How to use Data to Transform Omnichannel Consumer Experience (CX)

In the past, companies often believed that top-notch products or services were enough to win over customers. Today, however, that’s only part of the equation. The real game-changer is delivering an exceptional customer experience. Look at Apple - high end products supported with services. Yet they are continuously setting benchmarks in customer experience through all their touch points.

In today’s competitive landscape, creating a standout customer experience is essential. Interestingly, customers don’t view digital channels the same way businesses do. They effortlessly move between digital and physical interactions, seeing it all as part of one seamless journey. In their minds, there’s no divide between online and offline; they’re hyper-connected, digital-first consumers. The demarcation between digital and offline touchpoint don't exist any more.

Key Stats to Consider:

• Harvard Business Review found that 73% of consumers prefer using multiple channels for shopping. Only 7% shop exclusively online, while 20% stick to in-store. And with each additional channel they use, they tend to spend more: omnichannel customers spend 10% more online and 4% more in-store than single-channel customers.
• Google reported that omnichannel shoppers have a 30% higher lifetime value than single-channel shoppers, meaning they’re more valuable over time.
• Aberdeen Group noted that companies with strong omnichannel engagement strategies retain an average of 89% of their customers, compared to just 33% for companies with weaker strategies, i.e. growth in Total Customer Value (TCV) with data driven Consumer Lifecycle Experience (CLX).
• Invesp showed that companies with robust omnichannel engagement see a 9.5% year-over-year increase in revenue, while those with weaker strategies grow at only 3.4%.

To excel in omnichannel customer experience, brands must focus on hyper-personalization across all touch points—digital, physical, or a blend of both - Phygital, creating what we can call a “Digital-First Integrated Omnichannel Approach.”

Creating exceptional omnichannel consumer experiences often necessitates brands to adopt a data-driven approach to their digital transformation initiatives. Each component of their IT ecosystem, from establishing a foundation with data platforms to effectively integrating data and analytics across all components, plays a crucial role. By ensuring strong data & analytics connections among these interconnected blocks, brands can ultimately construct & enable an ecosystem that facilitates seamless omnichannel consumer experience.

This strategy relies on a three-layer interconnected framework:

• LAYER 1: Data Strategy → Digital Transformation
• LAYER 2: Digital Transformation → MarTech Transformation
• LAYER 3: MarTech Transformation → AI driven Omnichannel CX

Let me now explain how and where ‘DATA’ needs to be integrated and leveraged within each of these layers.

LAYER 1: Data → Digital Transformation

Digital transformation is more than simply adopting new software or technology. In fact, leading with technology would be a mistake. Even if technology is the catalyst for change, successful transformation begins with understanding the experiences of customers, defining processes and capabilities and solving for what technology is needed to meet your goals.?Integrate or leverage analytics tools and relevant APIs across these core building blocks in an IT ecosystem that relies on a variety of technologies to form a cohesive network to support a seamless customer experience, operational efficiency, and innovation.

Core Business Applications

• Enterprise Resource Planning (ERP) that integrates essential functions like finance, HR, procurement, inventory, and order management. It ensures data consistency and supports real-time decision-making across the organization.
• Customer Relationship Management (CRM): CRM is critical for managing customer interactions, sales, and marketing activities. It allows for data-driven personalization, improving the customer journey across channels.

Customer-Facing Solutions + MarTech Stack*

• E-commerce Platforms: Supports online sales channels for both B2B and B2C models. It should integrate well with other core systems like ERP, CRM, and Product Information Management to provide accurate, real-time information.
• Product Information Management (PIM): Centralizes product data, ensuring that accurate, consistent information is distributed across digital and physical channels, enhancing the omnichannel customer experience.
• DXP and CMS: Manages and delivers personalized content across customer touch points, including websites, mobile apps, and social media. DXP enhances customer engagement by delivering consistent messaging across channels.
• Customer Support: Leverages data in several impactful ways to provide a personalized, efficient, and proactive support experience. By connecting data across the customer journey, these technologies not only enhance immediate support interactions but also help businesses continuously improve products, services, and customer satisfaction.

