The Forrester Wave : Customer Analytics Solutions, Q2 2018

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1 Licensed for Adobe. Not licensed for further distribution. The Forrester Wave : Customer Analytics Solutions, by Brandon Purcell Why Read This Report In our 39-criteria evaluation of customer analytics solutions providers, we identified the nine most significant ones Adobe, AgilOne, IBM, Manthan, NGDATA, Salesforce, SAP, SAS, and Teradata and researched, analyzed, and scored them. This report shows how each provider measures up and helps customer insights (CI) professionals make the right choice. Key Takeaways Adobe, SAS, And IBM Lead The Pack Forrester s research uncovered a market in which Adobe, SAS, and IBM lead the pack. Manthan, AgilOne, Teradata, SAP, and Salesforce offer competitive options. NGDATA lags behind. Customer Insights Pros Are Looking For Insight Emancipation The customer analytics solutions market is growing because more CI pros and their stakeholders want easy and immediate access to insights. Additionally, this market growth is in large part due to the fact that data science talent is scarce, and brands that struggle to perform customer analytics fear disruption. Governance And AI On AI Are Key Differentiators As automated customer insights generation becomes commoditized, improved model monitoring and AI-driven prescriptive capabilities will dictate which providers lead the pack. forrester.com

2 by Brandon Purcell with Srividya Sridharan and Robert Perdoni Table Of Contents Related Research Documents Customer Analytics Solutions Emancipate Insights Caveat Emptor: Not All Customer Analytics Solutions Are Built The Same Customer Analytics Solutions Evaluation Overview Evaluated Vendors And Inclusion Criteria Vendor Profiles Leaders Strong Performers Contenders Supplemental Material It s Time To Raise The Bar With Analytics Now Tech: Customer Analytics Technologies, Q The State Of Customer Analytics 2017 Share reports with colleagues. Enhance your membership with Research Share. Forrester Research, Inc., 60 Acorn Park Drive, Cambridge, MA USA Fax: forrester.com 2018 Forrester Research, Inc. Opinions reflect judgment at the time and are subject to change. Forrester, Technographics, Forrester Wave, TechRadar, and Total Economic Impact are trademarks of Forrester Research, Inc. All other trademarks are the property of their respective companies. Unauthorized copying or distributing is a violation of copyright law.

3 Customer Analytics Solutions Emancipate Insights CI pros have always leveraged analytics technology to create the insights to win, serve, and retain customers. Traditionally, they have relied on generic advanced analytics toolkits to build custom models from scratch for a specific business purpose. However, they are increasingly relying on a new breed of analytics technology customer analytics solutions. 1 These solutions differ from their do-it-yourself (DIY) toolkit forebears in that they perform common customer analytics techniques automatically, emancipating insights from customer data with do-it-for-me (DIFM) capabilities. CI pros adopting customer analytics solutions benefit from: Business user accessibility. DIFM customer analytics solutions emerged as a response to the scarcity of data science talent that plagues many organizations. 2 Even enterprises that have robust data science teams often find the demand for those resources far exceeds the supply. Customer analytics solutions eliminate the need for data scientist intervention for common techniques such as behavioral segmentation, churn modeling, and customer lifetime value analysis. Most of the solutions in this evaluation have a business-user-friendly front end, giving nontechnical resources easy access to the insights they need to make key business decisions. Speed to insights and action. Customer analytics solutions deliver insights out of the box, so firms don t have to endure lengthy model development when they use them. Once the technology is deployed, many of these solutions automatically build churn models, calculate customer lifetime value, and visualize customer journeys. Additionally, since these solutions are designed not just for modeling but also for business users to implement model output, they help bridge the insights-toaction gap that plagues many CI teams. 3 A single view of the customer. One key benefit of customer analytics solutions is that they ingest and integrate data from multiple sources for analysis. Most CI pros know from painful experience that accessible, clean, and reliable customer data is a prerequisite for customer analytics. The top two challenges CI pros face in performing analytics are accessing data from multiple sources and ensuring its quality. 4 Most customer analytics solutions vendors perform identity resolution, deduplication, and many of the other gnarly data transformation processes to lay the foundational data layer for analytics. This single view of the customer is what customer data platforms (CDPs) promise, which is why some vendors in this evaluation have adopted that sobriquet. Caveat Emptor: Not All Customer Analytics Solutions Are Built The Same Despite the benefits of customer analytics solutions, they won t replace data scientists or more general-purpose DIY toolkits anytime soon. Most large enterprises will need to adopt a solution for common customer analytics needs alongside a toolkit for bespoke projects. Further, since this is a relatively new category, no vendor offers DIFM capabilities for every possible customer analytics technique. One of the main drivers for this evaluation was to determine which vendors have codified data science best practices for specific CI use cases into their solutions. CI pros considering a customer analytics solution should look for: 2

