Fundamentals

Understanding Hyper Personalized Customer Engagement

Hyper personalized customer engagement Meaning ● Customer Engagement is the ongoing, value-driven interaction between an SMB and its customers, fostering loyalty and driving sustainable growth. is about treating each customer as an individual, not just a segment. It’s moving beyond basic personalization, like using a customer’s name in an email, to crafting experiences that are uniquely relevant to their specific needs, preferences, and behaviors. For small to medium businesses (SMBs), this means leveraging data to create interactions that feel less like generic marketing and more like a one-on-one conversation with a trusted advisor.

Consider a local coffee shop. The barista remembers your usual order, asks about your day, and might even recommend a new pastry based on your past preferences. This is hyper-personalization in the physical world. The goal of advanced chatbot strategies Meaning ● Chatbot Strategies, within the framework of SMB operations, represent a carefully designed approach to leveraging automated conversational agents to achieve specific business goals; a plan of action aimed at optimizing business processes and revenue generation. is to replicate this level of individual attention in the digital space, at scale.

Hyper personalized customer engagement Meaning ● Tailoring customer interactions to individual needs, driving SMB growth through stronger relationships and targeted value. means treating each customer as a unique individual, offering tailored experiences that resonate with their specific needs and preferences.

Why Chatbots Are Essential for SMB Personalization

Chatbots are no longer just about answering frequently asked questions. They are evolving into powerful tools for proactive customer engagement and hyper-personalization. For SMBs with limited resources, chatbots offer several key advantages:

• Scalability ● Chatbots can handle a large volume of customer interactions simultaneously, 24/7, without requiring additional staff. This is crucial for SMBs aiming for growth without proportionally increasing operational costs.
• Data Collection ● Chatbots are natural data gathering points. They can collect information about customer preferences, purchase history, and behavior through conversations and interactions, providing valuable insights for personalization.
• Proactive Engagement ● Advanced chatbots can proactively reach out to customers based on triggers like website behavior, abandoned carts, or past interactions, offering personalized assistance or recommendations.
• Cost-Effectiveness ● Implementing and maintaining chatbots is often more cost-effective than hiring additional 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. or sales staff, especially for round-the-clock coverage.
• Consistent Brand Experience ● Chatbots ensure a consistent brand voice Meaning ● Brand Voice, in the context of Small and Medium-sized Businesses (SMBs), denotes the consistent personality and style a business employs across all communications. and messaging across all customer interactions, contributing to a stronger and more reliable brand image.

First Steps Avoiding Common Mistakes

Jumping into advanced chatbot strategies without a solid foundation can lead to wasted effort and poor results. Here are essential first steps and common pitfalls to avoid:

Defining Clear Objectives

Before implementing any chatbot, SMBs must define clear objectives. What specific business goals will the chatbot help achieve? Are you aiming to improve customer service response times, generate more leads, increase sales, or reduce operational costs?

Vague goals lead to unfocused chatbot strategies. For instance, instead of “improve customer service,” a clearer objective would be “reduce average customer service response time by 30% within the first quarter using a chatbot.”

Starting Simple

Resist the temptation to build a complex, AI-powered chatbot from day one. Start with a simpler chatbot that addresses basic customer needs and gradually add more advanced features as you gather data and understand customer interactions. Begin with a rule-based chatbot to handle FAQs and basic inquiries before moving to more sophisticated AI-driven personalization.

Choosing the Right Platform

Selecting the right chatbot platform is critical. For SMBs, ease of use, integration capabilities with existing systems (like CRM or e-commerce platforms), and pricing are key considerations. Many no-code or low-code chatbot platforms Meaning ● Chatbot Platforms, within the realm of SMB growth, automation, and implementation, represent a suite of technological solutions enabling businesses to create and deploy automated conversational agents. are available that are designed for users without technical expertise. Focus on platforms that offer features relevant to your objectives, such as personalization options, analytics dashboards, and integration capabilities.

