with greater frequency and personalization. Optimize marketing spend by reducing unnecessary campaigns and create focused outreach plan with optimized frequency

Create targeted campaigns with greater precision and accuracy

Target customers with personalized campaigns. Identify which channels would work for the users and target users based on their propensity to respond to those channels or selected campaigns. Furthermore, optimize communications based on the available data.

Use cases by Industry

Sample Prediction Uses

Marketers can create the following sample use cases using MoEngage Prediction.

Predict a particular outcome or conversion goal

User conversion refers to performing a particular business objective or outcome as defined by the marketer. Using predictions,

• Identify the propensity of users to perform a particular objective or goal.
• Identify segments of users who have high compared to low propensity and create targeted campaigns.

For example, users with a low propensity to convert are targeted with contextual offers and discounts. The users have more timely campaigns with higher frequency than users who have a higher propensity to convert.

Churn Rate Prediction

Churn rate refers to the percentage of users who stop using the app or service in a specified time period. Businesses must focus on ensuring a higher growth rate with lower churns. With predictions, marketers can pre-empt the loss of a customer and design necessary follow-up or nurturing proactively before it is too late.

For example, users with a high propensity to churn are targeted with contextual offers and discounts. The users have more timely campaigns compared with more timely campaigns with higher frequency than users who have a higher propensity to convert.

Lifetime Value Prediction

Customer Lifetime Value (CLV) refers to how much a customer is worth to the business and the average revenue generated throughout the entire span of the relationship with them. With predictive analytics, marketers can predict the future engagement with the users along with the revenue the engagement is likely to bring in.

Predict and plan marketing campaigns upfront

With predictions, marketers can clearly predict and pre-empt user actions and outcomes. Marketers create a suitable plan for the kind of campaigns required for the users. Risks are significantly reduced as decisions are made based on data, not based on assumptions that rely on instincts and some educated guesses.

The right type of content is identified for certain customers. Customize content creation and distribution based on user preference for content, channel, and time. When customers receive higher-quality communication, it increases the probability of sales conversions.

Similarly, based on the user propensity and the number of users likely to perform or not perform an action, marketers can suitably predict customer acquisition and overall retention costs needed to incur in the coming week or month and budget accordingly.

Upselling and Cross-Selling Opportunities

Predict and identify what the customer's needs and wants are. Identifying products are selling and the affinity of users towards a particular brand or product category. By predicting user propensity, marketers can target and create contextual campaigns to recommend suitable product categories to the user.