Fundamentals

Personalized customer engagement Meaning ● Customer Engagement is the ongoing, value-driven interaction between an SMB and its customers, fostering loyalty and driving sustainable growth. with AI is no longer a futuristic concept reserved for large corporations. Small to medium businesses (SMBs) can now leverage the power of artificial intelligence to create meaningful, individualized experiences for their customers, fostering stronger relationships and driving growth. This guide serves as your actionable playbook, stripping away the complexity and focusing on practical steps any SMB can take to implement AI-driven personalization.

Understanding Personalized Engagement

At its core, personalized customer engagement Meaning ● Tailoring customer interactions to individual needs, driving SMB growth through stronger relationships and targeted value. is about treating each customer as an individual. It moves away from generic, one-size-fits-all marketing and communication towards tailored interactions that resonate with specific needs, preferences, and behaviors. Think of it as the difference between a mass email blast and a carefully crafted message that speaks directly to a customer’s past purchases and expressed interests.

Personalized customer engagement means treating each customer as an individual, tailoring interactions to their unique needs and preferences.

Why is this important for SMBs? In today’s crowded marketplace, customers are bombarded with information and choices. Personalization helps your business stand out, capturing attention and building loyalty. It leads to:

• Increased Customer Satisfaction ● Customers feel valued and understood when interactions are relevant to them.
• Improved Conversion Rates ● Personalized offers and recommendations are more likely to drive sales.
• Enhanced Customer Loyalty ● Consistent personalized experiences Meaning ● Personalized Experiences, within the context of SMB operations, denote the delivery of customized interactions and offerings tailored to individual customer preferences and behaviors. build stronger, longer-lasting customer relationships.
• Efficient Marketing Spend ● Targeted personalization reduces wasted ad spend by focusing on the most receptive audiences.

Demystifying AI in Personalization

The term “AI” can sound intimidating, conjuring images of complex algorithms and expensive software. However, for SMB personalization, AI is often about leveraging readily available tools and features that simplify and automate tasks. We are not talking about building AI models from scratch. Instead, we are focusing on using existing AI-powered platforms to:

• Analyze Customer Data ● AI can quickly process large volumes of data to identify patterns and segments.
• Automate Personalized Communication ● AI can trigger personalized emails, messages, and content delivery based on customer actions.
• Recommend Products and Content ● AI algorithms can suggest relevant products or content based on individual customer profiles.
• Improve Customer Service ● AI-powered chatbots can provide instant, personalized support.

Essential First Steps ● Data Collection and Segmentation

Before implementing any AI-driven personalization, you need to understand your customers. This starts with collecting relevant data. Don’t panic ● you likely already have access to valuable data sources. Focus on gathering information from:

• Website Analytics ● Track website visits, page views, time spent on pages, and actions taken (e.g., form submissions, downloads). Tools like Google Analytics provide a wealth of data.
• CRM (Customer Relationship Management) Systems ● If you use a CRM, it likely stores customer contact information, purchase history, and communication logs. Even a simple spreadsheet can serve as a basic CRM to start.
• Email Marketing Platforms ● Platforms like Mailchimp or HubSpot track email opens, clicks, and engagement.
• Social Media Insights ● Social media platforms provide data on audience demographics, interests, and engagement with your content.
• Customer Feedback ● Surveys, feedback forms, and customer support interactions are goldmines of qualitative data about customer needs and pain points.

Once you have data, the next step is segmentation. Segmentation involves dividing your customer base into smaller groups based on shared characteristics. This allows you to tailor your personalization efforts to specific segments. Common segmentation criteria include:

• Demographics ● Age, location, gender, income (if relevant).
• Purchase History ● Past purchases, product categories, spending habits.
• Website Behavior ● Pages visited, products viewed, time spent on site.
• Engagement Level ● Email opens, clicks, social media interactions, 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. requests.
• Customer Lifecycle Stage ● New customer, repeat customer, loyal customer, churned customer.

Start with simple segmentation. For example, you might segment your email list based on purchase history (e.g., customers who have purchased product A vs. product B) or website behavior (e.g., customers who have visited your pricing page but not yet signed up). As you become more comfortable, you can create more granular segments.

