Handpicked Customer Lifetime Value Articles for Busy Customer Retention Leaders

Customer Value:

Customer Lifetime Value is probably one of the most important and valuable metrics but is also the least understood. Researchers have found that just 42% of companies are able to measure their CLV accurately! According to Dr. Philip Kotler, “Losing a customer does not mean losing a single sale but in fact losing the entire stream of purchases that the customer would make over a lifetime.”

These are some of the highly popular customer lifetime value articles from the last quarter curated by VOZIQ’s internal team.

What exactly is Customer Lifetime Value and why should you care?

This is a metric that is often misunderstood and, even more often, overlooked completely. In a nutshell, Customer Lifetime Value is the calculation of what a customer might be worth to your business over the course of doing business with you, perhaps over the course of a few years as opposed to a single transaction.

So, who should care about Customer Lifetime Value? For businesses that offer a customer multiple transactions over time, the concept of Customer Lifetime Value is pretty significant. For businesses such as home builders, who might only work with a customer once in their lifetime, this concept might not seem to matter. Isn’t this interesting?

Four steps to maximizing Customer Lifetime Value

Focusing in on, and maximizing, customer lifetime value isn’t difficult if you’re committed to it and have the right tools. Most B2C marketers use customer lifetime value as an input to determine how much is reasonable to spend to acquire a new customer i.e. customer acquisition cost (CAC). But for top-performing B2C companies in the world, CLV is the metric on which business decisions are made.

Here’s why:

  • It’s a faster path to revenue.
  • It’s an easier path to revenue.
  • For both reasons above, it’s a more profitable path to revenue.
  • And, of course, it justifies increased spend on customer acquisition.

How 5 brands grew their Customer Lifetime Value 2X in less than 1 year with Loyalty Programs?

Retaining customers isn’t easy, but it’s important to do for businesses of every size. After all, the sure fire sign of a healthy brand is a growing number of repeat purchases from already existing customers. Growing your customer lifetime value and building long term customer loyalty takes time, though. It can’t be created overnight. Earning increased repeat business and building customer lifetime value is doable and you can start right now.

Ignite growth by rallying your organization around a customer-first mindset

To drive growth, brands must attract and retain high-value customers. Marketers are struggling with channel and product-first mindsets that don’t allow them to put customers at the heart of their strategy. But some marketers are beginning to break through. Focusing on Customer Lifetime Value (CLV) is the first step. To do that, brands need to break down organizational barriers, evolve their data and measurement strategies, and embrace a test-and-learn approach.

We hope that you have enjoyed reading these articles. For busy customer retention leaders like yourself, our curators have compiled few more popular articles on various customer related topics from the last quarter.

Click the below image to download the complete list in PDF format.

Best handpicked resources recommended by VOZIQ

The 3 Dimensions of Operationalizing Customer Intelligence

Customer intelligence equips us to utilize customer related information for developing deeper customer relationships, which can be leveraged to improve profitability. It acts as the basis for targeting potential customers of various attributes, identified from existing profitable customers.

Due to the commoditization of services, products, there is an increased focus on service and customer interaction as a differentiator – customer experience, proactive service etc. help in positively influencing customer loyalty. These aspects, when combined with the enterprise analytics at a strategic level, lead to the Customer Intelligence. Through Customer Intelligence, a company can understand customer requirements, habits and is better equipped to handle customer issues so as to improve the overall customer experience.

Dimensions of Operationalizing Customer Intelligence

Customer Intelligence is multi- dimensional and primarily encompasses three different aspects:

  • Customer Information Integration: It is essential that the data garnered is clean and as per the guidelines given by the data users. The data compiled should be able to provide a 3600 view of the customer.
  • Customer Insights: This helps in the segmentation strategies and modeling. It is important to know the specific customer preferences, requirements and the customer value. The insight should be in depth and help in identifying the various potential activities of specific customers, such as customer moving to another brand, buying or responding to a particular event, campaign etc.
  • Customer insight Operationalization: The analytic customer insights need to be integrated to the front office applications so that individual customers can be addressed through customized treatments. Marketing and sales decisions can then be taken accordingly.

According to a study by McKinsey, it was found that extensive and best practice users of customer analytics outperform their competitors. For such companies, their business strategies aligned as per CI proves to be the competitive differentiator.

