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Tag: Customer Churn

customer lifetime value

3 Interesting Tips to Increase Your Customer Lifetime Value

Companies across industries invest a lot of time and resources in tracking the journey of their customer, improving customer service, and providing the best customer experience. However, most companies tend to struggle when it comes to customer journey tracking and measuring business performance in real-time. This is where the focus must be shifted to gauging customer lifetime value as this can help organizations to evaluate the future value, they can generate from their marketing initiatives. Also, it can help in designing an efficient marketing strategy with more concise budget planning. Measuring customer lifetime value is important for businesses to retain customers and gain long-term profits. In this article, we have listed a few important tips that can help companies to increase their customer lifetime value and understand how well are they resonating with their audience. Additionally, these tips can aid companies to reverse-engineer the experience of existing customers and reduce customer churn.

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CUSTOMER LIFETIME VALUE BLOG

Tips to Increase Customer Lifetime Value

Improve the onboarding process

To ensure sustainable business growth and increase customer lifetime value, companies should focus more on the onboarding processes as poor onboarding can be one of the major causes of churn. Onboarding is the point where any customer engages with a brand and where a brand can make the greatest positive impact. The process of onboarding can vary from industry to industry. However, there are a few interesting tips that can help businesses to engage their audience better. Businesses must make onboarding, an easy and fast process which can be done by simplifying the process with walkthrough guides, interactive how-to videos, wrapped tutorials and other content that might help customers in fulfilling their goals. Also, organizations should focus on communicating the value of their offering right from the start ensuring that communication is straightforward and is contributing to increase in customer lifetime value.

Wonder how to increase customer lifetime value, proactively boost engagement, increase acquisition rates, improve conversion and raise average order value? Talk to our analytics experts now!

Offer top notch customer service

Today customers are digitally empowered, and they expect better customer service at each of the touchpoints. Quality customer service is the key for businesses to grow and increase customer retention. Research reveals that customers are likely to switch brands after a single instance of poor customer service. Therefore, getting your customer service right should be your top priority in order to stay ahead of the curve and drivr profitable growth in the long run. Better customer service results in customer experience and this, in turn, can turn them into loyal clients.

Our customized analytics dashboards help businesses to map the customer journey across all customer touchpoints and gain a 360-degree view of each customer interaction with the business. Gain limited-time complimentary access to our analytics platform right away.

Foster better customer relationships

In the competitive business landscape, companies should focus on fostering good customer relationships in order to increase customer lifetime value. The key to nurturing good relationships with customers is to make them feel heard,  cared, and appreciated. Also, to improve relationships with customers, companies need to tap into their expectations and interests. This is where customer survey can help. Surveying customers can offer valuable insights into their likes and dislikes and can help in increasing customer lifetime value. This can further help in offering personalized services for better customer experience and satisfaction. Keeping a pulse on you’re your customers want  can also help in taking immediate action to improve customer satisfaction in case of a sudden dip in the sales and can boost customer lifetime value.

Want more tips on how analytics can help you  increase your customer lifetime value  and generate more revenue from your existing customers? Get in us with our experts to learn more.

Customer lifetime value

Customer Lifetime Value Analysis Helps Improve Retention Rate by 13% for a Telecom Company – A Quantzig Case Study

customer lifetime value

A world leader in the IT and telecom sector based out of Denmark was facing several predicaments due to their inability to identify new international business opportunities, improve retention rates, and ensure the right amount of investments towards profitable customer segments. To tackle such challenges, they approached Quantzig to leverage its customer lifetime value analytics solutions to profile the potential customers and develop a marketing strategy that can help them maximize retention, net profit and minimize re-marketing costs.

Quantzig adopts a holistic approach to help organizations develop unique methods for evaluating the customer lifetime value for each client across different industries. Request a FREE proposal now to gain in-depth insights into our customer analytics solutions.

Telecom Service

Business Challenge

The global telecom industry consists of several big players that make communication possible on a global scale, either through  phone or the Internet. The telecom industry is primarily driven by technological innovations and developments, which help to offer a wide range of communication services at low-cost margins.

The client, a leading telecom player with business operations spread across the globe, wanted to determine the net value of their customers by source, campaign, and channel. This would help them identify potential customers and ascertain their average revenue per user. The client, with the help of customer lifetime value analytics solutions, wanted to spend their resources smartly to acquire new customers and retain the most profitable ones with customer lifetime value analytics engagement. Also, with the help of customer lifetime value analytics solutions, they wanted to seek ways to improve business decisions about product development, sales, marketing, and offer reliable customer service and experience to the customers; thus, helping them to maintain a long-term relationship with customers.