Avoiding Common Pitfalls in Early Personalization

SMBs sometimes encounter roadblocks when starting with personalization. Avoiding these common pitfalls will ensure a smoother and more effective implementation:

1. Data Overload and Analysis Paralysis ● Don’t try to collect and analyze every piece of data imaginable. Start with the data that is most relevant to your personalization goals and readily accessible. Focus on actionable insights rather than getting lost in data.
2. Lack of Clear Goals ● Personalization should serve specific business objectives. Are you trying to increase sales, improve customer retention, or generate more leads? Define your goals upfront to guide your personalization strategy.
3. Over-Personalization or Creepiness ● Personalization should enhance the customer experience, not feel intrusive or stalkerish. Avoid using overly personal information or making assumptions that might feel uncomfortable. Transparency is key ● let customers know how you are using their data to improve their experience.
4. Ignoring Privacy Concerns ● Be mindful of data privacy regulations (like GDPR or CCPA). Collect and use 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. ethically and transparently. Provide clear privacy policies and give customers control over their data.
5. Technology Overwhelm ● Don’t feel pressured to invest in expensive or complex AI platforms immediately. Start with tools you are already using or free/low-cost options. Focus on mastering the fundamentals before moving to advanced technologies.

Quick Wins with Basic AI Tools

You don’t need to be a tech expert to achieve initial personalization wins. Many readily available tools offer built-in AI features that are easy to use. Consider these starting points:

AI-Powered Email Marketing

Email marketing platforms like Mailchimp, HubSpot Email Marketing, and Sendinblue offer AI-driven features to personalize your email campaigns. These features can include:

• Segmentation and List Management ● AI can help you automatically segment your email lists based on engagement and behavior.
• Personalized Subject Lines and Content ● Some platforms offer AI-powered subject line optimization and content recommendations to increase open and click-through rates.
• Send-Time Optimization ● AI can analyze data to determine the best time to send emails to individual recipients for maximum engagement.
• Product Recommendations ● If you sell products online, AI can help you include personalized product recommendations Meaning ● Personalized Product Recommendations utilize data analysis and machine learning to forecast individual customer preferences, thereby enabling Small and Medium-sized Businesses (SMBs) to offer pertinent product suggestions. in your emails based on past purchases or browsing history.

Simple Website Personalization

Even without coding, you can implement basic website personalization Meaning ● Website Personalization, within the SMB context, signifies the utilization of data and automation technologies to deliver customized web experiences tailored to individual visitor profiles. using tools like Google Optimize (for A/B testing Meaning ● A/B testing for SMBs: strategic experimentation to learn, adapt, and grow, not just optimize metrics. and basic personalization) or website builders like Wix or Squarespace, which often have built-in personalization features or integrations. Simple website personalization tactics include:

• Welcome Messages for Returning Visitors ● Personalized welcome messages can greet returning visitors by name or acknowledge their previous interactions.
• Dynamic Content Based on Location ● If you have a local business, you can display location-specific content to visitors based on their IP address.
• Pop-Ups Triggered by Behavior ● Use pop-ups to offer personalized discounts or promotions to visitors who are showing exit intent or have spent a certain amount of time on a product page.

Basic Chatbots for Customer Service

Free or low-cost chatbot platforms like Tidio or Chatfuel allow you to set up simple chatbots on your website or social media channels. Even basic chatbots can provide personalized support by:

• Greeting Customers by Name ● If a customer has interacted with your chatbot before, it can recognize them and greet them personally.
• Answering Frequently Asked Questions (FAQs) ● Chatbots can be trained to answer common customer questions, freeing up your team to handle more complex inquiries.
• Routing Customers to the Right Department ● Chatbots can ask initial questions to understand customer needs and then route them to the appropriate support team or resource.

Starting with these fundamental steps and quick wins will lay a solid foundation for more advanced AI-driven personalization Meaning ● AI-Driven Personalization for SMBs: Tailoring customer experiences with AI to boost growth, while ethically balancing personalization and human connection. in the future. Remember, the key is to start small, focus on practical implementation, and continuously learn and adapt based on your results.

