How AI Impacts the CRM Domain for Better B2C Services?

Customers form an imperative entity for any business. Knowing your customers and satisfying them with your product is somewhere an essential aspect to thrive in the market supremely controlled by customers' demands. And amid this CRM comes handy for companies to understand and interact with their audiences. "CRM stands for Customer Relationship Management. It's a technology used to manage interactions with customers and potential customers. A CRM system helps organizations build customer relationships and streamline processes so they can increase sales, improve customer service, and increase profitability," according to Salesforce.

Moreover, businesses have been using AI in space of CRM to boost the decision-making process. Some examples are online customer support, intelligent personal assistants, or process automation. The use of Customer Relationship Management (CRM) software and its value of providing a better service for consumers helps guide a business. As noted by Commercient, it is estimated that 2019 has brought an increase in spending on CRM as companies realize the importance of not just knowing, but understanding their customers. And according to the trends for 2019 (per Medium.com), CRM systems have evolved and integrated AI technology to improve the customer experience.

Let's understand how AI is changing the face of customer relationship management.

According to a market report, it has been estimated that by 2021, an additional US$394 billion in revenue could be gained from Artificial Intelligence adoption in CRM activities in the United States. There are many new developments in CRM software, such as face and voice recognition. Salesforce's Einstein and Zoho CRM's Zia are two examples of an AI-powered CRM that uses voice commands to access information.

Below are the three focus areas where AI makes it impact the most.

Lead scoring points sales reps toward prospects that are most likely to buy, and in the past, sales prospect algorithms classified each lead via fixed rules set by humans. With the advent of AI, lead scoring starts with these algorithms and adds to them user-defined factors. AI self-learns by weighing each factor that makes up sales reps' lead scores and consequently derives more attractive prospect lists than the sales prospect algorithms could on their own. The Einstein Platform by Salesforce and Coleman AI platform by Infor are good examples of how CRM software applications use AI for better predictive lead scoring.

Also, chatbots, robots that simulate human conversations, are increasingly used by CRM applications to assist customers in finding answers to queries and ensure that they're guided to a suitable channel (e.g., self-service or a company employee). Where chatbots, today, are currently focused on enhancing customer service platforms, in future they can potentially be applied to a variety of customer interactions. As predicted by Oracle, around 80 percent of major brands will be using chatbots by 2020. Also, Gartner Group predicts that 85 percent of customer interactions will be automated by 2020.

Further, leveraging AI for sales forecasting is the top growth area for sales teams. While historical sales data can be used to predict future sales, changing circumstances inevitably lead to inaccurate sales forecasts. AI serves a comprehensive understanding of each customer's needs and past behaviors, thereby reducing recurrent blind spots in sales and account scoring. AI also allows marketing and sales to assess the customer experience and customer journeys and to gain real-time insight into customer pain points, preferences, sentiments, and other buying triggers, all of which help to create more accurate sales forecasts. It has been predicted by Gartner that within five years more than 50 percent of the Fortune 500 will be using a combination of neural sales data and sales AI for better optimization of their sales.