Demystifying Predictive Analytics First Steps For Small Businesses

Predictive analytics, often associated with large corporations and complex algorithms, is surprisingly accessible and beneficial for small to medium businesses (SMBs). The core idea is simple ● use existing data to anticipate future customer behavior Meaning ● Customer Behavior, within the sphere of Small and Medium-sized Businesses (SMBs), refers to the study and analysis of how customers decide to buy, use, and dispose of goods, services, ideas, or experiences, particularly as it relates to SMB growth strategies. and optimize your 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. accordingly. This guide cuts through the jargon and provides a hands-on, no-nonsense approach to implementing predictive analytics, focusing on tools and strategies that deliver immediate value without requiring a data science degree or a massive budget. Our unique selling proposition is to empower SMBs to harness predictive analytics Meaning ● Strategic foresight through data for SMB success. using readily available, often free or low-cost tools, transforming customer service from reactive to proactive and driving sustainable growth.

Understanding Predictive Analytics In Simple Terms

Imagine you own a bakery. You notice that sales of croissants spike every Saturday morning. That’s a simple, descriptive analysis. Predictive analytics takes it a step further.

By looking at past sales data, weather forecasts, local events calendars, and even social media trends, you can predict how many croissants you’ll likely sell next Saturday. This allows you to bake the right amount, minimizing waste and maximizing profits. In customer service, predictive analytics works similarly. Instead of just reacting to customer issues as they arise, you can anticipate customer needs, potential problems, and even identify customers at risk of churning.

Predictive analytics for SMBs is about using readily available data to anticipate customer needs and optimize service proactively, not just reactively.

Essential First Steps Data Collection And Preparation

Before diving into predictions, you need data. The good news is, you likely already have valuable data sources. These include:

• Customer Relationship Management (CRM) Systems ● If you use a CRM like HubSpot, Zoho CRM, or Salesforce Essentials, you already have a goldmine of data on customer interactions, purchase history, and demographics.
• Website Analytics ● Google Analytics is a free and powerful tool that tracks website traffic, user behavior, popular pages, and conversion rates. This data can reveal customer interests and pain points.
• Social Media Platforms ● Social media insights from platforms like Facebook, Instagram, and X (formerly Twitter) provide data on customer engagement, sentiment, and trending topics related to your brand.
• Customer Feedback Surveys ● Even simple surveys using tools like SurveyMonkey or Google Forms can gather direct customer feedback Meaning ● Customer Feedback, within the landscape of SMBs, represents the vital information conduit channeling insights, opinions, and reactions from customers pertaining to products, services, or the overall brand experience; it is strategically used to inform and refine business decisions related to growth, automation initiatives, and operational implementations. on satisfaction, pain points, and expectations.
• Transactional Data ● Sales records, order history, and payment data contain valuable information about customer purchasing patterns and preferences.

The key at this stage is not to collect all data, but to focus on data relevant to your customer service goals. Start with a clear question ● “What customer service outcome do I want to improve using predictive analytics?” For example, you might want to reduce customer churn, improve first contact resolution, or personalize customer interactions.

Once you’ve identified your data sources, the next step is data preparation. This involves cleaning and organizing your data to make it usable for analysis. Common data preparation tasks include:

• Data Cleaning ● Removing errors, inconsistencies, and duplicate entries. For example, standardizing address formats or correcting typos in customer names.
• Data Integration ● Combining data from different sources into a unified view. For instance, linking CRM data with website analytics data using customer IDs.
• Data Transformation ● Converting data into a suitable format for analysis. This might involve converting dates to a consistent format or categorizing customer feedback into sentiment categories (positive, negative, neutral).

Avoiding Common Pitfalls Data Overload And Analysis Paralysis

One of the biggest mistakes SMBs make when starting with predictive analytics is trying to do too much too soon. It’s easy to get overwhelmed by the sheer volume of data and the complexity of advanced analytical techniques. Avoid “analysis paralysis” by starting small and focusing on quick wins.

Don’t try to build complex predictive models Meaning ● Predictive Models, in the context of SMB growth, refer to analytical tools that forecast future outcomes based on historical data, enabling informed decision-making. from day one. Instead, begin with descriptive analytics ● understanding what happened in the past ● before moving to predictive analytics ● forecasting what might happen in the future.

