Conversational AI for Retail: Transform Customer Experience

Retail businesses face mounting pressure to deliver instant, personalized customer experiences while managing operational costs. Conversational AI offers a powerful solution—enabling retailers to provide 24/7 support, personalize shopping journeys, and automate routine tasks without sacrificing the human touch that builds customer loyalty.

This technology combines natural language processing, machine learning, and voice capabilities to create intelligent systems that understand customer intent, respond contextually, and execute tasks autonomously. Whether answering product questions, processing returns, or guiding shoppers through purchases, these AI-powered solutions are reshaping how retailers connect with customers across every touchpoint.

What Is Conversational AI for Retail?

Conversational AI represents a significant evolution beyond traditional chatbots. While basic bots follow rigid scripts and predetermined decision trees, this advanced technology uses natural language understanding to interpret customer intent, context, and sentiment in real-time.

The system leverages several core technologies working in concert. Natural language processing enables it to comprehend human language with all its nuances—including slang, typos, and context-dependent meanings. Machine learning algorithms continuously improve performance by analyzing millions of customer interactions, identifying patterns, and refining responses. Generative AI capabilities allow the system to create contextually appropriate, personalized replies rather than selecting from pre-written templates.

In retail environments, these solutions operate across multiple channels simultaneously. Customers can interact via text messaging, voice calls, website chat widgets, social media platforms, or mobile apps—and the system maintains conversation context when shoppers switch between channels mid-journey.

The technology distinguishes itself through three key capabilities: understanding complex, multi-part questions; executing actions like order tracking or inventory checks; and learning from each interaction to deliver increasingly accurate responses. When integrated with retail systems like point-of-sale platforms, inventory management tools, and customer relationship management software, it becomes a powerful operational asset that bridges customer-facing service with back-end processes.

Why Retail Needs This Technology Now

The retail landscape has fundamentally shifted. Consumer expectations have reached unprecedented heights, with research showing that the majority of shoppers now expect personalized experiences and 86% willing to pay premium prices for superior customer service. Meanwhile, retailers struggle with persistent staffing challenges, rising operational costs, and the complexity of managing omnichannel customer journeys.

Customer behavior patterns reveal the urgency. Mobile commerce continues its explosive growth trajectory, with projections indicating that 62% of all e-commerce sales will occur via mobile devices by 2027. Shoppers demand immediate responses—research shows that many will abandon their carts if they can't quickly get answers to basic questions about products, shipping, or returns.

Traditional customer service models can't scale to meet these demands cost-effectively. Hiring enough human agents to provide 24/7 coverage across all channels requires substantial investment. Even well-staffed contact centers face limitations during peak shopping periods like holidays, product launches, or promotional events when inquiry volumes spike dramatically.

The competitive landscape intensifies pressure. Early adopters of AI-powered customer engagement report significant advantages in customer satisfaction scores and operational efficiency. Retailers who delay adoption risk falling behind competitors who can deliver faster, more personalized service at lower operational costs.

Economic factors compound the urgency. Inflation and economic uncertainty force retailers to optimize every dollar spent on operations. Automated solutions that can handle routine inquiries—which comprise 60-70% of typical customer service interactions—free human agents to focus on complex, high-value situations that truly require human judgment and empathy.

Key Benefits for Retail Operations

24/7 Availability and Instant Response

Customers don't shop on a 9-to-5 schedule, yet many retailers still operate support teams with limited hours. AI-powered systems eliminate this gap entirely, providing immediate assistance at any time—whether a shopper has a question at 2 AM or needs help during a lunch break.

The impact on customer satisfaction is measurable. Wait times drop to zero for common inquiries about store hours, product availability, order status, and return policies. During peak shopping periods when human agents face overwhelming volumes, the technology maintains consistent response quality without degradation.

This constant availability translates directly to revenue protection. When shoppers encounter obstacles during the purchase journey—confusion about sizing, questions about shipping options, or uncertainty about product features—immediate answers keep them moving toward checkout rather than abandoning their carts.

Personalization at Scale

Every customer wants to feel recognized and understood, yet delivering individualized experiences to thousands or millions of shoppers simultaneously presents enormous challenges. AI systems excel at this task by analyzing customer data in real-time and tailoring interactions accordingly.

