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Customer Experience Management Powered by Intelligent Automation

Customer Experience Management Powered by Intelligent Automation

Customer Loyalty vs. Customer Satisfaction

All enterprises should strive to create positive customer experiences that lead to customer loyalty. Customer loyalty is different from customer satisfaction.  It establishes a long-lasting relationship with customers. It is an ongoing process, not a single set of actions. Therefore, enterprises should develop a strategy for customer experience management.

Companies first focused on customer interactions by capturing and analyzing customer calls, emails and chat. Agent training soon followed to better serve customers during interactions. This approach helped to achieve customer loyalty, but it ignored other steps of the customer journey critical to Customer Experience Management (CXM).

4 Segments of Customer Experience Management

Customer experience management demands a view of the entire customer journey, consisting of the following four segments:

  1. Routing customer service requests to the right organization and agent rapidly with no need for the customer to repeat information
  2. Managing interactions for the best outcome for the customer
  3. Processing customer service requests quickly and accurately
  4. Capturing and analyzing customer feedback from all customer touch points during and after the completion of service, making changes and taking action toward improvement.

intelligent automation

Intelligent Automation Technologies Greatly Power Customer Experience Management

Enterprises can face major challenges in establishing these four critical segments effectively. This is where advanced Intelligent Automation (IA) technologies greatly enhance CXM programs.

The core components of IA is commonly used the same way in all four segments.

  1. Data and media capturing, unification, and big data management performed on all areas critical to the customer journey. This includes telecom platforms (network routers, IVR, PBX, ACD, etc.) and key performance indicators from workforce optimization, workforce management, customer relationship management and enterprise resource planning.
  2. Multichannel analysis (speech, desktop and text) of unified data and the creation of actionable knowledge
  3. Learning Decision Making Engines (LDME) driven by Artificial Intelligence (AI) further analyze actionable knowledge, make best decisions, and learn from historical data and analysis
  4. Automated actions driven by LDME launch actions and automate functions

 

Artificial Intelligence and Learning Machines are Key to Making IA a Practical Solution

customer journey

In customer service routing, IA automatically captures, monitors and analyzes the status and performance of all entities engaged in the customer journey. Routing makes the best decision and launches the action to intelligently route the service request in real time. It also captures customer information from each touch point and deposits it in a single place accessible to all agents and systems. This prevents customers from having to repeat information.

IA captures and analyzes customer interactions (calls, emails, chat, desktop transactions) to automatically conduct quality assurance, compliance management and customer sentiment analysis. Real-time coaching, workflow automation, reminders and notifications help automate agent interactions. Recent developments in AI-based chatbots and Intelligent Virtual Agent (IVA) technologies automate customer interactions while reducing enterprise expenses.

When it comes to processing customer service transactions, IA utilizes Robotic Process Automation to process repetitive tasks in various processes without errors. IA utilizes Business Process Automation to perform data collection, unification and analysis of transactions to automate business processes.

Finally, IA captures customer sentiment from every customer touch point during and after service. This can include social media content and customer surveys. IA analyzes the data and provides actionable knowledge to LDME that can offer conclusions, trends and actions.

Each of these four steps is designed to improve systems, processes and the interactions engaged in the customer journey.

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Transaction Data Unification Challenges

Enterprise data transactions

Associating and exchanging data and events related to the entire transaction life cycle and the data unification of transactions.

Sharing Enterprise Data

Today’s enterprises face a major challenge with associating and exchanging data and events related to the entire life cycle of the transactions. Transactions are processed by major and dispersed parts of their business operations, and especially in between telecom platforms, WFO products, and CRM software. Automating data unification and media from each transaction life cycle, processed by these discrete parts, can offer a holistic and enterprise-wide view for each transaction.

Data Unification – Automate or Manual Data Collection

For example, a CRM record can automatically and in a single place, contain the data and information related to the entire life cycle of a transaction. This can include the start of the transaction through telecom platforms, followed by:

  • Processing of the transaction by agents
  • Agent interactions
  • Information related to the recording
  • Monitoring and analytics of the transaction
  • Final steps in completing the transaction through the CRM software

Consequently, and without a data unification solution, enterprises are forced to manually gather the dispersed information. Manual operations result in time-consuming projects, higher costs, errors and deficiencies in the business process.

