The Forrester Wave : Customer Analytics Solutions, Q1 2016

Transcription

1 The Forrester Wave : Customer Analytics Solutions, Q by Brandon Purcell Why Read This Report In our 33-criteria evaluation of customer analytics vendors, we identified the 11 most significant software providers in this category Adobe, AgilOne, Alteryx, Angoss, FICO, IBM, Manthan, Pitney Bowes, SAP, SAS, and Teradata and researched, analyzed, and scored them. This evaluation details our findings about how well each vendor fulfills our criteria and where each vendor stands in relation to each other. This report will help customer insights (CI) professionals and marketers make the right choice for their customer analytics needs. Key Takeaways CI Pros Rely On Customer Analytics Tools To Understand Customer Behavior CI pros use customer analytics solutions to derive valuable insights from customer data. These tools perform multiple activities: descriptive analytics such as customer segmentation, predictive analytics such as propensity modeling, and data integration of different types of data. Four Vendors Lead The Pack With Comprehensive Offerings Forrester s research uncovered a market in which SAS, IBM, Adobe, and Pitney Bowes lead the pack. SAP, FICO, Teradata, Angoss, Alteryx, and Manthan offer competitive options, and AgilOne is close on the heels of these Strong Performers. Leading Solutions Enable And Accelerate The Insights Life Cycle The goal of customer analytics is to turn data into insight, insight into action, and also learn and optimize the results. The ability to manage and integrate data from multiple sources, quickly uncover valuable insights, take action on those insights, and measure success dictates which providers lead the pack. forrester.com

2 by Brandon Purcell with Srividya Sridharan, Megan Doerr, and Tyler Thurston Table Of Contents Notes & Resources You Don t Need A Ph.D. For Advanced Customer Analytics Don t Assume You Can Use Traditional Analytics To Uncover Deep Customer Insights Customer Analytics Solutions Evaluation Overview Evaluation Criteria Focus On The Breadth And Depth Customer Analytics Use Cases Included Vendors Cater To Business Users And Data Scientists Alike Approaches To The Insights Life Cycle Differ Significantly Vendor Profiles Leaders Strong Performers Contenders Supplemental Material Forrester conducted product evaluations in November and December 2015 and evaluated eleven vendor companies: Adobe, AgilOne, Alteryx, Angoss, FICO, IBM, Manthan, Pitney Bowes, SAP, SAS, and Teradata. Related Research Documents Deciphering A Fragmented Customer Analytics Ecosystem The Forrester Wave : Big Data Predictive Analytics Solutions, Q The Forrester Wave : Customer Analytics Solutions, Q TechRadar : Customer Analytics Methods, Q Forrester Research, Inc., 60 Acorn Park Drive, Cambridge, MA USA Fax: forrester.com 2016 Forrester Research, Inc. Opinions reflect judgment at the time and are subject to change. Forrester, Technographics, Forrester Wave, RoleView, 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 You Don t Need A Ph.D. For Advanced Customer Analytics In the age of the customer, understanding your customers to anticipate their needs, meet their expectations, and address their pain points is critical. 1 Whether CI pros sit in the marketing organization, a line of business (LoB), or in a centralized customer intelligence team, it is their job to employ advanced customer analytics to generate this insight. But often data scientists and Ph.D.s with knowledge of advanced analytical algorithms and programming languages such as R and Python are more adept at wrestling with mountains of messy customer data. Since the demand for these data scientists far exceeds supply, this job is increasingly the responsibility of marketers and CI pros who need marketer-friendly tools for data management and analytics. In fact, our data shows that among North American marketing decision-makers, 44% either agree or strongly agree with the statement I don t have the right set of customer and marketing analytics tools to help me produce and execute insights. Another 28% responded neutrally to the statement, leaving just over a quarter of marketers with the tools they need. 2 This presents a big market opportunity for analytics technology providers who must: Power systems of insight. If data is the fuel for systems of insights, then customer analytics is the engine, transforming data into insights that drive customer interactions. As customers interact with brands online, on mobile, or in stores, customer analyses are occurring in real time to determine the best messages, offers, and products to deliver to them. Systems of insight then automatically recalibrate by learning from the success or failure of these interactions. 3 To operate optimally, customer analytics solutions should sit at the center of systems of insight and integrate seamlessly with data warehouses and execution systems (see Figure 1). Address the entire customer life cycle. Until recently, most firms focused their analytical efforts on customer acquisition. In the age of the customer, however, marketers and CI pros need to focus on all phases of the customer life cycle. This means that in addition to offering acquisition analytics like lookalike targeting, customer analytics solutions providers need to offer analytical methods that help improve the customer experience, drive retention and loyalty, enable personalization, and deliver contextual marketing as well. 4 Focus on the business user. Because most marketers and CI pros are not familiar with programming languages, customer analytics vendors offer graphical user interfaces (GUIs) that provide a visual, intuitive analytical process. Marketers can now perform tasks that historically demanded specialized skill sets, such as data preparation or predictive modeling using automated or wizard-based functionality. Even the process of data preparation, where data scientists typically spend up to 80% of their time and effort, is now accessible to marketers and CI pros. 5 2

