Magic Quadrant for Digital Marketing Analytics

Published: 04 October 2017 ID: G00318002



Marketing leaders are asking their analytics teams to provide better insights into customers, prospects and journeys, and a more accurate assessment of the impact of marketing tactics. Use this research to find a digital marketing analytics tool to support your needs.

Market Definition/Description

This document was revised on 18 October 2017. The document you are viewing is the corrected version. For more information, see the Corrections page on

Target Audience

This Magic Quadrant is intended for chief marketing officers (CMOs), marketing analytics and data science practitioners, and other digital marketing leaders involved in the selection of systems to support marketing analytics requirements.


Gartner defines "digital marketing analytics" platforms as follows:

Digital marketing analytics platforms are specialized analytic applications used to understand and improve digital channel user experience, and prospect and customer acquisition and behavior, and to optimize marketing campaigns, with an emphasis on digital channels and techniques. They are stand-alone, end-to-end platforms, performing functions from data collection through analysis and visualization. They have demonstrated relevance to marketing through their ability to collect and ingest data from common marketing sources, and provide tools for standard marketing analytics use cases, and have significant adoption by marketing practitioners.

As Gartner defines it, digital marketing analytics encompasses five key areas. They represent the crucial ways analytics practitioners and the software tools they use are required to support marketing and advertising efforts to deliver more effective, timely and personalized multichannel experiences:

  • Customer and journey insights: Marketing analytics technologies continue to improve their ability to process person-level information at scale. At the same time, marketers are investing in building out multichannel identities for prospects and customers. The combination of identity and more detailed analysis gets marketers closer to their long-term goal of understanding customer journeys over time. Analytics tools must convincingly support this rising requirement.

  • Data integration: Marketers must be able to analyze data that sits on different systems and in different locations, in a wide range of forms and formats, including structured and unstructured data. In addition to the ability to collect data from channels such as websites and mobile apps, and to connect to outside sources via APIs and other methods, marketers need ways to manage and filter data and metadata.

  • Exploration: Marketing analytics is the discipline of finding useful patterns in data that can be used to improve multichannel marketing and customer experience. As such, analytics platforms increasingly must do more than report results — they must provide ways for users to explore data with visualizations, data mining and other tools, and to manipulate it to deliver better insight.

  • Advanced models: Moving beyond data exploration, marketing analysts rely on more advanced techniques to uncover trends and insights and improve the impact of marketing tactics. In particular, predictive analytics, machine learning and emerging artificial intelligence (AI) methods are providing new ways to understand the needs and motivations of customers and prospects, improve personalization and offers, and ultimately upgrade customer experience and lifetime value.

  • Execution system support: Marketing analytics activities are expected to lead to results, and tools must easily support common execution channels. Therefore, providers in this space must be easily connected to mainstay marketing systems such as multichannel campaign management, email, web content management, social marketing, customer experience and loyalty.

Magic Quadrant

Figure 1. Magic Quadrant for Digital Marketing Analytics
Research image courtesy of Gartner, Inc.

Source: Gartner (October 2017)

Vendor Strengths and Cautions


Adobe is a Leader in this Magic Quadrant. Its Adobe Analytics product is a multichannel analytics and segmentation solution that grew out of its 2009 acquisition of Omniture. It is positioned as the real-time intelligence engine at the core of the Adobe Experience Cloud. Most recently, Adobe has focused on features to enable and guide analysis of audiences and journeys. Leveraging machine learning, Virtual Analyst tailors alerts to users, and Segment IQ identifies similarities and differences among audiences. Propensity scoring, fallout analysis and Activity Map help the marketer better understand the customer journey and apply lessons to campaign design and targeting. Supporting enterprise marketers, from analysts to multichannel leaders and media planners, Adobe continues to more deeply integrate its Cloud offering by unifying all audience data (first and third party) across its core platform. Adobe's support for enterprise marketing analytics is shown by its focus on multichannel customer-centric, rather than channel-centric, analytics, particularly in customer journey mapping, and audience discovery and management.

  • Market understanding: Adobe's ongoing product development is informed by an understanding of the goals and needs of both technical marketing analysts and data-minded marketers. Client references tell us they chose and would continue to recommend Adobe for its vision and roadmap.

  • High performance: Multiple client references cite the tool's "powerful" features, reliability and flexibility. These customers see Adobe as the luxury performance engine of the analytics market.

  • Strong legacy: Adobe has a large — and largely satisfied — customer base and a stronghold in the enterprise analytics market. Adobe's awareness and consideration make it a contender in nearly every enterprise digital marketing analytics vendor evaluation.

  • Expertise required: Despite Adobe's ambitions to enhance AI-driven guidance, to get the most value out this powerful, high-cost solution, analyst expertise is recommended. In addition, frequent changes to naming and product bundling complicate the buying and upgrading processes.

  • Cross-cloud integration: Despite Adobe's strong integration across some products — for example, Analytics Cloud and Adobe Target — customer references note struggles in making analytics data available and maximizing its use across the rest of the Experience Cloud.

  • Available resources: Reference customers note that finding skilled practitioners to supplement in-house teams is challenging, and that Adobe's own support can be uneven.


AgilOne is a Niche vendor in this Magic Quadrant. AgilOne's mission is to help marketers make the most of first-party data through "actionable analytics." Positioning analytics as the fuel for its customer marketing engine, AgilOne emphasizes its ability to unify customer data across a variety of key marketing channels under a single ID. This ID becomes the foundation for highly targeted marketing campaigns powered by out-of-the-box models, including a number of predictive and propensity models. AgilOne replatformed last year for the release of v.6, emphasizing configurability and scalability. V6 also includes UI improvements for more intuitive reporting and dashboards. At the same time, it has repositioned its offering as a customer data platform (CDP). Supporting commerce and retail marketers, AgilOne's solution is tailored to enterprise brands seeking to use multichannel customer data to improve analytical decision making and power personalized marketing campaigns. AgilOne shines in use cases that leverage first-party customer data such as customer acquisition, loyalty and retention initiatives.

  • Customer data handling: The platform supports effective customer data management through intuitive and disciplined data integration, deduping, normalization and hygiene processes.

  • Predictive models: Transactional data from web and mobile, in-store behavior, and e-commerce transactions are linked to a master customer ID, which can then be augmented with third-party data to inform predictive models.

  • Segmentation: AgilOne comes with more than 300 prebuilt segmentation models, mostly tailored to retail use cases (e.g., recent buyers, lapsed customers). Users can also define custom and discovered segments around behaviors, and brand and product preferences.

  • Limited power user support: AgilOne is not an analyst workbench and is designed for the data-driven business user. Data export is available for those wanting to build custom models in their preferred environment.