Another pitfall is focusing on the wrong metrics. Vanity metrics like website traffic or social media followers might look impressive but don’t directly translate to improved customer service or business growth. Instead, prioritize actionable metrics that directly impact your customer service goals. Examples include:

• Customer Churn Rate ● The percentage of customers who stop doing business with you over a given period. Reducing churn is crucial for sustainable growth.
• Customer Satisfaction (CSAT) Score ● A measure of how satisfied customers are with your products or services, often collected through surveys.
• Net Promoter Score (NPS) ● Measures customer loyalty Meaning ● Customer loyalty for SMBs is the ongoing commitment of customers to repeatedly choose your business, fostering growth and stability. and willingness to recommend your business to others.
• First Contact Resolution (FCR) Rate ● The percentage of customer issues resolved on the first interaction. Improving FCR reduces customer frustration and support costs.
• Customer Lifetime Value (CLTV) ● The total revenue a customer is expected to generate throughout their relationship with your business. Predictive analytics can help identify high-CLTV customers.

Quick Wins With Simple Tools Spreadsheets And Basic CRM Reports

You don’t need expensive software or specialized skills to get started with predictive analytics. Spreadsheets like Microsoft Excel or Google Sheets and basic reporting features in your CRM system are powerful enough for initial analysis and quick wins.

Using Spreadsheets for Basic Trend Analysis ●

Let’s say you want to predict customer service ticket volume. Export your customer service ticket data from your CRM into a spreadsheet. Include columns for ticket creation date, time, issue category, and resolution time. Use spreadsheet functions to:

1. Calculate Daily/Weekly Ticket Volume ● Group tickets by date or week and count the number of tickets in each period.
2. Identify Peak Times ● Analyze ticket volume by time of day to identify peak hours for customer service requests.
3. Analyze Issue Categories ● Calculate the frequency of different issue categories to identify common customer problems.
4. Calculate Average Resolution Time ● Determine the average time it takes to resolve tickets for different issue categories.

By visualizing this data using charts and graphs in your spreadsheet, you can identify trends and patterns. For example, you might discover that ticket volume spikes on Mondays and for “billing issues.” This simple analysis provides actionable insights ● you can staff more customer service agents on Mondays and proactively address common billing questions in your FAQs or onboarding materials.

Leveraging Basic CRM Reports for Customer Segmentation ●

Your CRM likely offers basic reporting features that can help you segment customers based on their behavior and predict their needs. For example, you can create reports to:

1. Identify High-Value Customers ● Segment customers based on purchase history, order value, or lifetime value. These customers deserve prioritized and personalized service.
2. Identify At-Risk Customers ● Segment customers based on inactivity, declining engagement, or negative feedback. Proactive outreach to these customers can prevent churn.
3. Analyze Customer Service Interactions by Segment ● Compare customer service ticket volume, resolution time, and satisfaction scores across different customer segments to identify segment-specific service needs.

These basic CRM reports provide valuable insights for tailoring your customer service approach. For instance, you might offer proactive support to high-value customers or create targeted onboarding programs for new customers based on their industry or use case.

Starting with these fundamental steps and readily available tools allows SMBs to experience the tangible benefits of predictive analytics in customer service without significant investment or complexity. It’s about making data-informed decisions to improve customer experiences and drive growth, one step at a time.

Stepping Up Predictive Analytics Practical Techniques For Growing Businesses

Having established a foundation in predictive analytics with basic tools, SMBs are ready to explore intermediate techniques that offer more sophisticated insights and automation capabilities. This section focuses on leveraging readily available CRM features and accessible 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 to enhance customer service prediction and optimization. We move beyond simple trend analysis to explore customer segmentation, predictive CRM functionalities, and data visualization for deeper understanding and improved ROI. Our continued USP emphasizes practical, step-by-step implementation using tools within reach of growing SMBs.

Advanced Customer Segmentation For Personalized Service

While basic segmentation, as discussed in the Fundamentals section, is valuable, intermediate predictive analytics allows for more granular and dynamic customer segmentation. Instead of static segments based on demographics or purchase history, you can create segments based on predicted behavior and needs. This enables highly personalized customer service Meaning ● Anticipatory, ethical customer experiences driving SMB growth. interactions that drive satisfaction and loyalty.