4 Don t Assume You Can Use Traditional Analytics To Uncover Deep Customer Insights Advanced customer analytics needs a new, different breed of analytical methods that leverages machine learning and other advanced analytical techniques to find valuable patterns and anomalies in customer data. 6 Statisticians used to have to rely on a statistically representative sample of a population to test hypotheses and draw conclusions about the entire population. Thanks to significant advancements in computer storage and processing capabilities, CI pros can now analyze an entire population at once. 7 Additionally, customer analytics differs from traditional analytics by: Delivering output at the customer level. While traditional analytics deliver aggregated results in the form of dashboards and reports, the outputs of customer analytics are typically individual scores or segments for each customer. This difference requires a mind shift in terms of where to store these insights in a data warehouse, and how to operationalize them at an individual customer level. 8 For instance, brands such as McCormick & Company, MiniUSA, and Nike are using contextual marketing engines to drive real-time personalized interactions with individual customers. 9 Predicting the future. Traditionally, analysts looked at data about the past to better understand the past. Today, analysts are finding patterns in data about the past to predict and anticipate the future. For instance, many telecoms have mature customer attrition models that assign customers a propensity to churn and dictate which incentives to give customers to stay. 10 Similarly, predictive models can also determine the content and distribution lists of cross-sell and upsell campaigns. Analytics-savvy marketers are using customer lookalike modeling to target prospects with high predicted lifetime values. Customer experience pros are joining the predictive party as well, attempting to anticipate and optimize key CX metrics like engagement and satisfaction. 11 Leveraging new data types and sources. In addition to operating on traditional structured CRM data, customer analytics also leverages semi structured and unstructured data from transactional systems, digital properties, Internet of Things (IoT) connected devices, customer feedback channels, social media and other emerging sources. Including these new data types and sources in analyses is essential for producing a 360-degree view of the customer. Unfortunately, most companies only use 27% of their semi structured data and 31% of their unstructured data for business insights and decision-making. 12 3

5 FIGURE 1 Customer Analytics Is The Engine Driving Systems Of Insight Customer analytics Insights team All data Right data Insights-to-execution process Effective actions Possible actions Digital insights architecture 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 vendors. Evaluation Criteria Focus On The Breadth And Depth Customer Analytics Use Cases After examining past research, user need assessments, and vendor and expert interviews, we developed a comprehensive set of evaluation criteria. We evaluated vendors against 33 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 customer analytics offering. We looked at the strength of each vendor s functionality, analytics production (data to insights), analytics activation (insights to action and decisions), customer analytics use cases, delivery methods, and professional services. Strategy. A vendor s position on the horizontal axis indicates Forrester s assessment of its strategy and approach to the market. We compared the product and corporate strategies of each vendor in the context of Forrester s vision for the customer analytics market to assess how well each vendor is serving the current needs of the customer analytics market, as well as anticipating future needs through product investments and partnership strategies. 4

6 Market presence. To determine the size of each vendor s customer analytics business, we evaluated the vendor s target market, total revenue for the product(s), the size of each vendor s current customer base, new customer growth, and the number of employees devoted to its customer analytics product(s). Included Vendors Cater To Business Users And Data Scientists Alike Forrester included 11 vendors in the assessment: Adobe, AgilOne, Alteryx, Angoss, FICO, IBM, Manthan, Pitney Bowes, SAP, SAS, and Teradata. Each of these vendors (see Figure 2): Provides a comprehensive customer analytics software solution. Each vendor included in this Forrester Wave evaluation has a branded proprietary technology platform or product that enables customer insights professionals and marketers to perform key customer analytics methods such as customer segmentation, customer lifetime value analysis, churn and attrition analysis etc. Turn customer data into insights and insights into action. The customer analytics solutions included in this evaluation are able to ingest customer data from a wide variety of disparate sources, enable data preparation and visualization, possess a user interface that is intuitive for the business user as well as the data scientist, and facilitate effective distribution of insights across the organization. Empowers the business user with advanced customer analytics. The vendors we included offer a solution that delivers customer analytics to the marketing and customer insights organization with buyers and users supporting a customer or marketing insights type of role. Serves large enterprises in multiple verticals. Each vendor included in this Forrester Wave evaluation possesses a significant base of enterprise-class clients across at least three different industry verticals. 5

