Gartner published a report on the Personalization Engine Market Overview and the highlights are summarized below.
The Personalization Engine Market is defined as software that applies context about individual users to select, tailor and deliver messaging such as content, offers and other interactions through digital channels in support of three use cases:
- Digital commerce
- Customer experience (CX)
Personalization engines, sometimes called digital personalization engines, analyze customer data collected from outside software or from customer behavior to curate and tailor customer experiences including automated marketing efforts, websites, and product recommendations. These tools automate the process of segmenting, testing, and distributing 1:1 marketing efforts, ensuring campaigns are effective and memorable.
Personalization engines are often a feature of tools utilized by marketing teams. They are often built into or integrate with customer data platforms (CDP) and digital experience platforms (DXP) – they also work closely with content experience software and A/B testing software to create a full, personalized content creation and distribution cycle.
Personalization engines overlap with e-commerce personalization software in that both aim to tailor presented content to buyers; however, the former is a more generalized engine while the latter can only be used in e-commerce contexts.
Personalization Engine Market Overview
Marketing leaders are steadily increasing their investment in personalization — it now makes up 14% of marketing spend. Additionally, adoption of personalization engines is up 28% points since 2016. As more budget flows toward personalization tools, the market for stand-alone personalization engines continues to grow. At the same time, incumbent solutions are enhancing their technical capabilities, particularly in customer data management, to increase share of wallet and better manage data ingestion and integration. And vendors across the marketing technology landscape, such as marketing automation platforms, multichannel marketing hubs and email optimization solutions, are doubling down on embedded personalization capabilities. However, they may not offer the same breadth and depth of capabilities as a personalization engine.
As marketing leaders face a broader array of personalization solutions, they’re increasingly willing to trade up for performance improvement. 44% of client references queried as part of this research report are choosing a personalization engine to replace an existing solution. Vendors are paying close attention to customer success to maintain or improve retention rates. Alongside vendors’ best efforts to help marketers overcome impediments to personalization, users are reporting slightly higher personalization maturity. Retailers are among the most mature — representing a skew toward digital commerce as a leading use case for personalization. Other verticals like financial services, travel and hospitality, and even B2B companies are experimenting with a range of personalization use cases.
- The content recommendation engine market is projected to grow from $1.2B in 2017 to $4.95B by 2022, at a CAGR of 34% from 2017 to 2022.
- Increasing focus on enhancing customer experience, rapid digitalization, and need for analyzing large volumes of customer data are factors driving the market across the globe.
- Among components, the service segment of the content recommendation engine market is projected to grow at the highest CAGR during the forecast period.
- The high growth of the service segment can be attributed to the growing need to deploy development services, consulting services, implementation services, training services, support services, and others.
- The solution segment is expected to lead the market during the forecast period.
- The Asia Pacific content recommendation engine market is projected to grow at the highest CAGR from 2017 to 2022.
- The growth of the Asia Pacific market can be attributed to the rise in Over the Top (OTT) players and rapid digitization, which is expected to lead to the increasing deployment of content recommendation engine in the region..
Below are the key trends for personalization Engines – stand-alone personalization engines:
Personalization Engine Market Overview – Personalization Skews Toward Digital Commerce
All vendors in Gartner’s 2019 Magic Quadrant for Personalization Engines offer functionality to support personalized marketing, digital commerce and CX. However, they vary in breadth of capabilities, particularly in marketing and CX personalization. While the native ability to trigger personalized web and email content is standard some personalization engine vendors rely on integration to trigger personalized digital advertising. Despite expanded capabilities to support CX personalization, other personalization engine vendors depend on third parties for some or all survey functionality. It’s also worth noting significant variance in terms of reference clients’ evaluations of personalization engine vendors for the ease of API integration. Vendors also differ in terms of why they’re selected by client references. Digital commerce stands out as an area where personalization engine vendors have the fewest capability gaps. It is also the primary use case for 47% of client references, compared to only 31% and 19% who chose marketing and CX, respectively.