4 Analytical transparency to build trust. Many of the solutions we evaluated expose only the output of a model to business users, rather than the model itself. On one hand, this makes sense because most business users lack the technical knowledge or desire to understand the inner workings of machine learning algorithms. On the other hand, these models will be used to inform customer experiences, so marketers and other decision makers need to trust them to implement them. Some vendors strive to deliver the right balance of transparency, highlighting the most important variables in a model while keeping the math behind the scenes. Rapid deployment to match user needs and skills. Although customer analytics solutions offer insights out of the box, most solutions take one to six months to deploy (and a couple of them take up to 12 months). To perform automated data ingestion, integration, and ultimately machine learning, your brand s data must be mapped to the solution s data model. Some vendors, such as Manthan and AgilOne, typically perform this task through an initial service engagement that is included in their solutions pricing. Others also offer DIY data ingestion and preparation tools, such as IBM s BlueMix and SAP s Customer Data Cloud. Strong model monitoring and governance capabilities. Since the performance of machine learning models tends to degrade over time, some of the solutions in this evaluation continuously assess models and even swap stale models for fresh ones automatically. However, this doesn t mean CI pros can just set it and forget it. Models that perform best in theory may not always be operable in a live setting due to regulatory, privacy, or business constraints. CI pros will need to oversee these systems to ensure that as they learn, they remain accurate, ethical, and compliant. Automatic insight identification and prescriptive action. Some solutions we evaluated contain AI on AI automatically identifying interesting insights and prescribing action for users. IBM s Watson Marketing Insights automatically identifies high-risk and high-value segments to target and calculates the potential financial impact of retention. Adobe Analytics alerts users to anomalies in key metrics and automatically surfaces the root cause through contribution analysis. As the core customer analytics techniques in these solutions become commoditized, look for more innovative prescriptive capabilities to emerge. Customer Analytics Solutions Evaluation Overview To assess the state of the customer analytics solutions market and see how the vendors stack up against each other, Forrester evaluated the strengths and weaknesses of top customer analytics solutions vendors. After examining past research, user need assessments, and vendor and expert interviews, we developed a comprehensive set of 39 evaluation criteria, which we grouped into three high-level buckets: Current offering. Each vendor s position on the vertical axis of the Forrester Wave graphic indicates the strength of its current offering. Key criteria for these solutions include data (such as customer data management and data preparation); insights (including all the major customer 3

5 analytics techniques); action (execution and speed of deployment); usability (user interface and data visualizations); governance (model monitoring and GDPR compliance); business impact measurement; and time-to-value. 5 Strategy. Placement on the horizontal axis indicates the strength of the vendors strategies. We evaluated the completeness of vendors product vision, the direction of their product road map, amount of product investments, past market performance, depth of professional services, and breadth of partner ecosystem. Market presence. Represented by the size of the markers on the graphic, market presence scores reflect each vendor s customer analytics solution revenue, number of customers, and average deal size. Evaluated Vendors And Inclusion Criteria Forrester included nine vendors in the assessment: Adobe, AgilOne, IBM, Manthan, NGDATA, Salesforce, SAP, SAS, and Teradata. Each of these vendors has (see Figure 1): DIFM capabilities. The vendors in this evaluation provide a comprehensive customer analytics software solution consisting of DIFM customer analytics functionality, which provides customer insights directly to business decision makers without the need for support from data scientists. Client mindshare. The vendors we evaluated are frequently mentioned in Forrester client inquiries, vendor selection RFPs, shortlists, consulting projects, and case studies. A proven track record of revenue in customer analytics. The vendors in this evaluation earned at least $15 million from sales of their customer analytics solutions last year. Diverse vertical exposure. Each vendor included in this evaluation possesses a significant base of enterprise-class clients across at least three industry verticals. 4

