Marketing is a cornerstone of business success. Its aim is straightforward: to generate revenue by promoting, selling, and distributing products or services to the right audience. Beyond attracting new customers, marketing fosters loyalty and ensures repeat engagement, all while driving growth.
In the digital marketplace, where online presence is essential, e-marketing has emerged as a vital strategy. It offers businesses advantages like targeted advertising, measurable results, and reduced customer acquisition costs. Yet, the success of these efforts hinges on one key factor—customer engagement.
In this dissertation, Abraham Grobler, under the supervision of Professor Herman Engelbrech, explored how reinforcement learning can optimise e-marketing delivery times to improve customer engagement.
The Role of Open Rates in E-Marketing Success
E-marketing relies on tools like emails, text messages, and push notifications to connect with customers and drive engagement. But how do businesses measure success? Three metrics provide critical insights: delivery rate, open rate, and clickthrough rate.
- Delivery Rate: The percentage of communications successfully delivered to customers.
- Open Rate: The percentage of messages opened by customers.
- Clickthrough Rate: The percentage of users who visit a platform (website, app, etc.) via these messages.
Among these, the open rate stands out as a key indicator of whether a campaign captures attention. A high open rate often signifies that customers are intrigued enough to interact, creating opportunities for deeper engagement.
Challenges in Improving Open Rates
Despite its importance, boosting open rates is far from simple. Email, one of the most popular e-marketing methods, faces unique challenges:
- Inbox Overload: Customers receive a growing number of emails daily. As a result, each email competes fiercely for attention.
- Spam and Filters: Many marketing emails are automatically routed to spam or less-frequently checked folders.
- Content Fatigue: Customers are often inundated with repetitive or irrelevant messages, decreasing their likelihood of opening future emails.
To overcome these obstacles, marketers have experimented with strategies like crafting attention-grabbing subject lines, tailoring content to audience preferences, and tapping into customer motivations. But there’s another, often underutilised factor—timing.
Why Timing Matters in E-Marketing
Finding the best time to send messages can significantly impact open rates. Customers’ online behaviour varies widely. For instance:
- A corporate professional might check their inbox frequently throughout the day but only open emails deemed urgent or relevant.
- Another individual might review emails during predictable windows, such as commuting hours or lunch breaks.
By aligning email delivery with these behavioural patterns, marketers can enhance the likelihood of their messages being opened. Timing signals that a business understands its audience, creating a more personalised and engaging experience.
The Role of Machine Learning in Optimisation
Machine learning (ML) has already revolutionised customer engagement strategies, including open rate optimisation. Reinforcement learning (RL), a subset of ML, stands out for its ability to adapt through trial and error. RL systems aim to maximise long-term rewards by interacting with an environment and learning from outcomes.
Several studies have highlighted RL’s potential in e-marketing. For example:
- One framework demonstrated how RL could optimise delivery times by learning from customer interactions, even with incomplete data.
- Another study found that using RL algorithms to predict email delivery timing boosted open rates by over 14%.
These findings underscore RL’s power to uncover patterns and behaviours that traditional methods might miss.
Building an RL Framework for E-Marketing
Optimising open rates using RL involves several steps:
- Data Collection: Understanding customer behaviour is the first step. This includes analysing when customers are most likely to engage with emails.
- Customer Modelling: Creating a model that represents typical e-commerce customers. This simulated environment enables safe and scalable testing of different strategies.
- Algorithm Development: Implementing RL algorithms to interact with the customer model. These algorithms learn and adapt over time, identifying optimal delivery windows for specific audiences.
Over the course of developing these RL systems, extensive research into core RL concepts and their practical applications was conducted. Neural networks also played a pivotal role in enhancing RL strategies. During implementation, tuning, and training, challenges arose that required innovative solutions. One such solution was the introduction of Targeted Adaptive Exploration (TAE). This approach addresses limitations in traditional ε-greedy methods by balancing exploration and exploitation for each individual customer. By tailoring exploration strategies to customer-specific parameters, TAE significantly improves the system’s adaptability to evolving environments.
Additionally, a second challenge arose from the representation of the state space, which consisted only of customer IDs. This limited the ability to leverage the pattern recognition capabilities of Deep Q-Networks (DQN). To address this, a dynamic customer lookup table was integrated, allowing the RL agent to utilise neural network outputs to refine its action predictions. While this innovation enhanced the learning process, further refinement of the DQN agent is necessary, as results indicated it underperformed against baseline policies in some cases.
Practical Applications and Benefits
When applied effectively, RL can revolutionise e-marketing strategies. By sending emails at the right time for each customer, businesses can:
- Increase open rates.
- Improve customer satisfaction by demonstrating an understanding of their preferences.
- Drive higher engagement and conversions, ultimately supporting revenue growth.
Future Opportunities
This research opens up several avenues for further exploration:
- Enhanced Exploration Strategies: Developing methods to dynamically adjust exploration probabilities in tabular RL for evolving environments.
- Hybrid RL Agents: Combining tabular and neural approaches to handle incomplete state information while refining predictions.
- Customer Group Classification: Using neural networks to group customers by behaviour, simplifying scheduling while maintaining engagement.
- Real-World Integration: Transitioning RL systems to real-world customer interactions to validate and improve strategies.
- Expanded Customer Modelling: Incorporating additional dimensions like demographics, purchase history, and time-specific behaviours for a holistic view of customer interactions.
Final Thoughts
E-marketing’s success depends on connecting with customers in meaningful ways. Timing is a critical but often overlooked factor in boosting open rates. Reinforcement learning provides a cutting-edge solution by leveraging data and customer modelling to deliver personalised interactions. As businesses continue to explore innovative approaches, RL-based optimisation holds immense promise for enhancing engagement and achieving long-term success in e-commerce.
Read the complete research at: https://scholar.sun.ac.za/items/ec1d6445-670b-4329-8b75-718bfd421a98