  • Strategic shift: AgilOne's most recent positioning signals a shift toward the emerging CDP category. This prioritizes data ingestion and user-level data management but raises questions about the company's commitment to adding more advanced features for data exploration, visualization and discovery.

  • Implementation and deployment: AgilOne aims for rapid time to value, but client references report that reality may not match expectations.

AT Internet

AT Internet is a Niche vendor in this Magic Quadrant. A new entrant in this research, AT Internet has been in business for over 20 years. The company organizes its product offering around Analytics Suite, a set of tools and extensions for digital analytics (formerly web analytics). Features added over the last year include a browser extension for in-page analytics, a dashboard template library and Data Flow, which extracts large data volumes and feeds them to third-party platforms. While the company's offering is used by a minority of marketers in the United States, AT Internet's Analytics Suite has wide adoption in Europe. It complies with European data security, privacy and confidentiality requirements, with certifications from six countries: Sweden, Germany, the U.K., France, Spain and Greece. The Analytics Suite collects data through its SmartTag module. Marketers can create dashboards, perform ad hoc reporting and data mining, and access standard reporting within the platform. In addition, AT Internet offers both mobile app and mobile web reporting. Europe's comprehensive General Data Protection Regulation (GDPR) gives marketers cause to re-examine how their marketing analytics platforms comply with new rules and regulations. As a result, AT Internet stands to gain careful consideration.

  • EU privacy compliance: Marketers looking for an analytics product that complies with GDPR will find AT Internet's Analytics Suite conforms to those standards.

  • Customer-centric: Client references were highly satisfied with AT Internet's customer service, including its ability to use feedback from clients to enhance its product.

  • Dashboards and visualization: Recognizing the growing imperative for analytics to be a component of roles throughout the marketing organization, AT Internet has invested in building out its dashboard template library.

  • Advanced feature gaps: The Analytics Suite does not provide some methods that are commonly requested by marketers, including attribution modeling, anomaly detection and predictive analytics.

  • Limited customer analytics: Marketers seeking deep support for customer analytics, including customer journey analytics, would be better served by other platforms.

  • Global support constraints: Because of its low adoption rate outside of Europe, client references with needs in other regions reported challenges in sourcing talent and service providers who could use the software effectively.


ClickFox enters the Magic Quadrant this year as a Visionary. ClickFox Journey Science Suite is an enterprise analytics solution that serves large global enterprises that need deeper insight into customer journeys across their own channels. Launched in 2004, the suite offers entity-level data integration, cleansing and management to analyze a sequence of events over time. Based on a customer data object, a session object and journey description language, ClickFox provides the ability to define and explore journeys. Its newly released Fox engine supports dynamic traffic, event and attribution filtering; detailed path analysis; automated discovery and handling of similar or correlated events; and the ability to distribute journeys to operational systems via APIs. Visualizations include dynamic journey maps and are available through the Journey Watch module, powered by a partnership with ZoomData. Primary users are analytics teams at large enterprises (over $2 billion revenue). The company is launching a series of stand-alone apps for more rapid, lower-cost implementation, including Journey Trace. The app breaks journeys into multistep paths and identifies which steps are the most significant predictors of an outcome, such as a sale, and which steps impede that outcome. ClickFox's roadmap includes more support for scenario planning and automated optimization recommendations.

  • Innovative approach: ClickFox applies an approach to pattern detection, building its product around the assumption that journeys have a plot with a goal, such as a purchase. Years of statistical and technical work make it a leader in the field of "journey science."

  • Customer service: Reference customers praise the company's support teams and the "positive attitude" of its Midwest-U.S.-based staff.

  • Go-to-market alliances: ClickFox compensates for its limited sales and marketing capability by leaning on blue-chip partners such as McKinsey (also an investor), Capgemini, PwC and Cloudera.

  • Limited scope: While recognizing the strength of its core journey analytics features, reference customers did not employ ClickFox for predictive analytics, recommendations, A/B testing or AI use cases.

  • User interface: Reference clients noted that the product's user interface could use a refresh and greater responsiveness; however, they had not upgraded to the newer Fox release, which improves both.

  • Premium pricing: As a product for large enterprises supporting massive scale (one customer uses a 500 TB Hadoop cluster), ClickFox contracts typically range in the mid-to-high six figures, although its new stand-alone apps offer limited features at a much lower price.


FICO enters as a Niche player in this Magic Quadrant. The company's Marketing Solutions Suite (MSS) is an extension of its Decision Management Suite, which was released within the last year. The product is best-suited to marketers seeking an analytics platform that has multiple opportunities for add-on, in-depth specializations to solve advanced marketing analytics needs. This includes FICO Text Analytics, which automates insight discovery using text data mining, statistical analysis, sentiment analysis and machine-learning techniques. The Decision Management Suite also includes a new toolset, the FICO Analytics Workbench, which unites recent acquisitions (InfoCentricity and Karmasphere) with its legacy analytics tools. In addition, FICO offers full-service support for implementation and analysis. The Marketing Solutions Suite caters to marketers seeking advanced analytics and data integration capabilities. Sophisticated marketers looking to perform advanced projects — particularly those whose organizations have invested in FICO's other products — could be well-served by this tool.

  • Product integrations: In addition to integration among products, FICO offers technology for big data collection including batch data integration, an API framework for data services operations and support for real-time, streaming data collection.

  • Advanced modeling: Marketers looking to perform advanced analysis, such as propensity modeling, machine learning and applying neural networks, will find this tool useful.

  • Support resources: Client references gave high marks to the quality and availability of FICO's customer support.

  • Complex bundles: FICO's Marketing Solutions Suite can be bundled with a number of additional FICO products and managed service options. It had higher reported costs than most vendors assessed for this research, based on user surveys.

  • Advanced offering: The platform is best-suited to marketing organizations equipped to make use of the product's extensive features. Not all teams may have the resources to realize the promised value of the product.

  • Ambitious roadmap: Client references applauded FICO's product roadmap, while noting that many announced capabilities (such as multitouch attribution and audience data exchanges) have yet to be fielded.


Google declined to participate in the research process for this Magic Quadrant and identified no reference customers. Gartner's analysis is based on other credible sources, including previous vendor briefings, customer inquiries and publicly available information.

