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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 


The Ascent of Text Analytics

Use text analytics to uncover true customer sentiment.

Valuable customer insight from all data sources is not utilized to its full potential.

The New Frontier – Text Analytics

In this era of progressive improvements to the customer experience, many companies are searching for new avenues to gain insight into the customer journey using text analytics. Text analytics can be defined as a process for analyzing unstructured text, extracting relevant information, and transforming it into useful business intelligence. Large corporations such as Walmart, Amazon, and Google collect massive amounts of customer data in the Petabyte range and beyond. As enterprises of all sizes struggle to uncover true customer sentiment from unstructured data sources such as social media, surveys, notations in CRM software and service notes, blogs, and many other sources, this valuable customer insight is not being utilized to its full potential.

Preventing a Poor Customer Experience

However, most companies use only a fraction of the customer data that is readily available to them. One of the value propositions for using text analytics is its ability to analyze and classify huge quantities of text and automatically extract meaningful data that really needs to be scrutinized. This yields customer experience information that previously might not have been known or recognized, and used to prevent a repeat, poor customer experience. Text analytics reveals insights into why something is happening. For example, a visitor to a website might not be able to find a product or suitable answer to a question. Text analytics can be used to discover if the issue is related to content, FAQs, or navigation problems.

Root Cause Analysis

The data gleaned from text analytics can also be used to determine the root cause of problems (known or unknown), and companies can begin to anticipate or predict problems by analyzing customer interaction data. Just as speech analytics can unveil customer sentiment by automatically identifying key words and phrases from customer telephone calls, text analytics adds to the wealth of unstructured customer information that can be extracted, transformed and loaded into subject-oriented categories delivering new possibilities for enhancing the customer experience.

  • Trend analysis
  • Tool to make strategic business decisions
  • Predict and forecast customer behavior patterns
  • Deliver an improved customer experience
  • Companies are realizing that all employees must listen to their customers, or they will become fair game for the competition