7 FIGURE 2 Evaluated Vendors: Product Information And Selection Criteria Vendor Adobe AgilOne Alteryx Angoss FICO IBM Manthan Pitney Bowes SAP SAS Product evaluated Adobe Analytics Premium Complete And suite of complementary analytics solutions AgilOne Predictive Marketing Cloud Alteryx Analytics which includes Alteryx Designer, Alteryx Analytics Gallery, Alteryx Server KnowledgeStudio Version 9.5 KnowledgeSeeker Version 9.5 And suite of complementary analytics solutions FICO Model Builder 7.5 FICO Analytic Modeler Scorecard 3.0 FICO Analytic Modeler Scorecard Professional 6.0 And suite of complementary analytics solutions IBM Predictive Customer Intelligence And suite of complementary analytics solutions Customer360 TargetOne Spectrum Technology Platform V10.1 Portrait Miner Uplift and Explorer V6.1 SAP Predictive Analytics 2.3 SAP Hana (SPS 10) In-Memory Analytics Platform SAP Hybris Marketing version 1511 And suite of complementary analytics solutions SAS Customer Intelligence SAS Customer Intelligence 360 SAS Analytics Pro Package SAS Enterprise Miner And suite of complementary analytics solutions Version release date Teradata Teradata Aster Analytics including Aster Analytic Portfolio 6.20 Teradata Customer Interaction Manager (CIM) And suite of complementary analytics solutions 6

8 FIGURE 2 Evaluated Vendors: Product Information And Selection Criteria (Cont.) Vendor selection criteria 1. The vendor provides a comprehensive customer analytics solution that enables customer insights professionals and marketers to perform key customer analytics methods such as customer segmentation, customer lifetime value analysis, churn and attrition analysis etc. 2. The vendor has a solution that ingests customer data from a wide variety of disparate sources, enables data preparation and visualization, possesses a user interface that is intuitive for the business user as well as the data scientist, and facilitates distribution of insights across the organization. 3. The vendor focuses on delivering customer analytics to the marketing and customer insights organization with buyers and users supporting a customer or marketing insights type of role. 4. The vendor possesses a significant base of enterprise-class clients across at least three different industry verticals. Approaches To The Insights Life Cycle Differ Significantly Forrester s evaluation of customer analytics solutions uncovered a market with four Leaders, six Strong Performers, and one Contender (see Figure 3): SAS, IBM, Adobe, and Pitney Bowes lead the pack. It s not surprising that SAS, IBM, and Pitney Bowes are Leaders in this evaluation. They have continued to enhance their solutions since our last evaluation in They demonstrate their commitment to customer analytics by adapting their solutions to meet clients ever-changing data, business, and deployment needs. Adobe is the newest member of this group, and its appearance here is largely due to adapting its successful digital analytics capabilities to cross-channel customer analytics, as well as its singular focus on user experience. 14 SAP, FICO, Teradata, Angoss, Alteryx, and Manthan offer competitive options. It would be highly misleading to assume all six vendors in this category offer similar solutions. SAP and FICO are focused on large enterprise and offer dual interfaces an intuitive one for the marketer, and another for the advanced user of data scientist. Angoss and Alteryx offer analytics workbenches that firms often deploy in conjunction with larger scale tools. Teradata scored high because of its robust functionality, big data capabilities, and Aster AppCenter for sharing analytical processes. Manthan, a relatively new solution, impressed with its business user-friendly interface and out-ofthe-box metrics. AgilOne vies for customer attention. AgilOne barely missed the cut to be a Strong Performer, but don t be fooled. Its commitment to providing users with a prescribed set of analytical results resonates with marketers who would rather focus on insight activation than insight generation. 7

