Increase the Signal, Decrease the Noise
Identify Knowledge & Expertise
Leverage 360º Views
Customer service centers serve as organizational information hubs, resonating with the voices of the customers. They are strategic to an enterprise, as they are often the most recent and most frequent point of contact that the customer has with an organization.
Properly used, customer service centers can satisfy customers and improve retention. They can also drive revenue by cross-selling and upselling. To do this, they must manage the volume of interactions efficiently and control average handle time (AHT). Increasingly, they must achieve this with tighter budgets. Instant access and 360-degree views of all customer and product data is mandatory to enable customer service representatives (CSRs) to operate more efficiently.
With people and information spread across various locations, this task can seem daunting. The right mix of technology can enable customer service centers to overcome these challenges and run at peak performance. Below are four tips for CSRs to manage high volume of interactions:
Improving visibility into real-time customer data
CSRs need visibility into customer data across all contact and interaction points within the enterprise — regardless of location, repository and format. By aggregating all data and providing a single, secure access point to relevant and real-time customer and product information, a unified view of information can be formed to help CSRs respond to customers’ concerns and issues quickly and accurately.
Relieved of the burden of navigating multiple applications to find a single piece of relevant information, CSRs can immediately concentrate on the callers’ concerns and quickly resolve their issues — increasing first call resolution and reducing average handle time to minimize the volume of customer interactions. Automatically providing a unified view of customer information effectively enables the customer service center to improve productivity and reduce operating expenses.
High attrition has always been a major concern for customer service center managers. Rehiring and retraining costs directly impact the bottom line. More importantly, high turnover rates burden CSRs, affect productivity and hamper the customer service center’s ability to provide quality service.
Automating access to relevant information can help customer service centers lower attrition by minimizing the excessive pressure and stress of the customer service center environment, which is cited as a major reason for attrition.
Leveraging automated analytics on top of customer and product information, customer service center managers can quickly spotlight new products for training and push information out to their CSRs. Simplifying the way CSRs access customer and product information and providing ways for CSRs to easily collaborate and share knowledge reduces CSR stress and consequently turnover. When CSRs have the information needed to answer customer questions and resolve issues confidently, they are much better able to interact comfortably and build close and lasting customer relationships.
Accelerating time to proficiency
CSRs never know what inquiry or problem they will face on the other side of an inbound call. As such, they must be well-versed on the products, services and policies of their organization. Successfully training CSRs is vital to the success of the customer service center. The cost of attrition per CSR is high, with new employees taking up to three months to complete initial training in many industries.
This can be exacerbated as many customer service centers have myriad applications and repositories, such as CRMs, ERPs and external databases, that CSRs must learn to navigate to prepare for and complete a call. The ability to seamlessly connect to these applications and provide a unified view to information greatly reduces training time and cost.
Sharing CSR knowledge
Collaboration capabilities that promote knowledge sharing and retention — even if employees leave — enable the remaining CSRs to maximize and enrich each customer interaction. Enterprise data is continually growing; as a result, CSRs have even more information to learn and retain. In addition, customer service centers are often scattered across far-reaching locations without sufficient support for their distributed organization. A scalable, distributed platform for information access solves this problem and allows data to grow without compromising access or speed for CSRs. They can then concentrate on listening to customer concerns and ensuring complete satisfaction, enhancing the entire customer experience.
Companies that employ the right mix of technology in their customer service centers empower their CSRs to go beyond solving customer issues to being customer champions — listening and responding fittingly to their needs. By actively listening, CSRs can turn complaints into revenue. By having relevant information consistently and securely available, organizations can react quickly to customer demands, innovate business processes, profile new target markets and formulate ideas for new product features.
Consolidating silos and promoting the quick and easy transfer of information and insight captured in the customer service center across the entire enterprise allows executives to make informed decisions that positively impact the direction of the company.
Forces of global competition, narrow margins, higher product development costs, and tenuous exclusivity holds drive organizations to push innovation, seek cost cutting strategies, and go-to-market as quickly as possible. Demands change frequently while regulatory and compliance standards become even more stringent. Organizations must keep up, and the pressure on research and development (R&D) never stops. R&D is the critical driver within the organization, whether within a large aircraft manufacturer or a leading automobile company looking to develop cutting edge products and services or a pharmaceutical company accelerating time-to-market for new drugs or a CPG company reinventing waning products. R&D thrives on information: customer information, expert information, product information, scientific information, market information, and competitive information.
