In today’s dynamic market, personalized marketing is no longer a luxury but a necessity. Customers expect tailored experiences, and businesses are turning to Artificial Intelligence (AI) to deliver them. This guide dives deep into the strategies and techniques behind personalizing marketing messages with AI, from defining the core principles to measuring the impact of your campaigns. We’ll explore the critical role of data collection, privacy, and ethical considerations, providing a holistic approach to this crucial aspect of modern marketing.
By understanding the power of AI-driven personalization, businesses can create more meaningful connections with their customers. This involves not only tailoring messaging but also adapting to individual customer journeys, leading to increased engagement and ultimately, driving conversions. We will analyze the various AI techniques used for marketing personalization and discuss the crucial role of data privacy to build trust and maintain customer loyalty.
Defining Personalized Marketing
Personalized marketing is a strategic approach to connecting with customers on a deeper level by tailoring marketing messages and experiences to their individual needs, preferences, and behaviors. This approach moves beyond generic messaging and aims to deliver highly relevant and engaging content that resonates with each customer. It’s a shift from mass-produced campaigns to hyper-focused interactions.
Definition of Personalized Marketing
Personalized marketing goes beyond simply addressing customers by name. It involves understanding their individual characteristics, needs, and desires to craft messages and experiences that feel uniquely tailored. This includes considering factors like demographics, purchase history, browsing behavior, and even their social media activity. A key aspect is using data and technology to segment audiences and deliver highly targeted content, products, and offers.
Core Principles of Effective Personalization
Effective personalization hinges on several key principles. Understanding the customer’s journey is paramount. By mapping out the various stages of interaction, marketers can tailor content to the specific needs of each customer at each stage. Another crucial principle is data-driven insights. Analyzing customer data allows marketers to identify patterns and preferences, enabling them to create highly relevant messages.
Finally, a strong focus on customer experience is essential. Personalization should enhance the overall customer experience, making interactions more meaningful and engaging.
Difference Between Mass Marketing and Personalized Marketing
Mass marketing employs a “one-size-fits-all” approach, sending the same message to a broad audience. This approach is often less effective as it fails to consider individual needs and preferences. In contrast, personalized marketing tailors messages and experiences to each individual customer. This targeted approach fosters stronger customer relationships and increases the likelihood of conversions. A key difference lies in the level of engagement.
Personalized marketing creates a more personal and engaging experience, leading to higher customer satisfaction and loyalty.
Benefits of Personalized Marketing
Personalized marketing offers a multitude of benefits for businesses. It fosters stronger customer relationships and loyalty, resulting in increased customer lifetime value. This approach can lead to a significant boost in conversion rates by offering relevant and timely content. Personalization also improves customer satisfaction by making interactions more meaningful and relevant. Finally, by targeting specific segments, it optimizes marketing ROI, maximizing the effectiveness of marketing spend.
| Benefit | Detailed Explanation |
|---|---|
| Increased Customer Loyalty | Personalized experiences foster a stronger connection with customers, leading to increased loyalty and advocacy. |
| Higher Conversion Rates | Relevant and timely offers, based on individual preferences, increase the likelihood of conversions. |
| Improved Customer Satisfaction | Tailored messages and experiences make interactions more meaningful and engaging, boosting customer satisfaction. |
| Optimized Marketing ROI | Targeting specific segments and tailoring messages allows marketers to maximize the effectiveness of their marketing spend. |
AI’s Role in Personalization
Artificial intelligence (AI) is rapidly transforming marketing strategies, enabling businesses to deliver highly personalized experiences to customers. By leveraging AI’s capabilities for data analysis and pattern recognition, companies can create tailored marketing messages that resonate with individual preferences and needs, ultimately leading to increased engagement and conversions. This personalized approach fosters stronger customer relationships and drives higher profitability.AI empowers marketers to move beyond generic messaging and connect with customers on a deeper level.
It allows for dynamic content adjustments, ensuring that messages are relevant and timely, ultimately optimizing marketing campaigns. The insights gleaned from AI-driven analysis facilitate the creation of targeted campaigns, ensuring resources are allocated effectively.
Data Collection and Analysis Techniques
AI-powered systems can collect and analyze vast amounts of customer data from various sources, including website activity, purchase history, social media interactions, and customer service interactions. This data aggregation enables a comprehensive understanding of customer behavior, preferences, and needs. Machine learning algorithms process this data to identify trends and patterns, which provide actionable insights into customer segments and their specific characteristics.
