In today’s competitive digital landscape, customer experience is at the heart of business strategy. Retaining users and turning interactions into long-term relationships is key to staying ahead. Artificial intelligence (AI) and machine learning (ML) have emerged as powerful tools to personalise experiences, automate repetitive tasks, and enhance customer engagement.
By leveraging vast datasets and real-time feedback loops, businesses can create hyper-personalised experiences that evolve with user behaviour. So, how can ML help businesses foster deeper connections with their customers? Let’s dive into some key strategies.
Deep learning for deeper loyalty
Customer churn is a significant challenge, costing businesses a staggering $1.6 trillion annually. Studies show that customer-centric brands achieve 60% higher profits, making retention a top priority. However, traditional engagement strategies often fall short, relying on static frameworks and human-driven decision-making that limit scalability.
AI-driven solutions, on the other hand, operate in a fully data-driven, continuously evolving ecosystem. By leveraging vast amounts of data and automating key processes, ML enables businesses to create engagement models that dynamically adapt to user needs. This is especially valuable in industries like fitness, e-commerce, and ed-tech, where success hinges on personalisation, motivation, and continuous adaptation.
Rather than depending on predefined customer segments, ML evolves with user behaviour—offering tailored experiences that drive higher retention and long-term brand loyalty.
Focus on collecting the right kind of data
A solid engagement strategy starts with understanding why customers leave. Is it pricing? Missing features? A user experience that doesn’t meet expectations? Identifying these churn drivers requires a strategic approach to data collection, focusing on user behaviour, preferences, and feedback.
When businesses collect the right kind of data, they can create continuous feedback loops—allowing products to evolve in real-time. AI enables a shift from the traditional one-to-many approach to a hyper-personalised model, ensuring that customer needs are met at every touchpoint.
However, data collection should be intentional. Gathering excessive information wastes resources and raises compliance risks. Adhering to regulations like GDPR and CCPA and respecting third-party privacy agreements helps businesses maintain customer trust while avoiding legal pitfalls.
Identify key retention metrics
Which data points matter most to your business? Identifying retention-driving metrics allows you to create ML models that deliver measurable improvements.
For different industries, these metrics may vary:
- Fitness apps: Workout completion rates, session frequency, and progress tracking.
- E-commerce: Conversion rates, product page engagement, and cart abandonment.
- Ed-tech: Course completion rates, quiz engagement, and content interaction.
By pinpointing the data that influence user behaviour the most, businesses can build AI-driven engagement strategies that keep users coming back.
Uncover behavioural patterns
Looking beyond surface-level insights is crucial for optimising engagement. Businesses should focus on behavioural patterns that indicate engagement or disengagement.
For instance, instead of simply tracking workout completion rates, fitness apps can analyse whether users skip cooldowns—indicating that routines might be too long—or avoid certain exercises, suggesting difficulty. AI models can then adjust the user experience in real-time, balancing routines between exercises users enjoy and those they need for better results.
E-commerce platforms might track how browsing time within a category impacts conversion rates, while ed-tech companies could analyse how depth of feedback correlates with course completion.
Segmenting users based on their behaviour using clustering algorithms allows businesses to create more personalised experiences that resonate with different customer needs.
Start small and scale up
Before diving into complex ML models, it’s often best to start with simpler, rule-based systems to validate data quality and user response.
For example, many companies begin with basic recommendation engines before transitioning to more sophisticated ML models. In the case of a fitness app, rule-based workout recommendations can be introduced first, with ML gradually refining them based on user feedback, progress, and preferences.
Spotify follows a similar approach: new users receive genre-based playlists, which become highly personalised as the algorithm learns from listening habits.
Test, scale, iterate
Even after implementing ML, continuous optimisation is essential. Studies show that personalisation can increase recency, frequency, and value (RFV) scores by up to 86%—making it crucial to expand tailored experiences across multiple touchpoints.
However, AI models are not set-and-forget solutions. Over time, shifts in user behaviour can degrade model accuracy, requiring frequent monitoring and retraining.
For example, through continuous improvement, fitness apps have discovered that activity streaks drive engagement. Yet, instead of enforcing rigid daily streaks, adjusting goals based on individual habits—such as step data and workout frequency—can lead to better retention.
To keep engagement strategies effective, businesses should:
- Refine AI models through A/B testing
- Retrain models using updated datasets
- Monitor user feedback and adjust strategies accordingly
Final thoughts
Machine learning is reshaping how businesses approach customer engagement and retention. By focusing on the right data, implementing scalable AI solutions, and continuously refining models, companies can create deeply personalised experiences that keep users engaged and drive long-term loyalty.
For businesses looking to elevate customer relationships, integrating ML-driven engagement strategies isn’t just an advantage—it’s becoming a necessity.