To be at the forefront of innovation, R&D departments need complete visibility into both new and historical information across the entire enterprise as well as access to research from external public and premium information services. This is no simple task in today’s world where we are inundated with data — more data, more opportunities and more challenges. As a result, many companies depend on Cognitive Search and Analytics (CS&A) solutions to harness insightful, high-quality information and fuel innovation within their product and solution portfolios.
THE PRESSURE ON R&D
As organizations strive to create value, enhance customer experiences, and differentiate themselves from their competition, they have placed demands on their R&D departments to:
To meet these demands, R&D depends on complex scientific and engineering content that contains implicit conceptual relationships that can and should be semantically linked to simplify access to the knowledge embedded in that content.
HOW COGNITIVE SEARCH AND ANALYTICS HELPS
Cognitive Search and Analytics solutions amplify the expertise of R&D departments by surfacing insights from data across the enterprise, irrespective of location and format. From a single, secure access point, these solutions enable R&D professionals to unlock relevant and timely product research that helps make informed decisions. In addition, these capabilities are not limited to internal information; users can quickly access information from external Web sites and other applications, deriving relevant information and seamlessly integrating with internal enterprise information.
Cognitive Search and Analytics solutions enable enterprises to maximize the value of their intellectual property. Powerful search relevance and navigation capabilities enable researchers to find valuable pieces of past research and even parallel work going on without each group knowing about the other — eliminating duplicate work, reducing time spent in trials and shortening development cycles. These solutions allow employees to tag, bookmark and comment on documents, enabling collaboration and making teams more innovative, efficient and productive. Surfacing this existing knowledge enables workers to leverage the past work of distant or former researchers to benefit future research. Dynamically delivering relevant information, surfacing knowledge and enabling collaboration can decrease R&D costs significantly. Because R&D departments need to comply with a myriad of complex regulations, they need to be aware of relevant regulations without having to sift through the myriads themselves. This visibility enables R&D to stay abreast of regulatory mandates and efficiently manage compliance. Organizations can also leverage these solutions to send alerts to employees when there are new policy and compliance changes so that relevant R&D stakeholders are immediately notified.
Managing and maintaining product specifications is a critical function within R&D. Cognitive Search and Analytics solutions can access virtually any data source and expose changes when information is deleted or becomes outdated. These solutions can alert workers when any new information is created that impacts their specific process in the development cycle. These solutions also track and respect the access permissions accorded by each target application; only those with the correct privileges can access restricted information. Cognitive Search & Analytics solutions give researchers clear insight into product requirements and enable them to collaboratively develop safer, higher quality products that meet regulatory requirements.
RAPID RETRIEVAL OF RELEVANT INFORMATION MAKES THE DIFFERENCE
Extracting relevant information from vast and complex data volumes is a challenge that requires a sophisticated and scalable solution. The Sinequa Cognitive Search and Analytics platform handles all structured and unstructured data sources and uses Natural Language Processing (NLP), statistical analysis and Machine Learning (ML) to create an enriched “Logical Data Warehouse” (LDW). You can think of it as a repository of information about data and about relationships between data, people, concepts, etc. This LDW is optimized for performance in delivering rapid responses to users’ information needs. Users can ask questions in their native language or ask that relevant information be “pushed” to them in a timely fashion when it emerges. More than 150 connectors ready for use “out of the box” make the process of connecting multiple data sources fast and seamless. Company and industry-specific dictionaries and ontologies can be easily integrated, putting domain-specific knowledge “under the hood” of the Sinequa platform, making it an intelligent partner for anyone in search of relevant information.
The advanced semantic capabilities within Sinequa’s platform provide strong relevance in 21 different languages to assist organizations with even the most geographically and linguistically diverse workforce.
REAL-WORLD EXAMPLE: AMPLIFYING BIOPHARMA EXPERTISE
Consider one of Sinequa’s biopharma customers, a research-intensive organization dealing with a vast number of highly technical documents, produced both in-house and externally. The information in these documents varies according to the field of its origin – e.g. medical, pharmaceutical, biological, chemical, biochemical, genetic, etc. – and may deal with diseases, genes, drugs/active agents, and mechanisms of action. A lot of the information is textual, but there is also structured information, like molecular structures, formulae, curves, diagrams, etc. The volume of this information is on the order of magnitude of about 500 million documents and billions of database records.
