Testing

Category: Analytics

  • Customer Lifetime Value (CLTV) for a Digital Wallet Business

    Customer Lifetime Value (CLTV) for a Digital Wallet Business

    Customer Lifetime Value (CLTV) is a metric that measures the total revenue/profit that a customer is expected to bring over the course of their lifetime with the business. It is an important metric that calculates the worth of all customers. CLTV models can then help address issues such as

    “We have lost this high-value customer, how much can I afford to spend to win them back?” or

    “Which customers should I be targeting to maximize my return on investment?”

    Here are some reasons why understanding and knowing CLTV is essential to a business:

    1. It helps in targeting profitable customers: When the CLTV of each customer is known, it helps in identifying customers who need to be retained or targeted for more products/services and which customers can be let go if there is a risk of churn.

    2. It helps in understanding the kind of customers a business is acquiring: It is always desirable that a business acquires as many as high-value customers, so to evaluate this, calculating the CLTV of new customers relative to existing customers is important. If the CLTV of new customers is higher than that of existing customers, it is a positive for the business as this indicates that the new customers are of high value and profitable customers.

    3. Campaign Effectiveness: CLTV can also be used as a proxy to determine the effectiveness of campaigns where the purpose is to increase the value or revenue from customers. For instance, if a cross-sell or up-sell campaign is being done on a set of customers, and if the CLTV of those customers goes up in the near term, then we can reasonably say that the campaign has been successful.

    4. Business performance: CLTV can also be used as a business performance metric since it essentially tells us the value of all customers, which in turn tells us the value of the business. So, if CLTV increases over time, it is a positive sign for the business.

    5. ROI on Customer acquisition cost (CAC): For a business, (CLTV: CAC) ratio is a critical business metric. This tells us how much value the customer is bringing to the business for every dollar spent on acquiring the customer. This ratio tells you how profitable a customer will be over their lifetime. CLTV: CAC ratio can also yield insights into how efficiently the sales and marketing team are spending money to acquire customers.

    Calculating Customer Lifetime Value

    There are different ways to calculate based on different business models. Here, we will look at calculating CLTV for a digital wallet business.

    CLTV is calculated as follows:

    CLTV = ((T*AOV) AGM)) ALT

    There are four components when it comes to calculating CLTV:

    Component Definition Calculation
    T Average monthly transactions  No Of Transactions/ No of Active Months
    AVPT Average Value Per Transaction Total Transaction Value/ No of Transactions
    ALT Avg Customer Lifespan (In Months) 1/churn probability
    AGM Average gross margin (Revenue – Costs)/Revenue

    Out of these 4 components, Customer lifespan is somewhat difficult to calculate. If customer churn data is available for a longer period, i.e., 8-10 years, then we can arrive at customer lifespan using 1/ (average churn rate).

    Our Methodology

    We at Subex built a CLTV solution for a digital wallet company as part of our campaign intelligence offering.

    Here is the process we followed:

    • We took Active 90 subscribers as our base for this solution and calculated T, AVPT, and AGM at the monthly level using the last 90 days of data.
    • To calculate Average customer lifespan (ALT), we relied upon our churn probability prediction model, which was already integrated with our campaign intelligence solution, and thus, we arrived at customer lifespan. Since we wanted to reduce skewness in churn probability, we decided to create customer segments using RFM and then took the median of churn probability for each segment and used that to calculate the average customer lifespan.
    • We calculated Recency, Frequency, and Monetary scores and created a composite score by assigning weights to each score.

    RFM Composite Score: 60%(Monetary) +20%(Frequency) + 20%(Recency)

    • Using RFM composite scores, we created RFM segments based on percentiles with the following logic. For example, a very low segment contains customers with RFM scores between 0 and 15th percentile.
    RFM Segmentation Segmentation Logic
    Very Low 0-15th Percentile
    Low 15th – 30th Percentile
    Medium 30th – 45th Percentile
    Medium High 45th – 60th Percentile
    High 60th – 75th Percentile
    Very High 75th – 90thPercentile
    Elite Above 90th Percentile
    • For each of these segments, median churn probability scores were calculated, which were then used to calculate the average customer lifespan. This gave us all the components for the calculation of CLTV.

    Integrating Customer Retention Module

    We also built a customer retention module basis CLTV scores of customers. As mentioned earlier, CLTV provides insights on which set of customers’ needs to be prioritized for retention.

    The process of building this module was as follows:

    • We first created risk buckets based on the churn probabilities.
    Risk Bucket Distribution
    Low Risk 0-50%
    Medium Risk 50-70%
    High Risk 70-90%
    Very High Risk 90-100%

    So, a customer having a churn probability of less than 50% will belong to the Low-Risk bucket.

    • Using our RFM segmentation and risk buckets, we created a matrix to prioritize customers for retention.
    RFM Segmentation Vs Risk Bucket Low Risk Medium Risk High Risk Very High Risk
    Very Low P5 P4 P3 P3
    Low P4 P4 P3 P3
    Medium P4 P3 P3 P3
    Medium High P4 P2 P2 P2
    High P3 P2 P1 P1
    Very High P3 P2 P1 P1
    Elite P2 P2 P1 P1

    For prioritization, we created the order as follows: P1>P2>P3>P4>P5

    • Since a customer belonging to either high-risk or very high-risk segment and high, very high, and Elite RFM segment should be prioritized first in a retention campaign, we tagged them as P1. We then continued to move down the prioritization order for customers with lower risk and lower value.
    • When spending on campaign promotions to retain customers, the strategy should be to spend more on highly valuable customers first and then decrease the spending amount as we go down the prioritization order. We created a spending band for each of the priority groups as follows.
    Campaign Spend Bucket Lower Limit Spend Upper Limit Spend
    P1 20% 25%
    P2 15% 20%
    P3 10% 15%
    P4 5% 10%
    P5 0% 5%

    So, suppose a retention campaign is being run on customers in the P1 category. In that case, the maximum amount we can spend on the campaign is 25% of their respective CLTV, so even if we can successfully retain any of the customers, we will still make 3-4 times of our campaign spends.

