Conversational AI is the quickest way to convert your leads into prospective customers. It has already built an enormous application space in the technology and marketing domains. Businesses are adopting voice-based assistants to enhance their sales efforts and increase the lead generation processes. According to McKinsey, AI has the potential to add between $1.4 to $2.6 trillion in sales for businesses worldwide.
Many well-known brands are implementing conversational AI in their business models to offer customers with personalized experiences. AI helps businesses to understand the intent of the customer while engaging in conversations with them. In the present time, customers have a variety of options to make purchase decisions. Businesses can further leverage the NLP algorithm to respond to customer intent in the form of text or voice. Conversational AI uses an ML algorithm to continuously learn customer behavior, their language based on the conversations and help generate recommendations to ease the customer’s product choices. This method can be achieved by using powerful intelligent chat platforms. The conversational AI must be well trained and computed before customer handling. More customer-centric campaigns with marketing techniques coupled with product recommendations and predictive lead scoring can be used to drive sales and maximize revenue potential.
Customer-centric campaigns – Businesses now are more digitally-driven which is an added advantage in conversational AI implementation. Organizations’ websites have various customer-centric forms such as feedback forms, inquiry forms, subscription forms, etc which helps them to keep a record of all the data collected. And with the help of conversational AI, organizations can do predictive analytics and get insights in real-time to address the customers with more relevant products and services. Organizations can further segment the customers based on their intent by applying funnel analytics to get a more clear understanding of how their products and services are meeting their customer needs, a segment of customers’ desires, new innovations prospects are looking for and much more.
Market research – Many organizations do extensive market research before rolling out products and services in order to segment their target markets. Conversational AI eases their research analytics models by providing them with actionable insights and assistance on how to deal with various market segments. As conversational AI can automate repetitive tasks without human interventions the possibility of any error is minimal.
Sales enablement – The marketing and sales team collects hundreds of leads on a daily basis from several ongoing campaigns and sources. But only a fraction of them is capable of effectively utilizing the leads to final closure. Leads gathered need to be nurtured until the final sign-off. This process is wearisome but significant for the sales team in order to close a deal. It is widely observed that after an initial contact it takes 3-4 follow-ups by the sales team to get a response from the leads. One sales resource can reach out to one lead at a time which will be quite difficult for companies having big data. AI assistants can be integrated into any existing ERP and other databases to contact each leads in a personalized way and do multiple automated follow-ups using deep learning algorithms in order to qualify them as sales prospects within a short time span, unlike human representatives. These tasks can be easily automated using deep learning to close deals rapidly.
Customer relationship management – Sales CRM contains a wide range of leads information and quite often sales representatives are lost in those records while manually searching for lead details that lead to a loss of considerable time. AI helps the sales team to dig up old leads collected way back and somehow fallen off track for not communicating proactively. Conversational AI when integrated with sales CRM provides deeper insights on leads’ intent, demography, and other information required by the sales rep to engage with them proficiently. One huge advantage voice-based assistants can provide here is that the sales personnel are not required to be wary of technical know-how to understand the insights provided. They just need to speak in natural language with the tool and the NLP does the rest. The time and efforts saved by the sales representative can be productively utilized in communication with prospective clients to increase sales closures.
Predicting purchases – Predictive analytics and machine learning can predict customer purchases as the tool is consistently learning from human behavior and can alert the logistics departments about the deficiencies of required materials in their inventory well ahead of time in order to avoid any delay in sales. AI can track the movements of millions of customers on the website at once and decide the demand for definite products and services which encourages the sales team to come up with various promotions on those particular products and services.
Content selling – AI can analyze high volumes of data and track content engagement rates. This can help sales and marketing personnel to prioritize their follow-ups to provide personalized content to prospects at every stage of the customer journey and also share relevant content with the social networks thereby progressing towards a meaningful conversation and a fruitful conversion. AI and ML can be a great support to the sales team in designing sales campaigns and promotions and allows them to offer feasible sales discounts. AI algorithms analyze past price data such as discounted figures, promotional parameters, and sales history and calculate the price elasticity for certain products and services so that the team can set prices optimally personalized to the customers.
Conversational AI not only reduces human efforts in tedious tasks but also helps the sales team to generate more revenue from the market.