Unlock Customer Retention Power Simple Chatbot Data Strategies

Understanding Customer Churn Through Conversation

Customer churn, or customer attrition, represents the rate at which customers stop doing business with a company over a given period. For small to medium businesses (SMBs), a high churn rate Meaning ● Churn Rate, a key metric for SMBs, quantifies the percentage of customers discontinuing their engagement within a specified timeframe. can severely impact revenue, growth, and long-term sustainability. Acquiring new customers is often more expensive than retaining existing ones, making churn reduction a top priority.

Chatbots, now a common customer interaction tool, generate a wealth of data that, when properly analyzed, can offer early warnings of potential churn. This guide focuses on empowering SMBs to leverage this readily available data for immediate and measurable improvements in customer retention.

By understanding the signals within chatbot interactions, SMBs can proactively address customer needs and reduce churn.

Essential Chatbot Data Points for Churn Prediction

Many SMBs already utilize chatbots for customer service, sales inquiries, or lead generation. Unbeknownst to many, these interactions are goldmines of information. Here are fundamental data points to consider:

• Conversation Length ● Shorter conversations, especially for returning customers or complex issues, can indicate dissatisfaction or unresolved problems.
• Sentiment Analysis ● Most chatbot platforms offer basic sentiment analysis. Consistently negative sentiment signals frustration and potential churn.
• Frequently Asked Questions (FAQs) ● An increase in specific FAQs might point to emerging customer pain points or areas of confusion in your product or service.
• Drop-Off Points ● Identifying where customers abandon conversations within the chatbot flow reveals areas of friction or inefficiency in the interaction process.
• Keywords and Topics ● Analyzing the keywords and topics customers use can uncover unmet needs, complaints, or areas where expectations are not being met.
• Resolution Rate ● The percentage of issues resolved within the chatbot. Low resolution rates force customers to seek alternative support, increasing churn risk.

These data points, readily accessible in most chatbot platform dashboards, form the foundation for churn prediction. SMBs do not need complex data science expertise to begin extracting valuable insights.

Simple Tools for Immediate Data Extraction and Visualization

For SMBs, the emphasis is on actionable insights Meaning ● Actionable Insights, within the realm of Small and Medium-sized Businesses (SMBs), represent data-driven discoveries that directly inform and guide strategic decision-making and operational improvements. with minimal resource investment. Forget expensive data analysis Meaning ● Data analysis, in the context of Small and Medium-sized Businesses (SMBs), represents a critical business process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting strategic decision-making. software for now. Start with tools you likely already have or can access affordably:

1. Chatbot Platform Dashboards ● Most chatbot providers (e.g., ManyChat, Chatfuel, Dialogflow, Intercom) offer built-in dashboards that display basic metrics like conversation volume, resolution rates, and sometimes sentiment analysis. Explore these dashboards first.
2. Spreadsheet Software (e.g., Google Sheets, Microsoft Excel) ● Export chatbot conversation logs (often available in CSV format) and use spreadsheet software for basic analysis. Functions like AVERAGE, COUNT, and pivot tables can reveal trends and patterns.
3. Data Visualization Tools (e.g., Google Data Studio, Tableau Public) ● Free or low-cost data visualization Meaning ● Data Visualization, within the ambit of Small and Medium-sized Businesses, represents the graphical depiction of data and information, translating complex datasets into easily digestible visual formats such as charts, graphs, and dashboards. tools connect to spreadsheets or CSV files and create charts and graphs to make data easier to understand at a glance.

The initial step is data extraction and visualization. For example, exporting chatbot conversation logs to Google Sheets Meaning ● Google Sheets, a cloud-based spreadsheet application, offers small and medium-sized businesses (SMBs) a cost-effective solution for data management and analysis. allows for sorting conversations by length, sentiment, or keywords, revealing initial churn indicators. Creating simple charts in Google Data Studio Meaning ● Data Studio, now Looker Studio, is a web-based platform that empowers Small and Medium-sized Businesses (SMBs) to transform raw data into insightful, shareable reports and dashboards for informed decision-making. to visualize conversation volume over time, segmented by sentiment, can highlight trends requiring attention.