    Thus, by using CLTV, we created an end-to-end campaign management solution.

    It is also essential to know some of the pitfalls of CLTV because at the end of the day, CLTV is a prediction; therefore, caution is necessary when using it as a guide for making decisions.

    Pitfalls of CLTV:

    1. CLTV cannot be used to justify campaign expenditure, and it is only good as its assumptions.
    2. CLTV is not immune to changes in the macro environment. If inflation is high or there is some geo-political risk, CLTV cannot reflect it immediately.

    Get a 360-degree view of customers in a powerful all-in-one pane for monitoring and managing the complete life cycle of the customers

    Request a demo now!

  • Subex takes home two TM Forum Catalyst Awards

    Subex takes home two TM Forum Catalyst Awards

    We are very excited to announce that Subex has been announced as a winner across two categories in the prestigious TM Forum Catalyst Awards 2021. The Catalyst project, Measurements of Trust in AI Environment, a joint initiative with Dialog Axiata, Ncell Axiata, Axiata Digital Labs, BolgiaTen, Brytlyt, and AWS was recognized in the Sustainability category. Similarly, the Catalyst project, 5G Digital Marketplace Phase II, with Colt, Verizon, Cognizant, AWS, Servicenow, and STL was selected for the Best use of Open Digital Framework Category.

    TM Forum’s Catalyst Awards recognize the world’s leading companies for their outstanding proof-of-concept solutions to challenges facing communications service providers and their technology partners. The award for Sustainability recognizes the team whose project demonstrates the greatest potential to make our world a better place, based on the UN sustainable development goals (SDGs). On the other hand, the Best use of Open Digital Framework award recognizes the team that shows the most powerful use of TM Forum’s Open Digital Framework (ODF) assets in their solution and can quantify the benefits of using these assets.

    Steffen Roehn, Chairman of TM Forum and [StR] Partner of Bain & Company comments: “Throughout the past 18 months, the tech communications industry has mastered the challenge to keep the world connected. This meant to overcome a multitude of obstacles to ensure success for customers. We were extremely impressed by the incredible standard of the demonstrations and true innovation throughout the Catalyst projects this year as we reviewed the creativity of the teams in developing solutions to evolve our industry. I was privileged and proud to be a part of the process and I extend my congratulations to all the winners.”

    John Gillam, Chief Digital Officer, TM Forum comments: “Through the power of collaboration, we can connect, inspire, and ignite change for good to tackle some of the biggest barriers in telecoms. The TM Forum Catalyst Awards are a chance for us to honor the innovative and creative minds within our industry. This year, through the 41 Catalyst teams, we have seen evidence of the way in which we can unite to drive transformation within society, business, and the wider world. I’m delighted to congratulate this year’s Outstanding Catalysts, and the proof-of-concept solutions they have developed together.”

    To see the winners from this year’s TM Forum Catalyst awards, please visit the website here

    About the Catalyst Programs:

    Measurements of trust in AI environment

    Subex joined hands with Catalyst champions, Axiata, Ncell Axiata & Dialog to address the trust issues in AI systems by building a comprehensive framework to measure trust. As part of the catalyst program, the team explores how to bridge the AI trust gap across three use cases: Churn Prediction, Maintaining Model Trust in Real-Time Analytics, and Credit Rating.

    5G Digital Marketplace – Phase II

    Subex collaborated with Catalyst champions Colt and Verizon to demonstrate a digital marketplace involving cross-industry service compositions and the provisioning of appropriate 5G network slices to provide low-latency services faster. As part of the project, Subex’s Capacity Management solution will provide its proprietary predictive Machine Learning models to predict capacity needs. It will also apply advanced analytics on network slices to understand the impact on QoS (quality of service) and QoE (Quality of Experience) and provide an immersive customer experience.

  • Subex announced as a winner in the 2021 Pipeline Innovation Awards

    Subex announced as a winner in the 2021 Pipeline Innovation Awards

    Subex recognized for innovation in Artificial Intelligence category

    Subex, a pioneer in Digital Trust today announced that it has been selected as a winner in the 2021 Pipeline Innovation Awards. The company was declared as the winner in the innovation in Artificial Intelligence category for its no code, Augmented Analytics platform, HyperSense.

    The annual Pipeline Innovation Awards have provided the most credible recognition of technical innovation in the industry over the last decade.  Each year, the Innovation Awards program receives hundreds of nominations which are distilled to a select number of semi-finalists, who compete across more than 10 categories of technical innovation. Contestants submit extensive evaluation information to validate their innovation, which is objectively scored across over 20 different aspects of technical innovation. This information is provided to an esteemed judging panel consisting of key executives who leverage like technology to advance the way we work, live, play and communicate as a globally-connected.  The Judges exclusively select the most innovative competitor in each category.

