Principal Analyst Customer Engagement Practice - Ovum Ovum
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Customer engagement platforms: What are they and why are they superseding CRM?

17th Jan 2019
Principal Analyst Customer Engagement Practice - Ovum Ovum
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Move over CRM, it’s time for customer engagement platforms. Jeremy Cox explores some of the trends that will support the shift from CRM to CEP in 2019.

CRM in its 2019 guise will be vastly different from the old days when CRM was a system of record providing a modicum of support for sales, marketing, and service.

Indeed, the retirement of the acronym is long overdue, not least because to support today's demanding customer, enterprises must develop systems of engagement, not just record.

Welcome to the customer engagement platform.

Ovum defines a CEP as a platform that enables an enterprise to coordinate and intelligently orchestrate all customer engagement activities across its value chain in a way that delivers a symbiotic set of outcomes: superior experience for customers and profitable growth, improved operational efficiency, and lower costs for businesses.

The anatomy of a CEP consists of five enabling technology layers, culminating in a sixth orchestration layer.

The six layers are outlined here:

[Click to enlarge]

The ability to orchestrate the customer experience in real time depends on five enabling "layers" culminating in the orchestration layer. These layers are illustrative, and the configuration of vendor CEPs will differ.

  • Real-time customer engagement orchestration layer: Connects all interactions and data and delivers personalised content or next best action through every touchpoint.
  • Functional support layer: For CEPs to be effective, they must either provide direct functional support for customer-facing business units (CRM heritage vendors) or real-time input in support (customer intelligence and analytics heritage vendors). This includes support for marketing, sales, service, and, depending on the nature of the business, commerce and subscription billing to support subscription businesses, which can include the emerging product-as-a-service business model, where products are rented, not owned. The CEP vendor may partner with other vendors to support this capability, but it must be integrated to support real-time customer interactions and provide essential functional support to employees interacting with the customer.
  • Interaction intelligence tools and process automation layer: This layer includes intelligent tools (predictive and behavioral analytics, machine learning, natural language processing, robotic process automation, virtual assistants, etc.) to analyse, predict, and contextualise the data and infer customer intent. It also provides the automation capabilities to trigger a relevant response at the exact moment the customer interacts.
  • Security and compliance layer: This layer provides the security and governance measures and business tools necessary to protect sensitive customer data.
  • Unified customer data management layer: The unified customer data management layer brings together, either virtually or in a single data store, existing transactional and contextual interaction data, third-party data sources, big data, and IoT data, where relevant. It monitors and synthesises the data to create unified customer profiles, essential for effective personalisation or relevant and timely actions. Data quality is critical to fuel real-time intelligent orchestration capabilities.
  • The cloud infrastructure and integration layer: Given the speed of change, a cloud-based platform provides the optimum environment for customer engagement platforms.

Don't think CRM; think CEP

Classic CRM thinking and investing in CRM applications a department at a time will not lead to a coherent customer engagement capability. This approach to investment is still far too common, but it only proliferates silos and the fragmentation of data. Businesses must keep their eyes on the prize, delivering a positive customer experience that encourages repeat engagement to drive profitable growth.

To succeed in today's volatile, uncertain, complex, and decidedly ambiguous environment, organisations must be highly connected and able to sense and respond to change faster than ever. A customer engagement platform developed along the lines outlined in the six-layer model provides the essential mechanism to create consistently positive experiences for every customer. It provides the means to sense, respond, and adapt to ensure continued relevance. In short, to be customer-adaptive.

Let’s examine some key trends that will reinforce a move towards CEP in 2019.

Trend 1: Data management becomes a top priority to fuel AI-assisted customer engagement

Customer engagement demands a high level of data management, especially in high-volume B2C environments. Data and its quality are critical to fuel AI-assisted customer engagement. In most enterprises today, customer data is fragmented across multiple departments and systems.

Operational and data silos create barriers to customer engagement, and this is still the norm. The millions of daily interactions in high-volume B2C environments and countless customer journey permutations paint a picture of apparent chaos.

Dynamic orchestration of the customer experience relies on the ability to sense and respond to the customer's context, often in real time. Customer data must include traceability of the live interaction journey and any historical transactional information and stated preferences.

Most of the major CEP vendors are using or planning to use graph databases, particularly for identity management and the development of customer profiles.