The technology accesses purchase history, browsing behavior, demographic information, and previous interactions to contextualize each conversation. When a returning customer asks about "those shoes I was looking at," the system knows exactly which products to reference. It can suggest complementary items based on past purchases, offer relevant promotions aligned with shopping preferences, and adjust communication style to match individual customer preferences.

This personalization extends beyond product recommendations. The system can remember customer preferences for communication channels, delivery options, and payment methods—streamlining future interactions and reducing friction in the shopping experience.

Operational Cost Reduction

Labor represents one of the largest expense categories for retail operations. While human expertise remains essential for complex situations, AI systems dramatically reduce the cost per customer interaction by automating routine inquiries.

The economics are compelling. Traditional customer service interactions cost retailers $5-25 per contact when handled by human agents, depending on channel and complexity. Automated interactions cost a fraction of this amount—often between $0.50-5 per resolution. For retailers handling hundreds of thousands or millions of customer contacts annually, the savings compound quickly.

Beyond direct cost savings, the technology enables more strategic workforce allocation. Human agents freed from repetitive tasks can focus on high-value activities: resolving complex problems, building customer relationships, and handling situations requiring empathy and nuanced judgment. This shift typically improves both employee satisfaction and customer outcomes.

Implementation costs have decreased substantially as the technology matures. Cloud-based solutions eliminate infrastructure requirements, while pre-trained models reduce the time and expertise needed for deployment. Many retailers achieve positive ROI within 6-12 months of implementation.

Increased Sales and Conversion Rates

AI-powered engagement doesn't just answer questions—it actively drives revenue growth. The technology identifies purchase intent signals and responds with targeted interventions that move shoppers toward conversion.

Cart abandonment recovery represents a significant opportunity. When shoppers add items but don't complete checkout, automated systems can reach out via text or chat with personalized incentives, address common objections, or offer assistance. Retailers implementing these strategies report conversion rate improvements of 15-30% on abandoned carts.

Upselling and cross-selling become more sophisticated and less intrusive. Rather than generic product suggestions, the system analyzes the customer's current cart contents, purchase history, and browsing behavior to recommend genuinely relevant additions. This approach increases average order values while enhancing rather than detracting from the shopping experience.

Proactive engagement during the browsing phase helps capture customers who might otherwise leave without purchasing. When the system detects hesitation patterns—repeated visits to the same product page, extended time reviewing specifications, or comparison shopping behavior—it can offer timely assistance that addresses specific concerns and builds purchase confidence.

Omnichannel Consistency

Modern shoppers interact with retailers across numerous touchpoints: browsing on mobile apps, visiting physical stores, engaging via social media, and contacting support through various channels. Maintaining consistent experiences across these touchpoints challenges even sophisticated retailers.

AI systems excel at creating unified customer experiences. When a shopper starts a conversation via website chat, continues it through text messaging, and completes it with a phone call, the technology maintains full context throughout. The customer never needs to repeat information or explain their situation multiple times—a common frustration with traditional siloed support systems.

This consistency extends to physical retail locations. In-store associates can access the same AI-powered tools that support online customers, providing instant access to product information, inventory levels across locations, and customer purchase histories. Some retailers deploy QR codes that shoppers can scan to instantly connect with AI assistants for product comparisons, reviews, or additional specifications.

Data Collection and Customer Insights

Every customer interaction generates valuable data about preferences, pain points, and behavior patterns. AI systems capture and analyze this information at scale, surfacing insights that inform strategic decisions across the business.

The technology identifies trending questions and common customer concerns, helping retailers proactively address issues before they escalate. If hundreds of customers ask about a specific product feature, that signals an opportunity to improve product descriptions or marketing materials. Patterns in return reasons highlight quality issues or misaligned customer expectations that need attention.

Sentiment analysis capabilities detect customer frustration, satisfaction, or confusion in real-time. This enables immediate intervention when interactions turn negative and helps identify which products, policies, or processes create the most friction in the customer experience.

Predictive analytics leverage conversation data to forecast demand, identify emerging trends, and optimize inventory decisions. When the system detects increasing interest in specific product categories or features, retailers can adjust purchasing and merchandising strategies accordingly.

Top Use Cases in Retail Environments

Customer Service Automation

Routine customer service inquiries consume enormous amounts of time yet follow predictable patterns. AI systems handle these efficiently while maintaining quality standards.