Types of companies on the basis of their CI dimension

Usually, companies fall into either one of the four categories on their drive for these three customer intelligence dimensions.

  • Basic Level – In this case, the company does not have a 3600 view of the customer. It is primarily dependent on manual analysis of customer data. This may be due to a lack of knowledge or infrastructure required for analytics.
  • Foundational Level – Here, the company has the basic necessary parameters in place such as unique customer ID and customer segmentation.
  • Advanced Level – The company has a complete 3600 view of the customer and the customer campaigns and interactions are customized on the basis of the value segments.
  • Distinctive Level – Along with the 3600 view of the customer, the products and services are differentiated on the basis of the different segments and all customer interactions are insight driven.

Where does your company stand?

The goal of any company should be to reach advanced or distinctive level in order to leverage maximum benefits of CI. To do that, several important measures need to be adopted-

  • Integrated deployment of IT infrastructure, optimal analytics skills (in-house expertise), and smart execution/organization.
  • Top management incorporating analytics insights in key decision-making.
  • Developing a culture that appreciates and acts on customer analytics.
  • Putting new processes in place that managers and executives can readily understand and take actions on accordingly.
  • Engage and act to monetize social media insight with highly personalized offers and promotions.


Customer Intelligence dimensions and company categories combined are useful in creating a highly useful diagnostic matrix for the manager to find out the current level, intensity, and depth they would like to achieve in the future for developing strong customer relationships, niche offers and other proactive services.

A truly integrative approach to analytics is the gateway for extracting maximum value from customer intelligence. As a company progresses upward through its CI dimensions, its overall performance and efficiency in customer experience also gradually enhances.

Leading customer intelligence companies, such as VOZIQ, provide services and solutions that can be leveraged by enterprises across diverse verticals to move on to an advanced or distinctive level in Customer Intelligence.




Closing the Customer Intelligence Loop in Contact Centers

The customer intelligence build is utilized within the corporation at diverse points – e.g. by the CEO to frame strategies and in decision making, the Product Development Manager in product designing, by the marketing team for data mining and modeling, finance for measuring customer profitability, and even IT for data provisioning and reports.

One of the functions where the quickest impact of operationalization of CI is in contact centers, where the customers interact directly and need an immediate response. They are generally the first and preferred points of contacts for customers to communicate with a company about their products/services. Their efficiency and strategies determine what impression is cast on the customer and how well he/she is served. With the advent of CI, the contacts centers have seen a change in their operations. They are now inching towards a more insight driven interaction system with segmented profiling.

What does closed-loop customer intelligence mean?

For a contact center, customer insights are critical so as to ensure that the customer interaction experience is in line with corporate marketing and thus have consistent messaging and business and profitability goals. For this to occur, the contact center managers should be able to:

  • Understand who is interacting: The first aspect is to understand the person calling, e-mailing, faxing or chatting. The contact center needs to understand the individual, business or group that the contact represents. This understanding goes beyond mere identification of the customer and is more about understanding the customer as a person – the needs, pain points, context of the contact etc. This understanding leads to correct segmentation and subsequent provision of an appropriate response.
  • Responding to the customer in the best possible way: Once comprehensive customer intelligence is built, it should be used to match each customer contact with the best possible customer service representative for specialized handling of the interaction. For example, a frequent caller is routed to a more skilled agent who specializes in handling similar contacts. Similarly, high value customers are routed to agents empowered with responding to such customers.
  • Empowering CSRs to drive business impact: Based on customer intelligence and the insights into the contact handling record of contact service representatives (CSRs), tailored coaching programs can be created to empower the CSRs to leverage customer intelligence and deal with specific segments of the customers. This is a very crucial method to achieve business results, such as increased customer satisfaction, reduced customer churn, reduced repeat calls rates and so on.
  • Monitoring and reporting call center KPIs: The contact center managers should monitor and report Customer Intelligence results to top management – CSAT scores, customer retention numbers, first call resolution rates (FCR), etc.
  • Utilize the close customer relation to provide useful inputs to the CI analytics. The CI analytics team should focus on the operational realities, such as the descriptor data points for customer identification, routing engines, mapping the descriptor data points for right action/ treatments and success metrics identification.

How does closed loop customer intelligence help?