6 FIGURE 1 Evaluated Vendors: Product Information And Inclusion Criteria Vendor Product evaluated Version evaluated Date evaluated Adobe Adobe Analytics Spring release AgilOne AgilOne Customer Data Platform V6 IBM Watson Customer Experience Analytics Watson Marketing Insights Manthan Customer R3 NGDATA NGDATA s CDP Salesforce* Salesforce Marketing Cloud Salesforce Commerce Cloud Salesforce Einstein Analytics SAP SAP Marketing Cloud 1802 SAS SAS 360 Engage SAS 360 Discover SAS Event Stream Processing SAS Factory Miner SAS Marketing Optimization SAS Model Manager SAS Text Analytics Suite SAS Visual Analytics SAS Visual Statistics SAS Visual Data Mining and Machine Learning M Teradata Teradata Analytics Platform Teradata Customer Journey Teradata Think Big Analytics *Salesforce declined to participate in or provide information for our research. Scores are based on Forrester estimates. 5

7 FIGURE 1 Evaluated Vendors: Product Information And Inclusion Criteria (Cont.) Vendor selection criteria Each of the vendors we evaluated has: Do-it-for-me (DIFM) capabilities. The vendors in this evaluation provide a comprehensive customer analytics solution consisting of DIFM customer analytics functionality, which delivers customer insights directly to business decision makers without the need for support from data scientists. Client mindshare. The vendors we evaluated are frequently mentioned in Forrester client inquiries, vendor selection RFPs, shortlists, consulting projects, and case studies. Proven viability. The vendors in this evaluation earned at least $15 million from sales of their customer analytics solutions last year. Diverse vertical exposure. Each vendor included in this evaluation possesses a significant base of enterprise-class clients across at least three industry verticals. Vendor Profiles This evaluation of the customer analytics solutions market is intended to be a starting point only. We encourage clients to view detailed product evaluations and adapt criteria weightings to fit their individual needs through the Forrester Wave Excel-based vendor comparison tool (see Figure 2 and see Figure 3). Click the link at the beginning of this report on Forrester.com to download the tool. 6

8 FIGURE 2 Forrester Wave : Customer Analytics Solutions, Customer Analytics Solutions Strong Challengers Contenders Performers Leaders Stronger current offering Adobe Teradata AgilOne IBM SAS NGDATA SAP Manthan Salesforce Weaker current offering Weaker strategy Stronger strategy Market presence* *A gray bubble indicates a nonparticipating vendor. 7

9 FIGURE 3 Forrester Wave : Customer Analytics Solutions Scorecard, Current offering 50% Forrester s weighting 3.83 Adobe 3.17 AgilOne IBM 3.31 Manthan NGDATA Salesforce* SAP SAS 3.30 Teradata Data 20% Insights 50% Action 10% 1.50 Usability 10% Governance 6% Business impact measurement 2% Time-to-value 2% Strategy 50% Product vision 30% Product road map 30% Product investments 20% Performance 10% Professional services 5% Partner ecosystem 5% Market presence 0% Customer analytics solution revenue 40% Number of customers 40% Average deal size 20% All scores are based on a scale of 0 (weak) to 5 (strong). *Salesforce declined to participate in or provide information for our research. Scores are based on Forrester estimates. 8

10 Leaders Adobe Analytics fuels insight-driven customer experiences. Adobe has turned its mantra of Make Experience Your Business inward, by developing a marketer-friendly solution that does not skimp on advanced analytical functionality. Its solution excels at real-time conversion of insights into action, superior usability, and context-rich location and device usage analytics. Adobe is one of two vendors in this evaluation to receive a perfect score of 10 from client references on likelihood to recommend, with one commenting, They seem to recognize that it isn t just about producing actionable insights in a particular marketing silo, but rather about closing the loop with optimized experiences. Like most vendors in this space, Adobe has jumped on the artificial intelligence bandwagon, but most of the Sensei capabilities in Adobe Analytics existed long before being rebranded as AI. Still, Adobe s vision of using AI to automate insight discovery will continue to resonate with datadriven marketers. SAS offers a broad range of best-in-class analytics. The SAS brand has long been synonymous with advanced analytics, but recently it has focused on democratizing these capabilities for business users. While SAS supports all the customer analytics use cases evaluated in this report, many require custom development in SAS Visual Analytics, a business-user-friendly modeling workspace. Functionally, SAS impresses with differentiated capabilities, such as multivariate testing to determine optimal content variants and algorithmically driven cross-channel attribution. Strategically, SAS has committed to opening its platform to other tools, embracing the data science community s adoption of R and Python and positioning itself as the connective tissue enabling intelligence across the enterprise. Client references scored SAS poorly on the ease of importing data from multiple sources, and one stated, The UI could look better. This suggests SAS still caters to a more technical audience, but firms with these resources on staff will benefit from SAS s analytics leadership. IBM uses Watson to infuse marketing and customer experience with intelligence. Watson Marketing Insights (WMI) and Watson Customer Experience Analytics (WCXA) are a distinct departure from IBM s legacy data science toolkit, SPSS Modeler. WMI not only offers out-of-thebox access to churn propensities, lifetime value, and engagement metrics, it also recommends target audiences based on predicted business impact. WCXA excels at journey analytics, automatically identifying points of customer struggle. IBM s vision to own AI-powered marketing is aggressive, but its road map promises to combine analytical capabilities with more intelligent assistance for users. One client reference noted, Integration with [IBM s] other tools is lacking, which has always been a challenge for IBM. This issue may soon disappear, as IBM plans to unify solutions with a single user interface, allowing clients access to subscribed modules. Marketers and CI pros looking for strong insights out of the box should consider adding Watson to their teams. 9

