Original article can be found here (source): Artificial Intelligence on Medium
AI in Customer Assistance Implies Something beyond Chatbots
One of the major challenges for any organization is to provide its customers with highly personalized experiences. For this more and more organizations are collaborating with a chatbot development company or hiring chatbot developers to develop custom chatbots that interact with customers, solve their queries, and save their precious time.
According to a report by Research and Markets, the chatbot market size is projected to grow from USD 2.6 billion (last year) to USD 9.4 billion by 2024.
While chatbots have proven to be useful (i.e. offer 24/7 customer assistance) they haven’t been able to harness the true potential of AI i.e. fetch customer insights that organizations require to serve customers in a better way. For instance, chatbots still struggle to identify indicators of customer dissatisfaction.
According to usabilla.com survey, 46% of internet users in the US would prefer online support from a live person, even if a chatbot can save them 10 minutes. Another study carried out by Help shift shows the challenges that internet users face while dealing with chatbots.
Chatbot development hasn’t been able to impress customers as it hasn’t provided the level of personalization and authenticity that customers demand.
However, there are other sophisticated applications of AI that have been able to provide more humanizing touch and gather details that organizations need to understand customers.
Using AI in Customer Assistance (more than just chatbot implementation)
The following pointers discuss various ways how AI solutions can take customer assistance to the next-level:
ML Based Customer Assistance
In order to provide effective customer assistance, organizations collect a lot of unstructured data. This data is generated by monitoring the activities of customers and gathering their everyday conversations — it holds a lot of vital information that is useful in understanding the customer behavior.
Machine learning (ML), a subset of AI, can be utilized to build analytics solutions that derive actionable insights from the unstructured data that is collected.
Here are some ways that ML powered solutions can transform customer assistance:
Predictive Customer Assistance
Predictive customer assistance refers to the case where customers get the support not only before they know they need it, but before the problem ever occurs, i.e. proactively detecting major incidents.
Let’s take a case where a customer calls for assistance. The agent who is going to attend this customer knows everything about him because of the company’s CRM software — this includes who this customer is, when he last called, what was his last issue, etc.
Moreover, the agent can feed this CRM data to an ML based analytics solution, which can anticipate the next issue that the customer is going to face. This is based on the customer’s previous behaviors, which aligns with the patterns established by hundreds or even thousands of other customers. Analyzing the patterns allow the ML based solution to predict the customer’s needs and suggest the best way to manage them.
Thus, the agent can not only satisfy the customer by solving his current issue but delight him by providing suggestions to solve the upcoming issues.
Sentiment analysis is a field of AI that utilizes ML powered algorithms to determine if the tone of a message is positive, negative or neutral — it goes through the words/sentences (like Twitter’s tweets and Facebook’s posts) and establishes the attitude, feeling, or opinion of the person who wrote them towards a specific topic, product, or brand.
With sentiment analysis, organizations don’t have to wait for lagging feedback sources such as post-call surveys or sales surveys to understand the voice of their customers. Instead, they can spot all the sentiment trends as they occur, and accordingly adjust the business areas that impact the customer experience.
Moreover, organizations can also utilize these constantly evolving sentiment scores to recognize opportunities for training and development, and decide how to handle emerging issues.
ML-driven Content Creation
Nearly 81% of all customers attempt to take care of matters themselves before reaching out to a live representative. Furthermore, 40% say that help center searches don’t generate the help they’re looking for.
ML can be utilized to analyze the data that originates from customer support tickets and transform them into actionable insights for agents to apply to help articles.
These insights highlight how users describe their issues and if these descriptions are related to the content of the knowledge base. Moreover, agents can take these recommendations and adjust the help articles, making them more relevant.
Whether it is a call, an email, or a chat query, smart routing refers to identifying the caller/customer and the reason for the call to be assigned to the appropriate agent.
Smart Routing uses AI to develop models from the collected customer profiles based on aspects such as purchase pattern, preferred communication channel, and previous inquiries.
These models are mapped with service agent profiles, which include details like experience, knowledge, skills, customer interaction history, and more. The most suitable profile for the incoming call/query is selected i.e. prediction of the best match between service agent and employee. Moreover, customer and service agent models are constantly updated, with the focus on improving future experiences.
Intelligent Email Marketing
Gone are the days of creating instinctive, gut feeling based email marketing campaigns. Today AI is well equipped in crafting various elements of an email marketing campaign to offer personalization and entertain prospects/customers.
Creating optimal subject lines
AI can be used to create an appealing subject line that includes things like word choice, sentiment, emoji, and more.
Here, the idea is to utilize natural language generation (NLG) to produce a bulk of subject lines based on a company’s brand voice and then use natural language processing (NLP) to carry out a sophisticated sentiment analysis that predicts how subject lines will perform.
Segmenting email marketing lists
Segmenting audience before sending customized emails is a great thing to do. According to a study by Campaign Monitor, marketers are noticing a 760% increase in revenue by segmenting traffic.
However, segmentation can be based on infinite criteria. The question that rises is how to carry out segmentation that will make the most impact?
This can be done with AI, i.e. it can be utilized to analyze audience behavior, identify key patterns (like website dwell time and purchase pattern), and carry out segmentation in new ways.
Optimizing email send times
Email scheduling has proved to be a debatable thing for marketers — the thought about whether the audience will open an email at 10:00 a.m. or 3:30 p.m. still makes them scratch their head.
Email send times can be optimized with AI — here the idea is to use AI to assess/analyze heaps of data stored in a company’s CRM or corporate email software and then use the analysis to build a predictive model for each contact.
With this, each prospect/customer receives an email at the time they are most likely to open it, based on the data analyzed.
While chatbot development is a great start, it is only the tip of the iceberg when it comes to using AI to offer the next-level of customer assistance. There are more sophisticated AI applications such as ML-powered analytics solutions, smart routing & case segmentation, and intelligent email marketing that are beyond chatbots and are all set to take customer assistance to new heights.
This is because these applications harness the true potential of AI — they use current and historical data to make predictions, recognize indicators of customer dissatisfaction, and proactively resolve issues in order to delight the customers.
Author Bio: Anant Desai -A passionate & creative inbound marketing professional whose life revolves around content, technology and sports.