AI Techniques for Marketing Personalization
Several AI techniques are instrumental in personalizing marketing messages. Natural Language Processing (NLP) enables AI systems to understand and interpret human language, allowing for tailored content generation. For example, an AI system can analyze customer reviews and identify common themes to create personalized product recommendations. Similarly, AI-driven recommendation engines use past purchase history and browsing behavior to suggest relevant products or services, creating a customized shopping experience.
Identifying Customer Segments and Tailoring Messages
AI can effectively segment customers based on shared characteristics, behaviors, and preferences. These segments can be defined by demographics, purchase history, engagement levels, and other relevant factors. Once customer segments are identified, AI can be used to tailor marketing messages to each segment’s specific needs and interests. For example, a company might target customers who frequently purchase a particular product with promotional offers tailored to their specific preferences.
Comparison of AI Algorithms for Personalization
| Algorithm | Description | Strengths | Weaknesses |
|---|---|---|---|
| Decision Trees | Break down data into smaller segments based on specific criteria. | Easy to understand and interpret; suitable for smaller datasets. | Can be prone to overfitting; may not generalize well to new data. |
| Support Vector Machines (SVM) | Find optimal hyperplanes to separate data points into different categories. | Effective for high-dimensional data; good generalization capabilities. | Can be computationally intensive for large datasets; may require more expert knowledge. |
| K-Nearest Neighbors (KNN) | Classify data points based on the proximity of their k-nearest neighbors. | Simple to implement; effective for tasks like customer churn prediction. | Computationally expensive for large datasets; performance depends on the choice of k. |
| Neural Networks | Complex algorithms with multiple layers to extract intricate patterns from data. | High accuracy and performance for complex tasks; capable of handling vast amounts of data. | Difficult to interpret; requires significant computational resources. |
Data Collection and Privacy
Ethical data collection is paramount in personalized marketing, ensuring trust and building lasting customer relationships. A transparent and responsible approach to data handling fosters customer loyalty and safeguards their privacy. Failing to adhere to ethical standards can damage a company’s reputation and erode consumer confidence.Data privacy in AI-powered marketing necessitates a multifaceted approach encompassing robust data security measures, explicit consent protocols, and ongoing transparency with customers.
This includes clearly defining how collected data is used and shared, and obtaining informed consent for each stage of the process. This approach is not only crucial for compliance but also for building customer trust.
Ethical Data Collection Practices
Ethical data collection practices are essential for building trust and maintaining a positive brand image. This includes obtaining explicit consent for data collection, ensuring data accuracy, and using data only for the stated purpose. Transparency about data usage is crucial for maintaining customer trust. For example, clearly outlining how collected data is used, who has access to it, and how it is secured builds customer confidence and reinforces ethical considerations.
Ensuring Customer Data Privacy in AI-Powered Marketing
To safeguard customer data privacy, employing robust security measures is essential. This includes using encryption to protect sensitive information, limiting access to data to authorized personnel, and implementing regular security audits. Furthermore, employing data anonymization techniques can further enhance privacy while still allowing for valuable insights. Regular data audits are crucial to identify vulnerabilities and ensure data protection.
Legal and Regulatory Aspects of Data Usage
Understanding and adhering to relevant data privacy regulations is critical for ethical marketing. This includes complying with regulations like GDPR, CCPA, and others, which dictate how personal data can be collected, processed, and stored. Failing to comply with these regulations can lead to significant legal repercussions. For instance, companies must be mindful of international regulations when dealing with users from different regions.
Data Privacy Regulations
Data privacy regulations vary significantly across jurisdictions, requiring companies to tailor their practices accordingly. These regulations govern how personal data is handled and the rights granted to individuals.
| Regulation | Jurisdiction | Key Provisions |
|---|---|---|
| General Data Protection Regulation (GDPR) | European Union | Focuses on the rights of individuals to control their personal data, including the right to access, rectification, erasure, and restriction of processing. Strong emphasis on consent and data minimization. |
| California Consumer Privacy Act (CCPA) | California, USA | Grants California consumers specific rights regarding their personal information, including the right to know, delete, and opt-out of the sale of their data. |
| Brazil’s General Personal Data Protection Law (LGPD) | Brazil | Establishes a comprehensive framework for personal data protection, emphasizing data minimization, security, and the rights of individuals. |
| Others | Various countries/regions | Many other countries and regions have their own data privacy regulations. Companies operating globally must remain informed of these various regulations. |
Types of Personalized Marketing Messages
Personalized marketing messages go beyond generic advertisements. They cater to individual customer needs and preferences, resulting in higher engagement and conversion rates. By understanding different types of personalized messages and tailoring them effectively across various channels, businesses can build stronger customer relationships and drive significant returns.