Order tracking represents the highest-volume inquiry category for most retailers. Customers want immediate updates on shipment status, delivery estimates, and package locations. Automated systems access order management platforms in real-time, providing instant, accurate information without human intervention. When delays occur, they proactively notify affected customers and offer solutions like expedited shipping or compensation.

Returns and exchanges processes become significantly more efficient. The technology guides customers through return eligibility checks, generates return labels, and initiates refunds—all through conversational interfaces. This reduces processing time from days to minutes while improving customer satisfaction during potentially frustrating situations.

Product availability inquiries get resolved instantly. When customers ask whether specific items are in stock, the system checks real-time inventory across all locations and provides accurate answers. For out-of-stock items, it can offer alternatives, notify customers when items return to inventory, or locate the product at nearby stores.

Store information requests—hours, locations, parking, services offered—get handled automatically, freeing store associates to focus on in-person customer assistance rather than answering phone calls about basic information.

Personalized Product Recommendations

AI-powered shopping assistants transform how customers discover products by providing tailored guidance throughout the browsing journey.

These systems function as virtual personal shoppers, asking clarifying questions to understand customer needs and preferences. A customer shopping for winter boots might be asked about intended use (casual wear, hiking, work), preferred style, budget range, and size requirements. The technology then suggests options that match these criteria, explaining why each recommendation fits their needs.

Visual search integration enables customers to upload photos of items they like and receive suggestions for similar or complementary products. This capability proves particularly valuable in fashion and home goods retail, where visual appeal drives purchase decisions.

Style and fit guidance helps reduce return rates—a significant cost driver for online retailers. The system can ask about body type, style preferences, and past purchase satisfaction to recommend sizes and styles with higher likelihood of customer satisfaction.

Cart Abandonment Recovery

Shopping cart abandonment affects all online retailers, with industry averages hovering around 70%. AI-powered recovery strategies recapture a meaningful portion of this lost revenue.

The technology detects when customers add items but don't complete checkout, then initiates personalized follow-up. Rather than generic reminders, these interventions address specific objections. If a customer abandoned their cart after viewing shipping costs, the system might offer free shipping. If they left during payment entry, it might highlight security features or offer alternative payment methods.

Real-time intervention during the checkout process proves even more effective. When the system detects hesitation patterns—repeated back-and-forth between cart and product pages, extended time on the shipping options screen—it can offer proactive assistance through a chat prompt: "I noticed you're reviewing shipping options. Can I help you find the best choice for your needs?"

Timing optimization ensures follow-ups reach customers when they're most likely to convert. Machine learning algorithms analyze historical patterns to determine optimal outreach timing for different customer segments and product categories.

In-Store Experience Enhancement

Physical retail locations benefit significantly from AI integration, bridging the gap between digital convenience and in-person shopping.

QR code-based assistance allows shoppers to access detailed product information, customer reviews, and comparison data without searching for a store associate. Customers scan codes displayed near products to instantly connect with AI assistants that answer questions about features, compatibility, warranty terms, and more.

Store associate support tools powered by AI help employees deliver better service. When a customer asks about product availability, associates can query the system via voice or mobile device to check inventory across all locations, place orders for out-of-stock items, or arrange transfers from other stores.

Curbside pickup coordination becomes seamless. Customers notify the store of their arrival through automated systems, which alert associates and provide specific vehicle descriptions and parking locations. The technology manages the queue, ensuring efficient order delivery and minimal wait times.

Inventory and Stock Management

Real-time inventory visibility enhances both customer experience and operational efficiency. AI systems provide accurate stock level information across all channels and locations.

When customers inquire about product availability, they receive immediate, accurate answers drawn from live inventory data. This prevents the frustration of traveling to stores for out-of-stock items or placing online orders that can't be fulfilled.

Restock notifications allow customers to opt-in for alerts when out-of-stock items become available. The system automatically monitors inventory and reaches out to interested customers, capturing sales that might otherwise go to competitors.

Multi-location visibility helps customers find products across a retailer's network. If an item isn't available at their preferred location, the system identifies nearby stores with stock and can even reserve items or arrange transfers.

Post-Purchase Support

The customer relationship extends well beyond the initial purchase. AI systems provide ongoing support that builds loyalty and encourages repeat business.

Delivery updates keep customers informed throughout the shipping process without requiring them to track packages manually. Proactive notifications alert customers to shipping confirmations, transit updates, delivery attempts, and successful deliveries.