Behavioral Segmentation ● Analyze customer behavior across multiple touchpoints ● website activity, CRM interactions, purchase patterns, social media engagement ● to identify segments based on:

• Engagement Level ● Segment customers into highly engaged, moderately engaged, and low-engaged groups based on website visits, email opens, social media interactions, and support ticket frequency. Predict future engagement levels based on past behavior.
• Purchase Propensity ● Segment customers based on their likelihood to purchase specific products or services. Analyze past purchase history, browsing behavior, and demographics to predict future purchase patterns.
• Churn Risk ● Segment customers based on their predicted likelihood to churn. Analyze factors like declining engagement, negative feedback, service issues, and contract expiration dates to identify at-risk customers.

Needs-Based Segmentation ● Go beyond demographics and behavior to segment customers based on their underlying needs and motivations. This requires a deeper understanding of your customer base and can be achieved through:

• Customer Surveys and Feedback Analysis ● Use surveys and analyze customer feedback (from surveys, reviews, and support interactions) to identify common customer needs and pain points. Categorize feedback using 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. tools (many are available as CRM integrations or standalone low-cost options).
• Customer Journey Mapping ● Map out the typical customer journey Meaning ● The Customer Journey, within the context of SMB growth, automation, and implementation, represents a visualization of the end-to-end experience a customer has with an SMB. and identify pain points and opportunities for improvement at each stage. This can reveal unmet customer needs that predictive analytics can address.
• Predictive Modeling for Needs Identification ● Use 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 (often accessible through no-code AI Meaning ● No-Code AI signifies the application of artificial intelligence within small and medium-sized businesses, leveraging platforms that eliminate the necessity for traditional coding expertise. platforms) to predict customer needs based on their demographics, behavior, and past interactions. For example, predict which customers are likely to need proactive onboarding support or advanced product training.

Intermediate predictive analytics allows for dynamic customer segmentation Meaning ● Customer segmentation for SMBs is strategically dividing customers into groups to personalize experiences, optimize resources, and drive sustainable growth. based on predicted behavior and needs, enabling highly personalized customer service.

Leveraging Predictive Features In CRM Platforms

Modern CRM platforms are increasingly incorporating predictive analytics features directly into their functionality. These features are designed to be user-friendly and accessible to SMBs without requiring specialized data science expertise. Explore the predictive capabilities of your CRM system. Common predictive features include:

Lead Scoring and Prioritization ● Predictive lead scoring Meaning ● Lead Scoring, in the context of SMB growth, represents a structured methodology for ranking prospects based on their perceived value to the business. automatically ranks leads based on their likelihood to convert into customers. This allows sales and customer service teams to prioritize their efforts on the most promising leads. CRM systems often use machine learning algorithms to analyze lead data (demographics, behavior, engagement) and assign scores. Customer service can benefit by proactively engaging high-potential leads who are further down the sales funnel and may require specialized support.

Churn Prediction and Prevention ● Many CRMs offer churn prediction Meaning ● Churn prediction, crucial for SMB growth, uses data analysis to forecast customer attrition. models that identify customers at high risk of leaving. These models analyze customer data to identify patterns and indicators of churn. Customer service teams can use churn predictions to proactively reach out to at-risk customers, address their concerns, and offer retention incentives. This proactive approach can significantly reduce churn rates and improve customer loyalty.

Personalized Recommendations and Next Best Actions ● Some CRMs provide personalized product or service recommendations based on customer profiles and past behavior. These recommendations can be integrated into customer service interactions to offer relevant solutions and upsell opportunities. “Next best action” recommendations guide customer service agents on the most effective action to take in a given situation, based on predictive analysis of customer needs and preferences. This could include suggesting specific knowledge base articles, offering personalized support channels, or escalating issues to specialized teams.

Sentiment Analysis and Customer Health Scoring ● CRM-integrated sentiment analysis tools automatically analyze customer communications (emails, chat transcripts, social media posts) to gauge customer sentiment Meaning ● Customer sentiment, within the context of Small and Medium-sized Businesses (SMBs), Growth, Automation, and Implementation, reflects the aggregate of customer opinions and feelings about a company’s products, services, or brand. (positive, negative, neutral). Customer health scoring combines sentiment analysis with other data points (engagement, purchase history, support interactions) to provide an overall assessment of customer health and satisfaction. Customer service teams can use sentiment analysis and health scores to identify customers who are dissatisfied or at risk and prioritize proactive intervention.