Email Marketing Personalization
Email marketing remains a powerful tool for direct communication. Personalizing email campaigns involves tailoring the content, subject lines, and offers to individual recipients. This can be achieved through segmentation, dynamic content, and triggered emails. For example, a customer who frequently purchases hiking gear might receive promotional emails featuring new trail maps and accessories.
Personalized Product Recommendations
Product recommendations play a crucial role in e-commerce and other businesses. By leveraging AI-powered algorithms, businesses can suggest products tailored to individual customer preferences and past purchase history. These recommendations enhance the customer experience by offering relevant choices and increasing the likelihood of a purchase. For instance, if a customer frequently purchases books on history, the platform can recommend related authors or historical periods.
Other Forms of Personalized Marketing
Beyond email and product recommendations, personalization extends to other channels. These include targeted advertisements on social media platforms, personalized in-app experiences, and customized content on websites. Each channel presents unique opportunities for personalization, allowing businesses to reach their customers in a more meaningful and effective way.
Table of Personalization Techniques Across Channels
| Channel | Personalization Technique | Example |
|---|---|---|
| Dynamic content, triggered emails, personalized subject lines | A customer who abandoned a shopping cart receives a personalized email with a discount code. | |
| Social Media | Targeted ads based on interests and demographics | A user who frequently posts about photography sees ads for camera equipment and accessories. |
| Website | Personalized product recommendations, customized content based on browsing history | A user who browses articles on sustainable fashion sees recommendations for eco-friendly brands. |
| In-App | Personalized notifications, tailored content based on user activity | A mobile banking app provides tailored investment suggestions based on the user’s financial profile. |
Personalization Strategies and Tactics
Personalization in marketing extends beyond simply addressing customers by name. Effective strategies leverage a deep understanding of individual customer needs and preferences to craft tailored experiences that resonate with them on a personal level. This approach fosters stronger customer relationships, increases engagement, and ultimately drives conversions.
Effective Personalization Strategies
Personalization strategies are multifaceted and depend on the specific goals and resources of a business. Several effective strategies are widely employed. They typically involve combining data from various sources to build comprehensive customer profiles.
- Segmentation-Based Personalization: This involves dividing customers into groups based on shared characteristics, such as demographics, purchase history, or website behavior. Targeted messages can then be crafted for each segment, ensuring relevance and maximizing impact. For example, a retailer might send different promotional offers to customers who frequently purchase specific product categories, while offering other promotions to customers who have not purchased in a while.
- Product Recommendations: Leveraging AI-powered algorithms to recommend products or services based on past purchases, browsing history, and other relevant data points is highly effective. E-commerce platforms frequently use this technique to suggest complementary items or products that customers might be interested in. For instance, a customer who purchased a specific type of running shoes might receive recommendations for related apparel or accessories.
- Personalized Email Marketing: Tailoring email subject lines, content, and offers based on individual customer preferences can significantly boost open and click-through rates. A customer who has expressed an interest in a particular product category will receive tailored emails containing relevant information and promotions.
Customer Journey Mapping for Personalization
Understanding the customer journey is crucial for implementing effective personalization strategies. A customer journey map visually depicts the steps a customer takes when interacting with a brand, from initial awareness to purchase and beyond.
By mapping out this journey, businesses can identify specific touchpoints where personalization can have the greatest impact. For example, a customer who abandoned their shopping cart might receive a personalized email reminding them of the items they left behind and offering a discount to complete the purchase.
Personalizing Website Content for Conversions
Optimizing website content for personalized experiences is critical for driving conversions. Dynamic content that adapts to individual user behavior and preferences significantly enhances the user experience and increases engagement.
- Dynamic Product Displays: Instead of a static product page, a website can dynamically display products based on a user’s browsing history or interests. This personalized approach ensures that users see items relevant to their needs and preferences.
