Scale and enhance contact center efficiency – Learn how

There are mainly two factors driving the success of contact centers, namely, an efficient agent and a happy customer. However, scaling up operations becomes problematic in the absence of an agile and flexible operational structure as the business progresses. In order to meet the constantly rising demands and changing requirements of customers and increasing products and promotions, contact centers hire additional staff and train and prepare them for new products, promotions, and processes. This adds to operational costs and is highly time-intensive.

Courtesy of new-age technologies, the legacy approaches are fast receding into the past. Technologies like speech and sentiment analysis, conversational AI interfaces powered with NLP, intent identification, and RPA help scale customer support and enhance operational efficiency.

Role of technology in scaling contact center efficiency

New technologies are helping businesses deliver quick, predictive, and customized solutions to end-users.

Here are the different ways in which NLP, RPA, and other advanced technologies are amplifying contact center efficiency.

Accessibility of customer information: Consolidation of customer information based on historical interactions, past transactions, call logs, and behavioral patterns help agents to understand customer problems faster and resolve issues better and in no time. IVR (Interactive Voice Response), routing, and integration with NLP for sentiment analysis and incident automation through RPA support the agent in the business area.

Predictive and customized solutions: Contact centers use advanced capabilities to analyze customer requirements and sentiments aptly. With the help of past data, the automated system aptly predicts what customers want and provide customized solutions based on their needs.

Mitigating customer problems: Contact centers utilize the latest tech-based capabilities to better mitigate customer problems in less time. This translates into better operational efficiency, a motivated agent, and a delighted customer.

Analyze the overall performance of operations: With the operations being managed on different systems, multiple reports are generated, making it difficult for the contact center head to consolidate all reports and have a single view. AI and analytics help leaders consolidate the overall performance of the contact center operations, enabling quicker decision-making.

Reimagining customer experience with AssistEdge Engage

Businesses look for tech-enabled solutions only when they face a heavy influx of new customers, a new set of service requests or an increasing customer churn rate due to inefficient operations. AssistEdge Engage is a game-changing solution for agents. With the help of an Intelligent Automation platform like AssistEdge Engage, agents can easily double up customer experiences with actionable insights and customized solutions. They can handle more customer requests than earlier, resulting in better efficiency and productivity of contact centers and more business.

AssistEdge Engage offers the following benefits:

Conclusion

Scaling up contact center efficiency is a challenge with the existing disparate systems and the ever-growing systems as technology advances. AssistEdge Engage can help contact centers reimagine customer experience by leveraging the power of AI & Automation, thus improving contact center efficiency and productivity.

Process Discovery: Realizing the automation potential for your business processes

According to Gartner, 85% of big organizations are likely to deploy some form of RPA by the end of 2022. Yet, other reports show that early adoptions of RPA have failed, while 30-50% of such projects never took off. Wrong process selection, half-baked planning, and the absence of an end-to-end journey roadmap can be cited as some of the significant reasons for such failures.

Process discovery is considered the stepping stone for successful RPA implementation. Without discovering the proper process candidates for automation, the RPA project can never take off.

What is Process Discovery and how does it help with RPA implementation?

Process discovery refers to tools and techniques used for defining, mapping, and analyzing business processes fit for automation.

Like any new software implementation, RPA also requires proper planning, setting up a dedicated in-house team, selecting tasks across different departments which need immediate automation attention, and creating a blueprint for prioritizing tasks.

Unfortunately, businesses skip the planning stage altogether in their eagerness to jump on the bandwagon. This results in losses and failed RPA projects.

Other factors contributing to RPA failures are:

A tech-enabled approach to process discovery accelerates the RPA implementation project by addressing the aforementioned challenges and amplifying automation capabilities.

Process discovery solutions like AssistEdge Discover ensure the success of RPA projects. AssistEdge Discover leverages keystrokes and sophisticated neural network algorithms to create insightful business task maps.

How AssistEdge Discover amplifies the power of Automation through Process Discovery?

Firstly, AssistEdge Discover creates an Automation Blueprint by scanning empirical data and extracting subtle yet crucial process nuances. This Blueprint helps with the process discovery by identifying and prioritizing the proper use cases for automation.

The Automation Blueprint serves as a roadmap for scaling RPA implementation; it navigates the RPA CoE across the automation journey, assisting in the creation and validation of the business process value proposition.