    The innovation in Artificial Intelligence category recognizes innovations related to the application of artificial intelligence, machine learning and business intelligence solutions. For the same, Subex demonstrated the capabilities of its Augmented Analytics platform, HyperSense. HyperSense is an end-to-end augmented analytics platform, designed to help enterprises make faster and better decisions by leveraging artificial intelligence (AI) across the data value chain. HyperSense’s unique no-code capabilities allow users without a knowledge of coding to easily aggregate data from disparate sources, turn data into insights by building, interpreting, and tuning AI models, and effortlessly share their findings across the organization.

    “The Pipeline Innovation Awards have continually recognized the leading innovators that are transforming the industry, and the world, with the most significant technical advancements,” said Scott St. John, managing editor of Pipeline. “We are happy to see Subex recognized for their innovations in Artificial Intelligence in the 2021 Pipeline Innovation Awards program and applaud their advancements and contributions to the progress of the global landscape.”

    The Pipeline Innovation Awards program is open for nominations, and nominations are accepted from all technology companies, their agents, customers, and suppliers. Select companies are also nominated by Pipeline each year. Those that want to enter the competition can do so by clicking here.

    About Subex 

    Subex is a pioneer in enabling Digital Trust for businesses across the globe.

    Founded in 1994, Subex helps its customers maximize their revenues and profitability. With a legacy of having served the market through world-class solutions for business optimization and analytics, Subex is now leading the way by enabling all-round Digital Trust in the business ecosystems of its customers. Focusing on risk mitigation, security, predictability, and intelligence, Subex helps businesses embrace disruptive changes and succeed with confidence in creating a secure digital world for their customers.

    Through HyperSense, an end-to-end augmented analytics platform, Subex empowers communications service providers and enterprise customers to make faster, better decisions by leveraging Artificial Intelligence (AI) analytics across the data value chain. The solution allows users without coding knowledge to easily aggregate data from disparate sources, turn data into insights by building, interpreting and tuning AI models, and effortlessly share their findings across the organisation, all on a no-code platform.

    Subex also offers scalable Managed Services and Business Consulting services. Subex has more than 300 installations across 90+ countries. For more information, visit dev.enki.studio/test/.

    About Pipeline

    Pipeline is the world’s leading global publication that distributes rich multimedia content and produces programs, content, events, and activities that help service providers and enterprises make informed technology decisions. Pipeline has become the epicenter of industry and technical innovation, has well over 300,000 in annual global circulation, and is read by every major operator and enterprise in more than 150 countries. Pipeline is also read by premier global organization spanning the world’s top universities, government agencies, and financial institutions.  Through its rich content, engaging programs, global platform, and worldwide distribution Pipeline connects the world’s leading technical innovators with those that leverage advanced technology to transform the way we connect as a global society. For the latest content, go to and subscribe today www.pipelinepub.com and subscribe to Pipeline today.

    ###

    Media Contacts:

    For Pipeline:
    Scott St. John
    Managing Editor
    scott@pipelinepub.com
    +1 (734) 707-4988 x100

  • From Default to Debt: Stemming the Flow with Bad Debt Analytics

    From Default to Debt: Stemming the Flow with Bad Debt Analytics

    Telecommunications customer service teams have long been tasked with multiple roles – delivering superior experiences, being savvy with multiple tools, working with bots or assisted automatons, and curating omni-channel interactions to name a few. They are also responsible for ensuring that customers complete payments and clear their dues on time.

    While many solutions are available to enhance the operational efficiency of contact centers, this particular role – managing bad debt – requires a different approach, and here’s why. Globally, the incidents of fraud and defaulters are increasing, which put customer service teams on the backfoot as they struggle to make finance-related decisions. like:

    When late payments morph into bad debts

    The words ‘bad debt’ began as a way for banks and credit card companies to refer to customers who defaulted on their loan or credit card payments. Today, the telecommunications industry borrows this term from finance for postpaid and enterprise customers who have not cleared their dues as per their billing cycle. Bad debt is a real challenge within the telecommunications industry and a contributor to annual revenue losses. Bad debts can be as high as 2% of the total revenue for telecom operators.

    Billing to collection lifecycles in telecom are a bit different from the banking and lending industry. Banking and credit firms often deal with chronic defaulters through one-time settlements or loan restructuring. Telecommunication players, on the other hand, have a different play. Since initial debt recovery falls within the realm of customer service teams, the agents execute gradual actions like sending recurring payment reminders and barring outgoing calls. They often have no way of knowing whether a late payment is a genuine one or whether it is nefarious, leaning towards non-payment. Finally, as the default risk becomes severe, the job is outsourced to an external vendor – a third party collection agency – whose job it is to recover the payment or ‘bad debt’.

    The question is: Is there a way to predict the risk of bad debt way ahead of time?

    Here too, telecom can take a leaf out of the lessons and examples demonstrated by finance. Many global banks and lending agencies are investing in ways to capture 360-degree customer data in a bid to create large datasets that feed intelligent risk scoring models. Such models use atypical customer data to forecast the likelihood of credit risk and credit fraud. These predictions are then leveraged to make smarter decisions about lending, like levying higher interest rates, thereby bringing down the rates of default.

    Bad debt analytics

    An analysis of the data collected during each stage of billing to collections lifecycle will yield key insights. The five main stages are:

    Stage 1: Bill generation

    Stage 2: Pay-by-date

    Stage 3: Early delinquency stage (time of default > 30 days)

    Stage 4: Advanced delinquency or bad debt (time of default is between 60-120 days)

    Stage 5: Collection, disconnection or legal action (time of default > 120 days)

    Analytics models can ingest this lifecycle data to reveal patterns and correlations that provide a probability score of defaulting to bad debt. This entire process is known as bad debt analytics. It focuses on, among other things, minimizing how many customers move from Stage 2 to Stage 3. As seen in the image, stage 3 is the critical stage that separates late payments from defaulters.