- Personalized Recommendations: Website visitors can receive personalized recommendations based on their previous interactions, such as product views, searches, or even their location. This targeted approach helps users find products that match their interests.
A/B Testing Methodologies for Personalized Messages
A/B testing is a crucial component of optimizing personalized marketing messages. It allows businesses to compare different versions of messages or campaigns to determine which performs best.
- Experimentation with different segments: Comparing the response of different customer segments to various personalized messages helps in refining the strategy for maximum impact.
- Measuring key metrics: Tracking metrics such as conversion rates, click-through rates, and engagement levels for each variant helps to determine the most effective personalized approach.
Measuring the Impact of Personalization
Understanding the return on investment (ROI) of personalized marketing campaigns is crucial for optimizing strategies and demonstrating value. Effective measurement goes beyond simply observing increased engagement; it involves quantifiable metrics that demonstrate the tangible impact of personalization on key business objectives. This allows marketers to fine-tune their approaches and allocate resources effectively.
Key Metrics for Measuring Effectiveness
Precise measurement of personalized marketing campaigns relies on selecting appropriate metrics aligned with business goals. These metrics should reflect the desired outcomes of the campaign, such as increased sales, higher customer lifetime value, or improved brand loyalty. For instance, if the goal is to drive sales, metrics like conversion rates and average order value would be crucial. If the objective is to enhance customer engagement, metrics such as website visits, time spent on site, and click-through rates would be more pertinent.
Tracking Campaign Performance
Tracking campaign performance is a critical component of evaluating the impact of personalization. Robust tracking mechanisms allow marketers to monitor key metrics in real-time and adjust strategies as needed. Implementing tools for website analytics, CRM data integration, and marketing automation platforms allows for a holistic view of campaign performance. This detailed tracking facilitates a better understanding of which aspects of personalization are driving positive results and which require improvement.
Analytics Tools for Measuring Results
Utilizing analytics tools is essential for extracting meaningful insights from data collected during personalization campaigns. These tools provide comprehensive data visualization, enabling marketers to identify trends, patterns, and correlations related to personalization efforts. Common tools include Google Analytics, Adobe Analytics, and marketing automation platforms like HubSpot or Marketo. These platforms offer features like A/B testing, segmentation analysis, and campaign reporting, all critical for optimizing personalized marketing strategies.
Key Performance Indicators (KPIs) for Personalization
This table Artikels key performance indicators (KPIs) commonly used to measure the effectiveness of personalization initiatives. These KPIs provide a structured framework for evaluating campaign performance and identifying areas for improvement.
| KPI | Description | How to Measure |
|---|---|---|
| Conversion Rate | Percentage of users who complete a desired action (e.g., purchase, sign-up). | Divide the number of conversions by the total number of users exposed to the personalized message. |
| Customer Lifetime Value (CLTV) | Total revenue a customer is expected to generate throughout their relationship with the company. | Calculate the average revenue per customer and multiply by the average customer lifespan. |
| Average Order Value (AOV) | Average monetary value of orders placed by customers. | Divide the total revenue generated by the total number of orders. |
| Click-Through Rate (CTR) | Percentage of users who click on a personalized link or call-to-action. | Divide the number of clicks by the total number of impressions. |
| Engagement Rate | Measure of user interaction with personalized content. | Calculate the average number of interactions (e.g., likes, comments, shares) per user exposed to personalized content. |
Challenges and Considerations
Implementing AI-driven personalization strategies presents a unique set of challenges. These range from the complexities of data management and privacy concerns to the crucial need for consistent brand messaging and rigorous testing. Successfully navigating these hurdles is essential for maximizing the ROI of personalization efforts.Successfully implementing AI-powered personalization requires careful planning and execution. Overlooking potential pitfalls can lead to wasted resources and a diminished impact on customer engagement.
Careful consideration of these challenges, coupled with proactive strategies, will be critical to the success of any personalization initiative.
Potential Challenges in Implementation
Data quality and quantity significantly impact the accuracy and effectiveness of AI-powered personalization. Insufficient or inaccurate data can lead to flawed models and inappropriate recommendations, diminishing the positive impact of the personalization strategy. Robust data collection and validation processes are vital to achieving reliable results. Ensuring data privacy and compliance with relevant regulations is equally crucial. Failure to adhere to these guidelines can result in reputational damage and legal repercussions.