Customer intelligence is essential for moving the customer contact center to a profit or loyalty based operation from cost or service oriented operation. By closing the customer intelligence loop, businesses gain the ability to identify the business-critical trends in customer experience and take necessary action in a timely manner. This also helps the business to leverage the contact center customer data in a strategic manner in order to gain a competitive advantage in the market. Closing the loop also leads to the best alignment of operations for delivering a delightful customer experience. It greatly reduces operational costs by removing inefficiencies.

The Intelligence in Integrating Customer Data Sources

Customer data comes from various sources. However, most customer issues reach the company through contact centers. They act as a nerve center of customer interactions for organizations. So, it is safe to say that the effectiveness and efficiency of a contact center largely defines the customer experience for the organization.

As they act as ground-zero, or the first approach point for customer issues, companies need to be able to analyze the customer calls and integrate them with product and customer demographic data to yield crucial insights. Without the analytics of contact center customer data, businesses would not be able to earn customer loyalty, cross sell, upsell or take the necessary actions to stop a customer from moving to another brand. By analyzing contact center customer data, companies can:

  • Predict better and understand the parameters which affect customer loyalty
  • Identify products which have higher profitability
  • Design accurate campaign strategies for specific customer targeting
  • Better leverage the different messaging and communication platforms

Thus, mining contact center data can improve the overall customer experience as well as customer loyalty.

A 2014 Market Survey of Teradata found that, out of the 1,506 respondents, 92 percent agree that integrating data across teams can improve customer service. Forty-three percent of marketers say they now control their company’s customer data (up from 34 percent in 2013)—and a vast majority (83 percent) say they take an omni-channel approach to reaching customers.

Difficulties in Integrating Customer Data

The second important aspect in analytics and predictive churn management is integration of data from contacts across channels like CRM, call centers, customer survey, social media, etc. It is very important for companies to gain a unified, 360-degree view of their customers. However, this is easier said than done. Some factors that make it difficult to build a 360-degree customer view are:

  • Too many customer interaction points, and information from the different interactions are not usually stored together. The data could be anywhere in the organization—digital channels, CRM systems, customer support systems, call-centers, and so on.
  • Diverse ways of customer identification. Each department creates their own customer ID formats, leading to confusion.
  • The problem of Big Data. Companies have huge amounts of data, amounting to almost 100 million usage events in a single day! Storing this data, assigning individual subscriptions, indexing or reporting of this data is a huge problem.

Strategy for Integrating Customer Data

Integrating customer data from various sources would bring together the different pieces of the jigsaw puzzle and help in getting a clearer picture of the customer. Thus, through data integration, we get a holistic view of the customer to base the business strategy on. This in turn enables the organization to enhance sales by focusing on the most profitable customers and designing products, services and solutions which are in touch with the customer needs and wants, thus leading to increased customer loyalty and better marketing strategies.

While on the drive for customer data integration, there are a few key best practices to be followed.

  • The first step is to understand the data, clean and format it. The data set commonalities need to be identified, followed with analysis unit identification (location, household, product groups individuals, customer groups, etc.). The analysis unit is dependent on the business objective.
  • While integrating data, gaps or holes are found in certain data elements. The level of accuracy needs to be identified. This is followed by data mining, stating the hypothesis and analyzing the data.

The focus should be on unlocking customer-centric information. The analysis should be around the needs of the customer, what is important for the customer and why, the customer decision making map, basis for customer interaction and so on. Customer intelligence managers should analyze varied data sources to answer such questions.

Clearly, to complete the picture and unlock the full potential of analytics, the multi-channel customer information has to be intelligently integrated with the text analytics. The customer service agents need to be empowered with all the information on their screens working to provide inputs, suggestions and insight into what the customer is likely to purchase. This would help companies in effectively dealing with customer issues and planning more customer-centric strategies.

Why Market Analytics Trumps Market Research?

‘How can I please my customers?’ is a million-dollar question. Companies struggle to understand how their customers make purchase decisions regarding the products and services offered by the company. As most studies have established, the decision-making by a customer is mostly irrational. There may not be any logic to it.  Due to the explosion of social media, messaging apps etc., customer journey has become an extremely complex phenomenon. Customer expectations evolve too rapidly due to the access to information and social influence. A lot of these factors, apart from the actual product or service, affect customer decisions.

Since the very fate of your business is tied to the understanding of the customer, companies put a lot of effort and money into researching their customers, and are constantly on the lookout for more advanced and efficient methods of dealing with the deluge of data that is being generated every second.