Subject to permissions, journey data must flow from one interaction point to another. This presents a challenge where multiple systems are used to support individual departments – for example, marketing, sales, service, or commerce. In this scenario, data is not only locked away in system silos, but often recorded in different ways. Customers may be subscribers to newsletters in marketing automation systems, cases in customer care systems, and customers in commerce systems. The same customer might interact via the web, respond to an email campaign, and contact customer care to resolve a query or issue. Where customer data is fragmented across multiple systems, a mechanism is needed to recognise that it is the same individual in each interaction, not three distinct people.

With one or two exceptions, most of the CEP vendors are still trying to solve this challenge. This is one reason AI use cases have been limited to the specific applications being supported, such as guided selling for sales people, A/B testing and attribution in marketing, or a prioritised list of answers for a self-service application using NLP. The CEP vendors that have made the most progress with AI have a more unified portfolio of technology that more easily supports cross-cloud use cases.

The Ovum Decision Matrix: Selecting a Customer Engagement Platform, 2018–19 placed Pegasystems' Pega Infinity as the leader based on its progress with AI to support dynamic orchestration across the customer journey and, from an enterprise perspective, across multiple departments.

Big Data technologies are being adopted to solve the challenges of customer recognition and experience orchestration

An example of a Big Data technology that is helping solve the real-time challenges associated with massive interaction scale is graph databases.

Graph databases are good for sampling the problem and performing customer segmentation in high-volume omnichannel environments, where responses must be delivered in near real time. They are, however, still in their infancy, and with few exceptions, graph databases do not scale well.

Most of the major CEP vendors are using or planning to use graph databases, particularly for identity management and the development of customer profiles based not just on their transactional history (which is what CRM systems do) but also their interactions across multiple channels and customer journeys. These profiles containing behavioural data can then feed machine learning algorithms to understand behavioral patterns and trigger the most relevant action.

For instance, Oracle ID Graph is at the heart of the Oracle Data Management Platform. Elsewhere, in September 2017, SAP acquired Gigya, which is built on a graph database.

Trend 2: The rise of account-based marketing

ABM is an attempt to provide one-to-one marketing capability and to create a team effort between marketing and sales to more deeply penetrate targeted customer accounts. ABM is an adjunct to key account management, which shifts the focus from customer sales to developing deeper insights to help customers achieve their specific aims.

When well executed, this longer-term view of key account customers through ABM improves the corporate reputation; expands and deepens relationships, resulting in tighter bonds; and generates revenue growth.

Expect to see more ABM focus from CEP vendors CEP vendors with a strong B2B heritage are either planning or expected to offer more complete ABM capabilities in 2019.

  • Adobe acquisition of Marketo opens a more direct route to ABM.
  • Oracle was a founding member of the ABM Leadership Alliance and has one of the most complete offerings.
  • Pegasystems and Idio announced a partnership in February 2018 to deliver ABM capabilities.
  • Salesforce already provides AI support in a unified platform supported by Einstein AI.
  • SAP will announce enhanced ABM capabilities in the first half of 2019.

Trend 3: Microservices come of age

The rise of the CEP offers a big step forward for enterprises to engage with customers in more consistent, relevant, and helpful ways, across their various interaction journeys. Most enterprises are still at the early stages of digital transformation and are laying the foundations for the more agile and adaptive enterprise. Once the foundation is in place, some may also seek to change their business models and to add new capabilities at speed. The advantage of microservices is that these self-contained functional applications can be added to existing systems via APIs, usually via a PaaS as an extension. The diagram below provides an architectural view of microservices.

Ovum CEP 2

Microservices also foster experimentation. New functionality can be tested, and if it fails, it will not impact the integrity of existing systems. However, Ovum cautions not to consider microservices until the essential DevOps and CI/CD disciplines are in place. Enterprises increasingly are adopting the more agile development disciplines, and major vendors are creating their own microservices ecosystems and extension platforms to make it easier to find and consume the most promising and relevant microservices.

At SAP's Customer Experience Live event in September 2018, the vendor announced the launch of its SAP Cloud Platform Extension Factory and Kyma, an open source project designed natively on Kubernetes. It allows enterprises to extend and customise cloud-based and on-premises enterprise applications in a cloud-native way, using serverless computing or microservice architecture.

We can expect more vendors to support microservices and provide practical support to help enterprises extend the value of CEPs.

This article is adapted from the Ovum report '2019 Trends to Watch: Customer Engagement Platforms', which can be downloaded by subscribers.

Author: Jeremy Cox, [email protected]  Twitter: JeremyCoxCAE (the CAE stands for Customer-Adaptive Enterprise).

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