Initial Data Collection Strategies

Personalization relies on data. SMBs need to establish ethical and effective methods for collecting customer data Meaning ● Customer Data, in the sphere of SMB growth, automation, and implementation, represents the total collection of information pertaining to a business's customers; it is gathered, structured, and leveraged to gain deeper insights into customer behavior, preferences, and needs to inform strategic business decisions. through chatbots. This could include:

1. Welcome Surveys ● Use the initial chatbot interaction to ask new users about their preferences or needs. Keep surveys short and focused to maximize completion rates.
2. Website Behavior Tracking ● Integrate the chatbot with website analytics Meaning ● Website Analytics, in the realm of Small and Medium-sized Businesses (SMBs), signifies the systematic collection, analysis, and reporting of website data to inform business decisions aimed at growth. to track pages visited, products viewed, and time spent on site. This provides context for personalized interactions.
3. CRM Integration (Basic) ● Connect the chatbot to your CRM system to access existing customer data, such as past purchases or contact information, to personalize conversations.
4. Opt-In Forms ● Offer incentives for users to provide their information, such as exclusive content or discounts, in exchange for opting into personalized chatbot experiences.

Avoiding Over-Personalization

While hyper-personalization is the goal, there’s a fine line between helpful and intrusive. Avoid using overly personal information or making assumptions that might feel “creepy” to customers. Focus on using data to enhance the customer experience Meaning ● Customer Experience for SMBs: Holistic, subjective customer perception across all interactions, driving loyalty and growth. in a helpful and respectful way. Transparency is key; let customers know how their data is being used to personalize their interactions.

Essential Tools for Foundational Chatbot Personalization

For SMBs starting with chatbot personalization, several user-friendly tools are available. These tools often feature drag-and-drop interfaces, pre-built templates, and integrations with popular business platforms.

Choosing the right tool depends on the SMB’s specific needs and platform preferences. Many offer free trials or basic plans, allowing SMBs to test and find the best fit before committing to a paid subscription.

Starting simple, defining clear objectives, and choosing the right platform are crucial first steps for SMBs venturing into chatbot personalization.

Intermediate

Moving Beyond Basic Personalization Dynamic Content and Segmentation

Once the fundamentals are in place, SMBs can move to intermediate strategies to deepen chatbot personalization. This involves using dynamic content Meaning ● Dynamic content, for SMBs, represents website and application material that adapts in real-time based on user data, behavior, or preferences, enhancing customer engagement. and advanced segmentation to create more relevant and engaging customer interactions.

Dynamic Content in Chatbot Conversations

Dynamic content adapts to the individual user in real-time. Instead of static, pre-written messages, chatbots can generate responses based on user data, behavior, and context. Examples include:

• Personalized Product Recommendations ● Based on browsing history, past purchases, or items added to cart, the chatbot can recommend specific products or services. For an e-commerce SMB, if a customer has viewed several running shoes, the chatbot can proactively suggest popular models or related accessories.
• Location-Based Offers ● If the chatbot knows the user’s location (with permission), it can offer promotions or information relevant to nearby stores or services. A restaurant chain could offer lunch specials at the nearest location during lunchtime.
• Time-Sensitive Promotions ● Chatbots can offer deals or discounts that are only valid for a limited time, creating a sense of urgency and encouraging immediate action. “Get 15% off your order if you complete it within the next hour!”
• Personalized Greetings Based on Time of Day or Day of Week ● “Good morning, [Customer Name]! Starting your day with a coffee? Check out our breakfast menu.” or “Happy Friday, [Customer Name]! Ready for the weekend? We have special offers on cocktails tonight.”

Advanced Customer Segmentation for Targeted Messaging

Basic segmentation might involve grouping customers by demographics. Intermediate segmentation goes deeper, using behavioral and psychographic data to create more granular segments. This allows for highly targeted and relevant chatbot messaging.

Effective segmentation requires analyzing customer data from various sources, including CRM, website analytics, and chatbot interaction history. The goal is to identify meaningful segments that allow for highly tailored chatbot experiences.

Dynamic content and advanced segmentation enable SMBs to create chatbot interactions that are significantly more relevant and engaging for individual customers.

Integrating Chatbots with CRM for a Unified Customer View

For intermediate personalization, integrating chatbots with a Customer Relationship Management (CRM) system is essential. CRM integration Meaning ● CRM Integration, for Small and Medium-sized Businesses, refers to the strategic connection of Customer Relationship Management systems with other vital business applications. creates a centralized hub for customer data, allowing chatbots to access and update customer information in real-time. This leads to more informed and personalized interactions.