This calls for a need to upgrade the techniques and methods of carrying out research to understand customer behavior as traditional market research fails to deal with the explosion of data – the opportunities as well as the complexities of it. Companies need to differentiate themselves in order to gain a competitive advantage over their rivals and also effectively streamline their businesses. In this such scenario, expanding the focus from market research to market analytics can be a game changer for businesses.

How to differentiate between the market research and market analytics?

McKinsey & Co. suggests that companies can be a part of the research revolution by paying attention to four key areas: 1) Leverage the Internet to rapidly obtain details about consumers; 2) Keep the limitations of focus groups in mind; 3) Learn how people shop; and 4) Link consumer attitudinal and behavioral data.

Based on the above, certain questions related to differentiation between market research from market analytics arise.

  1. How is the value derived from big data?
  2. How is data integrated to gain actionable insights?
  3. How to link consumer attitudinal and behavioral data?
  4. How is the immensely vast internet leveraged for real-time data and analysis?

Market research with its traditional methodologies has limitations and is unable to answer the challenges posed by the rapidly burgeoning data and the need to integrate customer data from various data sources and to analyze it in real time.

Traditional Market Research Approach

Traditional Market Research primarily consists of getting an overall feel of the customers by addressing strategic questions broadly raised by the management. The research, in that sense, is largely driven by what you already know. Market research  broadly answers questions related to business aspects like product awareness, availability, packaging, placement, pricing, competition within the market, general sentiment of the customers within the target audience, brand loyalty, brand positioning, customer satisfaction aggregate and segment awareness, message effectiveness, and advertising recall among others.

Thus, market research helps in painting a broad, holistic picture of the customers and the marketplace. This is done by studying a sample from the universe. This study is time bound, and analysis done using statistical packages like SPSS and SAS. The findings of the study are presented to the management with or without strategic recommendations based on objectives.

Technology-Driven Market Analytics

The lives of people are increasingly being shifted to the virtual world. Online Media reveals anything and everything that a company needs to know about its consumers and the market. People discuss, comment, critique and post about products, services and provide details based on their general experience. They also rely more on the publicly shared opinion than a company’s advertised claims. This makes online media the most powerful platform for businesses to leverage their immense potential. However, to tap into the vast resource base, the traditional research techniques prove to be incompetent. This is exactly where the strength of analytics comes into the picture. Companies need advanced market analytics capable of transcending the confines of primary marketing research.

How Market Analytics trumps Market Research

There are a couple of factors which distinguish market analytics from traditional market research –

  1. Technology-driven: Market analytics is technology driven. This means it’s fast, accurate and can process large volumes of data significantly faster
  2. Discovery-oriented: Market analytics is more geared towards discovery of customer insights and prediction of customer behavior
  3. Big samples: Owing to the vastness of online media, the sample size for this type of analytics is virtually unlimited
  4. Data integration: Market analytics also has the ability to integrate customer data from multiple sources like contact centers, customer surveys as well social media
  5. Real-time analytics: Market analytics generates and communicates the customer insights in real-time via custom dashboards, email, and smartphone alerts
  6. Text analytics: Market analytics lends you the ability to analyze text data at scale
  7. Predictive analytics: Market analytics lends you the ability to map customer journeys more accurately, predict customer behavior and take pre-emptive actions

All these aspects of market analytics lead to very accurate customer insights which lead to concrete actions that deliver a very high ROI.

Who handles your Customer Analytics?

Processing all the customer data and creating actionable customer analytics is a very complex task. It requires coordinated efforts by skilled and specialized professionals working across multiple departments of a company; particularly customer experience, IT and marketing. However, the division of task varies from company to company depending on the scale of customer intelligence operations present in the organization.

In order to get to the bottom of who should ideally manage customer intelligence, we need to first understand what steps customer intelligence operations essentially encompass. It includes –

  1. The storage wherein the huge chunk of customer information extracted from various social media platforms, customer feedback, and other sources are stored together. This information can be structured or unstructured.
  2. The next step is processing the customer data to segregate the relevant from the irrelevant.
  3. The third step is analyzing. Once the processing is done, the relevant information is scrutinized for actionable insights to formulate further business strategies.