Data Management & Analytics

• Data Lakes/Warehouses: These solutions store vast amounts of structured and unstructured data, providing a foundation for analytics and AI/ML applications.
• Analytics and Business Intelligence (BI): BI tools transform raw data into insights, supporting decision-making across departments. Predictive analytics and AI help brands personalize experiences, optimize supply chains, and forecast demand.
• Master Data Management (MDM): MDM ensures that core business data is accurate, consistent, and shared across the enterprise, creating a unified view of customers, products, and suppliers.

Supply Chain and Logistics

• Supply Chain Management (SCM): Integrates supply chain planning, inventory management, and logistics, enabling real-time tracking and optimization across the supply chain.
• Warehouse Management Systems (WMS): Manages warehouse operations, optimizing stock movement, order fulfillment, and inventory control.

Infrastructure and Cloud Solutions

• Cloud Infrastructure (IaaS, PaaS, SaaS): Cloud services provide scalable and flexible resources, allowing businesses to adjust infrastructure according to demand. They also enable global accessibility and rapid deployment of applications.
• Network and Connectivity Solutions: A robust network infrastructure is essential for seamless data flow across on-premise, cloud, and hybrid environments, supporting omnichannel interactions and operational efficiencies.

Application Integration and Middleware

• APIs and Middleware Platforms: These integration layers connect applications across the ecosystem, ensuring smooth data flow between ERP, CRM, SCM, and e-commerce systems. APIs also allow integration with third-party services and enable microservices architectures.
• iPaaS (Integration Platform as a Service): iPaaS simplifies the integration of cloud applications, on-premise systems, and databases, making it easier to achieve a connected ecosystem that supports real-time data sharing.

Cybersecurity and Compliance

• Identity and Access Management (IAM): Manages user authentication and authorization, protecting sensitive data and ensuring regulatory compliance.
• Data Protection Solutions: Protects critical data at rest and in transit, using encryption, backup, and disaster recovery solutions to ensure business continuity and data security.
• Compliance Management Tools: Ensures that the business adheres to industry standards and regulations, such as GDPR, HIPAA, or PCI-DSS, particularly critical in omnichannel ecosystems where customer data is extensively shared and used.

Automation and AI/ML Solutions

• Robotic Process Automation (RPA): Automates repetitive processes, reducing manual work in areas like data entry, order processing, and customer service.
• Artificial Intelligence/Machine Learning: Powers personalization, recommendation engines, demand forecasting, and customer support chatbots, enhancing the omnichannel experience through data-driven insights.

Collaboration and Productivity Tools

• Collaboration Platforms: Tools like Microsoft Teams, Slack, Sharepoint, DAM, and project management software enable seamless communication and collaboration across departments.
• Knowledge Management Systems: Centralize knowledge for easy access, supporting internal teams and enhancing customer support capabilities.

KEY DRIVERS:

• A robust data strategy for dismantling data silos and eliminating fragmentation across online and offline channels. A unified data layer emerges as a pivotal element in this endeavor by providing a comprehensive and omnichannel view of customers and seamlessly bridging the gap between your MarTech and adtech ecosystems.
• Incorporate Identity Resolution, Management, Matching, and Integration to seamlessly bring fragmented data together, accurately identify, recognize, and connect with consumers at any time, across any channel or location, and deliver exceptional experiences.
• Optimize Back-End Processes: Inefficient back-end processes can significantly impact the customer experience. Salesforce’s research revealed that a whopping 64% of consumers expect companies to respond to them promptly. This means your back-end must be equipped with digital capabilities to support real-time interactions. To digitize these processes and better support customer-facing channels, start by identifying tasks that can be automated and make use of cloud solutions. Additionally, focus on developing streamlined workflows and removing redundancies to improve overall operational efficiency.

LAYER 2: Digital Transformation → MarTech Transformation

Qualtrics shows satisfied customers are five times more likely to be repeat customers and even loyal brand advocates who share the news of their great experiences with others. The discipline of seamlessly connecting omnichannel transactions through your martech ecosystem, is already complicated, but it has become more important than ever.

If your existing ecosystem is lacking, adding AI will only further complicate your efforts in AI. Hence, lets us first try to “Look Inside the Box .i.e. existing Martech Stack” and analyze how to leverage / optimize it to the best of its abilities, and then move to add the layer of AI on top of it.

