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Unlock growth with omnichannel analytics

Person smiling next to product engagement chart showing quarterly trends across mobile app, in-store app and online engagement — visualising omnichannel insights.
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Today’s customers navigate countless touchpoints, demanding seamless, personalised experiences at every step. Meeting this high bar requires moving beyond individual channel data. Omnichannel analytics offers the essential capability of unifying disparate information so you can truly understand the complete customer journey and drive significant improvements in customer satisfaction and operational efficiency.

The hurdle: Customers expect optimised digital experiences.

Customers expect businesses to deliver engaging experiences. If you send them a marketing message, it should speak to their interests on a personal level and come at the right time and place. If you recommend a product or service, it should be something they’ve searched for previously. Customers no longer judge businesses solely on their offerings and capabilities, but on the quality and coherence of the experiences they deliver across every interaction point.

Customers anticipate and increasingly demand, engagement that is consistent, deeply personalised and contextually relevant. A marketing message should resonate with individual interests, arriving precisely when and where it matters most. A product recommendation should reflect genuine prior interest or need. This expectation for seamlessness extends across all channels, both digital and physical.

What is omnichannel analytics?

Omnichannel analytics focuses on the integration and synthesis of data across these touchpoints. It “zooms out” to understand the entire process and how interactions on one channel influence behaviour on others, enabling seamless transitions and a truly unified customer view. Omnichannel analytics combines and integrates data around a single, persistent customer identity. This involves sophisticated processes like identity resolution and data stitching to connect disparate interactions across various platforms and devices over time. Without this unification, insights remain fragmented, preventing businesses from understanding the true, end-to-end customer journey.

omnichannel analytics across multiple channel levels

A comprehensive omnichannel marketing strategy must bridge the digital-physical divide. Relying solely on digital data provides an incomplete picture. Incorporating data from off-line interactions — such as in-store visits, call centre conversations or interactions with sales representatives — is essential for achieving a holistic understanding. This requires technology capable of integrating diverse data types from both online and off-line sources.

Creating a unified customer persona involves breaking down traditional data silos that often exist between departments like marketing, sales, customer support and product development. Establishing a single source of truth for customer data demands cross-functional collaboration and a shared commitment to prioritising the overall customer experience over channel-specific metrics.

Benefits of omnichannel analytics.

Implementing a robust omnichannel analytics strategy delivers a cascade of tangible benefits that are critical for success in the contemporary business landscape. These advantages span customer experience, operational efficiency, financial performance and strategic positioning.

An illustration including icons and labels for seven benefits of omnichannel marketing.

Enhanced customer experience (CX).

The most direct benefit is providing seamless, consistent and highly personalised experiences, irrespective of the channel a customer uses. By analysing data across touchpoints, businesses can uncover patterns, preferences and hidden needs when viewing channels in isolation. This deeper understanding allows for tailoring offers, content and interactions to individual customer profiles, significantly boosting satisfaction.

Improved operational efficiency.

A unified view of customer interactions exposes inefficiencies and areas for improvement within operational workflows, particularly in customer support. Analysing cross-channel data helps optimise resource allocation, identify recurring issues for proactive resolution and pinpoint common queries suitable for automation via tools like chatbots. This reduces operational costs and empowers support agents with complete customer context, leading to faster resolutions and improved service quality.

Increased revenue and return on investment (ROI).

Enhanced customer satisfaction and loyalty, driven by superior personalised experiences, directly translate into increased revenue. Studies show that customers engaging through omnichannel strategies tend to spend more than single-channel customers. Furthermore, omnichannel analytics helps identify lucrative cross-selling and up-selling opportunities based on observed behaviours and preferences across the journey. Businesses can allocate marketing and sales resources more efficiently, maximising ROI by pinpointing the most effective channels and touchpoints for driving conversions. Reduced friction throughout the purchasing process also leads to higher conversion rates.

Predictive decision-making.