Google is a Leader in this Magic Quadrant. The most widely used marketing analytics tool in the market, Google Analytics 360 is positioned within a broad suite of offerings under the 360 umbrella. The Google Analytics 360 Suite includes tools for web and mobile analytics (Analytics 360); site testing and personalization (Optimize 360); tag management (Tag Manager 360); and multitouch attribution (Attribution 360). Google also offers a flexible dashboard builder (Data Studio) and a survey instrument for brand marketers (Surveys 360). (Only Google Analytics 360 was considered in this evaluation.) Google continues to provide its suite in both free and paid tiers. The latter is aimed at enterprise users, supports higher data volumes and dimensions, and offers additional capabilities and services. Analytics Intelligence is a recently released set of features in Google Analytics 360 that use machine learning to make data exploration faster by letting marketers perform natural-language queries based on Google's foundational research. Using these features, marketers can perform natural-language queries based on Google's foundational research and receive automated data insights based on patterns and anomalies. These capabilities underline Google's strategic focus on greater machine-aided decision support for users throughout the organization.

  • Usability: Google's emphasis on usability and modular self-learning, as well as its large and engaged global user network, make its products easier to adopt and learn than most competitors' products. Likewise, its focus on shopping behavior and outcomes allows it to offer more action-oriented insights.

  • Product integration: By developing or significantly adapting its products in-house, Google is able to provide seamless integrations across its cloud offerings. In addition to marketing analytics tools, these include BigQuery for big data storage and processing, machine learning and deep learning libraries, and the DoubleClick suite of media tools.

  • Identity and benchmarks: Google's wealth of data about individual behaviors across devices and channels gives it a formidable edge to support people-based analytics and measurement. Its Analytics 360 platform provides anonymized benchmarks from its user base.

  • Customer-level analytics: Google's corporate policy to support consumer privacy by disallowing personal data in its analytics cloud may be commendable, but it forces marketers seeking a CRM-oriented approach to analytics to add additional steps to their data pipeline.

  • Developing advanced analytics: The company continues to add advanced features, particularly those useful to commerce marketers such as Smart Lists (a form of predictive model). However, its marketing analytics tools do not yet provide advanced features such as contribution analysis and predictive lifetime value.

  • Media-centric focus: As Google continues to build out data collection, integration and other connections across its media and analytics products, Google Analytics 360 is more relevant to users with significant Google media spend.


IBM is a Challenger in this evaluation. IBM primarily supports marketing analytics with its Customer Experience Analytics suite, which incorporates its web and mobile analytics products with Tealeaf session replay (which it calls behavior analytics) and customer journey analytics. IBM Customer Experience Analytics provides tools to help analysts gain a better understanding of customers' and prospects' paths through marketing channels such as their website, mobile apps, email, storefronts and call centers. IBM's strategy is to give marketers the ability to visualize and explore data at aggregate levels through web and mobile analytics, more closely via path and journey tracking, and finally at the individual session level, as needed, to locate points of struggle or identify ways to improve experience. The company provides additional advanced capabilities via its separate IBM Predictive Customer Intelligence suite, which incorporates IBM's SPSS statistical modeling software. Targeted at analysts across the enterprise, this product allows them to perform custom work including segmentation, next-best-offer recommendations, value-based segmentation and unstructured data exploration. IBM's commitment to cognitive computing, personified by its Watson brand, should increasingly benefit marketers in the future by providing automated pattern detection and other AI-powered features.

  • Experience analytics: IBM's analytics portfolio is broader than most competitors' portfolios, encompassing platforms from web and mobile analytics to user-level path analysis. Its Tealeaf product lets users scrutinize individual sessions to find points of struggle and is a differentiator.

  • Universal Behavior Exchange (UBX): UBX provides a large number of prebuilt connectors that enable easier data exchange among marketing and media partners, as well as across IBM's own product suite.

  • Product interface and usability: IBM received higher marks this year from reference customers for its user experience and design, the outcome of a significant multiyear revamp.

  • Portfolio integration: Positioning its Customer Experience Analytics as a single offering, IBM still has work to do to deliver a fully integrated suite to most customers. While IBM has case studies of customers using the entire portfolio, references reported using only part of it.

  • Digital analytics: IBM's web and mobile analytics product, IBM Digital Analytics, has not kept pace with the most recent releases from the market leaders. Reference customers mention a desire for an upgrade in its features.

  • Pricing and contract negotiations: While comfortable with the company's strategic support, some reference customers continue to give IBM lower marks for its pricing and contract processes, which can favor scaled services deals over one-off software subscriptions.


Optimove enters this Magic Quadrant as a Niche player. Its Relationship Marketing Hub includes predictive modeling, analytics and a campaign management tool. It supports marketing analytics by providing a single-sign-on tool that builds advanced customer models using a Markov decision process. The framework identifies audiences by their propensity to buy, convert or engage with particular marketing elements, such as emails. Optimove suits the needs of marketers seeking to identify opportunities to improve personalization, such as website or mobile app offers, or customer experience more generally, including recommending content for higher engagement. Beyond that, the company offers real-time user tracking and targeting through Optimove Realtime, and opportunities for third-party integrations based on individual requirements. Over the last year, the company has made a number of improvements to its analytics product that add more artificial intelligence and machine learning, including Optibot, which assists marketers in optimizing their activities by automatically recommending specific actions they can take to improve response. The platform's roadmap continues to build on its predictive modeling and automation capabilities, including building out customer journey views and better ROI projections. Optimove's product caters to marketers who focus on customer analytics, particularly those with a greater focus on performance marketing and revenue optimization.

  • Intuitive UI: Customers report that they find Optimove's interface to be intuitive and relatively easy to use while still providing sophisticated features.

  • Puts AI into action: Optibot, the platform's implementation of AI, helps marketers optimize campaign activities via automated recommendations. As such, Optimove anticipates a move in the market to machine-driven decision support.

  • Speedy time to value: Client references cited customer service and onboarding support as strengths, and clients noted quick initial implementation times as well as swift responses to their needs.

  • Category recognition: A newcomer to the space, Optimove has relatively low awareness among marketing analysts. Its profile should rise, as the company more than doubled its sales force in 2017.

  • User-driven visualization: The platform doesn't yet have capabilities for users to create custom dashboards. Client references also cited this as an opportunity for improvement.

  • Limited anomaly detection and alerting: Optimove alerts users when it detects anomalies, but its capabilities for users to define specific anomaly definitions or to automate detection are less flexible than the market leaders' capabilities.

Pitney Bowes

Pitney Bowes declined to participate in the research process for this Magic Quadrant and identified no reference customers. Gartner's analysis is based on other credible sources, including previous vendor briefings, customer inquiries and publicly available information.