MarTech transformation in an IT ecosystem is built on an interconnected network of technologies, each layer contributing to data collection, processing, personalization, and engagement. Here’s a breakdown of the essential building blocks and how data is integrated across each of these:

UNIFICATION OF DATA

1. Data Sources: Collection of 1st, 2nd, and 3rd Party Data

• First-Party Data: Directly collected data, such as customer information from CRM systems, website interactions, purchase history, app usage, and loyalty programs.
• Second-Party Data: Partnerships with other businesses to share anonymized data, allowing for broader audience insights without relying on third-party sources.
• Third-Party Data: External data sources provided by aggregators or data brokers that enhance customer profiles, segmenting and identifying broader audience behaviors.

2. Data Ingestion & Processing Layer

• Data Ingestion Platforms: Tools to gather data from multiple channels in real-time, such as social media, email, and web interactions. These tools include ETL (Extract, Transform, Load) processes to standardize data from different sources.
• Data Lake: A scalable repository that stores raw data (structured and unstructured) at scale, serving as a foundational layer for further data processing and analytics.
• Data Warehouse: Optimized for storing structured, processed data to support advanced analytics and reporting. Data warehouses enable quick querying and form the basis for BI and reporting tools.
• Inbound Data Processing: Involves real-time data streaming, transformation, and loading into data lakes or warehouses to support real-time analytics and personalization.

3. Data Governance and Compliance

• Data Governance Frameworks: Enforce data quality, consistency, security, and regulatory compliance (e.g., GDPR, CCPA). Governance frameworks define roles, responsibilities, and access to maintain data integrity across MarTech systems.
• Identity Resolution and Privacy Management: Tools to manage user consent, preferences, and privacy settings, ensuring compliance with data privacy laws while enabling personalized marketing.
• Master Data Management (MDM): Maintains consistency of core data entities (customers, products) across systems, ensuring a unified and accurate view in customer interactions.

PROCESSING, ACTIVATION AND MEASUREMENT

4. Customer Data Platform (CDP)

• Data Unification and Profile Creation: A CDP consolidates data from multiple touch points to create a 360-degree view of each customer, enabling advanced personalization.
• Segmentation and Targeting: The CDP uses AI/ML models to segment audiences and trigger dynamic campaigns based on behavior, preferences, and predictive insights.
• Real-Time Processing: A CDP processes real-time data to enable dynamic personalization based on behavioral & contextual data such as clickstream data, browsing behavior, and purchase history, including predictive modeling for next-best-action or product recommendations.

5. Experience, Content, and Creative Layer

• Digital Experience Platform (DXP): Coordinates customer engagement across digital channels, ensuring a cohesive and personalized experience at each touchpoint. DXPs integrate CMS, DAM, and CRM systems.
• Content Management System (CMS): Manages and organizes content delivery across web, mobile, and other channels. CMS platforms support personalization by integrating with CDPs to deliver dynamic, segmented content.
• Digital Asset Management (DAM): Stores and manages creative assets (images, videos, documents) for use across campaigns and channels, ensuring consistent branding and quick access for creative teams.
• Campaign Management Systems: Manages multichannel campaigns, supporting automation, segmentation, scheduling, and analytics to streamline content distribution.

6. Advertising Technology (AdTech)

• Programmatic Advertising Platforms: Automated ad placement tools (e.g., demand-side platforms) that leverage customer data for targeted advertising across online display, social media, and video channels.
• Audience Targeting and Segmentation: AdTech platforms leverage data from CDPs or DMPs (Data Management Platforms) to create audience segments, supporting targeted campaigns that align with customer preferences and behaviors.
• Cross-Channel Campaign Measurement: Tracks campaign effectiveness across digital channels, feeding back data to optimize future campaigns and refine audience segmentation.

7. Tracking, Optimization, and Analytics

• Web Analytics and Tracking Tools: Track customer interactions across digital properties, collecting data on engagement, conversions, and drop-off points to refine the customer journey.
• A/B Testing and Personalization Platforms: Tools for testing different creative or content variations, refining user experience, and optimizing conversion rates.
• Predictive Analytics and AI Models: Predicts customer behavior, supports segmentation, and provides insights for decision-making by applying machine learning to customer data.
• Data Visualization and Reporting: BI tools that visualize data in dashboards and reports, providing actionable insights to optimize marketing and business strategies.