Major challenges faced by the client:

  • To ascertain the net value of their customers
  • To seek ways to improve decision-making capabilities
  • To reduce customer churn rates and retain the most profitable customers

Our customer analytics experts help can help you build a customer lifetime value model to map the customer journey across all customer touchpoints. Get in touch with them right away!

Solution Offered and Value Delivered

By leveraging Quantzig’s customer lifetime value study, the client identified the necessary marketing efforts to reduce churn rates and ensured the right amount of marketing investments toward profitable customers.

Adopting a combined approach of Quantzig’s dynamic micro-segmentation and predictive modeling techniques, the client was able to accurately forecast the lifetime value of a customer based on their purchase history, demographics and other behavioral traits. Also, the telecom company was able to accurately predict the revenue generated by each customer which, in turn, helped them devise cost-efficient strategies aimed at improving the share of wallet from that customer. The solutions offered also helped them improve retention rate by 13% and build long-term relationships with the customers.

Our advanced customer lifetime value modeling solutions can help you identify profitable customer groups and optimize your marketing spend. Request a FREE demo to know more.

Outcome

Customer lifetime value analysis solutions helped the client to optimize interactions and conversations to drive repeat purchases, customer referrals, and minimize support costs. Furthermore, this engagement offered real-time and actionable insights into their customers’ journey which helped the client efficiently distinguish customers in terms of profitability.

Our customer lifetime value analysis also empowered the client to:

  • To improve retention rate by 13%
  • Accurately predict the revenue each customer generates
  • Devise data-driven marketing strategies to improve profitability
  • Reduce customer churn rates significantly

Request for more information below to know how our customer analytics solutions can help you proactively boost engagement and improve customer service.

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churn model

3 Major Challenges Enterprises Face in Building an Effective Churn Model

To succeed in today’s complex business scenario, companies need to build and deploy an effective customer churn analysis model in order to monitor churn rate and maximize customer retention. Acquiring new customers always costs heavily and this makes predictive churn model appealing for businesses that aim at retaining customers and maximizing profits. Although predicting customer churn seems to be easy initially it involves several challenges. We have listed some of these challenges in order to help companies adopt preventive measures before moving ahead in the process.

Our customer churn analysis helps companies to measure churn risk and enhance the effectiveness of retention campaigns. Want to know how? Get in touch with our experts right away.

Challenges of Building a Predictive Churn Model

Challenge #1: Lack of “silver bullet” methodology

One of the major challenges that companies face in building a predictive churn model revolves around the selection of a suitable churn modeling approach. But there is no single methodology to build a predictive churn model that can work in most situations. Machine learning techniques are mostly used by businesses due to their efficiency and ability to categorize and manipulate complex data sets. The approach of survival analysis, on the other hand, uses survival and hazard functions to predict which customer will churn during a particular period. So, the best solution to deal with this challenge is to compare the performance of several models and identify the most effective method for your business.

Quantzig helps its clients by offering accurate churn prediction models to design a data-driven customer retention strategy. Request a FREE proposal to gain better insights.

Challenge #2: Features and exploratory analysis

Businesses face several roadblocks and churn risk at this stage of building predictive churn models such as lack of information, target leakage and the need for optimal feature transformations. Along with the domain knowledge, businesses must also have the required skills and creativity to build robust predictive churn models. .  Therefore, it is important that companies execute careful exploratory analysis and build auxiliary models before embarking on building an overall churn prediction model. Exploratory analysis can also help in revealing reciprocity, irregularities, outliers, and, relationships between different functions, which wouldn’t be possible with domain knowledge alone. 

Our customer analytics solutions help businesses precisely predict future buying behaviors of customers and deliver relevant offers. Request a FREE demo to know more.

Challenge #3: Validating churn model performance

For accurate customer churn analysis, it’s essential to choose the correct metric to optimize and validate datasets. The precision of a churn model not only impacts performance but also affects decision-making. As such, businesses need to employ different strategies to validate the performance of a churn model prior to its implementation. Also, businesses need to monitor several versions of churn model to identify problems.