Now consider the more than 10,000 R&D experts within the organization trying to leverage this information daily. They need to be able to ask topical questions, find relevant people and documents, and explore the vast information landscape to discover knowledge. The Sinequa platform supports this by plowing through the hundreds of millions of documents and equally large amounts of structured data, analyzing the data, analyzing the natural language user queries, and classifying results by category in real time. With the data tamed and enriched, it is presented to the user via a simple, intuitive interface with faceted navigation aids that allow the user to filter results further based on structural attributes that are either explicit or were intelligently derived by the system. The interfaces, also referred to as search-based applications (SBAs) are configured to expose functionality that is very specific to an R&D expert, aligning the solution with the goals of the user.
The Sinequa solution has proven to be very valuable to the customer in question, putting both internal and external research–related information that scientists need for research, development, and decision making into a single virtual repository with advanced navigation and retrieval capabilities. It has also proved to be very beneficial to teams of research and development contributors by allowing experts around the world to collaborate more easily through a single research application. Features such as navigation by topic across multiple repositories, de-duplication of similar documents, and improved research capabilities have all made knowledge workers more efficient and innovative.
Sinequa’s Cognitive Search & Analytics platform leverages relevant customer and market information to give R&D organizations insight and the ability to react quickly to demands. Teams utilize this platform to collaborate and share information. Sinequa effectively eliminates data silos and delivers relevant information from data to users in their business context, such that they can make better decisions, drive innovation, reduce risk, and be more efficient, which in turn enables forward-thinking R&D departments that thrive on continuous product improvements and introductions to amplify the collective expertise of the organization.
If you are aiming at deploying a performant Enterprise Search platform you would do well to consider these 3 key criteria:
Strong Content Analytics
In order to be extremely effective and efficient, an Enterprise Big Data Search and Analytics platform should offer strong Content Analytics that combines indexing of both structured and unstructured data. Indeed, it’s the combination of both types of analysis that delivers more relevant results and insights to users.
In addition, a performant Enterprise Big Data Search and Analytics platform should also “put powerful NLP to work in surprising scenarios” according to Forrester Research. The semantic analysis (named entities extraction, text mining agents, etc.) coupled with the statistical analysis and machine learning algorithms enables data-driven businesses getting more relevant and contextual information from search results.
A Big Data Search & Analytics platform only deserves its name if it connects easily to virtually all data sources of an organization. If you need access to a new data source, you want to have it now, not in 3 months.
Multiple connectors to structured and unstructured data sources (internal and external to an enterprise) will help you cope with “data variety” and ensure that projects can start delivering value to users in a matter of weeks rather than months.
Your platform architecture should offer the necessary scalability to deal with your large and diverse amounts of Big Data. It should be scalable enough to combine statistical analysis of structured and unstructured data with linguistic and semantic analysis of texts in several major languages (NLP – Natural Language processing). Moreover, an out-of-the box Grid Architecture that allows you to flexibly adapt resources will help you gain agility and get faster response times.
So, is your Enterprise Big Data Search & Analytics platform as performant as you thought?
If not, request a demo here and see how you can get value from your big data easily and rapidly!
You are overwhelmed with business data coming from diverse and multiple data sources and with demands from various user populations to make use of this data? In that case you should look at how to construct your own App Store!
You may be wondering “How on earth am I going to that?”
But our customers achieved it! They built their own SBAs on the top of our advanced search & analytics platform. And now, they have created an “SBA factory” serving all kinds of business needs.
Sinequa ES is designed for high performance content analytics across functions and industries, it offers a powerful platform to create search-based applications (SBA) with hitherto unimaginable speed. This allows them to create Apps for any operational, individual and data need.
Our unique content analytics, including Natural Language Processing, produce a “rich” index, with information added on top of the original sources: meta-information, concepts and relationships between contents, etc. Only such a rich index can serve as a platform for an abounding and ever-growing set of Search Based Applications (SBA): Its richness ensures that even the SBAs you haven’t thought of as yet will find the information they need to serve their users in the index. If you need to delve into the original data sources, Apps become too difficult to construct.
Our customer AstraZeneca’s vision is to have Search nourish their next generation of business intelligence software and help create new applications. They have created a particularly innovative “App Store”.
Curious now to know more about these Apps and see how they have been deployed?