Benefits of CRM Integration

• Enhanced Personalization ● Chatbots can access customer purchase history, preferences, past interactions, and contact details stored in the CRM to personalize conversations. For instance, a chatbot can greet a returning customer by name and reference their previous purchases.
• Contextual Conversations ● CRM integration provides context for chatbot interactions. If a customer has an open support ticket or a pending order, the chatbot can access this information and provide relevant updates or assistance.
• Seamless Customer Journey ● Integrating chatbots with CRM ensures a seamless 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. across different touchpoints. Information collected by the chatbot is automatically updated in the CRM, providing a consistent view of the customer across all interactions, whether through the chatbot, website, email, or phone.
• Improved Lead Management ● For lead generation chatbots, CRM integration allows for automatic capture and qualification of leads. Chatbot conversations can be logged in the CRM, and lead status can be updated based on chatbot interactions.
• Data-Driven Optimization ● CRM data combined with chatbot interaction data provides valuable insights into 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 preferences. This data can be used to optimize chatbot flows, personalize messaging, and improve overall customer engagement strategies.

Practical Steps for CRM Integration

1. Choose a CRM with API Capabilities ● Ensure your CRM system offers an Application Programming Interface (API) that allows for integration with chatbot platforms. Popular SMB CRMs like HubSpot CRM, Zoho CRM, and Salesforce Sales Cloud offer robust APIs.
2. Select a Chatbot Platform with CRM Integrations ● Many chatbot platforms offer pre-built integrations with popular CRM systems, simplifying the integration process. Check the chatbot platform’s documentation for available CRM integrations.
3. Map Data Fields ● Identify the CRM data fields that are relevant for chatbot personalization Meaning ● Chatbot Personalization, within the SMB landscape, denotes the strategic tailoring of chatbot interactions to mirror individual customer preferences and historical data. and map them to corresponding chatbot variables or attributes. This ensures that data flows seamlessly between the CRM and the chatbot.
4. Automate Data Synchronization ● Set up automated data synchronization between the chatbot and the CRM. This can be real-time synchronization or scheduled updates, depending on the CRM and chatbot platform capabilities.
5. Test and Monitor the Integration ● Thoroughly test the CRM integration to ensure data accuracy and smooth functionality. Monitor the integration regularly to identify and resolve any issues.

CRM integration is a crucial step for SMBs to leverage chatbot personalization effectively, creating a unified and data-driven customer engagement strategy.

Personalized Customer Journeys with Chatbots

Beyond individual interactions, chatbots can be used to design personalized customer journeys. This involves mapping out different customer paths and creating chatbot flows that adapt to each journey, providing tailored information and support at each stage.

Mapping Customer Journeys

Start by identifying key customer journeys Meaning ● Customer Journeys, within the realm of SMB operations, represent a visualized, strategic mapping of the entire customer experience, from initial awareness to post-purchase engagement, tailored for growth and scaled impact. relevant to your SMB. These might include:

• New Customer Onboarding ● Guiding new customers through the initial setup, product tutorials, and key features.
• Product Discovery ● Helping customers find the right products or services based on their needs and preferences.
• Purchase Process ● Assisting customers with order placement, payment, and shipping information.
• Customer Support ● Providing solutions to common issues, troubleshooting guides, and escalation to live agents when needed.
• Loyalty and Retention ● Engaging existing customers with personalized offers, loyalty program updates, and feedback requests.

Designing Personalized Chatbot Flows

For each customer journey, design chatbot flows that incorporate personalization at key touchpoints. This involves:

1. Trigger-Based Interactions ● Set up chatbot interactions to trigger based on specific customer actions or events within their journey. For example, a chatbot can proactively engage a customer who has spent more than 5 minutes on a product page.
2. Conditional Logic ● Use conditional logic within chatbot flows to adapt the conversation based on customer responses or data. “If customer selects ‘yes’ to receiving newsletters, add them to the newsletter subscription list.”
3. Personalized Content Delivery ● Deliver personalized content, such as product recommendations, articles, or videos, within the chatbot flow based on the customer’s journey stage and preferences.
4. Seamless Handover to Live Agents ● Integrate a seamless handover mechanism to live agents within the chatbot flow for complex issues or when human assistance is preferred. Ensure the live agent has access to the chatbot conversation history and customer context.
5. Journey Tracking and Analytics ● Track customer progress through personalized chatbot journeys to identify bottlenecks, optimize flows, and measure the effectiveness of personalization efforts.