Still, want to know more about the critical roadblocks in building a precise customer churn model? Request for more information below.

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behavioral segmentation

Behavioral Segmentation: A Five-Minute Guide to go from Novice to An Expert

What is behavioral segmentation?

Behavioral segmentation is defined as the process of dividing the total market into smaller homogeneous groups based on customer buying behavior. This approach of market segmentation focuses more on the distinct actions of customers with regard to the product or service and less on the identity of customers. Behavioral segmentation allows you to categorize your existing and potential customers into smaller segments based on their purchase behavior. The main objective of behavioral segmentation is to identify niches and address their needs that are believed to be similar. This helps marketers to develop marketing strategies for every individual segment and create products to cater to their needs.

Market segmentation can help you analyze various customer groups and gain a competitive edge. Get in touch with our experts to know more.

Why is behavioral segmentation important?

With the competition getting fierce every day, companies are striving to win dynamic markets by designing and producing personalized products to serve the needs of their customers. Every consumer is unique, and this uniqueness is somehow based on their backgrounds and needs. This is the reason why they develop different habits in terms of buying specific products. Therefore, it becomes important for companies to aim at serving their customers based on their buying behaviors. This is where behavioral segmentation comes into the picture. One of the most important reasons why behavioral segmentation is important is because different individuals have different preferences and with this approach, businesses can yield higher profits than approaching different groups with the same marketing strategy.

BEHAVIORAL SEGMENTATION

Behavioral segmentation methods

Purchasing Behavior

This is one of the best behavioral segmentation methods that help to identify trends in customers behavior during the process of purchase decision-making. Analyzing the purchasing behavior of the customers can help in understanding how different individual approach the purchase decision, the barriers they face in the purchasing process. Also, by analyzing these entities and the relation between them, businesses can determine the moments in which customers are most likely to make a purchase.

Our analytics solutions help companies gain accurate insights into the behavior of their customers and marketing efforts. Request a FREE proposal to know more.

Occasion or Timing

Based on timings and occasions, the propensity of consumers to make purchases fluctuates. There are many customers who buy on specific occasions while there are others who buy on regular personal occasions and then there are a few who buy on rare personal occasions. Understanding when a group of similar customers is highly likely to make a purchase can help companies tailor their offers and maximize their profits. Also, it is considered to be one of the most beneficial behavioral segmentation strategies for businesses in terms of gaining customer loyalty.

Usage Rate

It is very important for businesses to know how often a customer uses your product or service. This can help companies in tailoring marketing initiatives and offer every individual a personalized customer experience. With the help of this behavioral segmentation method, businesses can easily identify their most reliable customers who provide them with a bulk of consumer-generated revenue. Also, it can help in targeting such customers who make purchases regularly but not at frequent intervals. This component of behavioral segmentation also helps in identifying customers who don’t make purchases unless some discounts are offered by the company.

Request a FREE demo to know how our analytics solutions can help you continuously track your consumers’ behavior.

customer analytics

A Holistic Guide to Customer Relationship Management

What is customer relationship management?

Customer relationship management comprises strategies, practices, and technologies that companies use to manage and analyze customer interactions and data throughout the lifecycle of a customer, with the goal of improving customer retention,  enhancing customer service relationships, and driving sales growth. A CRM system aids companies stay connected to their customers, streamline processes, and increase profitability. Also, CRM systems help organizations in sales management, productivity, and contact management. Furthermore, a customer relationship management strategy focuses on the relationship of an organization with service users, suppliers, colleagues and customers throughout its lifecycle with them. CRM system assembles customer data across different channels, or points of contact between the customer and the company through telephone, company’s website, direct mail, live chat, social media, and marketing materials.

Are you facing challenges in choosing customer relationship management tools or migrating from your existing software? Get in touch with our experts to know how our analytics solutions can help.

Benefits of customer relationship management

Benefit #1: Enhances customer service

Customer relationship management process provides businesses with several strategic benefits. One of such is the capability to add personalization to existing relationships with the customers. For businesses, it is possible to serve each client individually rather than as a group, by maintaining a repository on each customer’s profiles. Customer relationship management tools allow each employee to analyze the specific needs of their customers as well as their transaction file. The organization can occasionally adjust the level of service offered to reflect the importance or status of the customer. This can result in better customer service and can decrease customer agitation and builds on their loyalty to the business.