Product usage guidance helps customers get maximum value from purchases. The technology can provide setup instructions, maintenance tips, troubleshooting assistance, and recommendations for complementary products or accessories.

Warranty registration becomes frictionless when automated systems guide customers through the process conversationally, collecting necessary information and confirming registration completion.

Implementation Strategies

Assess Needs and Set Clear Goals

Successful implementation begins with thorough analysis of current pain points and desired outcomes. Retailers should identify which customer interactions consume the most resources, where customer satisfaction scores are lowest, and which processes create the most friction in the shopping experience.

Measurable objectives provide essential benchmarks for success. Rather than vague goals like "improve customer service," effective implementations target specific metrics: reduce average response time to under 30 seconds, achieve 70% automation rate for order status inquiries, increase customer satisfaction scores by 15 points, or decrease cart abandonment by 20%.

Stakeholder buy-in across the organization ensures smooth adoption. Customer service teams need to understand how the technology will support rather than replace them. IT departments require clarity on integration requirements and data security protocols. Marketing teams should align on how AI-powered engagement fits within broader customer experience strategies.

Choose the Right Use Cases to Start

Beginning with high-volume, low-complexity tasks delivers quick wins that build organizational confidence and demonstrate ROI. Order tracking, store hours inquiries, and product availability questions represent ideal starting points—they occur frequently, follow predictable patterns, and don't require nuanced judgment.

This phased approach allows teams to learn and optimize before tackling more complex scenarios. As the system proves its value with simple tasks, retailers can gradually expand to more sophisticated applications like personalized product recommendations, cart abandonment recovery, and complex customer service issues.

Prioritization frameworks help determine which use cases to implement first. Consider factors like inquiry volume, complexity level, current resolution costs, customer impact, and technical feasibility. Use cases that score high on volume and impact but low on complexity typically offer the best initial ROI.

Select the Right Platform and Technology

The build-versus-buy decision depends on organizational capabilities, timeline requirements, and budget constraints. Most retailers find that purchasing enterprise-grade solutions accelerates time-to-value and reduces technical risk compared to building custom systems from scratch.

Key evaluation criteria include natural language understanding capabilities, integration ecosystem compatibility, customization flexibility, scalability to handle volume fluctuations, and analytics depth. The platform should connect seamlessly with existing retail systems—CRM platforms, point-of-sale systems, inventory management tools, and e-commerce platforms.

At Vida, we've built our AI Agent OS specifically for business communication needs, with deep integrations across 7,000+ platforms. Our solution handles voice and text interactions across channels, routing intelligently to human agents when needed while maintaining full conversation context. This omnichannel approach ensures customers receive consistent experiences whether they call, text, or message through your website.

Design Conversational Flows

Effective AI interactions require careful design that maps to actual customer journeys. Start by documenting common customer scenarios: a shopper looking for product information, a customer tracking an order, someone initiating a return. For each scenario, outline the optimal conversation path that efficiently gathers necessary information and delivers the desired outcome.

Natural dialogue design avoids robotic, overly formal language. The system should communicate in your brand's voice—whether that's friendly and casual, professional and authoritative, or somewhere in between. Conversations should feel helpful rather than scripted, with the AI acknowledging customer frustration, celebrating successful outcomes, and adapting tone based on context.

Handoff strategies determine when and how the system escalates to human agents. Clear triggers—like customer frustration signals, requests for human assistance, or scenarios beyond the AI's capabilities—should prompt seamless transitions. When handoffs occur, human agents receive full conversation context so customers never need to repeat information.

Monitor, Optimize, and Scale

Continuous improvement separates effective implementations from mediocre ones. Track key performance indicators including automation rate (percentage of inquiries resolved without human intervention), average resolution time, customer satisfaction scores, containment rate, and cost per interaction.

Regular analysis of conversation logs reveals opportunities for enhancement. Look for questions the system struggles to answer, topics that frequently require human escalation, and customer feedback indicating confusion or frustration. Use these insights to refine responses, add training data, and expand capabilities.

Expansion roadmaps guide systematic growth from initial use cases to comprehensive AI-powered customer engagement. As early implementations prove successful, progressively add more sophisticated capabilities, additional channels, and deeper integrations with business systems.