Case Study ● E-Commerce SMB Personalized Purchase Journey

Consider a small online bookstore. They use a chatbot to personalize the purchase journey:

1. Welcome Message ● “Welcome to [Bookstore Name]! Looking for your next great read? Tell us what genres you enjoy, and we can offer personalized recommendations.” (Initial data collection).
2. Product Browsing Assistance ● If a customer browses the “Science Fiction” category, the chatbot proactively offers ● “Sci-fi fan? We have some new releases you might love! Check out ‘Project Hail Mary’ or ‘Dune.'” (Personalized recommendations based on browsing behavior).
3. Abandoned Cart Recovery ● If a customer adds books to their cart but leaves without purchasing, the chatbot sends a message after 30 minutes ● “Still thinking about those books in your cart? Complete your order now and get free shipping!” (Abandoned cart recovery with personalized offer).
4. Order Confirmation and Shipping Updates ● After purchase, the chatbot provides order confirmation and shipping updates ● “Your order is confirmed! You’ll receive tracking information soon. In the meantime, check out these other books by the same authors you purchased.” (Post-purchase engagement and cross-selling).
5. Post-Purchase Feedback ● A week after delivery, the chatbot asks for feedback ● “Hope you’re enjoying your new books! We’d love to hear your thoughts. Leave a review and get a discount on your next purchase!” (Feedback collection and loyalty building).

This personalized journey enhances the customer experience at each stage, increasing engagement and driving sales.

Personalized customer journeys using chatbots create a proactive and supportive experience, guiding customers effectively and enhancing satisfaction.

Advanced

AI-Powered Personalization Sentiment Analysis and Predictive Capabilities

For SMBs aiming for cutting-edge customer engagement, advanced chatbot strategies leverage the power of Artificial Intelligence (AI). AI-powered personalization Meaning ● AI-Powered Personalization: Tailoring customer experiences using AI to enhance engagement and drive SMB growth. goes beyond rule-based systems to understand customer sentiment, predict future behavior, and deliver truly dynamic and adaptive experiences.

Sentiment Analysis for Real-Time Personalization

Sentiment analysis, also known as opinion mining, uses Natural Language Processing (NLP) to determine the emotional tone behind text. Integrating 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. into chatbots allows for real-time adjustments to conversations based on customer emotions.

• Detecting Frustration and Offering Support ● If a customer expresses frustration or negativity in their chatbot conversation (“This is not working,” “I’m getting annoyed”), the chatbot can detect this sentiment and proactively offer assistance, such as escalating to a live agent or providing more detailed troubleshooting steps. This proactive support can de-escalate negative situations and improve customer satisfaction.
• Tailoring Tone and Language ● Based on sentiment, the chatbot can adjust its tone and language. For a customer expressing positive sentiment, the chatbot can respond with enthusiastic and friendly language. For a customer expressing neutral or negative sentiment, the chatbot can adopt a more empathetic and solution-oriented tone.
• Personalized Product Recommendations Based on Sentiment ● Sentiment analysis can be used to understand customer opinions about products or services. Chatbots can then offer personalized recommendations Meaning ● Personalized Recommendations, within the realm of SMB growth, constitute a strategy employing data analysis to predict and offer tailored product or service suggestions to individual customers. based on positively reviewed items or address concerns related to negatively reviewed items. For example, if a customer expresses interest in a product but mentions a concern about battery life (negative sentiment), the chatbot can proactively address this concern and highlight features that mitigate it.
• Proactive Feedback Collection Based on Sentiment ● After a customer interaction, sentiment analysis can determine the overall sentiment of the conversation. Customers with positive sentiment can be prompted to leave reviews or testimonials. Customers with negative sentiment can be contacted for follow-up to address their concerns and improve their experience.