Benefit #2: Encourages discovery of new customers

Customer relationship management tools help in identifying potential customers. They enable companies to keep a track of the existing clientele profiles and can use them to identify potential customers for maximum clientage returns. Addition of new customers to any business indicates the growth of that business. However, retaining customers is more than adding new prospects and a robust customer relationship management process helps here.

Benefit #3: Boosts customer revenues

Customer relationship management strategy ensures the effective coordination of marketing campaigns. It facilitates the filtering of the data and ensures the promotions target the right customers at the right time. Businesses can also use the data derived from customer relationship management tools to introduce loyalty programs that promote a higher customer retention ratio.

Benefit #4: Enhances effective cross-selling and up-selling of products

With customer relationship management, both cross and up-selling can be made easier. Apart from serving customers with the best offers quickly, these two forms of selling help employees in gaining a better understanding of their customer’s needs. With time, they can always anticipate related purchases from their customer.

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Benefit #5: Simplifies the sales and marketing processes

A CRM system facilitates the development of better and effective communication channels. Technological integrations like websites and interactive voice response systems can make work easier for the sales representatives as well as the organization. Consequently, businesses with a CRM have a chance to provide their customers with various ways of communication. Such strategies ensure appropriate delivery of communication and quick response to inquiries and feedback from customers.

Five Simple Steps to Get Better Results with CRM System

Step 1: Collect and store data

The first step in creating an effective customer relationship management system is to collect and store all the information you can get about every customer in your CRM system. So, you need to find out what industry they are in, their geographic data, why they chose your company, products, and services, demographic data. Eventually, you will have enough data to build better profiles of your customer. This will further help in creating targeted advertising to bring in more customers that meet your existing profile.

Step 2: Build a communication timeline

The second step is to determine the best frequency and order of communications with new prospects. Now you need to move on to hints on how to get the best from your product and service to begin building customer loyalty and relationships. Furthermore, you need to introduce your customers to complementary or add-on products they may have a need for. This cycle can be repeated so that most communications can add value to the customer and customer relationship.

Step 3: Analyze sales data

The next step in creating an effective customer relationship management strategy is to look at your most profitable and top spending customers. This way you can create more focused campaigns to reach out to them. Also, you need to speak to your existing customers and ask them to provide brief testimonials to draft a customer case study. This way you can show prospects how you have helped your customers facing similar predicaments.

Step 4: Make the data easily accessible in the field

Your salespeople need to know the status of issues, orders, and payments due so that they can act accordingly. Your engineering staff can use the data to create a history of issues and activities on customer sites that may aid them to fix current problems faster. If you provide links to field data, it makes you improve your customer responsiveness and help in building effective customer relationship management system.

Step 5: Personalize your communications

Personalization is the key to establish an effective customer relationship management. You can do this by using recent history collected in your customer relationship management database and showing your customers that you know what is happening to an individual customer. This can help you stay on the top of any issues proactively.

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Media and Entertainment Industry is Evolving with Big Data Analytics, Are You Keeping Up?

Big data analytics has become a huge game-changer in most, if not all, types of modern industries over the last few years. Some of the key big data applications for modern companies include enhancing customer experience, achieving cost reduction, better-targeted marketing, and improving existing processes. Several recent data breach incidents have also made enhanced security an important goal for big data analytics to accomplish.

Big data analytics in media and entertainment industry 

Media and entertainment companies are shifting to a content-centric model in order to embrace a customer-centric world. The ultimate aim of companies in the sector is to reduce customer churn by delivering top-notch customer experience. With smartphones and associated digital media becoming the major source of entertainment, it is high time for media companies to leverage big data analytics to create a connection with their customers. However, the ability to access, analyze, and manage large volumes of data while rapidly evolving the information architecture is becoming increasingly critical for companies in the media and entertainment industry who are determined to improve business efficiency and performance. Furthermore, media companies often face the challenge of correctly identifying why some customers subscribe and unsubscribe to their content or services. Media companies also face difficulty when it comes to analyzing why and how customers react to pricing and subscription models. Big data analytics can enable a better understanding of the consumer as the content is created to ensure success.

Analyzing large data sets is one of the biggest problems Contact USaffecting the business outcomes of media companies today! Get in touch with our industry experts to know how to beat other companies to the chase with the help of big data analytics.