8. Optimization and Feedback Loop

• Real-Time Optimization: Continuous analysis of customer interactions to adjust campaigns, content, and personalization efforts based on live data insights.
• Feedback from Sales and Customer Support: Integrating feedback from customer service and sales teams to refine targeting, improve product/service offerings, and enhance the customer experience.
• Campaign Performance and Attribution Analysis: Multi-touch attribution models evaluate the effectiveness of different channels, helping to optimize future budget allocations and campaign focus areas.

By establishing a cohesive, interconnected system of these MarTech building blocks, organizations can transform data into an actionable resource that drives hyper-personalized customer engagement, optimizes campaigns, and ultimately enhances the customer experience across all touch points in an omnichannel ecosystem.

LAYER 3: MarTech Transformation → AI driven Omnichannel CX

Lets recap, what we have covered so far…

In Layer 1, I outlined how a data strategy is the crucial component that shapes your digital transformation vision, implementation, integration, and customization roadmap. For each component of an IT ecosystem, a data lens must be adopted to configure and leverage it as an efficient switchboard, ensuring that each unit plays its role effectively to empower a data-led ecosystem.

And in Layer 2, building upon the layer 2 of data-led IT ecosystem, I then detailed out where and how to customize and leverage various building blocks of a MarTech Stack. Core is to optimize the existing platforms to the best of their abilities and use composable architecture with perfectly suited APIs to operate the Marketing Operations. I strongly recommend to do this before evaluating and adding the layer of our favorite - “AI or Gen AI”.

In Layer 3, instead of suggesting “Use AI everywhere” or repeat the most generic cases where AI is being broadly used such as AI-powered chatbots, voice assistants, recommendations, personalization, data insights, L1 and L2 support etc. My proposed approach is to go one or two level down across various components, identify the new use / upcoming cases and explore how AI / GenAI can be leveraged as a value addition for incremental effectiveness.

Here are my key recommendations on using AI / GenAI with a strategic combination of real-time data, personalization, automation, and predictive insights across different building blocks in IT and MarTech ecosystem.

Note: For each of these recommendations, I’ve also given my rating on their current adoption level, this is purely based on my various interactions with my clients and prospects, discussions with industry peers and my exposure to the published media.i.e. Adoption Level (AL): High / Medium / Low & Most Requested / Most Discussed)

1) Transition from Hyper-Personalised to Hyper-Innovative

• Customer Data Platforms (CDPs): In most of the cases, GenAI is being used to create personas and behavioral insights. Add the layer of "Predictive Personas i.e. We are talking about Humans, who get influenced and are dynamic in their behavior. No one can predict what they want? unless they tell you. Use GenAI to build models and predict how personas of specific target segments would evolve by taking inspiration from closer look-a-likes, market and consumer trends. Perfect example here is Temu (I know not a ideal one but we can’t ignore) or the latest sensation Vinted.com, a Lithuanian online marketplace for buying, selling, and exchanging new or secondhand items, mainly clothing and accessories.

(AL: Low but Most Discussed)

Don’t forget what the GOAT Steve Jobs said. Jobs held the perception that you must determine what customers are going to want, before they figure it out.

Content Management and Campaign Management:

• Dynamic Content Customization: AI-powered platforms dynamically adjust the content on digital channels (e.g., websites, apps) in real-time based on user profiles and interactions, ensuring each customer sees content relevant to them. (AL: High and Most Discussed)
• Create & Develop with GenAI: Use GenAI to create specific tools to co-create, replicate, generate content and creative as per your brand guidelines as well as image assets library in your DAM. And on top of other, use tools like Figma Dev, to bring design and development closer together. (AL: Low, Most Discussed and Requested)

CRM and Marketing Automation - Proactive Engagement and Predictive Outreach

• Predictive Customer Support: AI identifies common customer issues or pain points in advance, and can provide insights to reach out proactively with support, tips, or tutorials, reducing future service requests. (AL: High, Most Discussed and Requested)
• Next-Best-Action Recommendations: Machine learning algorithms analyze customer data to recommend the next best action, whether it’s a product upsell, an upgrade, or even a loyalty reward, increasing engagement and lifetime value. (AL: High, Most Discussed and Requested)
• Customer Journey Analytics: Use AI to track customer journeys across channels, identifying drop-off points, conversion bottlenecks, and opportunities to re-engage inactive customers. (AL: Medium, Most Discussed and Requested)

