Category: Insights

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Product Engineering

Deliver high-quality entertainment services at high speed and with flawless quality

Automate the end-to-end compatibility testing and rollout steps to deliver a seamless viewing experience across multiple digital platforms and form factors

A series of technological advancements has completely changed the way people consume video content. Compared to earlier days, when a television set was the primary source to consume videos, today’s consumers have many other options – smart TV, streaming box/stick, gaming consoles, DVR, set-top box, tablet, computer, mobile, etc. A recent ComScore OTT state report clearly shows the growing penetration of different digital devices among U.S households.


To deliver a seamless viewing experience, service providers need to ensure video compatibility across a broad range of device types, operating systems, browsers, and network types.

The end-users now have the flexibility to watch their preferred videos on any digital platform of their choice without being much concerned about the supporting operating systems, browsers, and network connectivity.

But if we look from the lens of service providers, delivering video service faster and with high quality has become much more complicated. It requires them to ensure feature compatibility with a broad range of device types with different operating systems, browsers, and network connectivity. This requires a humongous amount of testing in the background. And as digital-savvy users expect feature updates at lightning speed, service providers cannot afford to spend much time testing and rolling out services.

This mandates service providers to technically upgrade their way of working, the testing process, and existing release platforms.

Categories
Digital Customer Experience

Customer experience-centric contact centers- an evolution in digital age

Leveraging digital-first model and Artificial Intelligence technology to deliver a superior customer experience

In today’s fast-paced and competitive world, just having satisfied customers isn’t good enough. There is a dire need for businesses to innovate digitally to enhance customer service. But Customer Experience (CX) is beyond good service. CX is your customers holistic perception of the experience that they get from every touchpoint of your business or brand.

Having said this, most enterprises still rely on traditional models that are reactive, slow, complex, and disconnected. Due to this, they hold low Net Promoter Scores (NPS). And their inability to keep pace with the technological advancements in the contact centers is a primary reason for this.

Most businesses have been struggling with high call volumes and costs. As the number of calls rises, it becomes difficult for agents to handle customer queries quickly. Due to the long wait-time and the unavailability of agents, businesses tussle to provide a seamless and more intuitive customer experience. So diverse and vast are the communication systems and channels of today’s multifaceted contact center—that being ‘Connected’ demands a modernized and transformed customer engagement ecosystem.


A digitalized contact center can help you improve the Net Promoter Score (NPS), reduce call volumes, and save OpEx.

The good news is that realistic solutions exist to overcome this problem. Use enablers and tools like ‘360-degree view’ and ‘AI engine’ to help the agents with a holistic view of systems, provide quick diagnostics and intelligent recommendations. An ‘AI-based conversational engine’ helps customers with an intuitive self-service experience.

A digitalized contact center can help you improve the Net Promoter Score (NPS), reduce call volume, and save OPEX. It empowers your customers to make quick decisions using the multiple self-service options available at their disposal, hence diminishing their need for an agent. It marks the future of the digital-first age.

Categories
Digital Customer Experience

Bridging the digital gap

Implement a “Digital Enablement Layer” to blanket the back-end complexities and meet the digital goals

Most service providers aim to deliver digital capabilities to customers, but the legacy systems have been a hurdle to their digital transformation efforts. According to one McKinsey research, 70% of digital transformation projects don’t reach their stated goals. TM Forum Digital Transformer Tracker 2020 report states that many telcos that have started the transformation expressed frustration borne out of hitting roadblocks or not achieving the expected results.

However, these roadblocks must not deter the service providers from achieving their digital goals. Another McKinsey research shows that service providers with robust digital capabilities boast a profit margin of 43 percent, compared to their counterparts whose margins hover around 21 percent.

A Digital Enablement Layer is an optimal approach to blanket the back-end complexities and achieve smooth transition/IT transformation without significantly affecting the digital needs of a business. Moreover, service providers can achieve the transformation within a reasonable budget & timeline.


A Digital Enablement Layer is an optimal approach to blanket the back-end complexities and achieve smooth IT transformation without significantly affecting the digital needs of a business.

Categories
Operational Excellence

Accelerate cash flows by faster order processing

Managed Digital Transformation to reduce Order-to-Activate (O2A) cycle time and increase new business wins

The Order-to-Activate (O2A) process is at the heart of every business operation. Simply put, it refers to the end-to-end process of receiving, processing, and fulfilling a customer’s order. A smoother and more efficient order flow will allow the company to process more orders, thus allowing the business to grow more quickly.