Data Visualization For Deeper Insights And Communication

While spreadsheets are useful for basic analysis, data visualization tools offer more powerful and intuitive ways to explore and communicate predictive insights. Visualizations can reveal patterns and trends that are not readily apparent in raw data tables. For SMBs, user-friendly and affordable data visualization tools are readily available. Consider tools like:

• Tableau Public ● A free version of a powerful data visualization platform. Allows you to create interactive dashboards and visualizations from various data sources.
• Google Data Studio ● A free data visualization tool integrated with Google services. Easy to connect to Google Analytics, Google Sheets, and other data sources.
• Power BI Desktop (Free Version) ● Microsoft’s data visualization tool with a free desktop version. Connects to various data sources and offers robust visualization capabilities.
• Zoho Analytics ● Integrated with Zoho CRM Meaning ● Zoho CRM represents a pivotal cloud-based Customer Relationship Management platform tailored for Small and Medium-sized Businesses, facilitating streamlined sales processes and enhanced customer engagement. and other Zoho applications. Offers a user-friendly interface for creating dashboards and reports.
• HubSpot Dashboards and Reports ● HubSpot CRM includes built-in reporting and dashboard features for visualizing marketing, sales, and customer service data.

Creating Actionable Customer Service Dashboards ●

Design dashboards that focus on key customer service metrics and predictive insights. Examples of visualizations for customer service dashboards include:

• Churn Prediction Dashboard ● Visualize churn risk scores for different customer segments. Use color-coding to highlight high-risk customers. Include trends in churn rate over time and key churn drivers.
• Customer Sentiment Dashboard ● Display real-time customer sentiment analysis from social media and customer service interactions. Track sentiment trends and identify potential issues before they escalate. Visualize sentiment by customer segment or product/service category.
• Customer Service Performance Dashboard ● Visualize key performance indicators (KPIs) like FCR rate, resolution time, CSAT scores, and NPS. Track performance against targets and identify areas for improvement. Segment performance by agent, team, or issue category.
• Predictive Staffing Dashboard ● Visualize predicted customer service ticket volume by day of the week or time of day. Use this data to optimize staffing levels and ensure adequate coverage during peak periods.
• Customer Journey Visualization ● Map out the customer journey and overlay predictive insights Meaning ● Predictive Insights within the SMB realm represent the actionable intelligence derived from data analysis to forecast future business outcomes. at each stage. Identify drop-off points and opportunities to improve the customer experience. Visualize customer behavior flows and conversion paths.

Data visualization is not just about pretty charts; it’s about making data accessible and actionable for customer service teams. Well-designed dashboards empower agents and managers to understand customer needs, anticipate problems, and make data-driven decisions to improve service quality and efficiency.

By implementing these intermediate techniques, SMBs can significantly enhance their predictive analytics capabilities in customer service. Focus on practical application, leverage readily available tools, and prioritize actionable insights to drive tangible improvements in customer satisfaction, efficiency, and business growth. The journey from reactive to proactive customer service Meaning ● Proactive Customer Service, in the context of SMB growth, means anticipating customer needs and resolving issues before they escalate, directly enhancing customer loyalty. becomes increasingly impactful at this stage.

Advanced Predictive Analytics Cutting Edge Strategies For Competitive Advantage

For SMBs ready to push the boundaries of predictive analytics, this advanced section explores cutting-edge strategies and AI-powered tools that deliver significant competitive advantages. We move into the realm of sophisticated automation, proactive customer service, and hyper-personalization, leveraging the latest advancements in AI and machine learning. This section is for SMBs aiming for industry leadership through data-driven customer service innovation. Our USP here remains focused on practical implementation, but now at the forefront of available technology, guiding SMBs to adopt advanced techniques for sustainable growth Meaning ● Sustainable SMB growth is balanced expansion, mitigating risks, valuing stakeholders, and leveraging automation for long-term resilience and positive impact. and market dominance.

