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.
As organizations strive to create value, enhance customer experiences and differentiate themselves from their competition, they place tremendous demands on their R&D departments. From accelerating the delivery of innovative products to improving compliance to understanding consumer demands and improving responsiveness to gain and keep customer trust, R&D has a lot on its plate.
Global competition, narrow margins, higher product development costs and tenuous holds on exclusivity, drive organizations to push innovation, seek cost cutting strategies and go to market as quickly as possible. Consumer demands change frequently while regulatory and compliance standards become even more stringent. Organizations must keep up, and the pressure on R&D never stops. R&D is the epicenter of an 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.
R&D thrives on information: customer information, expert information, product information, scientific information, market information, regulatory 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 easy feat in today’s world where we are inundated with data — more data, more opportunities and more challenges. As a result, many companies depend on machine learning solutions to harness insightful, high-quality information and fuel innovation within their product and solution portfolios.
Here are six examples of how R&D departments are leveraging machine learning to improve their effectiveness and create competitive differentiation for their organizations:
Machine learning can leverage and build on relevant customer and market information to give R&D organizations insight and the ability to react quickly to demands. Teams are utilizing this technology to eliminate data silos and deliver increasingly 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. This in turn enables forward-thinking R&D departments that thrive on continuous product improvements and introductions to amplify the collective expertise of the organization.
As anyone doing business in Europe probably knows, the European Parliament adopted the General Data Protection Regulation (GDPR) just over a year ago in the Spring of 2016. The GDPR requires any company doing business in Europe to comply with strict new rules around protecting customer data. This has already introduced cause for concern among corporate security teams, as the GDPR takes a broad view of what constitutes personally identifiable information (PII). Companies will essentially need to provide the same level of protection for things like an individual’s IP address or cookie data as they do for name, address, and Social Security number. (more…)
As the data-driven age gives way to an information-driven economy where context is critical to surfacing useful insights from data, taking in relevance feedback from users, especially expert users, will play a major role in driving the benefits. This article explains the concept of a relevance feedback model and why you should care.
Assume you ask a person or a system to provide you with information on a certain topic. There may be many facets to this topic, and you may get information from a whole range of different aspects. If you are working with that person or that system on a permanent basis, you may want to tell them that only certain aspects and hence certain kinds of information are relevant to you – in the hope of getting only the more relevant answers from them the next time you ask. You give the person or system “relevance feedback”.
Now let’s concentrate on a system, a cognitive information retrieval system or a “system of insight”. In that context, a relevance feedback model (RFM) is the capability of the system to take your relevance feedback and “internalize” it in order to tune the results of your future queries to what is most relevant.
The system performs and automates this task by adjusting weights attributed to certain terms and their equivalents (i.e. terms with the same or similar meanings) within the data it processes.
Imagine you asked “what do we have on MRO”, and you got information back on maintenance, repair and operations, but you told the system that you are only interested in anything pertaining to “Mars Reconnaissance Orbiter”. The next time you ask, you will get information only pertaining to the latter and possibly on related topics like Mars landing craft, automated robots for planetary exploration, etc.
For one person and one query, that seem rather simple. But now imagine, that you have tens of thousands of colleagues and thousands of topics to cover. That is when the RFM benefits from machine learning algorithms, not only to detect the preferences of each person but also of groups of people with similar interests, similarities in documents, etc. to spread the user relevance feedback to other documents, queries and people on an ongoing basis in an automated way.
A key benefit of a relevance feedback model is to enable users, in particular expert users, to affect relevance appropriate to their environment without the IT department having to implement rules for relevance according to specific user groups. It allows administrators to decide by configuration which specific users within the organization will contribute as well as the exact factor of relevancy improvement.
The relevance feedback model can also go a long way towards improving the human-machine interaction. As the relevance of certain content increases significantly due to relevance feedback, the user experience starts to feel much more “conversational” – i.e offering one to three suggestions as “answers” to a query – than a traditional search interface offering a list of documents in response to a query.
The RFM provides a way to discover from everyone’s experience the information that best answers the question. Take the real-world case of a customer service representative (CSR) seeking an answer to a customer’s product question using the product name or code. In this case, the CSR will obtain a diverse set of documents including parts catalogs, how-to information, product specifications, packaging information, marketing material, etc. All of this information is relevant but only some of it may help the CSR answer the customer’s question.
Thanks to the RFM, the CSR would immediately see information she has already viewed when she searched similar things in the past because the RFM takes into account the user’s “click actions” and applies a tiny relevance boost accordingly. Perhaps even more powerfully, the RFM will also modify the order of the results by observing (over time) what information other CSRs spend time to discover, even when they dive deeply into the results list for relevant information. Organizations striving to take full advantage of the RFM will configure it so that the experts’ interactions with the system provide bigger boosts for important content and even ban inaccurate information from appearing in results lists.
As you can see from the example above, the RFM provides a collaborative way to modify search result order. It is neither a tagging nor a classification approach, both of which can be done at indexing time (extracting metadata from source, entity extraction with Natural Language Processing) or afterwards (classification through ML algorithm like clustering, similarity computation, and so forth). The RFM arguably represents a smarter approach by directly incorporating human decisions when presenting information that will best address a user’s query.
As information-driven organizations strive for ever higher degrees of accuracy for end users seeking knowledge, the ability to leverage relevance feedback from users, especially expert users, automatically at scale becomes increasingly mission-critical for optimal business performance.