Among these activities, storage and processing go hand in hand. The servers capable of processing the customer data require technical expertise to handle it. Hence, many customer intelligence professionals work in the IT department. Some companies may have their analysts working in business functions that use customer data to gain insights and make decisions. Since customer intelligence is still very much in its evolution stage, many companies may not actually have a separate group that consists of a team of professionals designated to solely overseeing its processing and analyzing.

Let us see some of the typical customer intelligence organization –

Customer analytics experts in business functions like marketing –

Customer intelligence experts working in marketing departments have an upper hand when it comes to modeling plans and policies more in sync with customer preferences. This is because the key to attracting customers and building customer loyalty lies with them. The customer data can be analyzed for insights that can be used to achieve more personalization in customer services. Marketing campaigns with a sharper focus on customer needs designed through analytical insights would reap far better results than those designed vaguely, without the highly reliable analytical data support to rely on. Since marketing functions revolve around the business, they generally don’t have customer intelligence experts handling the processing. That majorly remains the prerogative of the IT department.

Customer analytics experts in IT functions –

Since customer intelligence is very much a technology-driven process, in many companies, the IT department is the one where the core function is located. Technology is the backbone of customer intelligence. Customer intelligence professionals working in the IT functions know exactly the kind of technological infrastructure that is required to process customer data. The analysts can make recommendations for high-tech upgrade, weighing the scalability and the flexibility of the customer data with them. The needs and expectations of the analysts and the technology must match at all levels, so as to ensure that efficiency in processing is always maintained. They have to be mindful of the costs, reliability, packaging and ergonomics along with the pre-requisite technical knowledge.

Customer intelligence professionals working in different functions, having an expertise in their distinct capabilities, need to maintain a coordinated working pattern and a lucid communication channel to process and analyze the customer data for actionable insights.

Customer analytics experts in a separate group-

Some companies understand that understanding of the customer, creating actionable insights and utilizing them towards business goals is an interdisciplinary activity. A separate group of experts who understand data, technology as well business are most suited not only to bring together all the customer data, but also to transmit the customer intelligence to appropriate teams within an organization. This leads to better results and more success. Companies with a separate function generally had a higher ROI than those who did not.

Centralization of customer intelligence operations helps in multiple ways-

1) The experts can offer unbiased opinions strictly on the basis of customer intelligence, without intervention of vested interests of other company functions; whose judgment might be clouded in favor of its department’s well-being or constrictive due to a department-specific limited scope of understanding.

2) The customer intelligence professionals, together with their freedom, get a sense of assurance of job security and scope for progress, which is otherwise limited to other business functions due to the hierarchical structure of the businesses.

3) It helps in providing a singular view of customers, which is of absolute necessity for delivering better customer experiences. It saves the confusion that often arises with disorganized and multiple versions of customer information present in different departments.

4) It squashes the chance of being limited by traditional thinking. This is because it gives much space and independence for innovation and technological evolution outside the realm of typical business bounds.

5) It builds high levels of trust between the data scientists, who present insights on customer data, and the functional managers.

Having a separate centralized group of customer analytics professionals along with analysts in specific business units would establish a more cohesive, stable and healthier system of analytics communication. The analysts should be able to put in place the recommendations put forth by the central group, whose main task would be processing rigorous data and arriving at business intelligence through its data science capabilities.

What is your take on this?


5 Steps to Control Customer Churn

Effective Customer Churn Management Process

Controlling customer churn is vital for the success of any business. To improve customer retention and customer loyalty, companies need to first analyze customer churn and quantify its impact. This provides insight into the different customer groups that may need to be better addressed or need specific attention. Based on the industry vertical and the market, companies need to design a predictive churn model to identify potential customers who have a high probability of churn. This analysis can be the stepping-stone to improving customer loyalty and customer retention figures through marketing initiatives aimed at reducing the impact of customer churn.

Dr. Vasudeva Akula, CEO of VOZIQ, has elaborated on this topic in a LinkedIn post. Here is the link to the post – 5 Steps to Control Customer Churn.


Why your customers hate you?

Every business function, activity, goals and plan is directed towards enhancing the customer experience. It all boils down to developing a strong, satisfied and happy customer base which continually engages your company’s service or product and in turn spread the good word to other potential buyers.