9 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. FIGURE 3 Forrester Wave : Customer Analytics Solutions, Q1 16 Challengers Contenders Strong Strong Performers Leaders Current offering IBM SAS SAP Pitney Bowes Teradata Angoss FICO Adobe Alteryx Manthan AgilOne Go to Forrester.com to download the Forrester Wave tool for more detailed product evaluations, feature comparisons, and customizable rankings. Market presence Weak Weak Strategy Strong 8

10 FIGURE 3 Forrester Wave : Customer Analytics Solutions, Q1 16 (Cont.) Forrester s Weighting Adobe AgilOne Alteryx Angoss FICO IBM Manthan Pitney Bowes SAP SAS Teradata CURRENT OFFERING Products included Functionality Analytics production (data to insights) Analytics activation (insights to action and decisions) Customer analytics use cases Delivery methods Professional services STRATEGY Corporate strategy & vision Product strategy Partner ecosystem MARKET PRESENCE Target market Revenues for this product(s) Customers Employees 50% 0% 40% 20% 10% 25% 3% 2% 50% 50% 40% 10% 0% 10% 20% 50% 20% All scores are based on a scale of 0 (weak) to 5 (strong). Vendor Profiles Leaders Consider SAS if you are looking to drive innovation in customer analytics. As the only company in this evaluation whose sole focus is on analytics, SAS excels at analytics production that is, turning data into insights. It invests heavily in its customer intelligence suite to ensure that its solutions anticipate and even drive innovation in this space for marketers. With real-time data ingestion and integration, regular addition of new algorithms, and a robust selection of model management and deployment options, the limitations you will encounter with using SAS for customer analytics are less likely to be with the core functionality than with your team s ability to use them. Some clients we spoke with did cite issues with usability, saying that at first the user interface is not a joy ride, but that once it is up and running, it s smooth sailing. A few clients also noted issues integrating with existing marketing technologies, with one client experiencing 9

11 SAS and Google Analytics fighting each other. On the whole, however, client feedback was overwhelmingly positive, with clients noting SAS thought leadership in customer analytics and detailing quantifiable ROI. One client said they attribute profitability increases of up to 30% in some customer segments to SAS. Brands that view customer analytics as essential to their competitive strategy should partner with SAS to help them realize this vision. Consider IBM if your enterprise has diverse data, use cases, and deployment needs. IBM has a customer analytics solution for everyone. If you are looking for a tool to sort through mountains of messy customer data, IBM has it. If you are looking for predictive dashboarding, IBM has it. If you are looking for industry-specific solutions to industry-specific problems using industry-specific data, IBM has it. If you want to speak to your customer analytics solution like it s R2D2 (or BB8 for fans of Star Wars: The Force Awakens ), IBM will likely have it soon (in the form of Watson, which was not part of this evaluation). 15 The challenge with IBM is identifying which solution best meets your enterprise needs. According to client references, this confusion in the sheer breadth of analytics-related offerings is reflected in pricing. Predictive Customer Intelligence (PCI), for instance, is priced based on the number of customer records, and one client reference found IBM s way of defining a customer record needlessly painful. Another client reference said he wished IBM would listen to client feedback more often since it recently deprecated a functionality that (we) were using. IBM undoubtedly has a strong suite of offerings its challenge going forward will be to effectively communicate and differentiate those offerings in the market. Consider Adobe for a sleek marketer-friendly user experience to tie results to action. Adobe Analytics sits within the Adobe Marketing Cloud, giving marketers and CI pros a one-stop shop for data management, analysis, and deployment of insights. 16 With its origins in web analytics (Adobe acquired Omniture in 2009), the solution is particularly strong when analyzing data from digital channels and operationalizing insights through those channels. The tool is specifically designed for marketers who lack a technical background but would like to surface actionable insights in their customer data. There is also an interface for data scientists who wish to build custom models then deploy them to the business user. While Adobe doesn t offer the full array of algorithms the other Leaders in this evaluation do, its strong performance is due its fluent integration of data, analytics, and action for digital marketers. Pricing was the biggest source of frustration in among the clients we spoke with. Client references unanimously agreed Adobe should simplify its pricing structure. In addition, clients said that Adobe s services were still relatively siloed due to its aggressive acquisition strategy. On the product side, however, clients complimented Adobe s commitment to continual advancement and were surprised by the company s ability to integrate platforms from many acquisitions. Several clients noted that Adobe is highly responsive to client feedback when it comes to product enhancements. Adobe is a good fit for brands looking to use customer analytics in real time to drive digital marketing. 10