Big data applications in the media industry 

The future of the media and entertainment industry is largely dependent on the amalgamation of both digital and big data analytics solutions. The rapidly rising digital consumerism offers a plethora of opportunities for media and entertainment companies to leverage big data analytics for better customer engagement. Below are a few examples of how media companies can benefit from big data analytics:

Predict what audiences want

One of the primary big data analytics applications in media companies is to understand what the target audience wants. The amount of data that media companies gather gives them ample opportunities to leverage big data analytics capabilities to understand the demand of the genre of shows, music, content for a given age group on various different channels.

Ad targeting

Big data analytics gives companies in the media and analytics industry a better understanding of the digital media and the consumption behavior of users across various platforms. By using traditional demographic data, companies can personalize advertisements to suit the needs of specific users. Using big data analytics and offering micro-segmentation of customers to their advertising networks and exchanges, media and entertainment companies can also increase digital conversion rates.

Content monetization and product development

One of the key big data analytics applications in the media industry is that it helps in additional revenue generation for media and entertainment companies. Accurate data gives companies the ability to incentivize consumer behavior and, in the process, reveals the true market value of the content that has been generated.

Scheduling optimization

Using insights from big data analytics, media companies are able to understand when customers are most likely to view content and the devices that will be used to access content. With the scalability of big data analytics, this information can be analyzed at a granular ZIP code level to facilitate localized distribution.

Insights on customer churn

A serious menace that media companies find almost impossible to tackle is customer churn. It has been found that a considerable population of customers share their reviews through social media. Until the advent of big data analytics, combining and making sense of all the user-generated data from multiple sources, including social media was next to impossible. With the advent of big data analytics, it is now possible to know why customers subscribe and unsubscribe from a particular type of content. With the help of big data analytics, it becomes easier to clearly gain insights on what kind of programs they like and dislike.

On the flip side, implementing big data analytics in media and entertainment comes with its own set of challenges that media companies must overcome.

Big data challenges for media companies 

The unprecedented rate of growth of the media and entertainment industry is forcing companies in this sector to keep up with the rapid pace of change. The shift from traditional platforms to online channels paves the way for media and entertainment industry companies to implement big data analytics technology to gain a more profound understanding of customers’ needs. But, with the rapid adoption of digital technologies, the media industry does face a few big data challenges, which slows down their progression. To completely realize the potential of digital technologies, media companies should successfully tackle these big data challenges:

Data privacy

Numerous instances of the breach of personal data and media are making consumers more sensitive towards their data. Additionally, policymakers have also addressed their issues and have implemented regulations for companies handling personal data of customers. Regulations have also been imposed on companies that broker personal data to media houses. Such big data challenges can pose problems when it comes to accumulating sufficient user data, without which conducting accurate analysis is impossible.

Wonder how to overcome data privacy issues? All you need is the right analytics solution to combat such challenges.Get More Info

Lack of financial muscle

Media start-ups and SMEs often face challenges related to adequate financial capital. While accounting for cost factors to implement big data analytics, companies need to analyze several factors like data storage costs, infrastructure costs, data processing costs, and human resource costs. Although it is relatively easy to start a new company producing content, games, or apps, it can prove difficult to scale up without significant investments.

High bandwidth requirements

Today, audiences consume a large part of the content via online platforms. High-speed broadband is a must to facilitate this. However, the penetration rates of high-speed broadband services aren’t as impressive across the world as it is in metro cities. This largely reduces the potential customer base and allows data collection from only urban segment consumers. Analysts lose the opportunity to gain insights from customers who lack access to high-speed broadband and, therefore, resort to traditional platforms.

Piracy and copyright issues

Piracy and copyright issues have been a major concern for the media and entertainment industry over the past several years. However, the advent of the digital medium has created new big data analytics challenges such as the problem of account sharing. A large number of people gain access by sharing account information and passwords for the majority of video streaming sites. Analysts will have a tough time performing analysis on customer behavior and preferences as they cannot pinpoint the demographic details of the user. Furthermore, in some cases both the child and the adult may be using the same account, here an effective judgment is impossible on who prefers a specific genre.

Are big data challenges stopping your firm from leveraging the Request Proposaltrue potential of analytics-driven insights? Request a free proposal below to check out our portfolio of solutions and stay ahead of the curve.

How to overcome data security issues facing media companies? 

