Customer Growth: Does AI Help ?

AI for CRM Business Series : 1

The key to sustained business growth is to offer customer-centric products’ and services’ solutions. Customer-centricity dynamically evolves with our fast-paced lifestyles and with well-informed, data driven customers. That’s where businesses find opportunities to offer convenience and such flamboyant experiences – ones that customers are mesmerized and reluctant to move away from, unless flattered by better.

Various CRM functions engage with the customers on behalf of the organization. The sole objective: constant improvements in customer interaction, eventually in customer satisfaction that drives revenues. However, the question that continuously haunts the CRM Industry: Is there anyway that customer experience be made better? The answer has always been positive and it is technology that drives customer experience[1]. It was in form of Data Management, followed by Workflow automation and now with CRM focus on Customer Experience, Artificial Intelligence (AI) plays an important part[1]. Let’s understand how.

Predictive Lead Scoring is an automated lead scoring mechanism mainly based on historical customer data. It is based on using structured data sets obtained from large raw data, followed by identifying activity, interest and communication from leads along with other custom parameters defined by Sales representatives for score allocation (everything happening in background). This collected data helps in identification of potential customers and leads, increases the probability of lead conversion and an increased revenue. Predictive Lead Scoring helps Sales Representatives to identify easier targets and score on to achieving higher revenues.

Improved Service Quality at low cost is fast becoming a reality in this era of automation. Automating service desk operations and routine tasks is essential to deliver and improve customer experience, especially post-sales. The idea is to be virtually present around the customer every time making the pre- to post-sales transition seamless, bringing ease and user-friendliness to the overall experience. This instills confidence in customers about the company and offerings, keeps customers happily satisfied and moreover serves as excellent feedback source. Excellent brands thrive on customer service. Chatbots and voice-assistants have been pivotal in automating customer interactions 24X7 and 365 days a year. Good offerings have amazing customer support and great companies stand by their products and services.

In both the cases of Predictive Lead Scoring and automation, Natural Language Processing plays important role in identifying the sentiment, intent and mood of the customer based on communication. The response serves as useful feedback means in suggesting necessary communication that drives actions to retain customers, improve upon mistakes, make deals, build long-term relationships and sustain them. Improvements in Sales Pitch and conversations are based on understanding human language, especially dialects, accents and identifying the key words in conversations.

Suggestion and recommendation engines drive Sales in the Product and Service sectors. Machine Learning is used in observing customer buying patterns. It is based on large data chunks of specific age, gender, location and on various aspects to categorize and structure customer information to offer suggestions and recommend products and services. The insights from buying history and choices further improves the recommendations. Such practices have served to increase customer temptation and soar sales to a record high. It also has the potential to identify spending powers based on customer activity to gather Business Intelligence and prompt Sales representatives with necessary follow-ups and actions to boost revenue. Further, customers now align their requirements to the offerings with just a ‘touch’ on the screen. Image recognition and processing have been put to use in offering customer recommendations.

Case Classification is one area where Deep Neural Networks are used to automatically classify cases based on user history and trends. Cases related to known defects’ are routed to agents with expertise in dealing with typically known issues or provide instructions on the know-how to handle them. This improves customer service efficiency and makes agents extremely productive. The ultimate goal is to raise and maintain high customer satisfaction – Customer Efforts Score (CES), Customer Satisfaction Ratings (CSAT), and the Net Promoter Scores (NPS).

Reducing Customer Attrition requires unparalleled offerings and exceptional support. This has to do with product innovation and services offerings. However from the CRM perspective, customer attrition mainly reduces with constant efforts to improve customer experience. Organizations working tirelessly making all innovation and improvement efforts revolve around customer experience face no challenge but one – How to effectively manage high profits and Return on Investments (ROI)? The effective data handling from feedback (emails, customer reviews, social media, network platforms etc.) can be used to build strong Sales, Marketing and Support functions.

Training Sales Representatives using Sales Call transcriptions and gathered CRM Data has accelerated and refined the training flows. This low cost training accompanied by real-life scenarios brings Sales representatives up to professional proficiency without losing leads.

The Data Analysis improves lead score accuracy, identifies deals potentially at risks, forecasts revenues, identifies strengths and weaknesses of Sales representative/s. These AI use cases in CRM deploy Natural Language Processing, Machine Learning (Deep Learning), Image Recognition and Image Processing techniques to constantly improve and re-define the customer experience with an ability to make better decisions and constantly improve assistance in forecasting and making predictions.

Data is by far is the key ingredient in the AI revolution. More the customer information available, better is the organization equipped at targeting the user needs with the right offerings. Data possession also offers the potential to identify new product and service demands that fill in the gaps based on customer feedback (especially complaints). Handling data and putting it to good use requires either Machine Learning or other Artificial Intelligence techniques. Early AI investments surely guarantees better Data possession and thus positions businesses to stay ahead in competition. Does your business have this competitive edge ?