With smart A/B Testing, Churn Prediction, & Recommendation Engine, you get an all-star performance on your campaigns
Over decades, marketing has been matching its steps with technological advancements. Starting from Radio and TV to email, SMS, and social media, to now leveraging the immense capabilities offered by Artificial Intelligence (AI) and Machine Learning (ML).
While AI is already revolutionising the ways in which marketing content can be personalised for the users through features such as Send Time Optimisation and Subject Line Optimisation, it’s time to look at some of the applications that can boost your marketing activities across both web and mobile.
Smart A/B Testing:
A/B testing has always helped marketers select a campaign that ensures better performance. (The process of A/B testing is fairly simple – you have a significant sample, you send both the variants to the sample and learn which variant is getting maximum engagement. Then, you implement the winning variant for the remaining set of your customers).
The problem with this approach is, the possibility of a few customers still preferring the other variant is overlooked and the conversion rate is compromised somewhat. But, AI uses advanced algorithms to classify which customers from the remaining set might respond to which variant, based on the sample data. In other words, you’re able to identify a look-alike audience.
Additionally, reinforcement learning with the ‘Multi-armed Bandit’ algorithm is used to continuously iterate the learning and implementing phases of the A/B test. With this, the system repetitively tests and optimises both the variants to achieve a significantly higher conversion rate.
Churned customers are those who have decided to end their relationship with your brand. Customer churn occurs when customers uninstall an app, unsubscribe from newsletters, or stop engaging on the website. AI can help you predict customer churn using regression and classification ML algorithms to process historical customer data.
On Netcore Smartech, we use the ‘Logistic Regression’ model to understand the correlation between different variables and churn. The variables in this case include your customers’ historical activities and demographic information. A ‘Decision Tree’ model is used to classify the customers based on their likelihood to churn. Additionally, ‘XGBoost’ algorithm is applied to improve the accuracy of the churn predictions. Once you identify that a particular customer is likely to churn, it helps you create and deliver specific multi-channel campaigns tailor-made towards achieving personalised user retention.
AI-powered recommendation engines enhance the shopping experience for customers by giving product cues that are closely related to the customer’s current product choices, based both on browsing and purchase history. The recommendation engines recommend products that customers have more probability of buying, thus boosting turnover through cross-selling and upselling.
The product recommendations that you see in Amazon such as ‘Customers who bought Product A also bought Product B’ are driven by collaborative filtering, recommending items that are liked by similar users. The movie recommendations that you see on Netflix are driven by content filtering, providing content suggestions similar to those viewed by the user in the past.
On Netcore Smartech, we use classification-based collaborative filtering and content filtering to optimise these recommendations, thereby supporting a variety of recommendation scenarios across industries, right from e-commerce and travel to food delivery and media OTT.
The power of AI- and ML-driven multi-channel marketing is limitless. Not only do they help you suitably leverage a more comprehensive understanding of your customers’ behaviour across channels and devices but also enable you to craft intelligent campaigns engineered to boost engagement, monetisation, and retention in the long run.
Schedule a demo today to learn just how you can harness the incredible power of AI and ML to add more muscle to your marketing strategy.