Cross-Channel / Omnichannel Marketing Automation

• Automated Campaign Orchestration: Use AI to track each audience across omnichannel and manage a logical campaign targeting them across multiple channels (e.g., email, SMS, social media, in-app, stores, smart devices, IoT etc.), adjusting messages and timing based on real-time customer data and engagement. (AL: Low, Infancy level)
• Behavioral Retargeting on Digital Channels: AI can analyze browsing and purchase behavior to deliver personalized retargeting ads, reducing cart abandonment rates and maximizing conversion opportunities. (AL: High, Most Requested)
• AI-Driven Content Recommendations across Omnichannel: Use AI to automate content distribution across touch-points, delivering product suggestions, videos, or blog posts that match the customer’s profile and past interactions. (AL: Low, Infancy level)

2) Use AI behind the curtain (back-end) to empower customer support

AI-Powered Customer Service and Support:

• Sentiment Analysis for Agent Assistance: Use AI to analyze customer sentiment in real-time during support interactions. This allows agents to adjust their tone and approach based on customer mood, increasing satisfaction and reducing friction. (AL: Low, Most Discussed)
• Omnichannel Ticketing Systems: Implement AI-driven systems that unify customer support across channels (e.g., chat, email, phone) so that agents have a full view of each customer’s journey, allowing for seamless and consistent support. (AL: Medium, Most Discussed)

3) Innovate Phygital Journeys with AI (In-Store)

• In-Store Behavior Analysis: AI-driven facial recognition and IoT sensors can analyze in-store customer movements, helping brands understand foot traffic patterns, popular areas, and optimize product placements. (AL: Low, Most Discussed)
• Real-Time Data Integration: Collect data from multiple sources (e.g., social media, website, in-store sensors) and use AI to analyze it in real-time, giving insights into current trends, popular products, and customer preferences. (AL: Low, Most Discussed)
• Predictive Analytics for Demand Forecasting: AI can analyze historical data, seasonality, and external factors to predict future demand, enabling better inventory management and reducing stock-outs or overstock. (AL: Medium, Most Discussed)
• In-store Visual Search and Recommendations: AI-powered visual recognition tools allow customers to search by image, making it easier to find products that match their style preferences, even in-store, by scanning items with mobile apps. (AL: Low, Most Discussed)

4) Ad-Tech, Media Planning: Real-Time Performance Tracking and ROI Measurement

• Unified Analytics Dashboard: An AI-driven unified dashboard aggregates data from all touch-points (digital and physical), multi channels, multi region, multi products, multi formats etc. providing a holistic view of campaign performance, customer engagement, and operational efficiency. (AL: Low, Most Discussed)
• Customer Lifetime Value (CLTV) Modeling: AI can help measure the impact of omnichannel engagement on CLTV, providing insights into the long-term ROI of different marketing initiatives. (AL: Low, Most Discussed)
• ROI-Focused Optimization: AI continually analyzes channel effectiveness, marketing spend, and customer behavior to dynamically allocate resources to the highest-performing areas, maximizing ROI. (AL: Low, Most Discussed)

The fusion of data and AI across the core building blocks of your IT ecosystem

Bringing data and AI together across your IT ecosystem and MarTech stack offers a powerful way to reinvent customer interactions, innovate your offerings, and build lasting relationships. To unlock these benefits, start by assessing where you are now, envision where you want to be, and map out how data and AI can help you bridge the gap, aligning with both customer needs and company goals.

With data topped empowered with AI-powered solutions, brands can craft a seamless, personalized omnichannel experience that meets the high expectations of today’s digital-first consumers. This strategy not only boosts customer satisfaction but also drives revenue, streamlines operations, and builds long-term customer loyalty, ultimately leading to a healthy customer lifecycle experience (CLX) and total customer value (TCV).

By leveraging data and technology effectively, organizations can build a customer-focused ecosystem that goes beyond meeting expectations to truly exceeding them. In this rapidly evolving landscape, seamless digital transformation and customer-centric experiences will be the keys to sustainable growth and success.