Omnichannel analytics provides the foundation for truly data-driven organisational decision-making. A unified view allows marketing, product and operations teams to identify behavioural patterns, predict future actions and understand the “why” behind customer choices. It helps prioritise product features based on real user demand, effectively identifies and addresses customer pain points and reduces internal friction by providing a shared, single source of truth for customer sentiment and behaviour. Understanding true channel effectiveness — beyond last-touch attribution — enables smarter strategic choices.

Identify omnichannel insights in the customer journey.

Marketing journey map for Sevoi Resorts showing messaging strategies across stages: awareness, consideration, purchase, loyalty and post-purchase.
The value of omnichannel analytics lies in its ability to generate deep omnichannel insights. Omnichannel insights are the actionable intelligence needed to understand, map and optimise the entire customer journey.

It’s essential to have a 360-degree view of the customer. This involves consolidating data from every touchpoint — online and off-line — and connecting identities and interactions across channels, devices and time into a single, coherent customer profile. This unified view serves as the foundation for accurate customer journey mapping. Journey mapping involves visualising a customer's entire path, from initial awareness and discovery through engagement, purchase and post-purchase support.

Effective mapping requires identifying all key touchpoints, understanding the customer’s goals and mindset at each stage and recognising the transitions between different channels. Without the unified data layer provided by omnichannel analytics, any attempt at journey mapping will inevitably be incomplete, missing crucial interactions or transitions and leading to flawed conclusions. Data siloes create blind spots, particularly around off-line activities or the influence of support interactions on subsequent online behaviour.

A key application of these omnichannel insights is identifying friction points and gaps in the customer experience. By analysing the complete journey, businesses can pinpoint precisely where customers struggle, encounter obstacles or abandon processes. For instance, analytics might reveal that a confusing step in the online checkout process consistently leads to increased calls to the contact centre. This ability to connect experiences across channels allows businesses to identify the root causes of problems, often originating from where the symptom manifests. Discovering these friction points proactively — before they lead to widespread frustration or churn — enables businesses to intervene, optimise the experience and preserve customer relationships, moving beyond purely reactive problem-solving.

Furthermore, omnichannel insights illuminate the complex interplay between channels. Analytics can reveal how an interaction in one channel (such as viewing a social media ad) influences subsequent behaviour in another (such as visiting the website and making a purchase). Tracking these sequential steps across channels provides a much richer understanding of the customer’s decision-making process. This cross-channel perspective is vital for accurate marketing attribution modelling. Without it, businesses may misallocate resources by overvaluing final touchpoints and undervaluing channels that play critical roles earlier in the discovery or consideration phases, leading to inefficient marketing spend and missed opportunities.

Ultimately, these deep omnichannel insights fuel effective personalisation at scale.

Understanding individual preferences, historical behaviour across all touchpoints and predicted future needs allows businesses to tailor messaging, offers and experiences in real time, making interactions far more relevant and impactful. This transforms raw data into actionable intelligence that can be used by marketing, product, sales and operations teams to create seamless, consistent and truly customer-centric journeys.

How to build an omnichannel analytics strategy.