Predictive Personalization Anticipating Customer Needs

Predictive personalization uses machine learning algorithms to analyze historical data and predict future customer behavior and needs. Chatbots can leverage predictive analytics Meaning ● Strategic foresight through data for SMB success. to offer proactive and highly relevant personalized experiences.

• Predictive Product Recommendations ● Based on past purchase history, browsing behavior, and demographic data, AI algorithms can predict what products a customer is likely to be interested in next. Chatbots can proactively recommend these products, increasing the chances of upselling and cross-selling. For instance, if a customer frequently purchases coffee beans, the chatbot can predict they might be interested in a new coffee grinder or espresso machine.
• Personalized Content Based on Predicted Interests ● Predictive analytics can identify a customer’s content preferences. Chatbots can then deliver personalized content, such as blog posts, articles, or videos, that align with these predicted interests. A financial services SMB could use predictive analytics to identify customers likely interested in retirement planning and proactively offer relevant articles and resources through the chatbot.
• Predictive Customer Service ● AI can predict potential customer service issues based on past interactions and patterns. Chatbots can proactively reach out to customers who are predicted to experience issues, offering preemptive support and solutions. For example, if a customer’s order is predicted to be delayed based on shipping data, the chatbot can proactively notify the customer and offer compensation or alternative solutions.
• Dynamic Pricing and Offers ● Predictive analytics can be used to personalize pricing and offers based on individual customer behavior and purchase history. Chatbots can present dynamic pricing or personalized discounts to customers based on their predicted willingness to pay or likelihood to convert. This requires careful ethical consideration and transparency to avoid alienating customers.

AI-powered personalization, through sentiment analysis and predictive capabilities, enables chatbots to understand customer emotions and anticipate needs, leading to hyper-relevant and proactive engagement.

Contextual Personalization Real-Time Relevance

Contextual personalization focuses on delivering experiences that are relevant to the customer’s current situation and immediate context. Advanced chatbots can leverage real-time data Meaning ● Instantaneous information enabling SMBs to make agile, data-driven decisions and gain a competitive edge. to understand the customer’s context and adapt interactions accordingly.

Leveraging Real-Time Data for Contextual Relevance

• Location-Based Context ● If the chatbot has access to the customer’s location (with permission), it can provide location-specific information, offers, and services. A retail SMB with multiple locations can use location data to direct customers to the nearest store, provide local promotions, or offer location-based services like curbside pickup.
• Device and Platform Context ● Understanding the device and platform a customer is using (e.g., mobile, desktop, website, social media) allows for optimizing chatbot interactions for that specific context. For mobile users, chatbots can offer shorter, more concise messages and utilize mobile-friendly interfaces. For website users, chatbots can provide more detailed information and integrate with website features.
• Time-Based Context ● The time of day, day of the week, and even seasonality can be used to personalize chatbot interactions. A restaurant chatbot can offer breakfast menus in the morning, lunch specials during lunchtime, and dinner menus in the evening. Retail SMBs can promote seasonal products or offers relevant to the current time of year.
• Website Behavior Context (Real-Time) ● Advanced chatbots can track real-time website behavior, such as pages viewed, products clicked, and time spent on each page. This real-time data can be used to trigger proactive chatbot interactions that are highly contextual. If a customer is spending a long time on a specific product page, the chatbot can proactively offer assistance or provide more detailed product information.
• Referral Source Context ● Knowing how a customer arrived at the chatbot (e.g., from a social media ad, email link, website button) provides valuable context. The chatbot can tailor its initial message and conversation flow based on the referral source. For example, customers arriving from a social media ad can be greeted with a message that aligns with the ad campaign.

Creating Contextually Aware Chatbot Flows

Designing contextually aware chatbot flows involves incorporating logic that considers real-time data and adapts the conversation accordingly. This requires:

1. Data Integration ● Integrate the chatbot platform with relevant data sources that provide real-time context, such as location services, website analytics, and CRM systems.
2. Conditional Logic Based on Context ● Implement conditional logic within chatbot flows that branches based on contextual data. “If customer location is within 5 miles of a store, offer directions and in-store promotions.”
3. Dynamic Content Generation Based on Context ● Generate dynamic content that is tailored to the customer’s context. “Based on your current location in [City], here are the operating hours for our nearest store.”
4. Personalized Recommendations Based on Context ● Offer personalized recommendations that are relevant to the customer’s context. “Since it’s lunchtime, would you like to see our lunch menu?”
5. Testing and Optimization for Different Contexts ● Thoroughly test chatbot flows in different contextual scenarios to ensure relevance and effectiveness. Continuously optimize flows based on performance data and customer feedback.

