Customer Data
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Posted by Scott Parker

B2B customer support teams help organizations maximize the value of the products and services they’ve purchased. However, these individuals may be responsible for handling customers and products that range from the dozens to the hundreds of thousands. In addition, they’re rated on customer service metrics such as issue response and resolution times and the number of tickets that are opened and closed every day. Integrated survey tools enable customers to rank representatives right after interactions, keeping the pressure on teams to deliver exceptional service at all times.

How All Customer Service Teams Rank Their Top Priorities

How All Customer Service Teams Rank Their Top Priorities

Source: The State of Customer Service in 2020, HubSpot Report

Organizations place a premium on customer service and support, as leaders know it helps differentiate their businesses in the marketplace, reducing churn, driving revenues, and increasing buyer satisfaction. Fast, effective issue resolution increasingly separates leaders from laggards. According to a study of 1000 companies:

  • The average customer support response time is 7 hours and 4 minutes, however the top 5% of organizations respond in just 16 minutes.
  • The average customer support resolution is 3 hours and 10 minutes, but the top 5% resolve issues in 17 hours.
  • The average customer support team member is handing 21 different tickets each day.

Knowledge Management Improves Customer Support Delivery

Knowledge discovery can be a powerful resource for busy, stressed support teams, who are feverishly reviewing customer information as interactions unfold. Just like it sounds, knowledge discovery is “the overall process of converting raw data into useful information.” However, providing customer support teams with the information they need when they need it has historically been challenging.

Customer Information is often Siloed across Multiple Systems

While organizations typically do a decent job of capturing information, much of it may be trapped in business and technology silos. For example, customer information may span legal contracts, risk assessments, credit ratings, purchase orders, product usage, co-creation sessions, payments, social media posts, customer support calls or chats, engagement with chatbots and interactive voice response (IVR), website and knowledge base searches, webinar participation, resource downloads, and much, much more.

Now, with the advent of social, mobile, and the Internet of Things, data growth is soaring exponentially and may not be fully collected and analyzed. That’s a wealth of insight that could inform the customer experience that is going unused.

Customer Support Tools with Integrated Knowledge Discovery Help Spur Issue Resolution

Customer support tools typically integrate with critical systems, such as customer relationship management (CRM) and enterprise resource planning (ERP) solutions, giving staff insights into B2B buyers’ and product users’ needs. They also provide help desk software, ticketing and live chat, streamlining service interactions.

However, tools integrated with knowledge discovery provides so much more. Today, organizations can tap:

  • Deep knowledge bases: Instead of simple FAQs, B2B companies can provide help articles with detailed step-by-step directions, pictures, and hyperlinked content to aid with issue resolution. Search analytics also help service providers understand what customers are looking for, so they can develop new content or improve help documentation over time. According to Forrester, customers prefer knowledge bases over all other self-service tools.
  • Knowledge discovery software: Only enterprise search platforms can access, surface, and package insights from dozens or hundreds of databases at the time of need. Artificial intelligence (AI)-driven search tools such as Sinequa's Insight Engine provide staff with a single easy-to-use interface that displays relevant information across myriad systems, making it easy to answer questions and resolve issues.
  • AI-driven service: Organizations are using AI chatbots to gather information, offer automated answers to questions, direct customers to relevant content, provide updated wait times for interacting with support staff, and streamline conversations.

The ongoing pandemic has strained customer support teams and processes, as they handled a surge of customer questions, concerns, and complaints due to marketplace events and changing policies. Those organizations that proactively invested in customer knowledge discovery were better able to handle increased call, email, and chat volumes, while still delivering exceptional service.

Sinequa’s Insight Engine Improves Knowledge Discovery for Customer Support

Sinequa’s Insight Engine is the cognitive search tool you need to strengthen knowledge discovery and improve customer support. The platform includes 200 ready-to-go connectors and converters to more than 350 document formats, ensuring your support team can access all the information they need to serve customers. There’s no need for representatives to log in and out of up to 20 to 30 systems, which increases call or chat length and often angers customers. Instead, representatives can display mastery of the customer relationship, guiding interactions and providing the fast, targeted issue resolution that delights buyers and strengthens relationships.

Sinequa's Insight Engine Has Been Named a Leader in the 2021 Gartner Magic Quadrant for Insight Engines – the Fifth Year in a Row.

But that’s not all. Sinequa's Insight Engine is constantly improving. The platform harnesses AI technologies like natural language processing (NLP), and machine learning (ML) to enrich data, continually enhancing information relevance over time. And this process happens at scale.

How a Banking Call Center Improved Customer Service

Consider this example. A U.S. bank with more than $475 billion in assets was experiencing significant customer support challenges. Bankers had to access more than 30 different applications while taking 100 calls a day and struggling to meet an average handle time (AHT) goal of three minutes or less. Staff were taking shortcuts to meet AHT requirements, which increased security and compliance risks. In addition, call center turnover had soared to 35%, driving up hiring and training costs and compromising service quality.

The bank implemented Sinequa's Insight Engine in just three months to provide bankers with the information and tools they needed to serve customers. The business search engine increases the bank’s control by enabling granular access privileges. Bankers can access the information they’re entitled to, based on their role and security clearance, while providing fast, relevant and customized service. Now, banker shortcuts that create compliance risks are a thing of the past.

Sinequa's Insight Engine supports 88 different workflows and between 2M and 4M queries per month from a team of 80,000, handling complexity with ease. The platform has enabled the bank to decrease AHT in the call center by 16.6%, reducing annual costs by $49.8M. In addition, bank leaders expect to reduce turnover by 10%, saving another $7.8M. The bank is on track to migrate 33M data elements, which will provide even greater insight into customer needs.

In addition, Sinequa's Insight Engine makes it easier for representatives to tap the expertise of colleagues when needed. The bank is implementing an Expert Finder application, which will generate profit of $2.14 while further enhancing service.

You can get results like this when you strengthen knowledge discovery with an advanced search engine.

Learn how this bank improved customer support and unlocked millions of dollars in new revenues.

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