Additionally, the technical expertise required for implementation and maintenance of AI models can be substantial, potentially requiring investment in specialized talent or technology. Careful planning for resource allocation and training needs is critical.
Importance of Testing and Iterating
Testing and iterative refinement are essential components of any successful personalization strategy. Personalization models should be continuously monitored and adjusted based on real-time data and user feedback. This iterative approach allows for the identification and resolution of any inaccuracies or inefficiencies in the model. A/B testing various personalization approaches provides valuable insights into which strategies resonate most effectively with target audiences.
Furthermore, regular monitoring of key metrics, such as conversion rates and customer satisfaction scores, allows for the identification of areas needing improvement.
Maintaining Brand Consistency
Maintaining brand consistency is paramount in any personalization strategy. Personalized messages should not compromise the core brand identity and values. The brand voice and visual identity should remain consistent across all channels and touchpoints, even with the tailored messaging. Maintaining brand consistency ensures that customers recognize and connect with the brand despite the personalized content. Inconsistency can lead to confusion, erode trust, and damage the brand’s reputation.
A well-defined brand style guide and clear communication protocols are essential to maintaining brand cohesion.
Common Challenges and Solutions for Personalization
| Challenge | Potential Solution |
|---|---|
| Data quality and quantity issues | Implement robust data collection and validation processes. Invest in data cleaning and enrichment tools. Prioritize data accuracy and completeness. |
| Privacy concerns and regulatory compliance | Employ data anonymization and pseudonymization techniques. Obtain explicit consent for data collection and usage. Adhere to relevant privacy regulations (e.g., GDPR, CCPA). |
| Maintaining brand consistency | Develop a comprehensive brand style guide. Establish clear communication protocols and guidelines for personalization. Implement robust quality assurance checks. |
| Lack of technical expertise | Partner with AI specialists or agencies. Invest in training and development for in-house teams. Utilize readily available AI platforms and tools. |
| Measuring the impact of personalization | Establish clear key performance indicators (KPIs). Track relevant metrics (e.g., conversion rates, customer lifetime value). Analyze data regularly to identify trends and areas for improvement. |
Illustrative Examples
Personalization in marketing, driven by AI, is no longer a futuristic concept but a tangible reality. Numerous successful campaigns demonstrate its power to enhance customer experiences and drive significant business outcomes. These examples highlight how AI-powered personalization can transform interactions, foster loyalty, and ultimately, boost profitability.
Successful Personalization Campaigns Across Industries
Various industries have successfully leveraged AI-driven personalization strategies. These strategies, when implemented effectively, can yield substantial returns. For example, e-commerce platforms can use AI to recommend products tailored to individual customer preferences, significantly increasing conversion rates. Similarly, in the hospitality sector, AI can analyze customer data to offer customized recommendations, ensuring a more personalized and engaging experience.
- E-commerce: An online retailer, using AI-powered recommendations, saw a 20% increase in average order value after implementing a personalized product recommendation engine. This engine analyzed browsing history, purchase patterns, and demographics to suggest relevant products, leading to higher conversion rates.
- Finance: A financial institution used AI to segment customers based on risk profiles and financial goals. This allowed for personalized financial advice and product recommendations, resulting in a 15% increase in customer retention within the targeted segment.
- Healthcare: A healthcare provider used AI to personalize treatment plans based on patient history, medical conditions, and lifestyle factors. This approach improved patient outcomes and satisfaction scores.
A Case Study: Personalized Product Recommendations in E-commerce
This case study focuses on an online clothing retailer that implemented an AI-powered recommendation engine. The system analyzed customer browsing history, purchase patterns, and demographics to suggest relevant products. For instance, if a customer frequently viewed shirts and pants, the engine would suggest complementary items like belts and accessories.
| Customer Action | AI’s Response | Outcome |
|---|---|---|
| Customer browses men’s shirts | AI suggests similar shirts in various colors and styles, plus coordinating pants and accessories. | Increased browsing time and purchase likelihood. |
| Customer adds a shirt to cart | AI recommends related items like a matching belt, tie, or socks. | Higher average order value. |
| Customer abandons cart | AI sends a personalized email with a discount code and a reminder of abandoned items. | Reduced cart abandonment rates. |
Illustrative Customer Journey Personalized by AI
Imagine a customer, Sarah, browsing an online bookstore. AI observes her past purchases, highlighting her preference for historical fiction. The AI then tailors the recommendations to similar authors, genres, and historical periods. As she adds books to her cart, the AI suggests complementary items like bookmarks, notepads, or related author biographies. Further, post-purchase, the AI sends personalized recommendations for upcoming releases by her favorite authors, strengthening the customer relationship.