Intermediate

Having established a foundation in personalized customer engagement, SMBs can now explore intermediate-level strategies to deepen customer relationships and drive greater efficiency. This stage involves leveraging more sophisticated AI tools Meaning ● AI Tools, within the SMB sphere, represent a diverse suite of software applications and digital solutions leveraging artificial intelligence to streamline operations, enhance decision-making, and drive business growth. and techniques to automate personalization at scale Meaning ● Personalization at Scale, in the realm of Small and Medium-sized Businesses, signifies the capability to deliver customized experiences to a large customer base without a proportionate increase in operational costs. and optimize customer journeys.

Moving Beyond Basic Segmentation ● Dynamic and Behavioral Segmentation

While demographic and basic purchase history segmentation are valuable starting points, intermediate personalization requires moving towards more dynamic and behavioral approaches. Dynamic Segmentation means that customer segments are not static; they automatically update in real-time based on changes in 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 data. Behavioral Segmentation focuses specifically on grouping customers based on their actions, such as website interactions, app usage, email engagement, and purchase patterns.

Dynamic and behavioral segmentation Meaning ● Behavioral Segmentation for SMBs: Tailoring strategies by understanding customer actions for targeted marketing and growth. allows for real-time, adaptive personalization based on customer actions and evolving data.

Examples of dynamic and behavioral segmentation include:

• Website Activity-Based Segments ● Customers who have viewed specific product categories, added items to their cart but not completed checkout, or spent a certain amount of time on key pages.
• Email Engagement Segments ● Customers who frequently open emails, click on specific types of links, or have unsubscribed from certain email types.
• App Usage Segments ● For businesses with mobile apps, segments based on app usage frequency, features used, and in-app purchases.
• Customer Journey Stage Segments ● Customers classified by their current stage in the customer lifecycle (e.g., awareness, consideration, decision, loyalty).

Tools like HubSpot Marketing Hub, Marketo, and ActiveCampaign offer advanced segmentation capabilities that allow you to create dynamic and behavioral segments based on a wide range of criteria. These platforms often use AI to automatically identify and group customers with similar behaviors, simplifying the segmentation process.

AI-Powered Content Personalization ● Website and Email

Intermediate personalization extends beyond just segmenting audiences; it involves tailoring the content itself. AI can play a significant role in dynamically personalizing website content and email marketing Meaning ● Email marketing, within the small and medium-sized business (SMB) arena, constitutes a direct digital communication strategy leveraged to cultivate customer relationships, disseminate targeted promotions, and drive sales growth. messages to resonate with individual customer preferences.

Dynamic Website Content

Dynamic website content adapts in real-time based on visitor behavior, demographics, or other contextual factors. This goes beyond simple welcome messages and can involve:

• Personalized Product Recommendations on Homepage ● Displaying product recommendations on the homepage based on a visitor’s browsing history, past purchases, or stated interests.
• Tailored Content Blocks ● Showing different content blocks (e.g., blog posts, testimonials, case studies) on different website pages based on visitor segments or interests.
• Personalized Navigation Menus ● Adjusting the navigation menu to highlight sections of the website that are most relevant to a visitor based on their past behavior.
• Dynamic Landing Pages ● Creating landing pages that automatically adapt their headlines, images, and calls-to-action based on the source of traffic or the visitor’s profile.

Platforms like Optimizely, Adobe Target, and even more SMB-friendly options like Personyze offer tools for creating and managing dynamic website content. These platforms often use AI-powered recommendation engines to suggest the most relevant content variations for different visitor segments.

Personalized Email Content Generation

While basic email personalization involves using merge tags to insert customer names or company names, AI can take email content personalization much further. AI-powered tools can assist with:

• Personalized Subject Line Optimization ● AI can analyze subject line performance data to suggest subject lines that are most likely to resonate with specific segments.
• Dynamic Content Blocks in Emails ● Including content blocks within emails that are dynamically populated based on recipient preferences or behaviors (e.g., product recommendations, personalized offers, relevant articles).
• Personalized Email Newsletters ● Creating email newsletters where the articles and content are automatically selected and ordered based on each subscriber’s interests.
• AI-Driven Email Copywriting ● Some advanced AI tools can even assist with generating personalized email copy, although this is still an evolving area and requires careful review and human oversight.