Best Practices for Success

Maintain Human Touch and Empathy

Technology should enhance rather than eliminate human connection. While AI handles routine tasks efficiently, complex situations requiring empathy, nuanced judgment, or creative problem-solving still benefit from human expertise.

Clear escalation protocols ensure customers can reach human agents when needed. The system should recognize frustration signals, repeated unsuccessful attempts to resolve issues, or explicit requests for human assistance—and respond by connecting customers with qualified agents immediately.

Emotional intelligence in AI responses acknowledges customer feelings and demonstrates understanding. When customers express frustration about delayed shipments or defective products, the system should respond with appropriate empathy before moving to solutions.

Ensure Omnichannel Consistency

Customers expect seamless experiences regardless of how they choose to interact. Unified customer data across channels enables the system to maintain conversation context as shoppers move between touchpoints.

A customer who starts a conversation via website chat should be able to continue that same interaction through text messaging without repeating information. If they later call customer service, the human agent should have access to the complete interaction history across all channels.

Prioritize Data Privacy and Security

Customer trust depends on responsible data handling. Retailers must ensure AI systems comply with regulations like GDPR and CCPA, implement robust security measures to protect customer information, and maintain transparency about data collection and usage.

Clear privacy policies should explain what data the system collects, how it's used to improve customer experiences, and what controls customers have over their information. Customers should be able to opt out of data collection or request deletion of their data.

Design for Accessibility

Inclusive design ensures all customers can benefit from AI-powered experiences. This means providing both voice and text options to accommodate different preferences and abilities, supporting multiple languages for diverse customer bases, and following accessibility guidelines for customers with disabilities.

Voice interfaces help customers with visual impairments or reading difficulties, while text options benefit customers who are deaf or hard of hearing, or who prefer written communication. Multi-language support expands market reach and improves experiences for non-native speakers.

Set Clear Expectations

Transparency about AI involvement builds trust. Customers should know when they're interacting with automated systems rather than human agents. This doesn't diminish the experience—many customers prefer AI assistance for simple tasks because of the speed and convenience.

Managing expectations about capabilities prevents frustration. If the system has limitations, acknowledge them upfront and provide clear paths to human assistance for situations beyond its scope.

Overcoming Common Challenges

Technical Integration Complexity

Connecting AI systems with existing retail infrastructure presents technical challenges. Legacy systems may lack modern APIs, data formats might be inconsistent across platforms, and real-time synchronization requires careful architecture.

Success requires thorough technical planning before implementation. Document all systems that need integration, identify data sources and formats, and work with vendors who have experience integrating with your specific technology stack. Platform solutions with pre-built connectors to common retail systems significantly reduce integration complexity.

Data Quality Issues

AI systems perform only as well as the data that trains them. Incomplete product catalogs, outdated inventory information, or inconsistent customer records undermine accuracy and customer trust.

Address data quality proactively by auditing information sources, establishing data governance processes, and implementing validation rules. Regular data cleansing ensures the AI works with accurate, current information.

Change Management and Staff Adoption

Employees may resist AI implementation due to job security concerns or skepticism about the technology's capabilities. Successful adoption requires clear communication about how AI will augment rather than replace human workers.

Involve customer service teams early in the implementation process. Gather their input on pain points and desired capabilities. Provide comprehensive training on how to work alongside AI systems. Celebrate successes and share metrics showing how the technology improves both customer and employee experiences.

Customer Adoption Barriers

Some customers prefer human interaction or distrust automated systems. Rather than forcing AI engagement, offer it as one option among several. Ensure customers can easily reach human agents when desired.

Build trust through consistently positive experiences. When the technology reliably solves problems quickly and accurately, customer acceptance grows organically. Proactively communicate the benefits—instant availability, no wait times, consistent service quality.

The Future of AI in Retail

The technology continues evolving rapidly, with several emerging trends poised to further transform retail customer experiences.

Agentic AI represents the next evolution—systems that don't just respond to customer requests but proactively take actions to solve problems. Rather than simply providing order status when asked, these agents might detect shipping delays and automatically rebook deliveries, issue compensation, or arrange alternative solutions before customers even notice problems.

Multimodal experiences combine voice, text, and visual elements in unified interactions. Customers might speak product questions while viewing visual results, receive text summaries of voice conversations, or share images to get recommendations—all within a single, seamless conversation.