AI-Powered Predictive Customer Service Platforms

The landscape of predictive analytics is rapidly evolving with the rise of AI-powered platforms designed to democratize advanced techniques. These platforms often require minimal to no coding expertise, making them accessible to SMBs without dedicated data science teams. These platforms go beyond basic CRM predictive features and offer a comprehensive suite of AI-driven capabilities for customer service. Key features of advanced AI platforms include:

Automated Predictive Modeling ● These platforms automate the process of building and deploying predictive models. Users can upload their data, define their prediction goals (e.g., churn prediction, customer lifetime value Meaning ● Customer Lifetime Value (CLTV) for SMBs is the projected net profit from a customer relationship, guiding strategic decisions for sustainable growth. prediction, issue resolution time prediction), and the platform automatically selects and trains the most appropriate machine learning models. This eliminates the need for manual model development and tuning, significantly reducing complexity and time to implementation.

Real-Time Predictive Insights ● Advanced platforms provide real-time predictive insights directly within customer service workflows. As customer interactions occur, the platform analyzes data in real-time and provides agents with predictive information, such as churn risk scores, sentiment analysis, personalized recommendations, and next best action Meaning ● Next Best Action, in the realm of SMB growth, automation, and implementation, represents the optimal, data-driven recommendation for the next step a business should take to achieve its strategic objectives. suggestions. This real-time intelligence empowers agents to make informed decisions and deliver proactive, personalized service during every interaction.

Hyper-Personalization at Scale ● AI platforms enable hyper-personalization by analyzing vast amounts of customer data to create highly granular customer profiles and predict individual customer needs and preferences. This allows for tailored customer experiences across all touchpoints. For example, AI can personalize website content, email communications, chatbot interactions, and agent recommendations based on individual customer profiles and predicted needs. This level of personalization drives customer engagement, loyalty, and advocacy.

Proactive Issue Resolution and Prevention ● Advanced predictive analytics moves beyond reactive customer service to proactive issue resolution Meaning ● Proactive Issue Resolution, in the sphere of SMB operations, growth and automation, constitutes a preemptive strategy for identifying and rectifying potential problems before they escalate into significant business disruptions. and prevention. AI platforms can identify potential issues before they escalate or even before customers are aware of them. For example, predict potential service disruptions based on system monitoring data and proactively alert affected customers.

Identify customers likely to experience issues based on their past interactions and proactively offer support or guidance. This proactive approach reduces customer frustration, minimizes support costs, and enhances customer trust.

Predictive Customer Journey Optimization ● AI platforms can analyze the entire customer journey, from initial awareness to post-purchase engagement, and identify friction points and opportunities for optimization. Predictive models can forecast customer behavior at each stage of the journey and recommend personalized interventions to improve conversion rates, customer satisfaction, and lifetime value. For example, predict which customers are likely to abandon their online shopping cart and trigger proactive chat support or personalized offers. Identify stages in the customer journey where churn risk is highest and implement targeted retention strategies.

Advanced Automation Techniques For Customer Service Efficiency

Predictive analytics is a powerful enabler of customer service automation. By predicting customer needs and behaviors, SMBs can automate routine tasks, personalize interactions, and optimize workflows, significantly improving efficiency and scalability. Advanced automation Meaning ● Advanced Automation, in the context of Small and Medium-sized Businesses (SMBs), signifies the strategic implementation of sophisticated technologies that move beyond basic task automation to drive significant improvements in business processes, operational efficiency, and scalability. techniques powered by predictive analytics include:

AI-Powered Chatbots and Virtual Assistants ● Integrate AI-powered chatbots and virtual assistants that leverage predictive analytics to provide more intelligent and personalized self-service. Chatbots can predict customer intent based on their input and proactively offer relevant information or solutions. Personalize chatbot interactions based on customer profiles and past interactions.

Use predictive analytics to route complex issues to human agents efficiently. Advanced chatbots can even predict customer sentiment and adjust their communication style accordingly.

Automated Ticket Routing and Prioritization ● Automate the routing and prioritization of customer service tickets based on predictive analysis. Predict ticket urgency and complexity based on keywords, customer profiles, and past interactions. Route tickets to the most appropriate agent or team based on predicted expertise and availability.