Tools like Phrasee, Persado, and Smartly.io offer AI-powered solutions for email subject line and content optimization. For more comprehensive email marketing personalization, platforms like Iterable and Braze provide advanced segmentation, dynamic content, and journey orchestration capabilities.

Leveraging AI Chatbots for Proactive Customer Engagement

At the intermediate level, chatbots can evolve from simple FAQ answerers to proactive customer engagement Meaning ● Anticipating customer needs to enhance value and build loyalty. tools. AI-powered chatbots can initiate conversations with website visitors or app users based on triggers and behaviors, offering personalized assistance and guidance.

Proactive chatbot engagement strategies include:

• Welcome Messages with Personalized Offers ● Chatbots can proactively greet new website visitors with personalized welcome messages and offers based on the page they are viewing or their referral source.
• Abandoned Cart Recovery ● Chatbots can proactively engage with visitors who have added items to their cart but are about to leave the website, offering assistance or incentives to complete the purchase.
• Personalized Product Recommendations ● Chatbots can proactively suggest relevant products or services to visitors based on their browsing behavior or stated needs.
• Lead Qualification and Information Gathering ● Chatbots can engage with potential leads, ask qualifying questions, and gather information to personalize the sales process.
• Proactive Customer Support ● Chatbots can proactively reach out to customers who are exhibiting signs of frustration or confusion on the website or app, offering help before they initiate a support request.

Platforms like Intercom, Drift, and Zendesk Chat offer advanced chatbot features, including AI-powered conversational flows, natural language processing (NLP) for understanding customer intent, and integration with CRM and marketing automation Meaning ● Marketing Automation for SMBs: Strategically automating marketing tasks to enhance efficiency, personalize customer experiences, and drive sustainable business growth. systems. These tools enable SMBs to create sophisticated chatbot experiences that drive proactive customer engagement.

Case Study ● Personalized Product Recommendations for an E-Commerce SMB

Consider a small online clothing boutique. Initially, they used basic email marketing and website analytics. To move to intermediate personalization, they implemented an AI-powered product recommendation engine Meaning ● A Recommendation Engine, crucial for SMB growth, automates personalized suggestions to customers, increasing sales and efficiency. on their website and in their email marketing.

Implementation Steps ●

1. Tool Selection ● They chose a product recommendation engine plugin that integrated with their e-commerce platform (Shopify). This plugin used collaborative filtering and content-based filtering algorithms to generate personalized recommendations.
2. Data Integration ● The plugin automatically tracked customer browsing history, purchase history, and product views. This data was used to train the recommendation engine.
3. Website Personalization ● They added “Recommended for You” sections on their homepage, product pages, and cart page. These sections displayed personalized product recommendations generated by the AI engine.
4. Email Personalization ● They integrated the recommendation engine with their email marketing platform. They started sending personalized email campaigns Meaning ● Personalized Email Campaigns, in the SMB environment, signify a strategic marketing automation initiative where email content is tailored to individual recipients based on their unique data points, behaviors, and preferences. with product recommendations based on past purchases and browsing history. For example, customers who had purchased dresses received emails featuring new arrivals in dresses and accessories that complemented their previous purchases.
5. A/B Testing and Optimization ● They continuously A/B tested different recommendation placements and algorithms to optimize performance. They tracked metrics like click-through rates, conversion rates, and average order value.

Results ●

• Increased Sales ● Personalized product recommendations led to a 15% increase in overall sales within the first three months.
• Improved Conversion Rates ● Product pages with 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. saw a 20% higher conversion rate compared to pages without recommendations.
• Higher Average Order Value ● Customers who interacted with personalized recommendations had a 10% higher average order value.
• Enhanced Customer Engagement ● Personalized email campaigns had significantly higher open and click-through rates compared to generic campaigns.