The Automation Blueprint includes an Automation Prioritization Matrix, which uses minutely curated task-related data to provide suggestions throughout the automation journey.

Based on this, RPA CoE can use the Automation Blueprint to devise a detailed automation roadmap, giving a clear picture of workgroups/ departments, associated tasks and the number of FTEs. Further, the Automation Blueprint compares tasks against each other on selected parameters for making informed decisions.

Thus AssistEdge Discover and its Automation Blueprint capability not only help streamline the selection process for the right RPA candidate but also helps businesses realize the full automation potential.

Conclusion

Leaders focus on building a digitized, automated, and transformation-centric agile enterprise in the current business scenario. The role of the Automation Blueprint is to ensure the RPA program perfectly aligns with the key business objectives and the existing operational structure of the enterprises.

RPA investments generate higher returns for enterprises at a quicker pace. But, process discovery eliminates the chances of RPA projects failing for the reasons discussed above. AssistEdge Discover and its Automation Blueprint can prevent such failures by creating a proper roadmap for scaling RPA implementation.

 

Document AI: Unlocking enterprise document intelligence and actionable insights for data-driven decisions

The global AI market has already entered a new phase in the wake of the COVID pandemic. A major requirement for enterprises. According to recent reports, the annual AI software revenue will grow to nearly $100 billion by 2025.

What is Document AI?

Analyzing documents to unearth contextual meaning is an inherent human quality. Capturing data from a horde of documents of varying formats, like slides or posters, in advertising, infographics, and email, was beyond AI capabilities until now. Document AI uses Optical Character Recognition (OCR) and computer vision capabilities to recognize words, decode and interpret images, and other forms of media with 99% accuracy.

This holistic interpretation of documents is termed multi-modal image extraction, which extracts the pertinent information and converts it into structured data. It resembles how the human brain would have interpreted the same information. But, unlike humans, AI does that in a blink of an eye without compromising on the details.

5 Document AI benefits for enterprises

Document AI has found its way into various business use cases. With the help of NLP, AI document processing can bring tangible benefits to businesses, a few of which are described below:

Time saved

Document AI saves productive time by structuring unstructured data, indexing it, and ensuring easy searchability of the data. AI for documents has wide applications in insurance, healthcare, and other industries where piles of documents are analyzed regularly.

Optimal utilization of employee skills


With less time spent in the needle in a haystack search for pertinent data, employees have more bandwidth to use their inherent skills and expertise to cater to value-added services for which they were initially hired. This translates into better employee engagement and reduced turnovers.

Improved customer experience


With structured data easily searchable, businesses can witness massive improvements in customer service. For example, an insurance company takes hours to set up a customized policy by collating and evaluating all information about a customer’s unique circumstances. With AI document analysis, customers’ credit history, demographics, policy options, and possible exclusionary risks are highlighted in a searchable database; and setting up a bespoke policy can be done in a single phone call.

Improved document security

Data breaches have become a daily occurrence, thereby bringing security under the spotlight. Document AI can scan for sensitive information and automatically redact it when required. The same systems can be on the lookout for unusual activity, warning you of a possible data breach before it happens.

Unexpected business insights

Humans are smart individually but generally don’t deal well with scale. Artificial Intelligence is the opposite: the more data it has, the more unexpected insights it can produce. For example, AI can make correct medical diagnoses that trained doctors, miss. Likewise, when Document AI is fed with millions of business documents, it can extract unexpected insights for the company.

How does Document AI work for businesses?

Document AI can very well work for your business, however, depending on the following factors:

The breadth of AI: Evaluating whether or not Document AI can work with structure, data, and prediction problems unique to your business.

AI requirements: Companies need to understand their unique AI requirements. Most businesses expect a useful, integrate-able, and easy-to-consume AI.

IT readiness: This is an important factor to consider – the IT readiness of a company. Questions like “How will you scale? How can I integrate my documents? How do I bring the documents in? How do I pull the results out? What kind of security do you support?” are typical ones to find answers when bringing a new product into the data center.

Once these factors are in place, Document AI goes through the following steps:

Digitization of data: Company documents are a collection of different elements of varying sizes and formats, like graphs, charts, tables, logos and the text. Digitizing documents should be the first step before bringing in Document AI capabilities to address the data extraction clause.