  1. Deeply understand your customers and map their journeys. The foundation is a thorough understanding of your target audience. Utilise research and existing data to identify customer preferences, typical pain points and behavioural patterns across different segments. Develop detailed customer personas to guide strategic decisions. Critically, map the end-to-end customer journey, identifying every potential touchpoint — websites, mobile apps, social media, email, physical shops, call centres, third-party platforms and so on. Understand the customer’s goals, motivations and emotional state at each journey stage.
  2. Define clear goals and key performance indicators (KPIs). Establish specific, measurable goals for your omnichannel strategy that align directly with broader business objectives and identified customer needs. Crucially, all relevant departments should share and agree upon these goals, including marketing, sales, product and customer success, to ensure alignment. Define the KPIs you will use to measure progress towards these goals. Examples include customer retention rate, sales conversion rate, average order value (AOV), customer lifetime value (CLV), cross-channel engagement metrics, Net Promoter Score (NPS) and churn rate.
  3. Identify, consolidate and integrate data sources. Pinpoint all the systems and channels where valuable customer interaction data resides. This often requires building relationships with stakeholders across different teams (such as product, sales, IT and marketing) to understand data locations and gain access. The central task is to break down existing data silos and consolidate information into a unified view. Ensure that data is centralised, cleansed for accuracy and properly integrated for comprehensive analysis. Establish robust data collection processes and, critically, standardise naming conventions and event definitions across all systems to prevent data discrepancies that can undermine analysis.
  4. Select and integrate appropriate technology. Choose analytics tools and platforms, like Adobe Customer Journey Analytics, capable of handling the volume, velocity and variety of data generated across multiple channels and potentially specialised business units. Evaluate your current technology stack, identifying existing data integration capabilities, attribution challenges and data silos. Ensure the chosen analytics technology integrates seamlessly with data sources and activation and engagement systems— like marketing automation platforms, CRMs and customer data platforms (CDPs — to enable acting on the insights generated. Consider platforms offering low-code or no-code interfaces to potentially reduce dependency on specialised IT or BI teams for data access and analysis. Key technologies often involved include CDPs, CRMs, AI and machine learning engines and potentially ERP systems for inventory and order data.
  5. Enable personalisation and ensure seamless transitions. Use the unified data and chosen technologies, particularly AI and machine learning capabilities, to implement personalised content, recommendations and offers across channels. Focus on ensuring smooth and frictionless transitions for customers as they move between touchpoints (for example, starting a shopping basket on mobile and completing the purchase on desktop).
  6. Prioritise data security, privacy and governance. As data collection expands, robust security measures (like zero trust models) and transparent privacy practices become crucial. Implement strong data governance frameworks to ensure compliance with regulations (such as GDPR and CCPA) and maintain customer trust. This is not an optional add-on but a foundational requirement for long-term success.
  7. Continuously monitor, analyse and optimise. An omnichannel strategy is dynamic. Continuously monitor incoming data and track performance against defined KPIs. Analyse the results to uncover new omnichannel insights, identify emerging trends or friction points and spot opportunities for improvement or growth. Crucially, establish a feedback loop where these insights inform ongoing refinements to the strategy, campaigns, journey maps and even product development.
  8. Train and empower your teams. Ensure that all customer-facing staff — from sales associates to support agents to marketers — understand the omnichannel vision and are equipped with the knowledge and tools to deliver consistent, informed experiences across all touchpoints. Foster a culture of collaboration and data sharing across departments.

Generate omnichannel insights with Customer Journey Analytics.

Adobe Customer Journey Analytics can help you to achieve omnichannel intelligence, which is purpose-built to support modern, first-party data strategies and process large volumes of data quickly.

By leveraging Customer Journey Analytics, you can access tools to analyse the journey and extract omnichannel insights. Its drag-and-drop interface allows users to customise and perform complex analyses, visualising the customer journey sequentially across channels. Users can track steps, pinpoint friction points, identify actions leading to conversion, analyse integrated marketing campaign performance and even predict and mitigate churn. Tools like flow analysis and fallout analysis help visualise paths and drop-offs. The recent addition of Adobe Content Analytics allows businesses to analyse content performance directly within the context of the customer journey.

The fundamental strength of Customer Journey Analytics lies in its ability to connect customer identities and interactions across the full spectrum of channels — online and off-line — as well as across devices and over time. This creates the holistic, contextual map of engagement paths necessary for accurate end-to-end customer journey analysis, moving beyond the limitations of traditional analytics.

Addressing the critical challenge of data unification, Customer Journey Analytics offers remarkable flexibility. It can ingest and unify data from virtually any source, including websites, mobile apps, CRM systems, call centres, loyalty programmes, point-of-sale systems, streaming media and even voice assistants. It supports all customer data types (such as event, profile and lookup) in their natural state, without being bound to rigid hit-visit-visitor models. Capabilities like field-based identity stitching combine disparate IDs into a single person profile, while APIs facilitate the integration of historical data. Data prep tools help standardise and transform data from multiple sources and users can customise data views without affecting underlying datasets.