This case study demonstrates how an SMB can leverage intermediate AI personalization techniques, specifically product recommendations, to achieve measurable business results. The key was selecting the right tools, integrating data effectively, and continuously optimizing based on performance data.

Advanced

For SMBs ready to push the boundaries of customer engagement, the advanced stage delves into cutting-edge AI-powered strategies that can create truly exceptional and deeply personalized experiences. This level focuses on predictive analytics, hyper-personalization, and AI-driven customer journey optimization Meaning ● Strategic design & refinement of customer interactions to maximize value and loyalty for SMB growth. to achieve significant competitive advantages and sustainable growth.

Predictive Analytics for Proactive Personalization

Advanced personalization leverages Predictive Analytics to anticipate customer needs and behaviors before they even occur. Predictive analytics Meaning ● Strategic foresight through data for SMB success. uses historical data, 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. algorithms, and statistical modeling to forecast future outcomes. In the context of customer engagement, this means predicting:

• Customer Churn ● Identifying customers who are likely to stop doing business with you, allowing for proactive retention efforts.
• Purchase Propensity ● Predicting which customers are most likely to make a purchase, and what products they are most likely to buy.
• Customer Lifetime Value (CLTV) ● Forecasting the total revenue a customer will generate over their relationship with your business, enabling prioritization of high-value customers.
• Next Best Action ● Determining the most effective action to take with each customer at any given moment to maximize engagement and conversion.

Predictive analytics empowers proactive personalization by anticipating customer needs and behaviors, enabling preemptive engagement strategies.

Implementing predictive analytics requires:

1. Data Infrastructure ● A robust data infrastructure to collect, store, and process large volumes of customer data. This may involve a data warehouse or data lake.
2. Predictive Modeling Tools ● AI and machine learning platforms that provide tools for building and deploying predictive models. Examples include Google Cloud AI Platform, Amazon SageMaker, and Azure Machine Learning.
3. Data Science Expertise ● Access to data science expertise, either in-house or through partnerships, to build, train, and maintain predictive models.
4. Integration with Engagement Channels ● Integrating predictive insights into your marketing automation, CRM, and customer service systems to trigger personalized actions based on predictions.

For example, if predictive analytics indicates a high churn risk for a specific customer, the system can automatically trigger a personalized retention campaign, offering a special discount or exclusive content to re-engage the customer. If a customer is predicted to have a high purchase propensity for a particular product category, they can be targeted with personalized ads and email campaigns featuring those products.

Hyper-Personalization ● Individualized Experiences at Scale

Hyper-Personalization takes personalization to the extreme, aiming to create truly individualized experiences for each customer. It goes beyond segment-based personalization and focuses on tailoring every interaction to the unique preferences, context, and real-time behavior of each individual. Hyper-personalization is driven by:

• Granular Customer Data ● Collecting and analyzing a wide range of data points about each customer, including demographics, psychographics, behavior, preferences, and real-time context.
• AI-Powered Decision Engines ● Sophisticated AI algorithms that can process vast amounts of data in real-time to make individualized personalization decisions.
• Dynamic Content Generation ● AI-powered tools that can dynamically generate personalized content, offers, and experiences on the fly, adapting to individual customer needs and contexts.
• Omnichannel Personalization ● Delivering consistent and personalized experiences across all customer touchpoints, including website, email, mobile app, social media, and even offline interactions.

Examples of hyper-personalization tactics include:

• 1:1 Personalized Website Experiences ● Creating website experiences that are completely unique to each visitor, with personalized layouts, content, product recommendations, and offers.
• Dynamic Pricing and Promotions ● Offering personalized pricing and promotions to individual customers based on their purchase history, loyalty, and price sensitivity.
• AI-Driven Product Curation ● Creating personalized product catalogs or collections for each customer, showcasing only the products that are most relevant to their individual tastes.
• Personalized Customer Service Interactions ● Equipping customer service agents with real-time customer data and AI-powered recommendations to provide highly personalized support interactions.
• Contextual Personalization ● Adapting personalization based on real-time context, such as location, time of day, weather, or device being used.