Classification of data: Documents are classified and separated into specific groups after uploading those important paper files on a digital platform. So, if somebody uploads many documents, which is a collection of checks, invoices, purchases or sales orders, Document AI automatically separates them.

Analysis of extracted data: Once the data is extracted, it is important to add some sense. For example, take a form with the name “Alan” on it: just by looking at it, the AI doesn’t know what “Alan” means till it is paired with the keyword (field) “Name:,” printed on the left of the word “Alan.” So, the Document AI platform will do a full field value pairing to give meaning to the values in the document.

Evaluate content intent: Understanding the intent of the text blocks is essential. Whether it’s a termination clause in a legal document or explaining some medical procedure, Document AI can help by making a proper sense of the document data and the context of the information.

Comparison and analytics: Using advanced techniques and analytics, document comparison can understand the sentiment and historical implications and predict future outcomes and potential risks.

Converting into consumable information: Unless business users can consume the extracted data when needed, the whole point of processing documents and extracting information will have no meaning. Hence, actionable insights unearthed from unstructured documents are made consumable for businesses to use as and when needed.

 

Debunking 5 myths of AI Document Processing

As per an analyst report, 80-90% of enterprise data remains untapped and locked inside unstructured documents. In the wake of accelerated digital transformation, unstructured data can create serious bottlenecks for processing information, which is much needed to digitize processes.

Creating document digitization through AI document processing should be the first step for organizations to become digital businesses.  However, for businesses to make an informed choice between available AI document processing solutions, they need first to debunk a few myths.

Debunking five AI Document Processing myths

All those promises that document digitization solutions make are misleading and can convince business owners to make wrong choices. Hence, those myths need to be debunked.

Myth 1: 100% accuracy guarantee

Accuracy is deemed the wrong starting point when assessing the business use case for document processing solutions. Such claims require reference data. But, such reference data are derived out of a tiny sample set without guaranteeing how the solution will work on a large scale.

The only way to measure the performance accuracy of document digitization solutions is by implementing Intelligent Document Processing on the actual data set, followed by manual mapping of actual results vs. the expected ones.

However, manual mapping is not feasible when large data sets are involved. Hence accuracy is a proxy measure, commonly termed the confidence score. It means how confident the system is of correct extraction.

Unfortunately, many products fail to generate accurate extraction even with a high confidence score, and vice-versa. The level of data extraction accuracy stems from the kind of training data that the model was exposed to and the type of actual data it’s now running through.

This is why businesses should focus on savings in cost, effort, and time taken to carry out key business processes like loan applications.

Myth 2: Unconditional straight through processing


When AI document processing promises unconditional straight-through processing, it merely relies on the confidence score.

Businesses need to look for a system giving a reasonable accuracy to cut off into production and then benchmark exercises at regular intervals in production. One should start with a reasonable measure for basic ROI calculation and constantly improve the product performance with measurement and human feedback.

Unfortunately, most products lack benchmarks for tallying performance accuracy. Platforms like XtractEdge solve the problem with their learning and auto-tuning capabilities. These capabilities improve the product based on benchmarking data and auto-suggests corrections based on an ML model over time.

Myth 3: Implementation without calibration

A poor document quality will make a unique OOT model underperform on the client data. When choosing a document processing solution, PoC is the right step, as it allows calibration for the OOT model to work and adjust to client’s data needs. Calibration thus helps form a solid foundation, which continues from the PoC to implementation to production.

Myth 4: Customization – not required

No software platform can address all use cases for all industry verticals and business domains without customization.

There is no one-size-fits-all to align with every business need, regardless of their niches. There is an unthinkable level of complexity to consider regarding document digitization. Such complexities stem from the variety and variance of documents used in every organization, unique and different from those used by others.

Hence, customizing the product to deal with variables is essential.

There are five dimensions of document layout variance, such as:

Document processing platforms like XtractEdge solve the variance problem with an onboarding feature. XtractEdge automatically identifies and trains new layouts, improving the system as it runs. Further, if the classification goes wrong, the platform makes intelligent adjustments, like:

The onboarding queue thus saves the effort of checking every new layout manually.

Myth 5: Buy and forget

A document digitization product needs continuous training and improvement depending on the data quality and variance to handle exceptions.

And there should be a human-in-the-loop to oversee the quality of data extracted, variance and exceptions, working in tandem with the solution to get the best output.