Pitney Bowes is a Niche player in this Magic Quadrant. Known for its mapping, address validation and other data products, Pitney Bowes offers a number of tools that are used for marketing analytics: Spectrum Technology Platform, Portrait Suite and EngageOne Suite. The company's Spectrum Technology Platform provides enterprise data mining and data discovery capabilities aimed at analytics professionals across the enterprise. The platform includes Pitney Bowes' uplift modeling tool — Spectrum Miner (formerly Portrait Miner). It provides a way for marketers to predict the performance of marketing messages based on historical data and to use those predictions to optimize campaigns in email and other channels. Pitney Bowes' Spectrum Miner supports other common advanced analytical methods, including rule-based and automated segmentation, time series analysis, regression, decision trees, and predictive and propensity models. Pitney Bowes' marketing analytics capabilities are primarily used on-premises by skilled analysts, although a hosted (cloud) version of some of its products is available.

  • Data pipeline and workflow: The Spectrum Technology Platform is a developing set of tools that support the data science workbench, including data quality, governance, integration, workflow and enrichment, and an API. Its data science pipeline support exceeds that of most vendors in this evaluation.

  • Advanced modeling: Marketers can use Spectrum Miner for a number of advanced tasks. Reference customers have historically given it high marks for segmentation and visual exploration.

  • Uplift prediction: Incorporated into a disciplined campaign management process, uplift modeling can provide a powerful way to predict and optimize offers and messages to known customers.

  • Lack of new news: While continuing to support its existing products, Pitney Bowes did not announce any major marketing analytics product developments this year.

  • Limited industry breadth: Although conceptually industry-agnostic, the product is primarily used by analysts in telecom and insurance.

  • Unclear product vision: While strong in parts, as noted in previous Magic Quadrant evaluations, Pitney Bowes has yet to develop a strong case for wider adoption by marketing analysts in the face of growing competition in the enterprise analytics market.


SAP enters as a Challenger in this Magic Quadrant, graduating from being a Vendor to Watch in previous years. Building on the foundation of Hybris, the e-commerce platform it acquired three years ago, SAP has been developing and rolling out the components of its SAP Hybris Marketing Cloud in quarterly installments. In 2016, SAP embedded its SAP Analytics Cloud within the SAP Hybris Marketing Cloud. In early 2017, SAP continued to build out its marketing analytics offering by acquiring Abakus, a provider of multitouch attribution software that employed an innovative approach based on game theory. Underpinning the SAP Hybris Marketing Cloud is the SAP Hybris Profile, a real-time customer profile that can be updated with data from tags and connected systems. Related product modules include SAP Brand Impact (to measure ads and sponsorships) and SAP Customer Retention (customer journey overview). SAP is most relevant to marketers in large enterprises who have invested in SAP's products and have internal teams to support SAP Hybris deployment (although the company offers professional services). Strong growth of the SAP Hybris Marketing Cloud reflects SAP's commitment to providing additional products aimed at marketing analysts serving the needs of the enterprise CMO. The company continues to aggressively build (and add to) its marketing portfolio, presenting new competitive pressure on the Leaders.

  • Marketer-centric analysis: The SAP Hybris Marketing Cloud's interface is designed to be intuitive and can be grasped without pain by analysts with diverse backgrounds.

  • Global footprint: SAP's footprint is well-suited to marketers in large enterprises, who can take advantage of the company's ability to implement the product in multiple regions.

  • Strong segmentation capabilities: Client references were pleased with SAP's rule-based segmentation support and the flexibility of its segmentation tools.

  • Unproven attribution integration: SAP has yet to fully assimilate Abakus into the SAP Hybris Marketing Cloud. An announced fourth quarter 2017 integration and subsequent benefits of the acquisition have yet to be realized.

  • Self-service support: While client references were satisfied with their account management, they identified a desire for more opportunities to self-serve within the platform.

  • Complex data handling: Data preparation is complex, particularly for cross-functional global marketing teams. References reported needing to engage IT support for implementation, in many cases, which hampers operational efficiencies.


SAS is a Leader in this Magic Quadrant. Leaning on its pedigree in enterprise analytics, SAS has created a suite of complementary offerings tailored to meet most of the data-driven analyst's end-to-end requirements. SAS introduced Customer Intelligence 360 in 2016 as its digital marketing hub. It has been aggressively bundling analytics features over the past 12 months, including customer journey and attribution, a product recommender, multiarm bandits, and experimental design techniques that leverage factorial reduction. While these features cater to the technical analyst, SAS hasn't forgotten the marketer, who is the target for its new "guided analytics" features. With the addition of SAS Viya, SAS has modernized the SAS platform to ease cloud deployment across the analytics life cycle, support data integration and deliver real-time analytics. Noted for its robust feature set, and an attentive product and support team that closely partners with clients, SAS is best-suited to the enterprise marketing analytics team with advanced use cases and a requirement for flexibility and customizability.

  • Client focus: Customer references praised SAS' approach to client success through collaborative partnerships, training, best practices and a roadmap that shows it listens to and prioritizes end-user needs.

  • Comprehensive features: SAS' portfolio supports most marketing analytics requirements. It has strong capabilities in data management, modeling and statistics; web and mobile analytics; user-level customer and journey analytics; and features such as anomaly detection and text analytics.

  • Practical AI: Self-learning decision management solutions, neural networks and cognitive techniques for image and video classification are (or soon will be) embedded throughout the platform.

  • Expertise recommended: Although SAS offers access to analytics for the analytical marketer, as well as the marketing analyst, organizations without an advanced team will likely find a steep learning curve.

  • Complex product offering: Client references express frustration with the lack of cohesion across the SAS product portfolio. Issues include confusion about product bundles, inconsistent customer support, uneven product user experience and complex deployments. These factors hurt SAS' vision score relative to the other Leaders.

  • Integrations still under construction: Though SAS has added APIs in the last year, client references still flag integration — with external systems and, particularly, with other SAS product lines — as a pain point.


Thunderhead enters the Magic Quadrant this year as a Visionary. Formerly a Vendor to Watch, Thunderhead's One Engagement Hub is focused on the growing customer journey analytics (CJA) and orchestration market, encompassing both digital and offline channels such as call centers. Thunderhead One Engagement Hub collects data from the marketer's own sources such as websites and mobile apps using page tags and software development kits (SDKs). It also ingests data via uploads, APIs and prebuilt connections with common CRM systems such as Salesforce, Microsoft Dynamics and SugarCRM. One Engagement Hub creates taxonomies for customer attributes and interaction events, and provides analytics at the customer profile, journey and conversation level (i.e., estimating sentiment). Mapping customers and visitors to events, One Engagement Hub lets marketers segment and analyze journeys in detail via intuitive, interactive visualizations. Its algorithms include moment-of-truth analysis to identify key events associated with a particular outcome, like a sale; root cause analysis to determine likely starting points for paths of interest, such as those ending in an abandoned cart; and most-common-path analyses for pattern detection. It provides a query language and integrations with products such as Microsoft Power BI to enable additional data exploration.

