How AI-ML Helps Improve Conversion Ratio

AI-ML has become increasingly important in the business world due to its ability to process large amounts of data, make accurate predictions and decisions. According to a recent survey conducted by PwC, 72% of business leaders consider AI to be a business advantage in coming years.

Businesses have already started leveraging different AI-ML techniques to improve their business processes, business performance and, customer engagements.

In this blog we will understand how AI-ML models can help resolve business pain point to improve your Conversion Ratio and grow business. We will also see a live use case to understand it better.

How AI-ML Can help improve conversion ratio:

AI-ML models can help improve conversion ratios by analysing large amounts of data and identifying hidden patterns and insights that can improve the effectiveness of marketing and sales efforts. Here are some ways AI-ML models can help improve conversion ratio:

• Personalization: AI-ML models can analyse customer data, such as browsing behaviour, purchase history, and preferences, to create personalized recommendations and offers. This can increase the relevance and appeal of marketing and sales efforts, which can improve the conversion ratio.
• Lead scoring: AI-ML models can analyse customer data and behaviour to predict which leads are most likely to convert into customers. By scoring leads based on their likelihood of conversion, marketers and sales teams can focus their efforts on the most promising leads, which can improve the conversion ratio.
• Customer segmentation: AI-ML models can help marketers and sales teams segment their customer base based on various factors such as demographics, behaviour, and preferences. By doing so, they can tailor their messaging and offerings to each segment, which can increase the effectiveness of marketing and sales efforts and improve the conversion ratio.
• Chatbots and virtual assistants: AI-ML models can also help improve conversion ratios by using chatbots and virtual assistants to assist customers with the purchase process. By providing quick and personalized assistance, chatbots and virtual assistants can help customers make more informed decisions and improve the likelihood of conversion.
• Predictive analytics: AI-ML models can analyse customer data and behaviour to identify patterns and insights that can inform marketing and sales strategies. By using predictive analytics to anticipate customer needs and preferences, marketers and sales teams can create more effective campaigns that improve the conversion ratio.

Overall, AI-ML models can help improve conversion ratios by providing insights and tools that enable marketers and sales teams to create more personalized and effective marketing and sales strategies. By using data to inform decision-making and providing personalized assistance to customers, AI-ML models can improve the likelihood of conversion and help businesses achieve their goals.

How Intellify’s AI-ML Model Helps in Improving Lending Apps Conversion Ratio

Our client, a prominent Lending Organization, was struggling with consistent conversion ratio with no improvement. Despite the Loan Origination Dashboards presenting positive metrics regarding LO performance, branch-wise performance, and related data, decision-makers remained clueless about how to improve conversion ratio. The dashboards were capable of presenting real-time data with a Descriptive Analytics approach, leaving the organization without recommendations for resolving the business problem.

To address this challenge, Intellify deployed its industry expertise and different Data Science techniques to identify data points related to the conversion ratio pain point. We used our proprietary AI-ML framework to

• Analyse the data points from multiple sources.
• Identify the most affecting data points/factors.
• Categorize applicant personas.
• Analyse data from each cluster.

Our AI-ML model made 5 clusters of loan applicants based on certain demographic parameters, dividing them into Encouragers, Discouragers, and Neutral group of applicants.

The Encouragers were identified as the most promising cluster based on our AI-ML model’s analysis of their demographic attributes. Our BI dashboards guided the client to target the Encouragers with a specific marketing strategy. Our AI-ML framework provided insights into customer acquisition strategy modifications, enabling the client to make informed decisions.

As per our framework’s insights, the client modified its customer acquisition strategy to target Encouragers with personalized offers and incentives, leading to a remarkable 25% rise in the application conversion ratio in the next three months. The new prescriptive dashboards provided insights into the Encouragers’ demographic attributes and behaviour, allowing the client to tailor their marketing strategy to meet their unique needs.

The Intellify AI-ML model’s insights allowed our client to identify and focus on the most promising leads while also understanding the factors that contribute to conversion ratios.