Use Cases of CRM FS powered AI

  • 06/09/2021
  • AI

How CRM is powered by AI?


With technology advancing at a breakneck pace, financial services are feeling the need to increase efficiency, reduce costs and increase performance. CRM powered by AI promises to improve the speed and efficiency of already existing processes. Here are some examples of how CRM combined with AI can be used in financial services.

Use Cases of AI powered CRM

Process automation with AI powered CRM

AI-driven process automation can significantly reduce time-consuming administrative tasks that can be performed via CRM, allowing for a greater focus on customer retention and acquisition.

In addition, AI-driven process automation allows for simplification of procedures and their encoding into the system, resulting in increased accuracy, while closely monitoring risky transactions via risk scoring systems and faster response times via the financial services CRM.

Lead Qualification and lead management

In financial services, a large portion of sales reps’ time is spent on CRM-related activities, such as reporting and other administrative tasks. More often than not, sales reps don’t follow up with a lead because they forgot or are overloaded. Companies should devote more attention to customer relationship procedures and delegate other tasks to their sales staff. As the number of leads increases, it becomes more difficult to screen them manually. That said, every missed opportunity is a missed transaction. Financial services can’t afford to neglect leads, especially qualified ones. The combination of AI and CRM creates a true lead generation machine. This unstoppable combination allows financial services to get more done without sacrificing lead quality.

AI-enabled CRM solutions can automate the bulk of the analytical tasks associated with lead qualification. When a prospect expresses interest, CRM tools generate analytics based on a number of elements. In addition, AI technologies serve as the initial point of contact between a prospect and a company. Once a prospect has been qualified, a representative is responsible for closing the deal. Predictive analytics can rank accounts and prospects based on their behavior. This allows sales reps to prioritize the most qualified leads.

Predictive Analysis with AI powered CRM

With access to masses of customer data, financial services now know their customers better than they know themselves. This is made possible by CRM, which uses machine learning to infer data patterns from a 360-degree view of the customer. With CRM for financial services, financial companies can provide personalized messages and offers to consumers based on their transaction and interaction history. In this way, AI-enhanced predictive analytics can help increase financial services performance.

For example, AI-assisted suggestions based on predictive analysis of the customer’s portfolio can encourage customers to choose one particular financial asset over another.

CRM predictive analytics can help financial services improve their service in several ways, including:

  1. Enable representatives to spend more time on customer-related tasks.
  2. Gain a holistic view of the sales process and more easily influence desired outcomes.
  3. Leverage past data to get specific recommendations based on analysis of transaction history. Gain actionable insights in addition to opportunity indications to steer customers in the right direction.
  4. Contribute to the development of the tool by providing feedback and participating in the improvement of the suggestions provided by the AI.
  5. Identify whether opportunities are favorable or unfavorable and obtain recommendations for action based on the data.
  6. Gain insight and anticipate customer or investor issues before they are even aware they exist.

Content management

CRMs can automatically curate personalized emails, reviews and customer reports using AI. In addition, you can use this feature to write specific product descriptions, destination sites, social media posts and news articles.

Customer segmentation with AI powered CRM

CRM powered by AI can help you be more accurate in your customer segmentation strategy. Predicting customer behavior via micro-segmentation of the entire customer database through CRM allows the company to increase its returns by offering services to customers based on their previous investments or personal assets. Using sophisticated algorithms, you can easily categorize customers based on their demographics, investment behavior, preferences or dislikes.

You can analyze historical data on customer behavior and predict future behavior to provide tailored, targeted offers. The more targeted your marketing and outreach efforts are, the more leads you can get.

In addition, you can use all the data to target a specific group of potential customers rather than a broad segment of prospects. Plus, automated segmentation can generate accurate segments and launch targeted ads from a centralized data repository, all thanks to AI.

Sentiment analysis during calls

Understanding your customers’ emotions is essential to building trust with them. Because of the difficulty of developing trust without face-to-face contact, some research indicates that only a small percentage of customers trust representatives. AI can help solve this problem. Through sentiment analysis, AI-powered technologies can assess customer interactions during calls and determine their emotional state.

For example, AI technologies can perform real-time conversational analytics to determine customer emotions, call effectiveness, and the optimal way to respond.

Bottom line

With AI, you can take your CRM to new heights and achieve incredible revenue over time. Therefore, use AI-enabled CRM solutions to improve customer engagement, better understand individual prospects, and finally increase customer performance and satisfaction.

Please contact us if you have any questions.

A propos d'Asklogix

Asklogix est un éditeur de logiciels CRM, basées sur la plateforme Salesforce, et conçues pour les Services financiers..

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