“By 2020, the demand for an omnichannel customer experience will be amplified by the need for nearly perfect execution.” ― PricewaterhouseCoopers

Six years ago, John Bowen, then VP of Time Warner Cable, defined omnichannel marketing like this: “Omnichannel is viewing the experience through the eyes of your customer, orchestrating the experience across all channels so that it is seamless, integrated and consistent.” While marketers and CX leaders have been diligently working on “seamless and consistent,” less attention has been paid to the “through the eyes of the customer.”

That is changing.

From One-Way to Collaborative Customer Experiences

In a recent technology trends 2020 report, Accenture highlighted an emerging trend it calls “the I in experience,” a new model for CX that requires a shift away from one-way selling to using human input and choices to shape individual experiences. Unsurprisingly, martech will play a significant role in facilitating this shift. Accenture found three fundamental areas of change that demand we take a closer look at the types of martech we deploy:

Forensic Understanding of the Customer

This extends beyond the customer profiling and segmentation that most marketers do today to provide a deep understanding of how preferences are changing, what context is impacting activity and which devices are used when. Gaining this understanding requires a more rigorous approach to both data management and algorithmic analytics.

Related Article: Building a Big Picture Customer Experience for Now and the Future

Continuity of Experience

Seamless and consistent have long been on marketing’s radar. Continuity goes a step beyond. Customer journeys today are rarely contiguous: fragmented, start and stop journeys are much more the norm. Providing continuity requires that a company be able to understand where customers have been, predict where they are likely to go and provide options for the customer to shape their own individual journeys based on this knowledge.

Doing this effectively and in real-time requires not only journey analytics capabilities but also other optimization techniques that look at historical response and various testing methods such as A/B and multivariate testing that allow marketers to assess a range of marketing variables in order to match responses to individual journey steps.

Customer-Centric Decisioning

Keeping up with consumer technology advances and incorporating these into the customer experience is difficult. Many companies still guide customers through a journey that fits best with established business processes rather than allowing customers to actively shape and inform their own experiences. AI and machine learning embedded at the appropriate stages of the journey can help companies to present customers with appropriate decisions at the right times based mainly on a comprehensive understanding that is shaped by customer input rather than by company convenience.

Couple this trend for collaborative experiences with the technology capabilities that Gartner includes in its latest Multichannel Marketing Hub Magic Quadrant and the capabilities needed in a martech stack seem to extend exponentially. Requirements include collection and unification of customer data, detailed segmentation, targeting and personalization across campaigns and touchpoints and marketing attribution. Gartner also sees predictive analytics, optimization and data management as critical themes and foresees a time when independent categories such as AI/ML, personalization engines and customer data platforms overlap significantly with the multichannel marketing hub.

Related Article: Decisioning – The Only Way to Accelerate Analytics to Value

Learning Opportunities

Mapping Martech to the Marketing Lifecycle

The proliferation of capabilities mentioned across these omnichannel and multichannel trends and technology can make it hard for marketers to understand what is needed and when. One way to make some sense of this is to look at each step in the marketing process (the marketing lifecycle) and map martech capabilities into the lifecycle based on what marketers are trying to accomplish with each step.

Marketing Data Management

A true omnichannel view requires accurate and comprehensive customer data. A forensic understanding of the customer requires this, and Gartner also highlights its importance. Capabilities to look for here include:

  • A digital customer profile: Known and unknown digital activity should be linked and every digital interaction consolidated to the customer level. PII-free identifiers should help synchronize customer data sources — offline, geo-demographic, account level insights, call center interactions and more. And the data should be presented in an open data model that can be used to combine with non-digital customer data and shared across marketing and analytical tools.
  • Rigorous data management: Data management capabilities should include data access, data quality, data integration, data federation, data governance, master data management and data streaming.

Related Article: Where Omnichannel Experiences Get Stuck

Marketing Planning

Marketers should be able to streamline operations by managing all marketing processes across the entire marketing lifecycle for greater consistency, efficiency and effectiveness — from marketing strategy development and planning, to content creation, journey activation, and post-campaign analysis. Coordinated and consistent planning are vital for continuity of experience and are also an essential part of a multichannel marketing hub. Capabilities in an integrated planning tool should include:

  • Adaptive, agile planning methods: Planning should be facilitated top down, bottom up or via a combination. A variety of levels should be supported — brand, product, program, campaign — with the ability to adapt and change quickly and easily. Complete visibility should be built into time frames, costs, overruns and performance via calendars, user interface and contextual reporting.
  • Analytically informed planning: Integrated attribution, market mix modeling, data unification and embedded analytics should be available at the point of need. Planning tools should allow for better and more agile decisions, enable more effective budget use and facilitate adjustments in real time.
  • Cross-team and cross-application collaboration: Public APIs and UI-level connectors should be available to support integrations with other martech and collaboration systems. Marketing contributors should be able to work independently with the planning solution functioning as the main source of control and collaboration.

Journey Activation With Embedded Analytics

Activation goes well beyond personalization engines to incorporate rich functionality for inbound and outbound campaign design, powered by real-time decisioning that provides powerful customer analytics and artificial intelligence capabilities across all interaction types. Continuity of experience and customer-centric decisioning will require most of these capabilities, and the predictive analytics components and optimization techniques are also a Gartner critical theme. Capabilities for personalizing each journey step include:

  • Guided analytics and out-of-the-box reporting: Integrated analytical guides, such as automatically derived segmentation and applied optimization, should be available to empower true predictive and prescriptive marketing without the need for data scientists. Extensive out-of-the-box performance insights and AI driven attribution to maximize marketing investment are needed and techniques like automated segment discovery that uncover opportunities for cross-sell and up-sell also provide significant value. Out-of-the-box reports which require no data manipulation to help surface both business challenges and customer activity can be a valuable addition for marketers.
  • AI-powered decisions to extend and improve the customer experience beyond marketing: Artificial intelligence capabilities that can be integrated into marketing activities to provide additional insight to customer interactions should complement guided analytics capabilities. Embedded AI in response creation, testing and deliverability management will help to minimize costs and lower risk. Decision and engagement engines should empower marketers to make decisions quickly and scale and push those decisions across the organization in real-time.

Whether you are looking to uncover the “I in customer experience,” view omnichannel through the “eyes of the customer” or construct the perfect multichannel marketing hub, understanding which martech capabilities are needed throughout the marketing lifecycle will help to develop a technology strategy capable of producing contextually relevant engagements across the entire customer journey.

Related Article: Decisioning vs. Orchestration: What's the Difference?

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