    Tools to facilitate early detection of bad debt

    Bad debt analytics are a constellation of modern tools and technologies that can greatly assist customer service teams in making the right decision at the right time when late payment instances arise. It can also provide a host of correlations that inform onboarding about the propensity to default. Some of these tools:

    • Automated data capture – Telecom databases are replete with voluminous customer information that contain key insights. But first, this data lying in disparate systems across sales, marketing, finance, and network teams must be integrated into a single repository. Through automated data capture using connectors and APIs, Telcos can unlock data siloes, mechanizing the job of real-time data integration. This integrated data is what feeds bad debt analytics models for relevant output.
    • Machine learning models – These models accelerate the data-to-insight lifecycle by crunching data, understanding trends, and finding patterns that cannot be perceived by humans. An example is how one of our models uncovered that a majority of the defaulters in the handset sales department of a leading Middle East operator were migrant customers. This insight helped the telco adapt its credit processes to promote appropriate repayment behaviors and lower its bad debt risk.
    • Smart dashboards – Output from the models must be displayed in the right manner to the customer service teams so they can take or recommend the right actions at the right time. For instance, high risk of default in a specific location may indicate poor KYC practices by salespeople. Knowing this, the operator can immediately halt new sales transactions in that area and redirect staff for training.

    Learn how our Analytics Centre of Trust can help your business

    Schedule Demo

  • 5 Reasons you should attend the Telco Analytics Summit

    5 Reasons you should attend the Telco Analytics Summit

    “Improving analytics capabilities is one of the top priorities for operators to boost their organizational capability, to stop losing money, get a cutting edge over the competition, and develop a strong customer value proposition.”

    Telecom is going through unprecedented times with uncertainty around everything. The product/service mix is becoming more complex, evolving consumer behavior, rising business complexities, changing technologies, and much more. One thing which has continuously seen growth amidst all this is data. There has been multifold growth in the data generated by telecom operators.

    Subex is hosting its first virtual telco analytics summit on 17th November, and there is more than one reason for you to attend the two-day virtual event.

    Insights on the latest trends

    The event will cover topics across a variety of areas and functions within the telecom ecosystem. How analytics shapes the organization’s digital transformation journey to managing different aspects of the business and technology functions. Get exposure to the real-life challenges organizations face as they transition into digital businesses and how analytics helps them overcome those challenges.

    Speakers

    The event will host speakers from the leading telecom organizations worldwide, sharing their decades of experience with you. Meet both the user of Analytics tools and the consumers of Insights and learn how they are driving the transformation towards a data-driven culture. There is a mix of CXOs, Head of Departments, Analytics leaders, and practitioners to share their day in and day out with Analytics. Please find out our speakers now:

    • NAVDEEP KAPUR
      – Chief Commercial Officer, Econet
    • SANJAY BATHAM
      – Vice President – Bill and Pay (U2C) delivery, Jio
    • ARUN DUBEY
      – Chief Information Officer, Vietnamobile
    • SITHEMBUBUHLE NYATHI
      – General Manager, Digital Services & Analytics, Econet
    • HAYTHEM BENNACEUR
      – Senior Director Digital Transformation, Ooredoo Tunisia

    Real-Life Experiences

    Analytics helps organizations navigate through times of uncertainty by utilizing the vast amount of available data to increase revenue and profits across the value chain, spanning network operations, product development, marketing, sales, and customer service?

    How a CIO helps the organization establish a data-driven culture or a Chief Commercial Officer (CCO) speaks how he uses Analytics to transform the way the revenue center works at his organization. The need for telcos to establish a trusted data source to drive business objectives. These are some of the topics you will see at the summit.

    Alternative Learning

    The event will host the real-life practitioners who use Analytics and consume the Analytics output day in & day out to optimize, improve their work, and make informed business decisions. It will allow you to leave with new knowledge or a unique skill set. This is an opportunity for you to get inspired by the experience of these speakers.

    Format that suits you

    It’s a 2.5 hours event packed with insightful and engaging presentations from some of the best domain experts. There is a mix of keynote sessions, fireside chat, and panel discussions followed by Subex Data Science experts’ showcase session. Plus, you can attend it for free from the comfort of your home.

    Register here for the Telco Analytics Summit: Revenue Maximization through Data-Driven Decisions

    Save your spot now!

  • Agility, Tools & Best Practices of ETL

    Agility, Tools & Best Practices of ETL

    What is ETL and why do we need it?

    ETL is an advanced & mature way of doing data integration. We need to extract the data from heterogeneous sources & turn them into a unified format. In the subsequent steps, data is being cleaned & validated against a predefined set of rules.

    ETL projects a considerable performance boost with the ability to stream input from arbitrarily large XML, CSV, and FLF files and relational databases, and stream output to equally large XML, CSV, and FLF files or insert it into a database.

     ETL/ELT Process -Getting into few more Details

    Extraction:

    The first step of the ETL process is extraction. It is important to extract the data from various source systems and store it in the staging area first and not directly into the data warehouse. The extracted data is in multiple formats and can be corrupted.