11 Strong Performers Manthan s sweet spot is advanced customer analytics for retailers. Manthan positions itself as a customer marketing platform with its Customer360 and TargetOne platforms. It does a good job of addressing the three distinct personas in the analytics value chain business users, data scientists, and data engineers with distinct interfaces and functionality for each. Manthan specializes in problems unique to large retailers, such as defining customer churn and merchandizing and assortment optimization. Maya, its intelligent assistant that allows marketers to query data through speech, seems somewhat gimmicky today but may become more valuable as conversational computing takes off. Client references were satisfied overall, yet a few bemoaned its interface. One client said, The UI could be improved to [be] more user-friendly to non-analyst types. Despite these UI concerns, Manthan s robust customer data model underlies prebuilt KPIs and predictive scores, which retailers and other direct-to-consumer brands find alluring. 6 AgilOne empowers B2C marketers with DIFM analytics and strong identity resolution. AgilOne has fully embraced the customer data platform moniker, helping to define this nascent category with robust first-party identity resolution and real-time data availability. On top of that, AgilOne also epitomizes the new business-user-friendly ethos of the customer analytics solutions category, offering customer lifetime value analysis, churn propensity, behavioral clustering, and recommendation analysis out of the box. To date, this functionality serves AgilOne s target verticals of retail, travel, and leisure well, but the vendor may find it difficult to penetrate other direct-toconsumer industries without much reconfiguration. AgilOne s time-to-value was longer than other evaluated vendors as one client said, The integration was pretty rough. But, ultimately, usability wins out. The same client went on to say: The usability is amazing.... We wanted a platform that we could hire someone directly out of college and train them within four hours. This is possible with AgilOne. Teradata drives value for CI teams at large enterprises. Teradata is one of two vendors in this evaluation that earned a perfect score from clients on likelihood to recommend. And its client references included well-known brands with millions of customers and trillions of transactions. Teradata offers a business-user-friendly interaction management application, but most of the robust analytical functionality resides in Teradata Analytics Platform, which is designed for more sophisticated resources. Teradata packages these capabilities with strong consulting services to deliver common use cases, such as next best offer and customer path analysis in Teradata Customer Journey. For Teradata, adoption by nontechnical users will continue to prove challenging, but fortunately the vendor caters to Fortune 500 enterprises, whose data science teams thrive using Teradata Analytics Platform. As the head of data science at one client remarked, I really think they re quality I don t know if we d be where we are without them. 10

12 SAP Marketing Cloud delivers intelligence on big customer data. Seventy-seven percent of the world s transaction revenue touches an SAP system. Consequently, big data is at the core of SAP Marketing Cloud, which offers preconfigured marketing scenarios to users, including strong purchase propensity, product and offer recommendation, attribution, and channel preference capabilities. SAP plans to expand these capabilities and offer more advanced scenarios, such as optimization and conversations powered by SAP Leonardo, its own AI brand. On top of a strong customer analytics foundation, SAP offers executive-level dashboards with a scenario builder to answer what if questions. In terms of offering a comprehensive suite of customer analytics capabilities, SAP Marketing Cloud still has room for improvement one client reference noted, In my opinion, it is no analytical solution. The truth is that the solution provides some analytical functionality today that will continue to mature quickly based on its foundation of big enterprise customer data. Salesforce embeds advanced customer analytics in its offerings with Einstein. Salesforce declined to participate in or provide information for our research. That s surprising because Einstein s raison d être is out-of-the-box advanced customer analytics, eliminating the need for data scientist intervention. 7 In addition to offering recommendation and segmentation, Salesforce recently launched Einstein Prediction Builder, a business-user-friendly tool for creating custom predictive models that emulates a data scientist s best practices under the hood. Early adopters such as U.S. Bank are happy with their investment and already see measurable ROI. Salesforce s key limitation will be data. Einstein s models are only as good as the data used to train them, which means that if your Salesforce data is dirty or incomplete, your insights will suffer. Salesforce s answer is to continue to roll out trailblazing Einstein functionality and make a compelling case for importing all customer data into its platform. Contenders NGDATA is light on analytics, heavy on real-time orchestration. NGDATA is the other CDP in this evaluation. Unfortunately, for the purposes of this evaluation, it suffered from a lack of native customer analytics functionality, with the notable exception of a strong next-best-action capability. Most other models need to be created elsewhere and brought into the platform for deployment. What NGDATA lacks in analytics, however, it makes up for in its ability to ingest data, score records, and orchestrate the customer experience across channels in real time. Client references suggested NGDATA could invest in a more user-friendly user interface. Still, enterprises seeking to provide a data-driven customer experience should not overlook this vendor. Its ability to ingest insights, orchestrate action, and learn continuously by closing the feedback loop will become the new differentiator as traditional customer analytics techniques proliferate. 11