Contextual personalization, driven by real-time data, ensures that chatbot interactions are highly relevant and timely, maximizing customer engagement and satisfaction.

Omnichannel Personalization Consistent Experience Across Platforms

In today’s multi-channel world, customers interact with businesses across various platforms. Omnichannel personalization Meaning ● Omnichannel Personalization, within the reach of Small and Medium Businesses, represents a strategic commitment to deliver unified and tailored customer experiences across all available channels. ensures a consistent and personalized customer experience across all these touchpoints, including websites, social media, messaging apps, and even in-store interactions.

Building an Omnichannel Chatbot Strategy

An omnichannel chatbot strategy Meaning ● An Omnichannel Chatbot Strategy represents a synchronized approach to customer engagement across various digital touchpoints for SMBs, intending to provide seamless and unified experiences. aims to provide seamless personalization across all customer communication channels. This involves:

• Centralized Customer Data Platform (CDP) ● A CDP aggregates customer data from all channels into a unified profile. This centralized data source is crucial for delivering consistent personalization across channels. Choose a CDP that integrates with your chatbot platform and other customer communication systems.
• Consistent Brand Voice and Messaging ● Maintain a consistent brand voice and messaging across all chatbot interactions, regardless of the channel. This reinforces brand identity and ensures a cohesive customer experience. Develop brand guidelines for chatbot communication that are applied across all channels.
• Channel-Specific Chatbot Optimization ● While maintaining consistency, optimize chatbot interactions for each specific channel. Consider the unique characteristics of each platform (e.g., character limits on Twitter, visual focus on Instagram) and tailor chatbot content and format accordingly.
• Seamless Channel Switching ● Enable customers to seamlessly switch between channels during their interaction without losing context or personalization. For example, a customer might start a conversation on a website chatbot and then continue it on Facebook Messenger. The chatbot should maintain the conversation history and personalization across this channel switch.
• Integrated Analytics and Reporting ● Implement integrated analytics and reporting across all chatbot channels to track performance, measure personalization effectiveness, and identify areas for improvement. This provides a holistic view of chatbot performance across the omnichannel customer journey.

Practical Implementation of Omnichannel Chatbots

1. Select an Omnichannel Chatbot Platform ● Choose a chatbot platform that supports deployment across multiple channels and offers omnichannel capabilities, such as centralized data management and channel switching.
2. Integrate with CDP (or CRM with CDP Features) ● Integrate the chatbot platform with your CDP to access and update unified customer profiles. If you don’t have a dedicated CDP, leverage 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. with CDP-like features to centralize customer data.
3. Develop Omnichannel Chatbot Flows ● Design chatbot flows that are adaptable to different channels and maintain personalization across channel switches. Use conditional logic to adjust content and format based on the channel.
4. Test and Deploy Across Channels ● Thoroughly test chatbot functionality and personalization across all targeted channels. Deploy the chatbot on your website, social media platforms, messaging apps, and any other relevant customer communication channels.
5. Monitor and Optimize Omnichannel Performance ● Continuously monitor chatbot performance across all channels, analyze omnichannel customer journeys, and optimize chatbot strategies to enhance personalization and consistency across the entire customer experience.

Omnichannel personalization with chatbots ensures a consistent and seamless brand experience across all customer touchpoints, building stronger customer relationships and loyalty.

References

• Kotler, Philip, and Kevin Lane Keller. Marketing Management. 15th ed., Pearson Education, 2016.
• Rust, Roland T., and Christine Moorman. Strategic Marketing. 3rd ed., McGraw-Hill Education, 2017.
• Stone, Merlin, and Alison Bond. Direct and Digital Marketing Practice. 5th ed., Kogan Page, 2019.