    Fig: Four Steps of Staging Operation

    Transformation:

    The second step of the ETL process is transformation. In this step, a set of rules or functions are applied to the extracted data to convert it into a single standard format. It may involve the following processes/tasks:

    Filtering – loading only specific attributes into the data warehouse.

    Cleaning – filling up the NULL values with some default values, mapping USA, United States, and America into the USA.

    Joining:  Joining multiple attributes into one.

    Splitting:  Splitting a single attribute into multiple attributes.

    Sorting: Sorting tuples based on some attributes (generally key attribute).

    Loading:

    The third and final step of the ETL process is loading. In this step, the transformed data is finally loaded into the data warehouse.

    Need Agility in ETL Process & Tools to cope up In Current Business World:

    Scenario:

    With social media, IoT, and other Big Data drivers taking center stage, ETL has become imperative in consolidating transactional data in Hadoop (or equivalent) environments and transforming it into data warehouses that handle massive data scales. Posts on social media can contain videos, audios, images, maps, and other types. In addition, when integrating, data from social media (blogs, tweets, posts) is unstructured & it becomes a challenge to extract useful information. Business applications and connected devices produce continuous flows of data. ETL had to evolve from batch processing of predictable and well-structured data to handle semi-structured and nested data objects, such as JSON and XML, in micro-batches and in real-time.

    • ETL has evolved to support schema detection, as well as the ability to handle schema changes automatically to some extent.
    • When used with an enterprise data warehouse (data at rest), ETL provides a deep historical context for the business.
    • By providing a consolidated view, ETL makes it easier for business users to analyze and report on data relevant to their initiatives.
    • Organizations need both ETL and ELT to bring data together, maintain accuracy, and provide the auditing typically required for data warehousing, reporting, and analytics.

    Following best practices would ensure a successful design and implementation of the ETL solution.

    • Analyzing Source Data
    • Validation
    • Optimizing the ETL Solution
    • Error Handling, Logging and Alerting
    • Scheduling, Auditing & Monitoring ETL Jobs
    • ETL Auditing

    ETL Auditing in an extract, transform, and load process is intended to satisfy the following objectives:

    • Check for data anomalies beyond simply checking for hard errors
    • Capture and store an electronic trail of any material changes made to the data during transformation

     What is the comparison criteria for ETL tools?

    The ETL criteria underlying the categories have a direct relationship with the business and technical requirements for selecting ETL tools.

    Infrastructure Functionality Usability
    Platforms supported Debugging facilities Data Quality /profiling
    Performance Future Prospects Reusability
    Scalability Batch vs Real-time Native connectivity

    Conclusion:

     The quick takeaway from ETL Study is that consumer focus is based on the Agility of the ETL Tool/Process.

    The main trade-off is the performance keeping in consideration the continuous streaming of data from disparate lines of sources.

    Depending on the applicable Use Cases, ETL or ELT gets executed since both encompass Data transformation but in different Order.

    However, to choose the best pick, it is recommended to select ETL Tool Specific to Business needs, Data Requirements & commercial aspect of ETL Technologies. es.

    Want to create the right analytics strategy to transform your business.

    Explore More

  • What are the 5 ways that Business Intelligence Helps Telecommunications Providers?

    What are the 5 ways that Business Intelligence Helps Telecommunications Providers?

    Business Intelligence helps any organization transform the raw data into meaningful insights which can be leveraged by the organization to make business decisions such as CAPEX/OPEX investments, which products to launch, insights on customer behavior and much more. The importance of Business Intelligence in telecommunication has grown more than ever with the continuous growth in data as telcos ventured into new domains or launch new services. Here are the 4 ways that business intelligence can telecommunication drive more value-

    Customer & Campaign Intelligence: As telecom operators serves millions of customers, gaining individual customer level insights is very critical to provide personalized services that customers demand these days. Business Intelligence can provide that intelligence to operators by converting the raw data into consumable information. As a result, telcos can do targeted marketing campaigns, refine their pricing strategy and develop or launch products based on empirical consumer understanding.

    Proactive Customer Service: A good customer service can have a significant impact on reducing your customer churn. Customer issues and complaints are always going to be a challenge for telecom operators.  A Business Intelligence solution can provide process improvements to provide support and service. Customer care representatives can up their service for the most common customer complaints, finding stats on the complaints that customers waited on the phone longest about or even identifying support questions by email that took them the longest to respond.

    Network Intelligence– Telco customers are consuming more data than ever. They are streaming videos, playing online games, communication with colleagues or family/friends through video calls. This can cause network congestions, service interruptions resulting in loss of customer and revenue. Also, as 5G and IoT become mainstream, the high bandwidth and low latency requirements will require telcos to have clear visibility of their network performance tracking the activities happening at cell site level.

    Revenue Intelligence

    A Business Intelligence solution can help you gather information around revenue composition across different dimensions such as cell-sites, products/services, devices, geographies, customer segments and more. As a service provider, it is important for you to know the most profitable avenues that is driving the business growth.

    Product Performance

    A Business Intelligence solution can help capture information related to product usage, performance, revenue and margins. Based on the intelligence, operators can bundle their products and services for maximum ROI. The organization can decide to discontinue unprofitable products and launch profitable products.

    Build and Monitor Trusted Business KPIs in Real Time

    Schedule Demo

  • Anomaly Analytics: It’s Role in 5G and Assurance

    Anomaly Analytics: It’s Role in 5G and Assurance

    Introduction

    According to the latest trends and reports published by industry forums, by mid-2021 5G adoption would have reached around 250 million active subscribers from this year’s 80 million accounting for an increase of almost 200%. Though this increase is substantial, in the larger scheme of things, 250 million subscribers only account for 3% of the total mobile subscription globally. Furthermore, revenue predictions for 5G also state that 5G technology will not boost revenues but give a telecommunication service provider far more flexibility in the business models that can be deployed within the larger eco-system.

