Helpshift’s SensAI Predict auto-classifies tickets and automates service workflows. Using AI-driven proprietary technology, it improves time-to-first-response, lowers overhead, and enables agents to focus on doing what they love best about their jobs: helping customers.
In An Increasingly Automated World, Customer Service Is Lagging Behind
The technology that powers SensAI Predict — auto-classification and machine learning — is already ubiquitous in other industries and consumer-facing products. For instance, on the auto-classification side, logistics and delivery services offer automated routing engines that allow organizations to schedule work orders based on resource availability, inventory, and timeline. Automated classification is also used in more everyday settings, such as Spotify using behavioral and demographic data to offer personalized playlists to different cohorts of users, and even universities using predictive auto-classification to identify at-risk students. Machine learning has been applied even more broadly, and is used in everything from your Facebook Newsfeed to Amazon shopping recommendations, to retinal eye scans that can preemptively identify cardiovascular disease. Industries from travel, to healthcare, to education have implemented varying degrees of auto-classification and machine learning to offer better, more efficient consumer experiences.
Despite this prevalence of use cases, customer service traditionally has not incorporated even the simplest forms of auto-classification into its workflow — much less machine learning. Customers are bounced around from agent to agent, and when there is some form of routing based on ticket classification, this routing is either entirely manual, or reliant on static rules based on manually programmed keywords. It’s cumbersome and inconvenient for agents and customers alike.
To make matters worse, as customer service lags behind other industries, consumer expectations have grown to meet the service they receive in other areas. Today’s customers expect experiences to be digital native, fast, intelligent, and above all, efficient.
The efficiency that other industries have mastered with gusto has been sorely lacking from customer service — until now.
Enter SensAI: AI-Powered Machine Learning that Auto-Classifies Tickets
SensAI leverages the power of automation to auto-classify and automatically route tickets based on factors including:
- Issue urgency
- Customer data
- Technical level
- Agent capacity
- Category/ language
When a ticket is submitted, SensAI first puts the ticket into one of many classification ‘buckets’. It decides whether it is a shipping and delivery question…an account access question… a technical, ad hoc query, etc. Based on machine learning, keyword identification, and information collected through a chatbot (such as username, description of the problem, device type, etc.) the system can auto-classify tickets in real time. Once the machine learning engine has had time to learn through an ever increasing corpus of data, it auto- classifies with greater accuracy than human agents.
There’s no such thing as a triage backlog with SensAI. Because it ‘reads’ and interprets language at 10x the speed of a human, it can make decisions in a fraction of the time that a person could. This dramatically reduces error rate — such as customers getting transferred between agents — and also significantly reduces the wait time as a result.
After the system has auto-classified the ticket, it either automatically routes it to the agent best equipped to efficiently solve the customer’s problem, or follows a pre-programmed action, such as invoking a chatbot or suggesting a knowledge base article to deflect the ticket.
If the ticket needs to be routed to an agent, unlike other automated customer service solutions, the routing algorithm goes beyond assignment based on specialty and seniority alone: it also takes into account agent and team backlog, and will automatically adjust to account for these factors. Customer service managers can also set rules to ensure that no agent or team ever ends up with a backlog that results in a service level agreement (SLA) violation.
One of the biggest benefits of SensAI Predict is that once it is set up with adequate data and tags, it will continuously learn so that no additional manual effort is required. The algorithm processes and learns new categories, or ‘labels’ as they appear, which makes the system faster, more accurate, and easier to work with than traditional, keyword-based tagging systems.
SensAI Predict has five tangible benefits:
- Instant, more accurate ticket classification
- Faster time to ticket resolution
- Higher level responsibilities for supervisors
- Happier, more engaged agents
- Better CSAT ratings
No agent wants a huge backlog, and no customer wants a 40 minute wait time. SensAI offers a solution that helps customers and agents both get what they want: resolution and increased brand loyalty.
It’s also important to note that since this AI use case is not customer facing, and is only operating in the background, there is no need to fear rogue outbreaks due to hacking Natural Language Processing (NLP) technology.
Bringing Customer Service Into An Increasingly Automated World
Predictive auto-classification is only one of many applications of AI today. From the natural language processing used by Siri or Alexa, to decision-tree based chatbot used by Geico, to the insights provided through Twitter’s trending feature, AI is seamlessly woven into the fabric of our everyday lives.
SensAI similarly weaves the most advanced aspects of automation into every facet of the customer service journey. In our next deep dive, we will outline first how the SensAI chatbots can dramatically decrease ticket backlog and improve customer satisfaction, and then how to leverage the power of AI-driven, real-time insights to help customer service managers track and mitigate emerging issues across all bot and AI products.
Through predictive auto-classification, chatbots, and insights, SensAI gives the customer service industry the tools it needs to not only meet, but exceed the modern consumer’s expectations.