The ideal transaction data unification solution provides users with the ability to:

  • Define critical pieces of information and media they need to automatically unify from various enterprise products. This includes telecom platforms, WFO products (such as call recording, screen capture and desktop transactions, and automated quality assurance programs, etc.) and the CRM record objects
  • In real time and automatically collect the data, information and media from all user-defined products and platforms through APIs and software integrations
  • Aggregate the data and store in user-defined locations, such as the CRM screens

A supervisor, a claim or order validator, or a QA manager can utilize the CRM records, reports, charts and analytics snapshots to obtain the information related to the entire life cycle of a transaction. Users can click on user-defined CRM data fields and immediately access recorded calls, WFO-related data, or telecom information.

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First Call Resolution – You Don’t Know What You’re Missing

First Call Resolution

Call sampling for quality purposes misses a great deal of valuable customer information – a low first call resolution (FCR) rate leads to increased call volume, workload, agent payroll expenses, customer dissatisfaction, and loss of customers.

First Call Resolution

Enterprises record calls and customer interactions, performing Quality Assurance (QA) evaluations in order to improve their customer’s experience, and their own business performance. No matter how fine-tuned your enterprise processes are, every contact center gets repeat calls.  Industry reports indicate that approximately 65% of inbound calls are resolved on the first call. That means that 35% of customers are forced to call back to get their issue taken care of. Some customers won’t even bother to call back resulting in lost revenues, brand erosion, and lost customers. Repeat callers and the problem of first call resolution (FCR) is so important, because multiple calls for the same reason account for up to 15% of a contact centers annual budget.1

The question is how do you handle repeat callers, and are you even aware of how many times your customers called you back for the same reason. One of the components that is missing, especially in large contact centers with hundreds of agent calls, is a solution to listen to all of your customer calls, monitor all customer interactions, and only report or flag the instances that need to be reviewed.

Why First Call Resolution Matters1

  • Reduce operating cost –a huge opportunity to reduce your center’s operating cost.
  • Improve customer satisfaction – 1% improvement in first call resolution yields a 1% improvement in customer satisfaction; the absence of first call resolution is the biggest driver of customer dissatisfaction.
  • Increase opportunities to sell – when a customer call is resolved you increase the customer cross-selling acceptance rate by 20%.
  • Improve employee satisfaction – high employee satisfaction means higher first call resolution; stress is very high on employees who handle 2nd or 3rd customer calls.
  • Reduce customers at risk – if a customer’s problem is resolved in the 1st call, only 1% of those customers are at risk to go to your competitors.

Call Sampling

Quality Monitoring (QM) and Quality Assurance (QA) systems can help prevent bad habits from becoming common place, and ensure call center agents get all the details just right.  However, due to QA costs, most contact centers are forced to only sample calls for the purpose of QA – missing a great deal of available information that is valuable for improving the customer experience and performance.

The answer is to automate the entire QA process with automated speech analytics. AQA using speech analytics identifies and flags only the calls that need attention per pre-set guidelines or profiles. Discovering the reasons for repeat calls can be found using Speech and Desktop analytics and Customer Surveys.  In today’s business 100% of calls and transactions must be captured, monitored and evaluated in order to achieve the performance targets.  However, there is a major challenge in conducting quality and liability management for 100% of calls or desktop transactions.

Example 1 – by answering three simple questions it is easy to see why first call resolution matters:

Q1.      How many agents do you have?

A1.       50

Q2.      What is the average duration of calls recorded per agent per day?

A2.       6 hours

Q3.      What is the number of business day you work per month?

A3.       21 days

The formula here is a total of 50 agents X 6 hours of recorded calls X 21 days = 6,300 hours of recorded calls per month. Typically, a QA supervisor can only listen to a maximum of 120 hours of recorded calls per month. That means you need to have 50 QA supervisors to do 100% QA for 50 agents – at a huge cost of over $2.5M per year. That is why many contact centers still rely on call sampling – missing important calls, interactions and trends every day.

First Call Resolution Solution

The FCR solution is to apply analytics-based QA to automate and conduct 100% QA on all calls and interactions. This will enable you to discover and resolve problems with agents, processes or subjects (such as products or services), that cause repeat calls. Automating the QA process has become affordable for contact centers of all sizes, and certainly not at a premium of $2.5M/year for 50 QA supervisors required to QA without automation.