    This fact also means from an assurance perspective; the telecommunication service provider will need to invest in far more hardware and the associated services to onboard 5G services into the revenue assurance practice. While this may not be a significant investment initially considering the low adoption of 5G. However, this trend takes a significant turn as we go into the year 2022 to 2025, wherein the 5G service is expected to account for 30% adoption, thereby exponentially increasing your data volume, the hardware footprint and the OpEx required to perform daily revenue assurance activities for 5G services. At the end of the day with falling ARPUs, the discrepancies quantified will not justify the investment made towards onboarding 5G services into the traditional revenue assurance practice.

    You will need to look for a newer approach that provides a better cost vs benefit.

    Pressure on ROI from RA investments Traditionally revenue assurance involves the process of taking two or more datasets between the switch and billing and reconciling them to identify usage that was not billed due to issues in the data sent downstream. During 2G services, this volume for a tier 1 operator was anywhere between 100 million to 500 million xDRs, which required to be reconciled daily. However, with 3G and 4G service, this volume exponentially increased due to the larger mobile service adoptions and the associated services being provided. Today for a tier 1 operator with 100 million subscribers, the revenue assurance tool is required to reconcile anywhere from 5 billion to 10 billion xDRs daily. With ARPUs falling year on year, there is a significant pressure on the revenue assurance practice to justify the return on the investment made by the organization to plug leakages, a goal that will become significantly harder to reach.

    5G will only carry forward this trend at an even faster pace.

    Role of Anomaly Analytics

    As an example, let us look at a hypothetical revenue assurance setup. For an operator with 100 million subscribers, you will have anywhere from 10 billion to 5 billion xDRs generated daily. This data is then either stored in your data warehouse or your big data lake. Here the revenue assurance practice will require data to be processed individually, loaded in the tool, reconciled daily, and retained for 30 days with the goal being to identify revenue leakages. This setup would require significant investment on hardware which can run into millions of Dollars on its own, excluding the cost of license, deployment, support and manpower.

    Here we employ an Anomaly Analytics methodology, which significantly simplifies the TCO and the complexity of reaching the end goal. Taking the same hypothetical revenue assurance setup as above. The data stored in the data warehouse or big data lake can be aggregated accordingly to specific dimensions such as type of subscribers, cell site, rate plans, type of services, and measured against various metrics. In this hypothetical approach, only the aggregated data is processed using anomaly detection to monitor millions of KPIs, identify discrepancies quicker and correlate with other associated datasets. The approach here significantly uses lesser hardware and provides near real-time insights to only KPIs wherein the solution derives as a genuine discrepancy. More importantly, we achieve the same goal of identifying discrepancies.

    Traditional Reconciliation vs Anomaly Analytics Approach

    The point of concern between the traditional reconciliation and the anomaly analytics approach is that with the latter, the missing data identified by the prior is not readily available. In the anomaly analytics approach, one must dive into the data in the data warehouse or the data lake to identify the granular data. So we arrive at the question if it is worth investing is an anomaly analytics approach even if one does not have ready access to the granular data?

    As mentioned earlier, as a trend,we see a drop in ARPU; regardless of the service, the value of a single unit of measure, in this case, xDR, is trending downwards. As of last year, the ARPU dropped by 2% in the developed market (America and Europe) and by 18% in the developing market (South East Asia). This translates to the fact that if one identifies say 10,000 xDRs missing at a value of $1000, this value drops by a percentage every year due to the growing service usage and the drop in prices. Ultimately the question of going with an anomaly analytics approach for revenue assurance reconciliation is a question of “cost vs benefit.”

    Conclusion

    With newer 5G services, we see  a significant shift in our approach to revenue assurance and especially reconciliations. The term “single source reconciliation” has taken birth due to anomaly detection allowing the business to analyze the data and obtain insights on service, quality, billing, and service orchestration from a single source of data processed over a short period. While we are in the early stages of this approach, we are certainly expecting anomaly analytics to take the initial steps within a revenue assurance function and grow to become the mainstay within a couple of years.

    Want to know how Anomaly Analytics can play a role in 5G Assurance?

    Schedule a demo!

  • ¿Cómo afecta la canibalización del producto a su negocio de telecomunicaciones?

    ¿Cómo afecta la canibalización del producto a su negocio de telecomunicaciones?

    Maximizar el valor de por vida de un cliente y analizar su rentabilidad es muy importante y esencial para el negocio de telecomunicaciones en crecimiento. Según un informe de McKinsey, las Telcos serán testigo del crecimiento de mil millones de clientes adicionales de nivel medio, principalmente en mercados emergentes, para el año 2025. Para aprovechar esta creciente base de clientes, las Telcos buscan constantemente nuevas formas de interactuar y conectarse con su audiencia. La forma más efectiva de hacerlo es mediante el lanzamiento de productos frescos y emocionantes para satisfacer las cambiantes expectativas de los clientes. Si bien esta estrategia es altamente efectiva, sin embargo, puede conducir a la canibalización del producto, donde el volumen de ventas, los ingresos o la participación de mercado de un producto se reducen como resultado de la introducción de un nuevo producto por parte del mismo proveedor. Se puede desear la canibalización cuando queremos que el cliente cambie a un producto con ARPU más alto que ofrezca un mejor servicio y no deseado cuando el cliente cambie a un producto que genera relativamente un ARPU bajo. Subex ROC Insights ha estado a la vanguardia para ayudar a los operadores a analizar los datos de los clientes y el comportamiento de uso para obtener información que se traduzca en mayores ganancias con la canibalización deseada. Aquí queremos discutir cómo minimizar el impacto de la canibalización no deseada y compartir algunos casos de uso de la vida real también.