  • Journey analytics: One Engagement Hub provides a flexible cross-channel view of customer journeys that is particularly adapted to the needs of marketing analytics teams at midsize and larger enterprises in industries with highly engaged customers, such as retail, media and sports.

  • Implementation experience: Reference clients praised the Thunderhead implementation and onboarding process, which allowed for faster-than-average deployments of under three months, on average.

  • Journey visualizations: A marketer-focused tool, One Engagement Hub has a well-designed and intuitive interface, with rapid processing and refresh provided by a DataStax Enterprise Apache Cassandra cloud infrastructure.

  • Work in progress: Thunderhead One Engagement Hub has a relatively short track record, leading some reference clients to withhold judgment on its long-term potential.

  • Internal resource requirements: A number of reference clients mentioned that One Engagement Hub is a relatively complex strategic tool requiring dedicated internal support to realize its potential.

  • Early-stage third-party support. Thunderhead has recently established a portfolio of platform, system integrator and agency partners. However, reference customers relied on Thunderhead's internal Client Success team and reported limited access to quality third-party service providers. Presumably, more time and experience on Thunderhead's part will alleviate this challenge.

Vendors Added and Dropped

We review and adjust our inclusion criteria for Magic Quadrants as markets change. As a result of these adjustments, the mix of vendors in any Magic Quadrant may change over time. A vendor's appearance in a Magic Quadrant one year and not the next does not necessarily indicate that we have changed our opinion of that vendor. It may be a reflection of a change in the market and, therefore, changed evaluation criteria, or of a change of focus by that vendor.


  • AT Internet

  • ClickFox

  • FICO

  • Optimove

  • SAP

  • Thunderhead


Due to revised criteria that excluded vendors that focused on advertising and media analytics, four participants in previous years were dropped from this Magic Quadrant. Their removal is not a reflection on the quality of their products and services and does not signal a change in Gartner's opinion of these companies. Rather, it simply reflects the revised inclusion criteria for this year's Magic Quadrant.

  • AOL

  • Neustar

  • OptiMine Software

  • Visual IQ

One additional vendor was dropped this year due to an ownership change:

  • Webtrends announced in March 2017 that Oracle would acquire its Infinity platform, which it intended to incorporate into its Oracle Marketing Cloud.

Vendors to Watch

Several vendors demonstrated many of the qualities we associate with digital marketing analytics, but they didn't meet all of the inclusion criteria we established (see the Inclusion and Exclusion Criteria section, below). However, given the volatility of the marketplace, they are worth watching as the market evolves.

Eulerian Technologies

Eulerian Technologies is a web analytics and data management platform focused on performance marketers with significant advertising requirements. Built on its own stack and data collection methods, Eulerian provides a number of analytical tools useful for measurement and optimization, including rule-based and multitouch attribution reports; marketing frequency reports, which quantify the number of touchpoints prior to a sale; and path analyses, which identity common paths to purchase. Designed also to work alongside first-party data systems, Eulerian can ingest data from CRM and tag management systems, as well as other marketing and ad tech platforms, via APIs. It provides prebuilt and customizable dashboards, which offer some drill-down capabilities, as well as connectors to BI tools such as Tableau. While limited in its advanced analytics and providing no onboard predictive analytics, Eulerian has built a data management, segmentation and rules execution engine designed to support high-volume campaigns, particularly for e-commerce marketers.


Facebook quietly released an SDK and app analytics solution targeted to developers in 2015, and it has since expanded and extended the offering to support more channels, including web, Messenger bots, Facebook pages and offline. Underpinned by Facebook's 2 billion active user base, the offering promises people-based measurement with an emphasis on helping the marketer understand the customer journey across multiple digital touchpoints, including those outside Facebook. Targeted to product managers, marketers, analysts and developers in mid-to-large B2C organizations, Facebook Analytics is currently free and is separate from Facebook advertising analytics and Facebook Page Insights.


Splunk sees itself as a complement to marketing analytics tools by supporting three areas: digital marketing, product analytics and customer experience analytics. It is particularly useful for business users and advanced analysts who wish to perform custom analysis on marketing information in real time. For example, Splunk provides tools to analyze data sources like clickstream and mobile data, along with application and network performance data. Connectivity with relational sources enables enrichment of information across various channels such as clickstream, call center and mobile, making it easier to merge information from disparate sources. It connects to big data stores and unstructured data, and supports flexible ad hoc searches, native visualizations and dashboards. Splunk delivers analytics metrics in real time on product adoption, feature use, customer behavior, purchases and churn.


Founded by a former math researcher, based in France and launched in 2013, Tinyclues offers an advanced analytics platform for marketers who want to get more mileage out of their customer data. It applies proprietary artificial intelligence models to predict which customers are likely to buy particular products. For example, a large retailer could unleash Tinyclues on its e-commerce, loyalty and CRM data files, as well as product details, and it can identify likely future buyers of a certain product or product category. Because it predicts sales, rather than proxies like clicks or engagement, the platform works even for hard-headed performance marketers. It is particularly useful for finding buyers for strategic categories (e.g., high-margin products), reactivating lapsed customers and predicting buyers for new products. It does the latter by identifying relevant product features and finding buyers for similar products in the past, as well as other customers whose purchase patterns are correlated with these buyers. The company's roadmap includes expanding AI-based campaign planning and providing campaign auto-optimization options.

Inclusion and Exclusion Criteria

Providers needed to meet the following criteria to be included in this Magic Quadrant:

Revenue and New Customers

Providers in this evaluation must have had global revenue of at least $5 million in 2016, with at least 20% of installed base being users with at least two years' tenure. In addition, providers must have acquired at least five significant new customers of their analytics product(s) in 2016. Competition among providers in this segment is high, and the ability to acquire new customers and to grow is a signal of strength.

Marketing Focus

Research for this Magic Quadrant is conducted by analysts within the Gartner for Marketing Leaders group, which provides research and advisory service specific to marketers. The intended audience for this research includes marketing leaders, marketing analysts and technologists within the marketing function. Therefore, we require demonstrated, significant adoption by marketing practitioners.

Multichannel Marketing Support

Our audience includes both business-to-business and business-to-consumer users, so providers should have relevance to, and adoption by, both types. Our audience is also responsible for multichannel marketing analytics, so providers that focus on a single channel, such as mobile analytics or social analytics, are excluded. Finally, the market for multitouch attribution and marketing mix modeling tools — in other words, paid media analytics and measurement — is distinct enough in its inputs, analytical methods, outputs and user base to be treated as a separate market; for this reason, this year we are excluding providers who focus on multitouch attribution and marketing mix modeling.

