Marketing Tech

Data-Driven Customer Experience Management Research Report

By Catalyst Research Team

Data-Driven Customer Experience Management Overview

This research note is meant to provide a general overview of the Data-Driven Customer Experience Management (“Data-Driven CXM”) market. We define Data-Driven CXM as the systems that leverage customer intelligence to improve customer experience through positive interactions across consumer-facing functions (sales, marketing, customer success, retail operations, etc.) using methods availed by big data & predictive analytics technologies. For this research piece, we focus on Data-Driven CXM systems delivered to B2C verticals (e.g. retailers, CPGs, travel & hospitality, retail finance / insurance, etc.)[1] CXM technology is becoming table stakes; companies ranging from SMBs to Enterprise are buying and building Data-Driven CXM systems.

We view the Data-Driven CXM space as having three basic layers: Customer Interaction Platforms (“CIP”), Unified Digital Intelligence (“UDI”), and Legacy CXM.

Data-Driven CXM Market Layers

The CIP layer optimizes multi-channel interactions with customers by informing interactions with customer data derived from other digital and physical channels. Included within CIP are:

  • Behavioral Marketing: Real-time, omnichannel and personalized marketing and communications solutions
  • Testing, Optimization & Personalization: Systems that use testing & personalization technologies to optimize prospects’ and customers’ digital experiences
  • Emerging Engagement Channels: Systems that enable automated consumer interaction which are rapidly gaining business traction and consumer adoption

The UDI layer enables systems that capture consumer data across digital and offline touchpoints and run analytics on that data to glean powerful insights. Included within UDI are:

  • Consumer Data Governance & Identity: Systems that manage compliance for consumer data and identity
  • Consumer Data Orchestration: Systems that integrate, warehouse and run analytics on consumer data
  • Consumer Analytics: Systems that derive valuable consumer intelligence from consumer interactions

Legacy CXM systems include the “status quo” systems that most B2C companies today rely on to manage their customer interaction, including ad tech, marketing tech, CRM, customer success tech, sales enablement, marketing data systems, WCM and digital commerce platforms. CIP and UDI systems often “run on top of” Legacy CXM systems by ingesting data from them and leveraging their existing capabilities.

Data-Driven CXM businesses are deployed across a variety of use cases (personalization, marketing attribution, real time interaction management, etc.), but they all follow the fundamental Ingest, Activate, Orchestrate model:

  • Ingest: Extract first-party data from digital (email, CRM, web, etc.) and offline (POS, etc.) channels, as well as sync with second-party and third-party data, and normalize that data into a uniform schema
  • Activate: Transform raw data into real-time insights, including individual consumer profiles campaign & audience analytics, and customer journey modeling
  • Orchestrate: Push insights out to business users and into customer engagement channels (marketing, web, customer service, offline touchpoints, etc.) to drive successful consumer interactions

ROI of Data-Driven CXM

Through the “Ingest, Activate & Orchestrate” model, consumer-facing companies differentiate their goods and services based on customer experience (including quality and personalization) rather than through price, thus avoiding commoditization. This differentiation is increasingly important in the Internet Platform age, with Amazon, Google, and other platforms taking market share and margin based on convenience and price. Data-Driven CXM ROI is measured by greater customer engagement, revenue uplift and improved efficiency of customer-facing departments. In addition, B2C companies are investing in various systems to comply with existing and emerging consumer privacy regulation.

Key Trends Driving the Adoption of Data-Driven CXM

A host of factors have come together to drive adoption and prove this is an exciting time for Data-Driven CXM:

  • Digitalization:
    • B2C companies continue to add customer-facing channels (chatbots, web messaging, voice channels etc.) requiring more management and interaction between channels
      • CXM systems unify content and customer interaction across channels and add incremental digital channels that integrate into existing systems
    • Consumer preferences in the digital era require greater relevancy of interactions
      • Personalize digital and offline interactions
    • Consumer Tech:
      • Mobile – 95% of Americans own a cellphone & 77% own a smartphone[2]
        • CXM systems enable marketing campaigns across mobile push and SMS channels and leverage powerful geolocation data for real-time interactions
      • Premium Online Content – various online content providers have built userbases of 10’s of millions of people (e.g. Netflix with 118mm subscribers, Spotify with 140mm active users & 70mm subscribers[3], etc.) and are building rich profiles on those userbases
        • CXM systems integrate rich consumer data from premium content providers
      • Social Media – 69% of American adults now use at least some form of social media2
        • Inform existing CXM channels with social media intelligence including consumer demographic data, psychographic data, preferences, behaviors, etc.
        • Leverage social media, especially messaging platforms, as both a marketing and customer service channel
      • Consumer IOT systems – various in-home or retail IoT systems (voice interaction, iBeacon, RFID, etc.) are gaining wide-scale traction
        • Integrate psychographic data into CXM systems
        • Leverage location data to provide geotargeting marketing campaigns
      • Marketing:
        • Diffuse technology systems – lack integration at application, data and process / human levels
          • CXM systems unify disparate data silos as well as streamline integration at the application level
        • Decrease in cookie-based consumer tracking – 64% of tracking cookies are disabled by browsers[4], which in turn is largely caused by rise of mobile and in-app browsing
          • CXM systems provide alternative means of consumer identification (such as deterministic matching using email addresses and device IDs) and enable B2C companies to move beyond audience segmentation to true 1:1 / personalization
        • Marketing attribution challenge
          • CXM systems run segmentation analytics on top of customer data sets for more accurate attribution
        • Underlying Technology:
          • Emergence of machine learning / deep learning
            • CXM systems leverage deep learning technology for a variety of use cases
          • Artificial intelligence has the potential to be injected into virtually every digital function of businesses; by 2020, 85% of customer interactions will be managed without a human[5]
            • Build new engagement channels leveraging AI (chatbots, recommendation engines, etc.)
          • Privacy Regulation and Identification:
            • GDPR & ePrivacy regulations
              • Provide requisite identity protection and anonymization to comply with new regulations (consumer identity & governance)
              • Ingest & activate new sources of first-party data to mitigate the loss of existing third-party data

Market Opportunity

Investments in customer experience pay off. A survey analysis concluded that a one percent increase on Forrester’s CX Index (an annual benchmark of customer experience among global brands) increased annual revenue by over $100mm for certain businesses, including mass market auto manufacturers, big box retailers, retail banks and luxury hotels chains[6]. Forrester also concluded that the “leaders” on its CX index grew revenue on average 5.1x faster than its “laggards”[7].

There is consensus that integrating Data-Driven CXM technologies allows companies to improve their customers’ experience: 91% of executives in the US say their use of data analytics has allowed them to deliver at least moderately better customer experiences. However, only 36% of executives say they have real time integration across all customer channels; only 14% of executives claim that their consumer data is structured on a cross-functional and synchronized basis; and only 6% of executives claim to have 100% visibility into all the interactions their customers have with their organization[8].

Given how early the market is and the difficulty in defining a market that cuts across both business functions and verticals, it is difficult to estimate the market size of Data-Driven CXM solutions at this time. However, there are various market estimates of sub-segments that demonstrate the high growth and potential of the market:

  • Customer data platform (“CDP”) market growing at a rate of 50% to $1.0bn in 2019[9]
  • Data management platform (“DMP”) market growing at a rate of 22% to $2.5bn in 2025[10]
  • Global chatbot market growing at a rate of 24% to $1.3bn in 2025[11]

[1] We further distinguish “Customer Experience Management” from other contexts in which the term is used, including as synonyms for customer service software or for customer feedback / review management

[2] Pew Research Center; 2018

[3] Company Press Releases

[4] Flashtalking, 2017

[5] Gartner Research

[6] Forrester: Drive Revenue with Great Customer Experience, 2017

[7] Forrester: Make the Case That CX Transformation is Both Important and Urgent

[8] Forbes Insights: Data Elevates the Customer Experience

[9] Raab Institute: Customer Data Platform Industry Profile 2017

10] Forrester: Ad Technology Forecast, 2017

[11] Grand View Research: Global Chatbot Market, 2017

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