Example 2 –

  • A contact center has 100 agents
  • Repeat call rate is 60%
  • That means 60 agents are handling only repeat calls
  • Using speech and desktop analytics, repeat calls are reduced by 20%
  • This is a savings of 20 agents handling repeat calls

20 * $5,000/Month → $60,000/Month → $720,000/Year

A significant savings with a real return on investment.

Benefits of an Automated QA Solution

  • Users can define QA scoring templates
  • Speech analytics will automatically analyze each call against the QA template and records the scores
  • Speech analytics use phonetics engine that processes the analysis extremely fast
  • Results can be directly and automatically disseminated to various organizations, or
  • Can be further evaluated by a small group of QA supervisors

Implementing Automated Quality Assurance

As with any advanced technology such as analytics, it is important to find a vendor that will work with you to fulfill your specific needs and environment. This should first begin with a complete review of your issues, challenges and root causes of concern, and the capability to uncover trending information. The only way to really understand the issues and create a solution is to use your own data and implement a solution using your own operating processes. Implementing an automated quality assurance solution has a demonstrable return on investment and can improve your customers as well as your employees’ satisfaction.

 

Fully Automated Quality Monitoring for High Volume Contact Centers http://ubm.io/2eqgP8i

1SQM Group

 

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Cross-Channel Analytics Transforms the Enterprise

Cross-Channel Analytics Transforms the Enterprise

Capturing and analyzing 100% of the customer experience at every touch point determines the alignment and the optimization necessary to significantly improve customer satisfaction.

Cross-channel Analytics Delivers Customer Engagement Management

OnviSource recently announced a new cross-channel analytics, enterprise-wide solution called OnVision. OnVision leverages an entire suite of workforce optimization (WFO) products to deliver insightful information and trend analysis related to customer interactions and experience across all channels and customer touch points.

“Inside-out” versus “Outside-in”

WFO is traditionally used as an “inside-out” approach to optimize the performance of the workforce within an enterprise in order to improve customer satisfaction. Today’s market is rapidly transforming to an “outside-in” method with first capturing and analyzing 100% of customer experience at every touch point. This determines the alignment and the optimization that is necessary to significantly improve customer satisfaction, and resulting in customer retention, growth, new revenues and favorable branding.

The transition of WFO to this new approach, called Customer Experience Management (CXM), requires an omni-channel method to capture relevant customer experience data from every channel; including calls, emails, chats, desktop transactions, customer surveys and social media. However, capturing 100% of information from all channels can produce a massive amount of data, or Big Data.

What Cross-channel Analytics can do for You

Cross-channel analytics can be leveraged in many ways including looking for data on a specific “subject” you want information on, to providing important data on events or issues you are not even aware of yet. For example, a customer may contact your business from multiple channels before making a purchase. They may fill out an online form, call for information, or send an email inquiry. All of these unique interaction touch points can identify and assist the customer. Unfortunately, the same customer often gets conflicting information, even pricing, depending on which channel they contacted. OnVision can dramatically improve the overall messaging and alignment for customer engagement.

Another example, this time from a corporate view, where a bank issues a new credit card. The bank wants to understand customer sentiment regarding the credit card launch from multiple sources such as social media (Facebook, Twitter, etc.), call center inquiries, email, text, and chat feedback. OnVision can combine all of the multi-channel analytics into a subject search, in this case, the credit card, and automatically report on the campaign from all of the different channels into one combined report. OnVision can also provide “proximity” searches such as “I like this credit card”, or “this is not a good credit card offer”, “I hate this credit card”, into a cross-channel analytics report.

Often businesses are not even aware of what they need to change to improve operations, customer service, products or processes. OnVision can discover as yet unknown issues or trends. For example, a sudden spike in complaints about a new product, across multi-channels, that might not have triggered an alert if they were only identified coming from one channel. Applying cross-channel analytics gives you trending and predictive analytics and a universal view of the entire enterprise.

The multi-channel analytics generates a huge amount of data. OnVision greatly assists managing the big data. It automatically analyzes the captured data from each channel and produces channel-dependent actionable knowledge. OnVision consolidates and filters customer interaction data across the entire enterprise into seamless, usable and actionable data. It is the next step in the evolution of workforce optimization and big data management, providing a customer centric approach to customer loyalty and improved satisfaction. OnVision fulfills the need for a solution in which WFO and omni-channel analytics are unified with big data management techniques. This provides a true customer experience management, or the next generation WFO, in a universal view.
Connections Magazine – Transforming WFO into CEM 

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