Avoiding Common Pitfalls in Early Data Analysis

When starting with chatbot data Meaning ● Chatbot Data, in the SMB environment, represents the collection of structured and unstructured information generated from chatbot interactions. analysis, SMBs can easily fall into traps that lead to misleading conclusions or wasted effort. Avoid these common pitfalls:

• Data Overload ● Don’t try to analyze everything at once. Focus on 2-3 key metrics initially. Start with conversation length and sentiment as they are often strong churn indicators.
• Correlation Vs. Causation ● Just because two data points move together doesn’t mean one causes the other. For example, shorter conversations might correlate with churn, but the root cause might be poor chatbot design, not customer dissatisfaction itself. Investigate the ‘why’ behind the data.
• Ignoring Qualitative Data ● Numbers tell part of the story, but reading actual conversation transcripts provides crucial context. Qualitative analysis helps understand the reasons behind the quantitative trends.
• Lack of Actionable Insights ● Analysis is useless without action. Ensure your data analysis leads to concrete, implementable changes in your chatbot, customer service Meaning ● Customer service, within the context of SMB growth, involves providing assistance and support to customers before, during, and after a purchase, a vital function for business survival. processes, or product/service offerings.
• Perfection Paralysis ● Don’t wait for perfect data or sophisticated tools to start. Begin with basic analysis of readily available data and iterate as you learn. Progress over perfection is key for SMBs.

For instance, if data shows a spike in negative sentiment related to shipping times (identified through keyword analysis), don’t just assume customers are generally unhappy. Investigate if there’s a real issue with shipping delays or if communication about shipping expectations is unclear in the chatbot flow. Qualitative review of transcripts will provide clarity.

Quick Wins ● Addressing Obvious Churn Triggers

Even basic chatbot data analysis Meaning ● Chatbot Data Analysis, within the Small and Medium-sized Business (SMB) context, represents the systematic process of examining the information generated by chatbot interactions. can reveal immediate opportunities to reduce churn. Focus on these quick wins:

1. Improve Chatbot Flow for Common Pain Points ● If FAQs or keyword analysis reveals recurring questions or complaints (e.g., “return policy,” “shipping cost,” “out of stock”), proactively address these within the chatbot flow. Provide clearer answers, links to relevant information, or options for immediate resolution.
2. Optimize for Resolution ● Analyze drop-off points. If customers frequently abandon conversations when trying to reach a human agent, streamline the escalation process. Ensure a seamless transition to live chat or phone support when necessary.
3. Personalize Early Interactions ● Use initial chatbot interactions to gather basic customer information (e.g., order history, past issues). Personalizing responses and acknowledging past interactions can improve customer experience Meaning ● Customer Experience for SMBs: Holistic, subjective customer perception across all interactions, driving loyalty and growth. and reduce frustration.
4. Proactive Support for At-Risk Customers ● Identify customers exhibiting negative sentiment or repeatedly asking about cancellation or refunds. Trigger proactive outreach (via chatbot or email) offering assistance or addressing their concerns.

Imagine an online clothing retailer. Basic chatbot data reveals a high volume of questions about sizing and fit, often accompanied by negative sentiment and conversation drop-offs. A quick win is to integrate a size guide directly into the chatbot flow, perhaps even with interactive elements or customer reviews for similar body types. This proactive approach directly addresses a pain point, improving customer experience and reducing potential returns (a key churn indicator in e-commerce).

Real-World SMB Example ● Restaurant Online Ordering Chatbot

Consider a local pizza restaurant using a chatbot for online orders. Initially, they just used the chatbot to take orders. However, by looking at chatbot data, they uncovered hidden churn indicators.

By acting on these simple data-driven insights, the restaurant improved its chatbot flow, integrated order tracking, and adjusted chatbot capacity, leading to a noticeable decrease in order cancellations and repeat customer orders increased, demonstrating a direct impact on retention through basic chatbot data utilization.

Deepen Churn Insights Advanced Chatbot Data Analysis Methods

Systematic Data Collection and Tracking for Predictive Analysis

Moving beyond basic dashboard metrics requires a more systematic approach to data collection and tracking. For intermediate analysis, SMBs should focus on setting up processes to capture and organize chatbot data for deeper insights. This involves:

• Detailed Conversation Logging ● Ensure your chatbot platform logs comprehensive conversation data, including timestamps, user IDs (if available and respecting privacy), full conversation transcripts, sentiment scores (if available), and any custom data points relevant to your business (e.g., order IDs, product categories viewed).
• Data Export Automation ● Set up automated exports of chatbot data to a central repository, such as a cloud spreadsheet (Google Sheets) or a database (even a simple one). This avoids manual data pulls and ensures data freshness.
• Custom Event Tracking ● Implement custom event tracking within your chatbot flows to capture specific user actions that are indicative of churn risk. Examples include ● repeated requests for refunds, expressions of dissatisfaction with specific product features, or multiple inquiries about account cancellation.
• Data Integration (Basic) ● If possible, start integrating chatbot data with other customer data sources, such as CRM systems Meaning ● CRM Systems, in the context of SMB growth, serve as a centralized platform to manage customer interactions and data throughout the customer lifecycle; this boosts SMB capabilities. or email marketing platforms. Even basic integration allows for a more holistic view of customer behavior and churn risk.

For example, an e-commerce SMB might set up custom event tracking to log every time a customer asks about their return policy after adding items to their cart. This could be a stronger churn signal than simply asking about the return policy in general. Automated daily exports of chatbot logs to Google Sheets can then be used for trend analysis.

Systematic data collection and integration are crucial for identifying meaningful patterns and predicting customer churn Meaning ● Customer Churn, also known as attrition, represents the proportion of customers that cease doing business with a company over a specified period. with greater accuracy.

Uncovering Churn Patterns Using Intermediate Data Analysis

With structured data collection, SMBs can employ intermediate data analysis techniques to identify more subtle churn patterns. These techniques are still accessible to non-data scientists and provide deeper insights:

1. Cohort Analysis ● Group customers based on when they started interacting with your chatbot (e.g., by month or week). Then, track churn rates within each cohort over time. This reveals if churn is worsening over time or if certain cohorts are more prone to churn.
2. Trend Analysis ● Analyze trends in key metrics (conversation length, sentiment, resolution rate, specific FAQs) over time. Look for sudden spikes or dips that might indicate emerging churn risks. Visualizing trends in charts (using tools like Google Data Studio) is very effective.
3. Correlation Analysis (Basic) ● Use spreadsheet functions or basic data analysis tools to explore correlations between different chatbot data points and customer churn (or lack thereof). For example, is there a strong negative correlation between conversation length and customer retention?
4. Segmentation Analysis ● Segment customers based on chatbot interaction patterns (e.g., frequent users vs. infrequent users, users who primarily use the chatbot for support vs. sales). Analyze churn rates within each segment to identify high-churn customer groups.

For a SaaS SMB, cohort analysis might reveal that users who onboarded through the chatbot in Q3 have a significantly higher churn rate than those onboarded in Q2. Trend analysis could show a recent spike in negative sentiment related to a new product feature. Segmentation might show that customers who primarily use the chatbot for technical support have a higher churn rate than those who use it for sales inquiries.

No-Code/Low-Code Platforms for Deeper Insights

While spreadsheets are useful for basic analysis, no-code and low-code data analysis platforms empower SMBs to perform more sophisticated analysis without coding. These tools offer user-friendly interfaces and pre-built functionalities for data exploration, visualization, and even basic predictive modeling:

1. Google Looker Studio (formerly Data Studio) ● More advanced visualization and dashboarding capabilities than basic spreadsheet charts. Connects to various data sources, including Google Sheets, databases, and even some CRM systems. Offers interactive dashboards and more sophisticated chart types.
2. Tableau Public ● A free version of a powerful data visualization tool. Similar to Looker Studio but with a different interface and strengths in certain types of visualizations. Good for exploring complex datasets visually.
3. Zoho Analytics ● A business intelligence and analytics platform that integrates well with other Zoho products (CRM, etc.) but can also connect to external data sources. Offers data preparation, analysis, visualization, and reporting features.
4. Power BI Desktop (Microsoft) ● A free desktop application for data analysis and visualization. Powerful and widely used in businesses of all sizes. Connects to various data sources and offers advanced analytics capabilities.

These platforms allow SMBs to create interactive dashboards that monitor key churn metrics in real-time, perform drag-and-drop analysis, and explore data without writing code. For instance, using Looker Studio, an SMB can create a dashboard that tracks cohort churn rates, sentiment trends by topic, and segment-specific churn, all updated automatically from their chatbot data exports.