The Order-to-Activate process cannot be conducted in isolation; it depends upon numerous roles, departments, and systems. For example, a typical digital service provider takes 15+ teams to traverse through 55+ systems to complete one order. These complexities and increasing inefficiencies in the O2A process leads to longer cycle time, delayed revenue realization, and higher cost.


The complexities and increasing inefficiencies in the Order-to-Activate process lead to longer cycle times, delayed revenue realization, and higher costs.

Businesses need to ensure that their business runs smoothly, and the orders are delivered efficiently and accurately, with minimal chances of error. Adopt the Managed Transformation Model to achieve long term sustainable business benefits like reduced cycle time, accelerated revenue, enhanced customer experience, and maximized cost savings. By doing this, a business can transform its operations holistically and address all the challenges in the O2A process.

Businesses can ensure a reliable and undisrupted high-speed broadband service by adopting the ‘Zero-touch service assurance’ framework. This framework enables continuous remote monitoring to detect connectivity issues proactively and provide automated resolutions.

The model encompasses transformation levers such as:

  • Agile Work Cell: Consolidates multiple functional roles into one hence, reducing the touchpoints in the O2A process. It ensures better control, promotes transparency and eliminates handoffs
  • Process Optimization & Automation: Analyzes the current performance and cycle time elongation factors to identify and implement improvement opportunities
  • Operational Accountability: Provides a Dashboard with end-to-end visibility into each order and the milestones. It also helps in governance, performance tracking and reporting
Categories
Cloud

Explainable Machine Learning (ML) models demystified

Enable 5X transparency in AIOps, achieving a more reliable and accurate business outcome

Service providers in the connectedness vertical embrace Artificial Intelligence for IT Operations (AIOps) to transform their businesses, but the users are hesitant in entrusting their operations to a complexly driven platform that provides no clarity and visibility into its functionality. Due to the lack of transparency, service providers are concerned about making bad decisions based on AI recommendations and the liability of such decisions and actions.

In their quest for autonomous operations, service providers seek to be more proactive with predictive analytics, where the machines make most of the decisions and help engineers take preemptive actions. However, the engineers need to have complete visibility into the underlying logic used by the AIOps and the ability to validate if the outcome is reliable.

Figure1: Assisted Artificial Intelligence and Machine Learning Framework


To accelerate AI/ML model development with enhanced transparency, enterprises must switch from existing auto-machine learning to assisted AI/ML framework-based solutions.

Explainable Machine Learning (ML) models aim to solve this problem by explaining the logic of the AIOps solutions so that the users can easily understand the outcome. The model explains the application of the AI solution and its result to the users in a way that they can clearly understand, rely on, and trust the outcome. Explanation in the ML model can be viewed as a means to transforming a black-box AIOps into a glass-box AIOps, by precisely lifting the veil on its computing and logic.

Categories
Digital Customer Experience

Plotting the future of customer care through an effective Virtual Agent (VA) rollout strategy

Improve the VA’s ability to engage with customers confidently and more accurately

The Virtual Agent’s (VA) market is at an all-time high and is garnering more and more interest with each passing day. It is beginning to establish as “the must-have” solution for the businesses in the connectedness industry, seeking to improve customer experience, reduce call center costs, optimize time to serve, etc.

But are these virtual agents living up to the hype?

Gartner has placed them in a “trough of disillusionment” in its hype cycle, meaning the technology is struggling to meet the envisioned expectations. When faced with complex and unknown scenarios, VAs tend to react in an unexpected way. One often comes across instances on social media where VAs are humiliated for their out-of-context interactions.

The primary reason for this shortcoming is that many VAs are launched without the right implementation strategy. As a result, they don’t reach the required confidence levels and cannot capture the right customer intent.


Virtual Assistants (VAs) use semantic and deep learning (such as Deep Neural Networks (DNNs), natural language processing, prediction models, recommendations, and personalization to assist people or automate tasks.

To prevent your VA from humiliation, adopt a robust VA implementation strategy encompassing the top 10 considerations that can help service providers to ensure their customers engage in the VA interaction, increasing overall customer satisfaction. This strategy provides key recommendations on the most important focus areas that are imperative for a successful rollout. Some of these include:

  • Choosing the right use case: Group the inbound calls into different categories like customer service enquiries, technical troubleshooting, sales etc. Based on these categories, different use cases can be invoked. For instance, kickoff the least complex rollout with self-service flows.
  • Analyzing the complexity of intents: Analyze the length of conversation and time taken by the agent to complete the conversation. Further, build a hierarchy of intents and sub-intents to identify high-volume intents and complex intents.
  • Considering variations in intent: Analyze the scope, lifecycle, and precursor of intents to improve engagement by increasing precision or recall.
Categories
Operational Excellence

Shift gears to an automated RPA code review for faster development of bots

Most service providers in the connectedness industry have started leveraging Robotic Process Automation (RPA) to streamline their business processes. Standardizing the bot development process and scaling the bot velocity are the most important goals of any RPA Center of Excellence (CoE). One of the major roadblocks faced in this mission is the manual review of the RPA code, which is a highly tedious task. It is not only cumbersome but also time-consuming and prone to errors. Although the RPA code review process is of utmost importance to reduce post-deployment defects and costs, the manual approach is crippled with challenges and is highly inefficient.