AI-Driven Customer Retention
AI can significantly enhance customer retention by identifying at-risk customers and proactively addressing their concerns. A telecom company, for example, used AI to identify customers exhibiting churn patterns. The AI then sent personalized messages, offering incentives and tailored solutions to retain these customers. This approach successfully reduced customer churn by 10%.
Content Structure and Formatting
Effective content structure is crucial for conveying personalized marketing messages clearly and engagingly. A well-organized presentation helps readers understand the concepts and strategies presented, leading to increased comprehension and better implementation. This section details various methods for structuring content to enhance personalization efforts.
Blog Post Structure for Personalization
A blog post about personalization should follow a logical flow, guiding the reader from introduction to practical application. Begin with an engaging introduction that clearly states the topic and its relevance. Following this, break down complex concepts into digestible segments. Use headings and subheadings to create clear divisions, making it easier for readers to navigate and locate specific information.
Include compelling examples and real-world case studies to illustrate the discussed concepts and strategies. Conclude by summarizing key takeaways and offering actionable steps for readers to implement personalization in their own marketing campaigns.
HTML Table for Data Presentation
Tables are powerful tools for presenting data effectively. They allow for a structured and organized representation of information, enhancing readability and comprehension. For instance, a table could showcase different types of personalized marketing messages, alongside their respective benefits and implementation strategies. The table should include clear column headings and rows that contain specific data points. Data should be presented in a concise and understandable manner.
Consider using color-coding or formatting to highlight key information and create a visually appealing presentation.
Example of a Personalized Email Template
A well-designed email template plays a significant role in a personalized marketing strategy. It should be tailored to the recipient’s specific needs and interests. A personalized email template should contain a clear subject line that resonates with the recipient. The body of the email should contain personalized content, referencing previous interactions or specific preferences. Include calls to action that are relevant to the recipient’s context.
A template could incorporate dynamic content elements, such as personalized product recommendations or targeted offers. Employing a visually appealing design and a responsive layout are crucial for optimal user experience.
<html>
<head>
<title>Personalized Email Template</title>
<style>
/* Styling for the email template
-/
</style>
</head>
<body>
<div style="font-family: Arial, sans-serif; margin: 20px;">
<h1 style="color: #333;>Hi [Customer Name],</h1>
<p style="color: #555;">Based on your recent activity, we've curated some personalized recommendations for you.</p>
<table style="width: 100%; border-collapse: collapse; margin-bottom: 20px;">
<tr style="background-color: #f0f0f0; font-weight: bold;">
<td style="border: 1px solid #ddd; padding: 10px;">Product Name</td>
<td style="border: 1px solid #ddd; padding: 10px;">Price</td>
</tr>
<tr style="border: 1px solid #ddd; padding: 10px;">
<td style="border: 1px solid #ddd; padding: 10px;">Product A</td>
<td style="border: 1px solid #ddd; padding: 10px;">$25.00</td>
</tr>
<tr style="border: 1px solid #ddd; padding: 10px;">
<td style="border: 1px solid #ddd; padding: 10px;">Product B</td>
<td style="border: 1px solid #ddd; padding: 10px;">$10.00</td>
</tr>
</table>
<p style="color: #555;">Click the button below to explore these offers.</p>
<a href="#" style="background-color: #4CAF50; color: white; padding: 10px 20px; text-decoration: none; border-radius: 5px;">Explore Now</a>
</div>
</body>
</html>
Wrap-Up
In conclusion, personalizing marketing messages with AI is a powerful strategy for boosting engagement and driving business growth.
This guide has explored the entire spectrum of personalization, from defining its principles to the crucial role of data privacy and ethical considerations. By understanding the techniques, measuring the impact, and addressing the challenges, businesses can leverage AI to create truly personalized customer experiences. We encourage readers to embrace the opportunities presented by AI and tailor their marketing strategies to resonate with individual customers.