13 Engage With An Analyst Gain greater confidence in your decisions by working with Forrester thought leaders to apply our research to your specific business and technology initiatives. Analyst Inquiry To help you put research into practice, connect with an analyst to discuss your questions in a 30-minute phone session or opt for a response via . Learn more. Analyst Advisory Translate research into action by working with an analyst on a specific engagement in the form of custom strategy sessions, workshops, or speeches. Learn more. Webinar Join our online sessions on the latest research affecting your business. Each call includes analyst Q&A and slides and is available on-demand. Learn more. Forrester s research apps for ios and Android. Stay ahead of your competition no matter where you are. Supplemental Material Online Resource The online version of Figure 2 is an Excel-based vendor comparison tool that provides detailed product evaluations and customizable rankings. Click the link at the beginning of this report on Forrester.com to download the tool. Data Sources Used In This Forrester Wave Forrester used a combination of three data sources to assess the strengths and weaknesses of each solution. We evaluated the vendors participating in this Forrester Wave, in part, using materials that they provided to us by April 11. Vendor surveys. Forrester surveyed vendors on their capabilities as they relate to the evaluation criteria. Once we analyzed the completed vendor surveys, we conducted vendor calls where necessary to gather details of vendor qualifications. 12

14 Product demos. We asked vendors to conduct demonstrations of their products functionality. We used findings from these product demos to validate details of each vendor s product capabilities. Customer reference surveys. To validate product and vendor qualifications, Forrester also fielded a survey with three of each vendor s current customers. The Forrester Wave Methodology We conduct primary research to develop a list of vendors that meet our criteria for evaluation in this market. From that initial pool of vendors, we narrow our final list. We choose these vendors based on 1) product fit, 2) customer success, and 3) Forrester client demand. We eliminate vendors that have limited customer references and products that don t fit the scope of our evaluation. Vendors marked as incomplete participants met our defined inclusion criteria but declined to participate or contributed only partially to the evaluation. After examining past research, user need assessments, and vendor and expert interviews, we develop the initial evaluation criteria. To evaluate the vendors and their products against our set of criteria, we gather details of product qualifications through a combination of lab evaluations, questionnaires, demos, and/or discussions with client references. We send evaluations to the vendors for their review, and we adjust the evaluations to provide the most accurate view of vendor offerings and strategies. We set default weightings to reflect our analysis of the needs of large user companies and/or other scenarios as outlined in the Forrester Wave evaluation and then score the vendors based on a clearly defined scale. We intend these default weightings to serve only as a starting point and encourage readers to adapt the weightings to fit their individual needs through the Excel-based tool. The final scores generate the graphical depiction of the market based on current offering, strategy, and market presence. Forrester intends to update vendor evaluations regularly as product capabilities and vendor strategies evolve. For more information on the methodology that every Forrester Wave follows, please visit The Forrester Wave Methodology Guide on our website. Integrity Policy We conduct all our research, including Forrester Wave evaluations, in accordance with the Integrity Policy posted on our website. 13

15 Endnotes 1 See the Forrester report Now Tech: Customer Analytics Technologies, Q See the Forrester report The State Of Customer Analytics See the Forrester report Vendor Landscape: Customer Analytics Service Providers, Q See the Forrester report The State Of Customer Analytics GDPR: General Data Protection Regulation. 6 KPIs: key performance indicators. 7 See the Forrester report Mega Vendors Use AI To Change The CRM Game. 14

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