Developing Intermediate Churn Prediction Indicators

Based on intermediate data analysis, SMBs can develop more refined churn prediction Meaning ● Churn prediction, crucial for SMB growth, uses data analysis to forecast customer attrition. indicators. These are signals derived from chatbot data that, when combined, provide a stronger indication of churn risk:

• Negative Sentiment Trend ● Not just a single instance of negative sentiment, but a consistent trend of increasing negative sentiment in recent chatbot interactions for a specific customer.
• Decreasing Conversation Frequency ● A customer who previously interacted frequently with the chatbot but now interacts less often, especially if combined with other negative signals.
• Unresolved Issue History ● Customers with a history of unresolved issues reported through the chatbot are at higher churn risk. Track resolution history and flag customers with persistent unresolved problems.
• Specific Keyword Combinations ● Certain keyword combinations might be strong churn predictors. For example, “cancel account” + “dissatisfied with service” + “competitor pricing” is a stronger signal than just “cancel account” alone.
• Low Engagement with Proactive Offers ● If you use the chatbot to send proactive offers or promotions, low engagement (no clicks, no responses) can indicate disinterest and potential churn.

For example, a telecommunications SMB might identify a churn prediction indicator as ● “Negative sentiment in the last two chatbot interactions” + “No chatbot interaction in the past week” + “Inquiry about competitor pricing in a previous conversation.” This combination of signals provides a much stronger churn prediction than any single metric in isolation.

Proactive Retention Strategies Based on Intermediate Insights

Intermediate churn insights enable more targeted and proactive retention strategies. Instead of generic retention efforts, SMBs can tailor interventions to specific customer segments or individuals at risk:

1. Personalized Outreach Based on Sentiment ● If a customer’s chatbot interactions show a recent negative sentiment trend, trigger personalized outreach. This could be a proactive chatbot message offering assistance, a follow-up email from customer support, or even a phone call for high-value customers.
2. Targeted Offers for At-Risk Segments ● If cohort or segmentation analysis reveals high-churn customer groups, create targeted retention offers specifically for these segments. This could be discounts, extended trials, or value-added services.
3. Proactive Issue Resolution for Unresolved Issues ● Identify customers with a history of unresolved issues reported through the chatbot. Proactively reach out to these customers to understand their ongoing issues and offer solutions. This demonstrates commitment to customer satisfaction.
4. Re-Engagement Campaigns for Low-Frequency Users ● For customers with decreasing chatbot interaction frequency, initiate re-engagement campaigns. This could involve personalized chatbot messages highlighting new features, special offers, or asking for feedback.

For instance, if an online subscription service identifies a segment of users who are consistently expressing frustration with account management through the chatbot (based on sentiment and keyword analysis), they could launch a targeted campaign offering personalized onboarding assistance or simplified account management guides to this specific segment. This proactive approach is more effective than a generic retention campaign.

SMB Case Study ● E-Commerce Customer Support Chatbot

An online shoe retailer implemented an intermediate chatbot data analysis strategy to improve customer retention. They moved beyond basic metrics and focused on deeper insights:

By adopting these intermediate techniques, the shoe retailer gained a much clearer understanding of churn drivers and implemented targeted retention strategies, resulting in a measurable reduction in customer attrition and improved customer lifetime value. They demonstrated that even with readily available tools and a focus on actionable insights, SMBs can achieve significant improvements in retention through chatbot data analysis.

Maximize Retention Impact Cutting-Edge AI and Automation

Advanced Data Analysis Techniques for Granular Churn Prediction

For SMBs seeking a significant competitive edge, advanced data analysis Meaning ● Advanced Data Analysis, within the context of Small and Medium-sized Businesses (SMBs), refers to the sophisticated application of statistical methods, machine learning, and data mining techniques to extract actionable insights from business data, directly impacting growth strategies. techniques, often powered by AI, can unlock granular churn prediction capabilities. These methods go beyond basic correlations and identify complex patterns and subtle churn signals within chatbot data:

• Machine Learning Classification Models ● Train machine learning Meaning ● Machine Learning (ML), in the context of Small and Medium-sized Businesses (SMBs), represents a suite of algorithms that enable computer systems to learn from data without explicit programming, driving automation and enhancing decision-making. models (e.g., logistic regression, decision trees, random forests) to predict customer churn based on chatbot data features. These models learn from historical data to identify complex relationships between chatbot interactions and churn outcomes.
• Clustering Analysis ● Use clustering algorithms (e.g., K-means, DBSCAN) to segment customers into distinct groups based on their chatbot interaction patterns. This can reveal previously hidden customer segments with unique churn characteristics and needs.
• Natural Language Processing (NLP) and Sentiment Analysis Meaning ● Sentiment Analysis, for small and medium-sized businesses (SMBs), is a crucial business tool for understanding customer perception of their brand, products, or services. (Advanced) ● Employ advanced NLP techniques to extract deeper insights from chatbot conversation text. This includes topic modeling to identify recurring themes, intent recognition to understand customer goals, and nuanced sentiment analysis to detect subtle emotional cues.
• Time Series Analysis ● Apply time series models to analyze chatbot data metrics over time and forecast future churn trends. This is particularly useful for detecting seasonal churn patterns or predicting the impact of specific events on churn rates.