Platforms like Evergage (now Salesforce Interaction Studio), Dynamic Yield (now Mastercard Personalization), and Amperity are designed for hyper-personalization. These platforms offer advanced AI capabilities for real-time decisioning, 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. generation, and omnichannel personalization orchestration.

AI-Driven Customer Journey Optimization

Advanced personalization extends to optimizing the entire customer journey. AI-Driven 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. optimization involves using AI to analyze customer journeys, identify friction points, and personalize the journey at each stage to improve conversion rates, customer satisfaction, and lifetime value.

Key aspects of AI-driven customer journey optimization include:

• Customer Journey Mapping and Analysis ● Using AI to analyze customer journey data and identify common paths, drop-off points, and areas for improvement.
• Personalized Journey Orchestration ● Using AI to orchestrate personalized 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. across multiple channels, ensuring consistent and relevant experiences at each touchpoint.
• AI-Powered Journey Segmentation ● Segmenting customer journeys based on behavior, preferences, and goals, and tailoring the journey for each segment.
• Real-Time Journey Optimization ● Continuously monitoring customer journeys in real-time and using AI to dynamically adjust the journey based on customer behavior and feedback.
• Predictive Journey Optimization ● Using predictive analytics to forecast customer journey outcomes and proactively optimize the journey to achieve desired results.

For example, AI can analyze customer journeys to identify that a significant percentage of customers are abandoning the purchase process at the payment stage. Based on this insight, the system can automatically trigger personalized interventions, such as offering alternative payment options, providing reassurance about security, or offering a small discount to incentivize completion. AI can also personalize the onboarding journey for new customers, guiding them through the key steps and features based on their individual needs and goals.

Customer journey orchestration platforms like Kitewheel (now Pega Customer Journey Management), Thunderhead, and Pointillist offer advanced capabilities for AI-driven customer journey optimization. These platforms enable SMBs to design, manage, and optimize complex customer journeys across multiple channels, leveraging AI to deliver personalized and seamless experiences.

Case Study ● Hyper-Personalized Experiences in a Subscription Box SMB

Consider a subscription box SMB that curates and delivers personalized boxes of beauty products. To achieve advanced personalization, they implemented a hyper-personalization strategy driven by AI.

Implementation Steps ●

1. Expanded Data Collection ● They significantly expanded their data collection efforts, gathering not only demographic and purchase history data but also detailed preference data through surveys, quizzes, and feedback forms. They also tracked real-time browsing behavior and social media interactions.
2. AI-Powered Preference Analysis ● They implemented AI algorithms to analyze the vast amount of preference data and build detailed individual customer profiles. This included understanding individual preferences for product types, brands, ingredients, scents, colors, and even packaging preferences.
3. Dynamic Box Curation ● They developed an AI-powered box curation engine that dynamically selected products for each subscriber’s box based on their individual profile and real-time product availability. No two subscribers received exactly the same box.
4. Personalized Content and Communication ● They personalized all communication, including email, website content, and even packaging inserts. Subscribers received personalized product recommendations, usage tips, and content tailored to their individual beauty profiles.
5. Feedback Loop and Continuous Optimization ● They implemented a robust feedback loop, actively soliciting feedback from subscribers on their boxes and using this feedback to continuously refine the AI algorithms and improve personalization.

Results ●

• Increased Customer Retention ● Hyper-personalization led to a significant increase in customer retention rates, with subscriber churn decreasing by 30%.
• Higher Customer Satisfaction ● Customer satisfaction scores increased dramatically as subscribers felt that the boxes were truly tailored to their individual needs and preferences.
• Premium Pricing and Increased Revenue ● The ability to offer hyper-personalized experiences allowed them to justify premium pricing and increase average revenue per subscriber.
• Stronger Brand Loyalty and Advocacy ● Subscribers became strong brand advocates, actively recommending the subscription box service to others due to the exceptional level of personalization.

This case study showcases the transformative potential of advanced AI-driven hyper-personalization for SMBs. By investing in data, AI technology, and a customer-centric approach, SMBs can create deeply personalized experiences that drive significant business value and competitive differentiation.

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