Software as a Service

The product must be available as a SaaS deployment, although on-premises versions may also be supported (optional). Professional services may be available (optional), but more than 65% of marketing-analytics-related revenue should derive from software products.

Basic Capabilities

Providers in this evaluation should support application capabilities in all of the following domains:

  • Extensibility — supports analytics across multiple platforms, including web and mobile, multiple channels, and multiple data formats. For example, providers that support only social, mobile app, advertising, text or image analytics are specifically excluded.

  • Data collection and onboarding — supports the marketer's collection of data for analysis from channels such as websites, email systems and mobile apps, through tags, SDKs, APIs, feeds or other methods.

  • Data access — enables users to access other common marketing data sources through custom integrations and/or published APIs.

  • Visualization and exploration — provides prebuilt and customizable dashboards and data visualization tools for marketers, as well as the ability to perform ad hoc data exploration.

  • Filtering and manipulation allows analysts to prepare data such that it becomes available for subsequent analysis, including the ability to filter, apply custom mappings and preprocessing rules, and define metadata.

  • Batch and real-time processing — supports both scheduled and ad hoc batch data processing, and makes data available for analysis at or near the time of collection.

  • Management and deployment facilitates data, metadata and model management, integration and deployment to other applications; includes defining user roles and permissions for workflow.

  • Usability and interface has intuitive user experience design, including integration of different platform components into a coherent user interface, ideally with single sign-on (SSO).

Advanced Analytic Capabilities

In addition to the above basic capabilities, providers in this evaluation should have some level of native support for at least three to four of the following advanced analytic capabilities:

  • Segmentation — including rule-based segmentation and automated segment discovery (clustering).

  • Recommendations — delivering predictive recommendations for products and content, using methods such as collaborative and content filtering and matrix factorization.

  • Propensity models — using machine-learning techniques to develop propensity models useful to marketers, including propensity to buy (e.g., items, brands) and propensity to engage (e.g., content).

  • Predictive valuation — providing analytical methods to score potential customer value based on behavioral and other inputs; includes customer lifetime value (CLTV) predictions.

  • Anomaly detection — using automated statistical methods to identify and analyze the components of events or metrics that are outside an expected range of values.

  • Contribution analysis — identifying the attributes and data elements that are most likely to contribute to an observed anomaly or other event for the purpose of understanding key drivers.

  • Text analytics — applying analytical methods to unstructured text to gain insights and perform classification, including sentiment analysis.

  • Version optimization — using advanced methods, such as multivariate testing, multiarmed bandits and evolutionary algorithms, to perform rapid testing and optimization of campaign elements and versions.

  • Journey analytics — understanding how individuals and segments interact across channels over time, using approaches such as pathing analysis, game theory and Markov chains.

  • Artificial intelligence — applying AI methods, such as neural networks and deep learning, to improve targeting and customer experience on marketing channels and perform specialized tasks such as image analysis.

Evaluation Criteria

Ability to Execute

We elected to weight Ability to Execute toward existing product features and the reported experience of reference customers. As part of a rapidly changing category, digital marketing analytics providers will be rewarded for product and service excellence, more than the ability to sell and market that so often leads to success in more mature or commoditized markets.

Table 1.   Ability to Execute Evaluation Criteria

Evaluation Criteria


Product or Service


Overall Viability


Sales Execution/Pricing

Not rated

Market Responsiveness/Record


Marketing Execution

Not rated

Customer Experience




Source: Gartner (October 2017)

Completeness of Vision

We elected to weight Completeness of Vision toward the vendor's market understanding, particularly how such understanding is expressed in terms of product innovation and strategy. While factors such as business model and vertical go-to-market tactics are important, we believe category volatility, rapidly shifting user requirements and advances in underlying technologies mean that providers will survive and thrive based largely on the clarity of their vision and their ability to respond to market needs.

Table 2.   Completeness of Vision Evaluation Criteria

Evaluation Criteria


Market Understanding


Marketing Strategy


Sales Strategy


Offering (Product) Strategy


Business Model

Not rated

Vertical/Industry Strategy

Not rated



Geographic Strategy

Not rated

Source: Gartner (October 2017)

Quadrant Descriptions


Again this year, the Leaders quadrant consists of three providers: Adobe, Google and SAS. All are innovating in response to market demands and show an eagerness to serve their generally loyal customer base. They continue to support basic marketing analytics requirements while adding more and more advanced features. Emerging from web analytics, Adobe and Google provide a comprehensive suite of tools that apply their analytic capabilities to the operational needs of a wide range of marketers. For example, both integrate analytic features such as user segmentation with systems that personalize websites and mobile apps, and other systems that target advertising campaigns. Google's ability to provide free products, including its Data Studio visualization tool, as well as its impressive R&D lab, challenges Adobe — which does not sell media — to pursue larger enterprises with more comprehensive tools and service. SAS also targets the enterprise marketer with more complex analytics needs, and it offers a tool for almost all of them. In the future, new channels and formats may emerge that will challenge the Leaders' ability to keep up. Low-cost storage, up-skilled practitioners and open-source libraries — some provided by Google — challenge these providers to improve usability, integration and services. Likewise, more advanced capabilities embedded within operational systems, such as content management and multichannel campaign management platforms, maintain pressure on the Leaders to evolve.


The Challengers quadrant includes IBM and a new entrant, SAP. Both vendors are firmly rooted in the enterprise, serve large global markets with diverse requirements, and provide significant levels of services via both their own resources and a network of partners. As is common with longstanding enterprise software vendors, both entered the marketing analytics space largely through acquisition — IBM via its Coremetrics acquisition in 2010, and SAP via its 2013 acquisition of Hybris. Since then, both have worked to build out a set of analytics products to serve the needs to CMOs at larger companies, aligning other tools — such as IBM's SPSS statistics workbench — to form a suite. While supporting many basic and advanced requirements, the Challengers represent relatively small lines of business within very large global organizations. Competing for internal resources and R&D, they are at the mercy of shifting corporate priorities. On the other hand, being a part of a global giant can yield benefits in the form of foundational technologies, such as IBM's Watson and SAP's Hana big data infrastructure. The Challengers will have to increase commitment to marketers' needs, and evidence agile development and rapid updates to maintain their position or become Leaders.