    Impacto de la canibalización de productos

    El mayor impacto de la canibalización de productos es la pérdida de ARPU existente y los ingresos respectivos, con los clientes actuales que se mueven a un plan de bajo margen. Este resultado es debido al lanzamiento de múltiples productos tan cerca uno del otro que se comen la cuota de mercado del otro. Por ejemplo, una empresa de telecomunicaciones líder en India mantuvo los precios cercanos y entrelazados con una pequeña diferencia entre los planes para las llamadas locales a sus propios teléfonos de red. Si bien la compañía de telecomunicaciones usó sus planes de precios para aumentar estratégicamente su participación en el mercado al darles a los clientes incentivos para hablar con otros usuarios de la misma compañía de telecomunicaciones, esto, sin embargo, resultó en la canibalización del producto.

    Análisis de productos: una medida vital para prevenir la canibalización

    El impacto de la canibalización del producto puede minimizarse mediante el análisis de productos. El análisis de productos ayuda a las empresas de telecomunicaciones a obtener recomendaciones sobre cómo dirigirse a los clientes. El análisis de productos ayuda a tomar la decisión correcta con respecto a los productos que se lanzarán analizando el rendimiento del producto y optimizando los productos según los requisitos del cliente. Les ayuda a ser proactivos en términos de:

    • Estimación de la contribución de ingresos por producto: la mayoría de los ingresos de una empresa de telecomunicaciones se deriva del uso de los clientes. La analítica del producto ayuda a las Telcos a determinar varios factores, como el volumen total de datos, minutos y SMS transmitidos a través de la red y el contenido consumido en múltiples canales, como varias plataformas OTT. Dado que los operadores pueden aprovechar los análisis anteriores, podrán crear productos únicos que cautiven a los nuevos clientes.
    • Comprender el tipo de cliente que consume cada producto: la analítica del producto ayuda a las empresas de telecomunicaciones a comprender a los clientes en términos de teléfonos, tiempo de uso, la proporción de voz y datos, comportamientos de uso de SMS y tipo de contenido consumido. Esto le permitirá a las Telcos la ventaja de tener una imagen clara al desarrollar nuevos catálogos de productos. Los perfiles de los clientes y los conocimientos sobre su patrón de uso pueden ser útiles para convertirse en grandes contribuyentes. Esto puede ser aplicable para estudiantes que regresan a la universidad después de un descanso, o personas que viajan por vacaciones. Estas actividades conducen a un mayor uso, por lo tanto, si les ofrece productos relevantes, está creando una canibalización deseada que ayuda a un cliente satisfecho con un ARPU más alto. En uno de los proyectos, el equipo de ROC Insights analizó el comportamiento de uso del cliente e identificó una mayor actividad nocturna para algunos usuarios. Esta información ayudó al operador a encontrar un enfoque hiperpersonalizado para apuntar a estos clientes con paquetes nocturnos que resultaron y generaron mayores ingresos y un ancho de banda de red optimizado.
    • Mapeo del comportamiento de uso de los clientes: la analítica del producto ayuda a las Telcos a rastrear la conducta de los clientes. Obtienen información profunda sobre qué ofertas los atraerán, por qué eligieron una red, cuáles son sus comportamientos de uso de datos, voz y contenido OTT, etc. Un ejemplo de esto sería la campaña de “recuperación” que ejecutamos para un cliente después de analizar el comportamiento de éste. Vimos una caída periódica en el uso en múltiples regiones con mayor inactividad y abandono. Esto se contribuyó principalmente debido a que los estudiantes universitarios iban de vacaciones semestrales. Y después de las vacaciones, no todos los clientes estarían activos, algunos de ellos se mudarían a la red de la competencia. El operador lanzó paquetes personalizados que resultaron en una mayor actividad, márgenes y menor rotación.
    • Percibir la huella de la red de los clientes: al registrar y analizar los patrones de uso de la red por hora del día, por cliente y con un análisis preciso de los datos, los operadores pueden formar una buena imagen de cómo los diferentes tipos de clientes contribuyen al valor económico. Los nuevos productos se pueden diseñar en consecuencia.
    • Medición del impacto de la canibalización luego de nuevos lanzamientos y campañas: es vital medir de manera proactiva el impacto de la canibalización, ya que esto ayudará a las empresas de telecomunicaciones a determinar la rentabilidad y el ROI del nuevo producto. La ayuda de Product Analytics determina la cantidad de nuevos clientes que la red ha adquirido y cuántos de ellos cambiarán de productos existentes, lo que los equipará para tomar medidas correctivas para minimizar el efecto de la canibalización.
    • Segmentación de clientes en términos de contribución y huella de red: análisis precisos ayudarán a Telcos a determinar qué clientes vale la pena buscar e invertir y en qué productos pueden ayudarlos a lograr este objetivo. Ayuda a evitar la adquisición de clientes de bajo valor a un alto costo, así como a administrar selectivamente a sus clientes actuales para obtener el mejor rendimiento financiero.