Augmented and virtual reality integration will enable AI assistants to guide customers through immersive shopping experiences. Imagine virtually trying on clothing with an AI stylist providing real-time feedback and suggestions, or visualizing furniture in your home while the assistant answers questions about dimensions, materials, and delivery options.

Emotional AI advances will enable systems to detect and respond to customer emotions with greater sophistication. By analyzing voice tone, word choice, and conversation patterns, these systems will adapt their approach based on whether customers feel frustrated, excited, confused, or satisfied.

Generative AI capabilities will create increasingly natural, contextually appropriate responses. Rather than selecting from pre-written templates, systems will compose unique replies tailored to each customer's specific situation, communication style, and needs.

Market adoption will accelerate as implementation costs decrease and success stories proliferate. Industry analysts project that by 2028, the majority of retail customer interactions will involve some form of AI assistance. Retailers who build strong AI foundations now will be positioned to capitalize on these advancing capabilities.

Choosing the Right Solution

Key Features to Evaluate

Natural language understanding capabilities determine how well the system comprehends customer intent, context, and nuance. Look for solutions that handle complex, multi-part questions, understand industry-specific terminology, and adapt to different communication styles.

Integration ecosystem breadth affects how completely the AI can access and act on your business data. Comprehensive integration with CRM, inventory, order management, payment processing, and marketing platforms enables more sophisticated, helpful interactions.

Customization flexibility allows you to tailor the system to your specific brand voice, business processes, and customer needs. Evaluate how easily you can modify conversation flows, add new capabilities, and adjust behavior based on your unique requirements.

Analytics and reporting capabilities provide visibility into system performance and customer interactions. Robust dashboards should track automation rates, customer satisfaction, common inquiry types, escalation patterns, and business impact metrics.

Scalability ensures the system handles volume fluctuations without performance degradation. Consider both technical scalability—can it handle 10x traffic during holiday shopping?—and functional scalability—how easily can you add new use cases and channels?

Vendor Evaluation Criteria

Industry experience and expertise matter significantly. Vendors who understand retail-specific challenges, terminology, and workflows will deliver better results than generalist providers. Ask for retail customer references and case studies demonstrating relevant experience.

Customer support and training determine how quickly you can deploy and optimize the solution. Look for vendors offering comprehensive onboarding, ongoing technical support, and resources for continuous improvement.

Pricing models vary widely—from per-interaction fees to flat monthly rates to usage-based tiers. Understand total cost of ownership including implementation, training, maintenance, and scaling costs. Calculate expected ROI based on your specific use cases and volume projections.

Implementation timeline affects how quickly you realize value. Some solutions deploy in weeks, while others require months of customization and integration work. Balance speed with thoroughness—rushing implementation often leads to suboptimal results.

Getting Started with Vida

At Vida, we've designed our AI Agent OS to address the specific communication challenges retail businesses face. Our platform handles both voice and text interactions across all customer touchpoints—from phone calls and SMS to website chat and social messaging.

What sets our approach apart is the depth of integration and intelligence. With connections to over 7,000 platforms, our AI agents can access your inventory systems, order management tools, CRM data, and customer histories in real-time. This means they don't just answer questions—they take action, solving problems autonomously while knowing exactly when to escalate to your human team.

Our retail customers use our platform to automate order tracking inquiries, handle product availability questions, process returns and exchanges, recover abandoned carts, and provide 24/7 customer support. The system learns your brand voice, understands your products, and improves continuously based on real customer interactions.

Implementation is straightforward. We work with you to identify high-impact use cases, configure the AI with your business knowledge, integrate with your existing systems, and train your team on working alongside AI agents. Most retailers see measurable results within the first month of deployment.

Whether you're looking to reduce customer service costs, improve response times, or create more personalized shopping experiences, our team can help you design an AI strategy that delivers real business value. Visit vida.io to learn more about how our AI Agent OS transforms retail customer engagement, or explore our AI call center solutions and AI receptionist capabilities designed specifically for retail operations.

The retail landscape continues evolving at an unprecedented pace. Conversational AI provides the foundation for meeting rising customer expectations while controlling operational costs. Retailers who implement these systems thoughtfully—starting with clear use cases, choosing the right technology partners, and continuously optimizing based on results—position themselves for sustainable competitive advantage in an increasingly digital marketplace.