Organizational data is often scattered across systems, hidden within text, and locked within the minds of employees. Given the disparate channels, how might this data be unified and delivered to support agents engaged in service calls? Assaf Melochna and Shahar Chen, former colleagues at workforce management and service optimization company ClickSoftware, advocate an AI- and machine learning-based solution. The two cofounded Aquant, which algorithmically mines and analyzes data from various sources to learn manufacturing, utilities, and telecom companies’ unique service languages and map customer problems to solutions.
Today, in anticipation of substantial growth, Aquant announced that it has raised $30 million in a series B round led by Insight Partners, with participation from Angular Ventures and Silvertech Ventures, bringing its total funding to $40 million in the last 14 months. CEO Chen said the investment will enable the company to meet market demand by growing its engineering, client services, and go-to-market teams. “The expectations from customers of manufacturing companies are growing, just as skilled service workers are retiring. This makes exceptional service delivery more critical, and more challenging, than ever,” he added.
At a high level, Aquant captures the knowledge of subject matter experts by extracting insights from data silos like customer relationship management platforms and enterprise resource planning software. It analyzes the text of customer comments and field technician notes and validates its findings with top performers before delivering prescriptive insights to every member of the service team.
In this respect, Aquant is riding a cross-industry wave of customer experience automation adoption. Gartner predicts that 25% of customer service and support operations will integrate virtual customer assistant or chatbot technology across engagement channels by 2020, up from less than 2% in 2017. It’s common sense — Gartner reports that organizations report a reduction of up to 70% in call, chat, or email inquiries after implementing a virtual customer assistant, and 33% savings per voice engagement.
Chen says Aquant takes only days to learn a service language from customer tickets, parts catalogs, inventory, supply chains, internet of things alerts, and more. Its AI algorithms identify patterns and make decisions as they interpret the differences in the way service issues are described. Aquant then extrapolates context and intent and maps problems to the right solutions as it prioritizes technicians’ job schedules based on business goals.
Aquant aims to optimize part pickup, eliminate the inefficiencies of assigning a depot for each customer, and mitigate the risks that threaten service business (chiefly noncompliance, machine failure, and customer churn). An intelligent triage feature platform automates answers to customer queries by enabling field service agents to arrive at jobs with an understanding of the issue, in part by surfacing answers to agents’ questions through an existing customer relationship management platform.
Using predictive analytics, Aquant can predict when customer complaints are the result of error or environmental factors versus product failure. The system automatically prompts team members to respond accordingly, in theory eliminating wasted dollars spent on dispatching technicians and diagnosing and replacing parts, and it recommends solutions based on cost-effectiveness while searching for anomalies in warranty claims.
Aquant says its clients — Johnson & Johnson, DSL, the Home Depot, Smart Care Equipment Solutions, Edwards, Stryker, KLA Tencor, BD, TFI Food Equipment, Orbotech, Glory, 3D Systems, Rational, and Haemonetics — experience on average a 34% reduction in repeat visits, a 26% reduction in parts consumption, a 15% increase in first contact resolution, and a 30% reduction in cost per warranty claim.
Perhaps it’s unsurprising, then, that the global customer service automation market is anticipated to be worth $6.23 billion by 2022, up from $1.56 billion in 2016. Top brands include Kustomer, which recently raised $60 million to further develop its customer service process automation platform, and Directly, which this week nabbed $20 million. Zinier is another — it raked in $90 million in early January for its field service tools that embed AI and machine learning.
“Our mission is to invest in high-growth software companies that are disrupting industries to create more efficiency and maximize productivity,” said Insight Parnters managing director Peter Sobiloff Peter, who plans to join the company’s board of directors. “It was clear to us that Shahar and Assaf have a deep understanding of the field service software landscape and, with this, have a unique opportunity to build something disruptive in the space. Aquant’s centralized SaaS platform and AI technology has the ability to help manufacturers make smarter and faster decisions, which leads to real ROI with both revenue growth and cost savings.”
Aquant has offices in New York and Tel Aviv.
Author: Kyle Wiggers.
Source: Venturebeat