Companies in the media and entertainment industry are collecting and using a huge amount of data. This gives rise to concerns over security issues that can compromise even locally generated information. With several instances in recent times reporting data breaches especially in the media and entertainment industry, it has become imperative for companies in this sector to make data protection a priority and establish strict security measures. We, at Quantzig, have identified some of the critical ways in which companies in the media and entertainment industry can guard data against breaches in both big data analytics deployments and any software that accesses the data:

Ensure endpoint security

Trusted certificates at each endpoint will help to ensure that the company’s data remains secure. Additional measures that companies in the media and entertainment industry should use include regular resource testing and allowing only trusted devices to connect to their network through the use of device management (MDM) platform.

Prevent insider threats

It is not just external threats that media companies must combat but also several internal security risks which include disgruntled or simply careless employees. Such challenges are faced by media companies where employees who work with the data are not fully educated on proper security practices and behavior, including data scientists and software developers. So, it is vital to provide digital security training to all employees.

Analyze and monitor

A big data analytics solution that includes tools for both analysis and monitoring in real time can raise alerts in events where a network intrusion is detected. But this can result in large amounts of network data. The goal is to provide an overall picture of what’s happening over sometimes large networks from moment to moment. Companies in the media and entertainment industry may not have the resources to monitor and analyze all the feedback generated, including false alarms as well as real threats. Here, big data analytics itself can be used to improve network protection. The company’s security logs can be mined for abnormal network connections. This will make it easier to identify actual attacks as opposed to false alarms.

Final thoughts

Research suggests that a large number of companies across industries have already invested in or are planning to put in their money in big data analytics due to the huge impact of big data analytics globally. The media and entertainment industry stands out as a prompt adopter of big data analytics solutions because a huge volume of data is generated digitally in this vertical and enables change in the consumer research programs.

We understand the various challenges that companies face in analyzing the feasibility of big data analytics for their business. Our big data analytics solutions are tailor-made to suit the requirements of each business. Request a free demo for more insights.

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Telco Case Study: Customer Analytics to Study and Capitalize on Customer Behavior

What the Client Wanted

Deploy customer churn analytics-based solutions to study and capitalize on customer behavior.

The Outcome

Leveraging customer churn analytics helped the client to identify the drivers that contributed to customer churn. This engagement entailed the implementation of analytics-based solutions to scrutinize, understand, and capitalize on customer behavior. It even enabled them to develop a customer churn model that precisely tracks deviations in customer behavior.

Overview of the Telecom Industry

Data and analytics are key factors that have transformed many industries globally. It not only acts as a tool to cut down costs and enhance customer experience but also helps drive future growth. Also, digitization has played a major role in improvising business functions across industries and the telecom industry is no exception. In fact, it enables organizations to reimagine their business functions and systems, and future-proof their businesses to withstand the growing competitive pressure.

Moreover, the growing need to deliver a superior customer experience has proven to be a critical driver for telecom analytics in the telecom industry. Telecom analytics enable telecom firms to understand the modern customer’s needs and help personalize services in real-time.Request Proposal

However, to gain a stronger foothold in the industry, telcos have to focus on providing holistic services. Implementing innovative market trends helps leverage the efficiency of services. Quantzig’s customer churn analytics is one such solution that aims to change the way the telecom industry functions.


Telecom Industry Challenges

  • Ongoing regulations in the telecom industry: Telecom industry players have been facing many regulatory challenges owing to the complexity of the surrounding environment. Leading telecom firms have also started embracing such regulatory pushes to turn them into customer-focused benefits.
  • Offering personalized customer experiences: Enhancing customer experience is the key to improve brand loyalty and profit margins. To do so, telecom companies need to gain detailed insights into the customer’s journey and understand the various aspects affecting their behavior.

About the Client

A leading player in the telecom industry.

Client’s Challenge

To identify customer behavioral patterns and develop a reusable customer churn model, the client – a leading telecom client – approached Quantzig. The client wanted to leverage Quantzig’s vast experience in customer churn analytics to their benefit and gain actionable insights to capitalize on customer behavior.


Benefits our customer churn analytics engagement

Customer Churn

To gain in-depth insights into our customer analytics solution

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Business Impact

Quantzig’s customer churn analytics solution helped the client to develop a reusable churn model based on the customer’s behavioral pattern. The customer churn model benefitted the client in numerous ways and enabled them to take proactive measures to reduce customer churn. It also assisted the telecom industry client to devise and test the churn score and key drivers based on each customer’s propensity to churn.