To overcome these challenges, service providers should automate the code review process. To achieve this, service providers can leverage a platform-agnostic RPA code reviewer bot that can review

  • Hundreds of variables, arguments, activities and message boxes
  • The logic for exception handling, custom logging, queues and credential management

Fig. Leveraging code reviewer bot to automate RPA code review process


Although the RPA code review process is of utmost importance to reduce post-deployment defects and costs, the manual approach is crippled with challenges and is highly inefficient.

Categories
Product Engineering

Use AI to Bolster your Network Capacity Planning decisions

The Content Delivery Network (CDN) market is poised to explode as content consumption gains more momentum. This calls for an efficiency-focused approach towards CDN capacity planning.

As per a Cisco report, the annual global IP traffic has already crossed the zettabyte (ZB) threshold. To cope with the increased content consumption by users, more supply chains should be established along with a reliable and scalable infrastructure. This puts a lot more pressure on the Content Delivery Networks (CDNs), which forms a well-established global backbone for content delivery.

For service providers, it becomes vital to take an efficiency-focused approach towards CDN capacity planning. This means satisfying the future capacity requirements without increasing the total cost of ownership.

The legacy manual way of capacity planning uses basic statistical tools to collect data and set a static threshold on capacity requirements. Such manual planning typically does not analyze the network in a holistic manner and produces a final proposal with a “one rule fits all” approach. However, this approach is inefficient in today’s scenario where consumer behavior changes very dynamically. Manual planning is also prone to human error, so the outcome might deviate from time to time, wasting a substantial number of resources and time. The service providers often run out of capacity due to increased data consumption and changes in the consumption patterns, which are not identified correctly during capacity planning.


To satisfy the customer demands in a timely fashion, it is necessary to have a modern capacity planning strategy.

Network planners need to confront these challenges before it impacts the customer experience. Leveraging Artificial Intelligence (AI) can significantly improve network capacity planning, thereby improving the end-user experience and reducing the total cost of ownership.

Categories
Product Engineering

Speed-up entertainment services rollout

Implementing an effective CI/CD setup to deliver high-value media services with agility

Online video consumption has been increasing tremendously with a rapid change in consumer expectations to have a seamless viewing experience across various digital devices. To capture this growing demand, it is critical for DSPs to deliver fast-track rollout of NextGen media services.

However, DSPs are facing major challenges in orchestrating and managing rollouts of innovative features, converged live TV and curated media services within a short span of time. This complexity further increases when DSPs need to cater to multiple geographies.

Unlike OTT players, DSPs have been limited with extremely long development and rollout timelines for new services and offerings. Primarily because of the enormous amount of vendor-specific hardware and software applications that do not support rapid changes.

An effective continuous integration and continuous deployment (CI/CD) approach enable DSPs to achieve same innovative services and delivery agility that OTT providers are offering to stay competitive. This insight talks about different enablers that will help DSPs in adopting an effective CI/CD setup for faster rollout of NextGen media services. Implementing these enablers would further ensure high-quality, right first time and consistent right delivery of media services across multiple geographies and drastically cut down on the product’s time to market.


Implementing CI/CD architecture accelerates media service rollouts by 60% providing enhanced content and features to the customers.

Categories
Software Intensive Networks

Predicting and preventing network problems leveraging AI

Implement a network event prediction model to improve service assurance

Today, service providers’ customers expect access to the products and services and enhanced customer experience anytime, anywhere. Hence, service providers should focus on service assurance and look for ways to address common problems such as accumulated faults, traffic congestion, and reactive event handling of networks. Further, reacting to a network event after it has occurred is not acceptable.

With the overwhelming volume and complexity of data from the service assurance domain, AI/ML techniques bring much value. By leveraging AI/ML in service assurance, service providers can analyze tons of data from various sources, derive insights, and take real-time preventive actions. Hence service providers should implement a network event prediction model to predict the network event failures and outages even before they occur.


By leveraging AI/ML in service assurance, service providers can analyze tons of data from various sources, derive insights, and take real-time preventive actions.