Prioritize tickets from high-value or at-risk customers. Automated routing and prioritization ensures faster response times, improved agent efficiency, and enhanced customer satisfaction.

Predictive Knowledge Base and Content Recommendations ● Optimize knowledge bases and self-service content using predictive analytics. Predict which knowledge base articles or FAQs are most relevant to a customer’s query based on their input and profile. Personalize content recommendations based on customer needs and past interactions.

Use predictive analytics to identify gaps in knowledge base content and proactively create new articles to address emerging customer issues. This enhances self-service effectiveness and reduces the burden on human agents.

Automated Proactive Outreach Campaigns ● Leverage predictive analytics to automate proactive customer outreach campaigns. Predict which customers are likely to benefit from proactive support, onboarding assistance, or product updates. Automate personalized email or in-app messages based on predicted customer needs and preferences.

Use predictive analytics to optimize campaign timing and messaging for maximum impact. Proactive outreach builds stronger customer relationships and reduces reactive support volume.

Long-Term Strategic Thinking And Sustainable Growth With Predictive Analytics

Advanced predictive analytics is not just about short-term gains; it’s about building a long-term strategic advantage and driving sustainable growth. SMBs that embrace a data-driven culture Meaning ● Leveraging data for informed decisions and growth in SMBs. and invest in advanced predictive capabilities can achieve significant competitive differentiation. Key strategic considerations include:

Building a Data-Driven Customer-Centric Culture ● Foster a company culture that values data and uses predictive insights to inform all customer-facing decisions. Train employees on how to use predictive analytics tools and interpret predictive insights. Encourage data sharing and collaboration across departments to create a holistic view of the customer. A data-driven culture empowers employees to make better decisions and deliver superior customer experiences.

Continuous Improvement and Optimization ● Predictive analytics is an ongoing process of learning and optimization. Continuously monitor the performance of predictive models and automation systems. Regularly review and refine predictive models based on new data and changing customer behaviors.

A/B test different customer service strategies and automation approaches to identify what works best. Continuous improvement Meaning ● Ongoing, incremental improvements focused on agility and value for SMB success. ensures that predictive analytics remains effective and delivers ongoing value.

Ethical Considerations and Data Privacy ● As SMBs leverage more advanced predictive analytics techniques, it’s crucial to address ethical considerations and data privacy concerns. Ensure transparency in data collection and usage practices. Obtain customer consent for data collection and personalization.

Comply with data privacy regulations (e.g., GDPR, CCPA). Use predictive analytics responsibly and ethically to build customer trust and maintain a positive brand reputation.

Investing in Scalable Infrastructure Meaning ● Scalable Infrastructure, in the SMB context, refers to an IT and operational framework meticulously designed to handle increasing workloads and demands without hindering performance or requiring substantial reinvestment. and Talent ● As predictive analytics becomes more integral to customer service operations, SMBs need to invest in scalable infrastructure and talent. Choose AI platforms and tools that can scale with business growth. Consider hiring or training employees with data analysis and AI skills.

Build partnerships with external experts or consultants as needed. Investing in infrastructure and talent ensures that SMBs can effectively leverage predictive analytics as they scale and grow.

Reaching the advanced level of predictive analytics in customer service requires a strategic mindset and a commitment to continuous innovation. By embracing AI-powered tools, advanced automation, and a data-driven culture, SMBs can transform their customer service into a powerful engine for competitive advantage Meaning ● SMB Competitive Advantage: Ecosystem-embedded, hyper-personalized value, sustained by strategic automation, ensuring resilience & impact. and sustainable growth, setting new standards in customer experience and operational excellence. The future of SMB customer service Meaning ● SMB Customer Service, in the realm of Small and Medium-sized Businesses, signifies the strategies and tactics employed to address customer needs throughout their interaction with the company, especially focusing on scalable growth. is undeniably predictive and proactive.

References

• Kohavi, Ron, et al. “Online Experimentation at Microsoft.” Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2010.
• Provost, Foster, and Tom Fawcett. Data Science for Business ● What You Need to Know about Data Mining and Data-Analytic Thinking. O’Reilly Media, 2013.
• Reichheld, Frederick F. “The One Number You Need to Grow.” Harvard Business Review, vol. 81, no. 12, Dec. 2003, pp. 46-54.