Customer Analytics Solution Insights

Customer analytics is gaining extensive applicability in a number of industry domains and are poised to witness wide-spread adoption in the coming years. Also, with the innumerable challenges that have unfolded in today’s rapidly changing telecom landscape, it becomes crucial for telcos to predict customer churn and devise appropriate telecom analytics based business models that offer real-time insights into customer behavior.


To know more about the benefits of devising an effective customer churn model

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Why Should Your Business Calculate Customer Lifetime Value?

Customer delight, customer satisfaction, user experience, and customer relationship have been the focus of every marketer in this new age. But why are marketers so obsessed with those terms instead of capturing new customers? For starters, it is predicted that it is many times more expensive to get a new customer than to retain the existing ones. Then, there is also the fact that these customers do not generate revenues for one purchase. They generate revenues over their lifetime as long as they are with the brand. Calculate a $20 revenue every month for 20 years, and the figure suddenly looks gigantic. However, calculating customer lifetime value is not an easy task as it all depends on factors such as expected churn rate, discounted rate, and profit margin per customer. ManyRequest Solution Demo organizations still go through the pain of making all such calculations to estimate customer lifetime value (CLV). So why is calculating customer lifetime value so important?

Generate ROI on customer acquisition

Calculating customer lifetime value gives you insights on your most profitable customers. It helps companies redirect their marketing budgets to channels, which boost the lifetime value of a customer rather than gross profit on the initial purchase. As a result, companies are looking to maximize customer value against the cost of acquiring them. Focusing on CLV will dynamically change the economics of an acquisition strategy. It frees up the budget from acquiring unprofitable customers, which can be used to acquire profitable long-term customers.

Create effective messages and targeting

A thorough CLV analysis paves way for effective customer segmentation. Segmenting customers based on customer lifetime value can increase the relevance of your marketing communications, which, in turn, brings about a higher degree of personalization. Also, a company can gain insights into what category of product each particular segment of customers prefers.

Define business objectives and metrics

Calculating customer lifetime value is very important to create a benchmark for future growth and expansion. It is also an excellent metric to determine the worth of your business. It can, therefore, help businesses define growth, turnover, future sales, and net profit. Additionally, the CLV computations can be tweaked to figure out lifetime gross margins and costs based on the average order value. Also, other KPIs such as acquisition cost, growth in average shopper value, churn rate and repeat purchase growth can be balanced with CLV computation.

Enhance retention marketing strategy

Evaluating the causes of a marketing campaign is a pretty dicey thing. Mostly marketers commit the mistake of factoring in the instant revenue of a marketing campaign to calculate ROI of the campaign. However, it does not value the subsequent revenues bought by the customer along the way. One good way to assess marketing campaigns is the impact it made on the average CLV of the customer segment you were targeting. Consequently, it aligns the marketing department focus to improve customer retention.

Cross-selling and up-selling opportunities

CLV calculation provides a business with deeper insights into customer behavior. Based on the individual pattern of buying, businesses can accurately cross-sell and up-sell the right product to the right customer. Such opportunities directly increase the revenue base of the company.

To know more about the importance of calculating customer lifetime value:

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3 Interesting Ways Banks Can Curtail Customer Churn Rate | Quantzig

Acquiring new customers is a more expensive process when compared to the retention of old ones. This is one of the main reasons why leading companies have put reducing the customer churn and improving their customer retention capabilities on their priority list. The banking sector is among the industries recording the largest rate of customer churn every year.

The rising competition in the market, which gives customers the liberty of choice and better offers, is one of the primary contributors to customer attrition for retail banking companies. To state the obvious, providing effective, meaningful service is key to reducing customer churn. But how can companies in the banking sector get there and bring down customers leaving them for competitor brands?  To identify early signs of potential customer churn, banks first need to start getting a holistic 360-degree view of the customer base and their interactions across multiple channels such as bank visits, calls to customer service departments, web-based transactions, mobile banking, and social media interactions. This would allow them to detect early warning signs of customer churn such as reduced transactions or stoppage of auto-pay or negative experiences, and you can take specific actions to prevent churn. 

At Quantzig, we understand the impact that customer churn rate can have on your business. And to help banking and financial services companies provide the best customer service and reduce customer churn rate, our team of experts have highlighted three strategies that they must consider:

How to Curtail Customer Churn Rate?

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