Personalization Engine Market Overview – CX Personalization Capabilities Expand
According to Gartner’s 2018 Magic Quadrant for Personalization Engines, only 18% of vendor deployments were in support of CX personalization. Not much has changed in that regard. This year, only 19% of client references cited customer experience as the primary use case that drove them to buy a personalization engine. Nevertheless, personalization engine vendors have expanded their CX personalization capabilities. Most offer the native ability to gather user feedback. They enable teams to design and execute user surveys, gather and analyze survey data, append response data to user profiles, and segment and target users based on their responses. While CX may not be their primary use case, 81% of client references rate CX as very or critically important, and 56% are using the CX personalization capabilities offered by their personalization engine vendor. That said, while most vendors have expanded their vision and technical capabilities to personalize CX, they still have room to improve the effectiveness of their execution. On average, reference clients rate vendors 4.3 out of 5 in this area, compared to average ratings above 4.5 for digital commerce and marketing personalization.
Personalization Engine Market Overview – Personalization Providers Prioritize Client Success
While most client references (approximately 90%) report overall satisfaction with personalization engine vendor integration/deployment and service/support, clients also expressed numerous usability gaps. Most personalization engine vendors in this Magic Quadrant have added a customer success capability, including access to a customer success manager or team and some level of on-boarding services. Still, the level of client support available varies across vendors. Some personalization engine vendors offer more consultative “getting started” support around initial use case design immediately post-purchase, while some vendors take a managed services approach throughout the course of a customer relationship. Other ways vendors support customer success include monthly reports focused on ROI, incremental revenue and peer benchmarks. They build customer success teams capable of supporting both strategy articulation and technical execution.
Personalization Engine Market Overview – Personalization Engines Enable Distinct Marketing Roles and Requirements
Personalization providers are balancing the advancement of their overall personalization capabilities with the need to empower less technical marketers. As a result, some vendors are creating segmented user interfaces designed to support both nontechnical and technical client stakeholders’ needs. Other personalization engine vendors are moving in a similar direction, evolving their products to enable key tasks for different types of end users. For example, personalization engine providers referenced supporting greater autonomy for nontechnical team members by adding functionalities such as step-by-step campaign creation, pre-populated testing templates and simple content editing/preview tools. Vendors are also building more sophisticated functionality for the developer audience, such as allowing developers to embed personalization functionality as they build new apps or websites, or allowing them to modify templates within the code.
Personalization Engine Market Overview – Personalization Engines Continue to Overlap with CDPs
Most personalization engine providers evaluated in this research claim to have a CDP to help provide reliable data ingestion and management — but few sell this capability separately. The benefits of a CDP-supported personalization engine include accelerated cross-channel, cross-solution and cross-source data integration. Plus, a CDP helps make some built-in personalization engine machine learning possible through standardized data feeds. However, this creates a MarTech dilemma for marketers as they build out their multiyear technology roadmaps. Marketers who already have a solution for integrating and managing customer data — whether homegrown or managed via a CDP or multichannel marketing hub — should consider whether adding a personalization engine will require them to sunset the existing technology. Marketers planning to select a personalization engine first and building their data and analytics infrastructure out from there will likely have an easier first move. However, they should carefully consider setting expectations with peers in IT around what customer data the personalization engine will require and manage, and how that will impact and integrate with other enterprise systems of record.
Personalization Engine Market Overview – Data Inputs Expand to Better Support Personalization
Personalization engine vendors are diversifying their data ingestion abilities. Specifics vary by vendor but generally fall into a handful of categories:
- Customer context (location, time of day, local weather)
- Brand context (inventory level, presence of local store, current store traffic)
- Predicted preferences (engagement by channel, effects of incentives, customer lifetime value)
- More detailed interaction data (dwell time, hover-over behavior).
Three key needs drive this advancement:
- The first is marketing’s need to develop capabilities that don’t require personally identifiable information to comply with existing and upcoming privacy regulations such as GDPR and the California Consumer Privacy Act (CCPA).
- Second, broader input data has the potential to improve machine learning core to many of these platforms.
- Third, marketing teams must better manage “cold start” problems — such as new individual, new item and new product category — that are common to some use cases (such as fast fashion, online auction houses and companies focused on new customer acquisition).
To take full advantage of the growing breadth of data availability — for targeting, triggering, segmenting and predicting — marketers will need to stay keen to how new data sources are being utilized to improve personalization.