Now imagine what would happen if your customers are dissatisfied with their experience and start hating your service and products? It would mean triggering off bad publicity that, in this age of instant communication, would spread like wildfire and turn your years of hard-earned reputation to dust in no time! It takes only one bad decision or inadvertent negligence on your part to tarnish your image in the market and let your competitors’ take advantage. No business entity ever desires this fallout. Yet, despite that, companies often fail to fully comprehend their market due to incomplete of incorrect evaluations and analysis, and may even deliver less satisfactory customer experiences. Here are some common mistakes that must absolutely be avoided. 

So, why your customers hate you?

Not taking into account the end-to-end journey (wholesome customer experience)

Most of the companies with an intention of enhancing customer service, focus their attention entirely on grabbing attention of the customer to buy their product and the feedback later after the use. These, however, are merely touchpoints and in no way descriptive of the entire customer experience. It reveals nothing about the entire customer journey and the overall impression the customer forms in his mind. 

Inferior product services due to an absence of customer intelligence

Many a times a company delivers inconsistent product services due to its lack of information about the customers they are engaging with. This can cause a major setback to the quality of customer service it provides. Not possessing the critical information about the customer can lead to highly disarrayed response when the customer approaches for help. For example the most commonplace instance. Suppose there’s a customer who had engaged with your company in the past and after facing certain issues had lodged a complaint. The next time the same customer decides to buy your product/service and contacts you, if you don’t know what you are dealing with in the absence of his/her history, you might again make the same mistake and lose that customer forever!

Acting on incomplete customer data and distorted perceptions of customer expectations

81% of consumers say they would be willing to pay more for better customer service. 70% have stopped buying from a brand because of bad service. Every company has customer data in your possession. However, more often than not, the problem is that this data is unstructured, incomplete or incomprehensible. No actionable insights can be gained out of it. So in the absence of relevant customer intelligence, the company might rely on the disorganized information which would produce an erroneous projection of customer expectations and that would eventually lead to bad service.

Absence of a detailed roadmap of delivering optimized customer journey

The customer journey is like a roller coaster ride with its share of ups and downs. If you don’t have any roadmap or a blueprint before releasing the product/service in the market, you’ll be totally clueless about assessing its reception and overall evaluation of its performance with the customers. Rest everything that follows will just go haywire.

Customer satisfaction is at the root of any company’s success. Voice of customers determines how you will fare in the market and how your company’s policies and strategies will shape. If that is ignored or incorrectly assessed, you would only have disgruntled customers walking away to other options.

Here is how you can offer a satisfying customer experience –

So what can you do to avoid the above-mentioned mistakes?

  • Working on actionable customer intelligence that takes into account the chief customer issues that otherwise can be easily overlooked and using that to realign and re-strategize policies and services more in coherence with customer expectations.
    Before undertaking any endeavor, you must do a thorough research that is inclusive of complete customer sentiments analysis and tracking of customer needs and wants, their brand experiences and behaviors. Real-time tracking ensures updated accounts capturing the pulse of the customers on a timely basis.
  • Delivering customer satisfaction by taking into account the end-to-end customer journey, using analytics to gain insights into customer preferences and sentiments, gauging impact of various initiatives and their impact on customers and drawing comprehensive observations through it.
    According to a research by Mckinsey & Company, a company’s performance on journeys is 35 percent more predictive of customer satisfaction and 32 percent more predictive of customer churn than performance on individual touchpoints.
    A lot of content is generated on a daily basis on social media platforms. These conversations, apart from generating customer data, also extract relevant competitive intelligence-competitor strategies, what is working for them and what is not, their own customers, and so on.
    According to this, a blueprint can be developed that would be devoid of all common mistakes and inclusive of all good workable aspects assembled from the whole market and restructured and redesigned for your specific customers. Map out every touchpoint the customer has with your company and analyze the kind of interaction that happens at each one of them.
  • Combining the data from operations, marketing, sales, customer feedback and competitive research to extract relevant bits of actionable intelligence to further strategize on a priority basis.
    Once the intelligence is obtained, it should be operationalized with visual scorecards, event alerts, and role-based reporting for increased efficiency.
    With this, customer expectations can be taken into account while product designing. This would eventually help build customer loyalty and customer advocacy by aligning their offerings to their real needs.
  • The above process should always happen in synergy of analysts, technology, services, marketers and sales executives. All need to work cohesively in order to arrive at rightfully gauged customer-centered solutions.

Having insightful customer analysis in hand allows for process optimization, timely intervention, product ideation and development, and customization of communication. Having diligently followed the above, you can address the real customer pain points, and deliver an outstanding customer experience.