For instance, an SMB can use machine learning to build a churn prediction model that considers hundreds of chatbot data features (e.g., conversation length at different stages, sentiment scores for specific topics, frequency of certain keywords, resolution times, agent handling time). Clustering analysis might reveal distinct customer segments like “proactive self-servers,” “reactive support seekers,” and “passive information gatherers,” each with different churn propensities and needs.

Cutting-edge AI and automation are key to unlocking the full potential of chatbot data for proactive churn management and personalized retention.

Leveraging AI-Powered Tools for Automated Insights

The complexity of advanced data analysis is mitigated by the availability of user-friendly, AI-powered analytics tools. These platforms democratize access to sophisticated techniques, enabling SMBs without data science teams to leverage AI for churn prediction and automation:

1. No-Code AI Platforms (e.g., Obviously.AI, Akkio, Levity) ● These platforms offer drag-and-drop interfaces for building and deploying machine learning models Meaning ● Machine Learning Models, within the scope of Small and Medium-sized Businesses, represent algorithmic structures that enable systems to learn from data, a critical component for SMB growth by automating processes and enhancing decision-making. without writing code. They often include pre-built models for churn prediction, sentiment analysis, and text classification, making AI accessible to non-technical users.
2. AI-Powered Chatbot Analytics Platforms (e.g., Dashbot, Chatbase) ● Some chatbot analytics platforms are incorporating AI features directly into their dashboards. These might include automated churn prediction scores, AI-driven sentiment analysis with more nuanced emotion detection, and anomaly detection to flag unusual churn patterns.
3. Cloud-Based Machine Learning Services (e.g., Google Cloud AI Platform, Amazon SageMaker Autopilot, Azure Machine Learning) ● Cloud providers offer managed machine learning services that simplify the process of building, training, and deploying AI models. “AutoML” features in these platforms automate many of the complex steps, making them more accessible to SMBs.

For example, an SMB could use Obviously.AI to connect to their chatbot data (exported to Google Sheets or a database) and build a churn prediction model in minutes, without writing a single line of code. The platform would automatically handle data preprocessing, feature selection, model training, and deployment, providing a user-friendly churn prediction dashboard.

Automating Retention Actions Based on Advanced Analysis

The real power of advanced chatbot data analysis lies in automating retention actions. AI-driven churn prediction enables SMBs to move from reactive to proactive retention, triggering personalized interventions in real-time based on predicted churn risk:

1. Automated Personalized Chatbot Messages ● Integrate churn prediction models with your chatbot platform. When a customer is identified as high churn risk, trigger automated, personalized chatbot messages offering proactive support, special offers, or addressing specific concerns identified through NLP analysis of past conversations.
2. Dynamic Customer Segmentation and Personalized Experiences ● Use clustering analysis to create dynamic customer segments based on real-time chatbot interactions. Deliver personalized chatbot experiences, content, and offers tailored to each segment’s needs and preferences.
3. Automated Escalation to Human Agents for High-Risk Customers ● For high-value or high-risk customers identified by churn prediction models, automate escalation to human agents for personalized support. Ensure agents are equipped with context from chatbot interactions to provide informed assistance.
4. Predictive Customer Service Routing ● Use AI to predict the best agent or support channel for each customer based on their chatbot interaction history and churn risk score. Route high-risk customers to experienced agents or specialized retention teams.

Imagine a SaaS SMB using an AI-powered churn prediction model. When a customer’s chatbot interactions trigger a high churn risk score, the system automatically sends a personalized chatbot message ● “We noticed you might be experiencing some challenges. Would you like to schedule a free consultation with one of our product experts to address your specific needs?” This proactive, personalized outreach, triggered by AI-driven insights, is far more effective than generic retention efforts.