    ¿Cómo controlar el efecto de canibalización?

    Para minimizar el efecto de canibalización, los Telcos deben asegurarse de que los productos estén diseñados perfectamente. Cada producto debe estar claramente posicionado para el segmento de usuarios al que está dirigido, con una diferenciación muy clara en términos de beneficios y con puntos de precio correctos para separar los segmentos objetivo considerando la necesidad del cliente y la capacidad de pago. Si nos dirigimos a un cliente de alto uso de datos con un mejor paquete de voz y SMS, es una pérdida de ingresos, esfuerzos y las posibilidades de abandono aumentan significativamente. Con menos planes de precios bien diferenciados, las posibilidades de que los planes se canibalicen entre sí son mínimas, ya que cada plan apunta a un segmento diferente.

    La canibalización de productos es un riesgo inevitable al que se enfrentan las empresas de telecomunicaciones, pero con medidas proactivas regulares y extensas, las compañías de telecomunicaciones pueden anticipar tales situaciones de canibalización y contener tales ocurrencias.

    Optimice sus productos de telecomunicaciones como Bundling, Recomendación de producto, Análisis de sensibilidad, Halo de producto y Efecto de canibalización para mejorar ARPU.

    Haga clic aquí

  • Is Product Cannibalization Eating into Telcos’ Revenue

    Is Product Cannibalization Eating into Telcos’ Revenue

    Maximizing a customer’s lifetime value and analyzing their profitability is highly important and essential to the growing telecom business. According to a report by McKinsey, Telcos will witness the growth of an additional billion middle-tier customers, mainly in emerging markets, by the year 2025. To tap into this growing customer base, Telcos are constantly seeking new ways to engage and connect with their audience. The most effective way to do so is by launching fresh and exciting products to cater to changing customer expectations. While this strategy is highly effective, it can, however, lead to product cannibalization—where the sales volume, revenue, or market share of one product is reduced as a result of the introduction of a new product by the same provider. Cannibalization can be desired when we want the customer to switch to a higher ARPU product that delivers better service and undesired when the customer switch to a product that relatively generates low ARPU. Here we will talk about the undesired cannibalization.

    Impact of Product Cannibalization

    The biggest impact of product cannibalization is the loss of existing ARPU and respective revenues, with current customers moving to a low margin plan. It results due to the launching of multiple products so close to each other that they eat into each other’s market share. For example, a leading telco in India kept the pricing close and intertwined with a little difference across the plans for local calls to its own network phones. While the telco used its pricing plans to strategically increase its market share by giving customers incentives to talk to other users of the same telco company, this, however, resulted in product cannibalization.

    Product Analytics—a Vital Measure to Prevent Cannibalization 

    Product cannibalization impact can be minimized by wielding product analytics. Product analytics help telcos gain recommendations on how to target customers. Product analytics helps make the right decision regarding the products to be launched by analyzing product performance and optimizing products based on customer requirements. It helps them to be proactive in terms of:

    • Estimating the revenue contribution per product: The majority of revenue for a telco is derived from customers’ usage. Product analytics helps telcos determine various factors such as the total volume of data, minutes, and SMS transmitted over the network and content consumed over multiple channels like various OTT platforms. With operators being able to tap into the above analytics, they will be able to create unique products that captivate new customers.
    • Understanding the type of customer consuming each product: Product analytics helps telcos understand customers in terms of handsets, time of usage, the proportion of voice, and data, SMS usage behaviors and type of content consumed. This will enable Telcos the advantage of having a clear picture when developing new product catalogs.
    • Mapping the usage behavior of customers: Product analytics help Telcos track the psyche of the customers. They gain deep insights into what offers will attract them, why they chose a particular network, what their data, voice and OTT content usage behaviors are, etc.
    • Perceiving network footprint of customers: By recording and analyzing network usage patterns by time of day and by the customer and with accurate analysis of the data, operators can form a good picture of how different types of customers contribute to the economic value. New products can then be designed accordingly.
    • Measuring the impact of cannibalization post new launches and campaigns: It is vital to proactively measure the impact of cannibalization as this will help telcos determine the profitability and ROI of the new product.Product Analytics help determines the number of new customers the network has acquired and how many of them will switch from existing products – which will equip them to take corrective measures to minimize the effect of cannibalization.
    • Segmenting customers in terms of contribution and network footprint:Accurate analytics will help Telcos determine which customers are worth pursuing and investing in and what products can help them achieve this goal. It helps in avoiding the acquisition of low-value customers at a high cost, as well as selectively managing their current customers for the best financial yield.

    How to Control the Cannibalization Effect?

    Product cannibalization can be managed by understanding products that offer unique features, meets customer needs, reduces the customer’s total costs, creates a high usage value product and is innovative in being the first of its kind in the market.

    To minimize the cannibalization effect, Telcos need to make sure the products are designed perfectly. Each product should be clearly positioned for the segment of users it is aimed at, with very clear differentiation in terms benefits, and with right price points to separate target segments considering customer need and ability to pay. With fewer well-differentiated pricing plans, the chances of plans cannibalizing each other are minimal as each plan targets a different segment.

    Product cannibalization is an inevitable risk that Telcos face, but with regular and extensive proactive measures, telecommunication companies can anticipate such cannibalization situations and contain such occurrences.

    Optimize your telecom products such as Bundling, Product Recommendation, Sensitivity Analysis, Product Halo and Cannibalization Effect to improve ARPU.

    Download Whitepaper