The Bottomline

While various AI document processing solutions make many claims, they need not deliver the same output when used in real business use cases. Hence, companies should be aware of the myths mentioned above and the real story behind those myths before narrowing down on an Intelligent Document Processing solution.

 

Contact Center Automation: Creating happy customers with Intelligent Automation

Building end-to-end journeys successfully can craft unique customer experiences. Unfortunately, contact centers struggle to meet consumer demands of getting round-the-clock assistance. Business leaders know that striving for a balance between faster problem resolutions and offering personalized services can help such contact centers survive the unprecedented challenges. And that is a one-way ticket to winning customers.

Considering the current scenario, contact center automation is the only bet to keep customers happy. With the help of Intelligent Automation, contact centers can improve the quality and efficiency of their services and deliver business benefits at various touchpoints.

How does Intelligent Automation handle the pressing problems of contact centers?

Statistics suggest that global business spending on contact centers will increase to $407 billion or more by 2022.

Customer relationships and loyalty have taken center stage forcing contact centers to abandon traditional methods and switch to an omnichannel model of connecting with end-users. This new approach is presenting a new set of challenges for the contact centers, such as: The challenges mentioned above beg for a contact center automation solution beyond call routing and scheduling. In order to keep pace with the evolving consumer demands, businesses and contact centers need to invest more in tech-enabled solutions like Intelligent Automation, other than increasing the manpower, adding to operational costs, and eventually suffering from loss of business.

Benefits of using Intelligent Automation in contact centers

Contact center automation embedded with Intelligent Automation capabilities garners better interactions between businesses and customers. IA provides contact centers with an intuitive and unified dashboard with a single sign-in to enter and update information on various systems simultaneously, without shifting between applications.

Moreover, call center automation uses interactive voice response, virtual assistants, and automatic call distributors to help agents access the required data in real-time for acting promptly on customer problems.

An automated system proactively identifies callers and creates a personalized experience based on customer needs as anticipated. The interface makes cross-sell recommendations too! Hence, AHT and call-abandonment rates are reduced, revenues are maximized, and executives are freed from mundane and tedious tasks.

The following are a few benefits of using IA in contact centers:


Predictive intelligence: Predictive intelligence helps contact center executives anticipate consumer intent by extracting information from past interactions, call logs, transaction history, and behavioral patterns. This improves first-call resolution.

Automated call triage and resolution: Automating the triage process eliminates the bias element from the customer support process. This evenly distributes the workloads to conserve manpower resources.

Unified agent desktop: The omnichannel system connecting disparate systems and applications makes service and call handling metrics easily accessible. This makes customer interactions more effective.

Actionable business insights: With the help of customer information aggregated from multiple sources, businesses generate actionable insights for process optimization and performance management.

Virtual assistants: Virtual assistants help with first-level support, guiding customers in self-service and addressing FAQs, freeing agents to address more complex issues.

Reimagining Contact Center Automation with AssistEdge Engage

Customer experience can make or break a business. Hence, contact center automation powered by Intelligent Automation capabilities can provide a competitive edge to businesses. However, the success of automated contact center solutions boils down to identifying and implementing the right solution.

AssistEdge Engage is a ready-to-deploy, configurable solution that adds automation capabilities to contact centers. Leam more about its features.

Intelligent Automation for contact centers – Use case

Prices in the telecom industry are homogenous; hence, companies have to rely heavily on quality service and customer experience. And contact centers are the medium to increase customer engagement. The companies are challenged to balance efficiency, quality, and satisfaction of both agents and customers.

There are other bottlenecks faced by telecom companies when it comes to:

A European multinational telecommunications company was faced with similar challenges. The company stresses continuous customer experience with its services but lacks the much-needed Intelligent Automation solution. With AssistEdge Engage, the fourth largest telecom operator globally could witness a 40% reduction in average AHT across operations.

Intelligent Automation is the future of contact centers

Contact centers depend on repetitive and time-intensive tasks, which are integral to their business models. But, such repetitive tasks weigh down their operational costs from hiring extra resources.

With Intelligent Automation, contact centers have the bandwidth to rethink their approach to operations and find new ways to automate customer experience. Contact center automation solutions like AssistEdge Engage will positively impact their functioning, enhance operational efficiencies and improve the overall experience for both agents and customers.