Integrating Chatbot Data with CRM and Business Systems

For a holistic customer view and optimized retention strategies, advanced SMBs integrate chatbot data with CRM systems and other business platforms. This integration creates a unified customer profile and enables cross-functional churn management:

• CRM Integration ● Connect chatbot data with your CRM to enrich customer profiles with interaction history, sentiment scores, churn risk predictions, and chatbot-identified needs. This provides sales, marketing, and support teams with a complete customer context.
• Marketing Automation Integration ● Integrate chatbot churn predictions with marketing automation Meaning ● Marketing Automation for SMBs: Strategically automating marketing tasks to enhance efficiency, personalize customer experiences, and drive sustainable business growth. platforms. Trigger personalized email campaigns, targeted ads, or loyalty program adjustments based on churn risk scores derived from chatbot data.
• Customer Data Platform (CDP) Integration ● For SMBs with larger data volumes and more complex systems, a CDP can centralize chatbot data along with data from other sources (website, apps, transactions). This enables advanced customer segmentation, personalized experiences across channels, and holistic churn management.
• Feedback Loop Integration ● Integrate chatbot data analysis insights back into chatbot design and training. Use churn prediction results and NLP-identified customer pain points to continuously improve chatbot flows, content, and agent training, creating a virtuous cycle of improvement.

For example, integrating chatbot data with a CRM allows a sales team to see a customer’s churn risk score directly within their CRM profile. If a high-value customer is flagged as high churn risk based on chatbot interactions, the sales team can proactively reach out with personalized retention offers or address potential issues before churn occurs. Marketing automation integration can trigger targeted email campaigns offering personalized solutions to customers identified as at-risk by chatbot analysis.

Long-Term Strategic Thinking Data-Driven Retention Culture

Ultimately, leveraging chatbot data for churn reduction is not just about implementing tools and techniques; it’s about building a data-driven customer retention Meaning ● Customer Retention: Nurturing lasting customer relationships for sustained SMB growth and advocacy. culture within the SMB. This requires a long-term strategic perspective:

• Data Literacy Across Teams ● Promote data literacy across all customer-facing teams (sales, marketing, support). Train teams to understand chatbot data insights, churn prediction indicators, and how to use data to improve customer interactions.
• Continuous Monitoring and Iteration ● Establish processes for continuous monitoring of chatbot data, churn metrics, and retention strategy performance. Regularly iterate on analysis techniques, prediction models, and retention actions based on ongoing results.
• Experimentation and A/B Testing ● Embrace a culture of experimentation. A/B test different chatbot flows, retention offers, and communication strategies to identify what works best for reducing churn. Use chatbot data to measure the impact of experiments and optimize accordingly.
• Customer-Centric Data Ethos ● Ensure data privacy and ethical data usage are paramount. Be transparent with customers about how chatbot data is used to improve their experience. Focus on using data to serve customers better, not just to maximize retention at all costs.

Building a data-driven retention Meaning ● Data-Driven Retention, within the sphere of SMB growth, centers on leveraging factual insights, extracted via automation and sophisticated analytics platforms, to improve customer lifetime value. culture is a journey, not a destination. It requires commitment from leadership, investment in tools and training, and a willingness to continuously learn and adapt. However, for SMBs that embrace this strategic approach, leveraging chatbot data for churn reduction becomes a sustainable competitive advantage, driving long-term growth and customer loyalty.

Innovative Tools and Approaches Recent Advancements

The field of chatbot data analysis and AI-driven retention is rapidly evolving. SMBs should be aware of recent advancements and innovative tools that can further enhance their churn reduction efforts:

Staying informed about these innovations and experimenting with new tools will enable SMBs to continuously refine their chatbot data analysis and retention strategies, maintaining a competitive edge in customer experience and loyalty. The future of churn reduction is increasingly driven by AI and automation, and SMBs that embrace these advancements will be best positioned for long-term success.

References

• Kotler, Philip, and Kevin Lane Keller. Marketing Management. 15th ed., Pearson Education, 2016.
• Reichheld, Frederick F., and Phil Schefter. “E-Loyalty ● Your Secret Weapon on the Web.” Harvard Business Review, vol. 78, no. 4, July-Aug. 2000, pp. 105-13.
• Rust, Roland T., Katherine N. Lemon, and Valarie A. Zeithaml. “Return on Marketing ● Using Customer Equity to Focus Marketing Strategy.” Journal of Marketing, vol. 68, no. 1, Jan. 2004, pp. 109-28.