Two new entrants make up the Visionaries group this year: ClickFox and Thunderhead. They both serve the developing category of customer journey analytics, which provides marketers and other customer-facing analysts with tools and processes to gain an understanding of customer and prospect behavior over time. They both focus on answering how (and why) customers and segments traverse first-party channels such as websites, mobile apps, email and call centers in pursuit of particular paths or goals. Initially applied primarily to customer service and experience design analytics — e.g., determining what people were doing (or trying to do) on a website before complaining to a rep — CJA is emerging as a powerful tool in the hands of marketing analysts. Marketers use it to develop multilayered views of different types of customers as they navigate complex cross-channel and cross-device relationships with a brand. While taking different strategic and feature-set approaches, outlined in their individual descriptions in this Magic Quadrant, both Visionaries are challenged to expand their product offering, and scale development and go-to-market teams, to continue to grow.

Niche Players

As usual, the Niche quadrant includes a diverse mix of vendors with different strengths and particular market focus. This year, the Niche quadrant includes returning vendors AgilOne and Pitney Bowes. It also includes new entrants AT Internet, FICO and Optimove. These players face the challenge of migrating their offerings and brand images toward a broader scope in a rapidly changing marketplace. Each has clear strengths in a particular analytics area; however, as a group, they lack the scope of the Challengers and the compelling market vision of the Leaders and Visionaries. All are themselves attempting to grow while providing a part of marketing analysts' requirements, yet they have either not articulated or not yet shown to the market how they can migrate their product toward market-leading growth. Given market volatility and potential acquisition activity, any of the Niche players could credibly move to the Challengers or Visionaries quadrant next year. In addition, Magic Quadrant capabilities are re-evaluated each year to reflect changes in market priorities, and this can affect the relative scoring of any vendor.


The past several years have brought a dramatic acceleration in the application of technology and real-time data to marketing. At the same time, marketers have responded by building out analytics teams, acquiring technologies and delivering more useful support to the organization. Yet CMO (and CEO) expectations still outpace the skills available in most organizations (see "Presentation for Marketing Organizational Design and Strategy Survey 2016: Marketing Leaders' Ambitions Outstrip Capabilities" ). As digital marketing budgets continue to rise (see "CMO Spend Survey 2016-2017: Budgets Climb (Again!) to 12% of Revenue as Marketers Juggle More Demands" ), analytics teams are asked to contribute to greater growth and retention, and to measure the impact of marketing more accurately. It is difficult to find a CMO mandate — from improving customer experience to increasing marketing share and brand perception — that doesn't require significant support from an already-taxed analytics team.

Market Evolution

While web analytics continues to function as a mature market, the broader marketing analytics ecosystem is undergoing rapid and significant change. This third Magic Quadrant for Digital Marketing Analytics is motivated by five major trends:

  • Channel convergence: Consumers increasingly have an abundance of channels and devices with which to interact with brands and to purchase products and services. Marketers must understand and analyze customer journeys that cross multiple channels and devices, as experience becomes the new battleground for competitive differentiation.

  • Customer data and analytics: As the technology infrastructure supporting marketing improves, customers engage more frequently in digital channels, and the value of some third-party data declines, marketers look to their own and partner data to give them an edge. In the past year, Gartner marketing analysts have seen a notable increase in client inquiry on the topics of customer data and customer journey analytics. Cross-device user matching, user-level data collection and time-series-based analytics converge to open up the possibility of useful customer journey understanding and optimization for marketers.

  • Customer experience overlap: As marketers focus more attention and gain more value from their own customer and prospect or visitor data, organizations are having more trouble drawing a line between customer experience and marketing analytics. As silos between channels such as email and websites are being dissolved, steadfast divisions between voice-of-customer and marketing analytics — or dare we say CRM and marketing — are less meaningful. As marketers examine the customer journey more closely, they increasingly pull in data and insights from other customer experience (CX) systems.

  • Predictive analytics: Improving customer experience and the impact of marketing and advertising increasingly requires the application of advanced analytical techniques that were previously reserved for custom projects. For example, personalization of content and offers on websites, mobile apps, emails and other channels, benefits from predictive modeling. Predictive, text, graph, machine learning, emerging AI and other techniques are being adopted by marketing teams.

  • Usability imperative: As the demands on internal analytics teams grow, marketers continue to ask vendors to provide new features more rapidly and to improve the usability of their user interface, setup and maintenance. Better usability increases efficiency and makes the tools more relevant to a wider group of users who may have nontraditional analytics backgrounds, as well as overstressed advanced analysts who are looking to save time. Already, platforms in the Leaders quadrant are adding natural-language-query capabilities to their tools.

Need for integration. For decades, disconnected data collection, reporting and analysis was a fact of life for most marketers — one for which technical solutions did not readily exist outside of costly or complex ad hoc work-arounds. Relatively recent improvements in data processing speed, big data storage and frameworks, and active and well-funded academic and corporate research, all have created an environment in which it is possible to combine insights across channels at increasingly detailed levels.

At the same time, consumer behavior fragmentation and immersion in digital experiences has made supporting a more coherent experience critical. Both demand and supply forces are compelling marketers to seek a single view of the customer, holistic analytics and insights, and techniques advanced enough to make a difference. For these reasons, we believe that vendors in the space will have to provide more integrated solutions that transcend recent point categories, including mobile, social and B2B lead-scoring vendors.

Category Volatility

This report reflects information collected in the second and third quarters of 2017. Marketers should consider this snapshot a baseline, but also weigh more recent and ongoing developments, as they research solutions. They should also be wary of vendor promises regarding future features or release dates — particularly in this category, many vendors overpromise or are overoptimistic. Always ask what is currently in production, what is in beta and what is not. Meanwhile, demand explicit assurances regarding data ownership and reuse (including benchmarking), and privacy and security policies.

Market Overview

Booming Business

All signs point to a healthy analytics market for some time to come. More than 40% of U.S. and U.K. marketing leaders say analytics is one of their top five investment areas, and 57% intended to increase their spending on marketing analytics products and services in 2017, according to Gartner's CMO Spend Survey 2016-17 (see "Survey Analysis: 57% of Marketing Leaders Expect to Spend More in 2017" ). The sharpest increases in analytics spending were expected in manufacturing (65% of respondents), high tech and financial services (both 60%). This growth comes on top of near-ubiquitous adoption of more mature categories, such as web analytics, which is deployed or about to be deployed by 92% of marketers, and digital marketing or customer analytics (89%). (For more on technology adoption rates by industry and marketer type, see "Marketing Technology Survey 2016: How Marketers Use Technology to Run, Grow and Transform Their Organizations." )

Changing Dynamics

The market for digital marketing analytics is evolving and remains characterized by consolidation in mature categories, such as web analytics, and ongoing fragmentation in others, such as mobile app and social analytics. In the traditional web analytics — now called digital analytics — category, a handful of vendors led by Adobe and Google dominate enterprise and midmarket users. Google's free analytics products are widely adopted and have forced competitors to focus on advanced features, niche markets or enterprise buyers (even as Google offers its own paid enterprise-grade products). Specialized channel analytics tools still exist, particularly for tracking engagement with mobile and video formats.

Concurrently, a number of point solutions exist for specific advanced analytics use cases, most notably lead scoring for B2B marketers, and predictive analytics to determine offers, product and content recommendations for e-commerce companies and publishers; these were too narrowly scoped for inclusion.

Media measurement. Media measurement — including attribution, marketing mix modeling and combined solutions — encompasses a potpourri of software tools, professional service providers and nascent features included within broader analytics products. This year, we also excluded these providers as representing a distinct, coherent market that deserved its own separate treatment.

We segment the competitive landscape into two broad categories: diversified software providers and customer analytics providers.

Diversified Software Providers

These include Adobe, FICO, Google, IBM, SAS and SAP. Emerging from the web analytics (Adobe, Google, IBM) or enterprise analytics market (FICO, SAS, SAP), these companies have rapidly added features and new capabilities as the market has evolved. With the exception of Google, these companies primarily serve large enterprise customers with diverse needs. They all embed digital analytics within a larger analytics and media context, providing a modular approach that gives marketers a menu of products and bundles of features from which they can choose, depending on their needs. In addition to generally targeting the enterprise, these providers are investing heavily in serving the advanced or power user, improving decision support, and upgrading user experience. Their challenge remains in adding features fast enough to serve the market while integrating these within already complex and costly product sets.

One vendor, AT Internet, serves a similar market to digital analytics products from Adobe, Google and IBM. It differs from them in having a narrower offering, focusing primarily on the European market, and in its adoption by both enterprise and smaller marketers.

Customer Analytics Providers

These include AgilOne, ClickFox, Optimove, Pitney Bowes and Thunderhead. Companies in this category focus on providing a single view of the customer based on first-party data sources. They differentiate themselves by stressing data quality and hygiene, as well as accurate metadata, and their maintenance of a master customer record that captures key attributes, which in turn forms the basis of the analytics engine. These are practical solutions, designed to improve the performance of specific marketing campaigns. Their utility makes them attractive to a segment of users, and their emphasis on customer data maintenance aligns them with the "user-centric" movement of most marketers. They have the potential to expand their offering with a focus on predictive analytics for a wider set of users, and on advanced measurement and data exploration.

Adjacent providers. As a fast-growing market, digital marketing analytics is attracting attention from large technology incumbents, such as Facebook (see Vendors to Watch), as well as traditional business intelligence (BI) and analytics providers who see marketing as a logical source of new users. A few BI platforms — such as Tableau and Qlik Sense — are already widely used by marketing teams for specific requirements, such as visualization or data exploration. (Microsoft Power BI and ZoomData are alliance partners of Thunderhead and ClickFox, respectively.) These platforms currently lack support for key marketing requirements, but their addition of features requested by marketing users could turn them into a new competitive magnet for marketing analytics budgets.

AI startups. The AI revolution has brought a host of new entrants clamoring for marketing leaders' attention. All promise to provide deeper insights, faster, using newer methods such as deep learning, genetic algorithms and neural networks. Many are simply using machine-learning methods and calling them AI — and, in fact, the past year has seen a troubling expansion in jargon-heavy pitches short on specifics. At the same time, exciting advances are being made in the space. The adoption by marketing clouds of AI avatars and projects, such as Adobe Sensei, Salesforce Einstein and IBM Watson, signal major investments. Marketers are advised not to seek an easy answer, but instead should master the (non-AI) fundamentals. Reference customers for this Magic Quadrant indicated that only 7% of their marketing analytics product usage included some form of AI (loosely defined). Watch this space.

Evaluation Criteria Definitions

Ability to Execute

Product/Service: Core goods and services offered by the vendor for the defined market. This includes current product/service capabilities, quality, feature sets, skills and so on, whether offered natively or through OEM agreements/partnerships as defined in the market definition and detailed in the subcriteria.

Overall Viability: Viability includes an assessment of the overall organization's financial health, the financial and practical success of the business unit, and the likelihood that the individual business unit will continue investing in the product, will continue offering the product and will advance the state of the art within the organization's portfolio of products.

Sales Execution/Pricing: The vendor's capabilities in all presales activities and the structure that supports them. This includes deal management, pricing and negotiation, presales support, and the overall effectiveness of the sales channel.

Market Responsiveness/Record: Ability to respond, change direction, be flexible and achieve competitive success as opportunities develop, competitors act, customer needs evolve and market dynamics change. This criterion also considers the vendor's history of responsiveness.

Marketing Execution: The clarity, quality, creativity and efficacy of programs designed to deliver the organization's message to influence the market, promote the brand and business, increase awareness of the products, and establish a positive identification with the product/brand and organization in the minds of buyers. This "mind share" can be driven by a combination of publicity, promotional initiatives, thought leadership, word of mouth and sales activities.

Customer Experience: Relationships, products and services/programs that enable clients to be successful with the products evaluated. Specifically, this includes the ways customers receive technical support or account support. This can also include ancillary tools, customer support programs (and the quality thereof), availability of user groups, service-level agreements and so on.

Operations: The ability of the organization to meet its goals and commitments. Factors include the quality of the organizational structure, including skills, experiences, programs, systems and other vehicles that enable the organization to operate effectively and efficiently on an ongoing basis.

Completeness of Vision

Market Understanding: Ability of the vendor to understand buyers' wants and needs and to translate those into products and services. Vendors that show the highest degree of vision listen to and understand buyers' wants and needs, and can shape or enhance those with their added vision.

Marketing Strategy: A clear, differentiated set of messages consistently communicated throughout the organization and externalized through the website, advertising, customer programs and positioning statements.

Sales Strategy: The strategy for selling products that uses the appropriate network of direct and indirect sales, marketing, service, and communication affiliates that extend the scope and depth of market reach, skills, expertise, technologies, services and the customer base.

Offering (Product) Strategy: The vendor's approach to product development and delivery that emphasizes differentiation, functionality, methodology and feature sets as they map to current and future requirements.

Business Model: The soundness and logic of the vendor's underlying business proposition.

Vertical/Industry Strategy: The vendor's strategy to direct resources, skills and offerings to meet the specific needs of individual market segments, including vertical markets.

Innovation: Direct, related, complementary and synergistic layouts of resources, expertise or capital for investment, consolidation, defensive or pre-emptive purposes.

Geographic Strategy: The vendor's strategy to direct resources, skills and offerings to meet the specific needs of geographies outside the "home" or native geography, either directly or through partners, channels and subsidiaries as appropriate for that geography and market.