12 Consider Pitney Bowes for customer centricity and smooth marketing integration. Pitney Bowes Portrait Explorer interface, which displays each customer record as a small baseball card highlighting key attributes of the customer profile, is highly differentiated and puts the customer literally and visually at the center of all analyses. Pitney Bowes facilitates difficult analytical processes like data preparation for the less technically savvy marketer or customer insights. With firm roots in location analytics (due to the acquisition of MapInfo in 2007), Pitney Bowes is wellpositioned to leverage the growing volumes of contextual mobile and IoT data. It also offers a variety of industry-specific, demographic, and firmographic, data products for further data enrichment. Client references cited the ability to analyze data from emerging channels and touchpoints as a key area for improvement. Pitney Bowes does not currently offer text analytics, but it plans to add that capability. Pitney Bowes more technical clients would like them to add additional modeling techniques to their offerings, while less technical clients say their training materials need improvement. All client references were highly satisfied with analytics activation how the tool integrates with existing marketing applications to execute on insights which is of primary importance for many brands. Strong Performers Consider SAP for either automated modeling or analytics-driven marketing. SAP is a tale of two products. On the one hand, there is SAP Predictive Analytics, an analytical workbench for business analysts and data scientists that came from the 2013 KXEN acquisition. Its link analysis functionality visualizes the correlation between variables, simplifying data exploration. On the other hand, there is SAP Hybris Marketing, which has modeling functionality of its own, as well as model management and deployment capabilities. Its strength is in operationalizing models created within the solution or elsewhere. As a result of these two similar products, SAP faces a significant strategic challenge they either must clarify the differences between the two or integrate them. From a functionality perspective, client references bemoaned SAP s lack of ETL abilities, citing data management as the largest area in need of improvement. Also, while SAP s breadth of different algorithms is a strength, it is also drawback since clients would like more guidance on when to employ them. Despite this, all SAP s client references we surveyed said they would not switch to another vendor for the same or lower price. If SAP clarifies and streamlines its product strategy, they may very well join the ranks of the customer analytics leaders. Consider FICO for robust modeling and dual interfaces. FICO Analytic Modeler Scorecard broadens the accessibility of customer analytics by allowing marketers and CI pros to develop models using a logical step-by-step approach that overlays business requirements on top of mathematics. The resulting models appear with explanatory text describing how the model was built, detailing how it works, and assessing its performance. This circumvents the need for a data scientist to interpret results and gives CI pros the language they need to explain their analyses to 11

13 internal stakeholders. The emphasis on explaining analysis to nontechnical resources throughout the process is a strong differentiator for FICO. On the other hand, FICO also offers Model Builder for data scientists looking to do more advanced analytics. Client references mentioned there is room for improvement in FICO s ability to integrate into marketing execution systems, saying the analytics activation phase required too much intervention from IT. Another client said that their pricing model, particularly for large organizations, is challenging and can be expensive. From an analytical perspective however, clients referenced FICO s thought leadership and strength in predictive modeling, not surprising for the company that created and universalized the FICO score. Brands looking for a tool to democratize analytics across the enterprise may find it with FICO. Consider Teradata if you have code-savvy analysts and massive amounts of data. Of all the vendors included in this Forrester Wave evaluation, Teradata is the only one that doesn t offer a workbench for customer analytics. This means that Teradata Aster Analytics users must know how to code. On the other hand, Teradata s Customer Interaction Manager is very marketer-friendly and with Aster provides end-to-end execution of customer analytics and campaign management. From an analytical perspective, Aster offers an impressive array of algorithms that are optimized to run at scale on all types of data. Aster s AppCenter is a key differentiator and best in class method for sharing data preparation and modeling processes. Teradata s client references see a gap in driving analytics to the front line, and would like to be able to export their models into a real-time streaming infrastructure. Teradata plans to release a real-time scoring engine to Aster Analytics later this year. It is also developing an analytics workbench as well as an automatic code generator to democratize the use of Aster. Teradata Aster can provide firms with rich customer analytics, but firms which are dependent on a visual GUI may want to wait for Teradata to roll out its new interface for marketers. Consider Angoss as a standalone or complementary tool for DIY advanced analytics. Angoss KnowledgeStudio is a powerful analytical tool that allows users to build complex repeatable analytical processes in an intuitive GUI. Although there is some modeling automation, it is geared toward an advanced business analyst or data scientist. It offers a wide range of data preparation capabilities, a necessity for companies looking to mine customer data for insights. Its customer analytics suite comes to life when paired with other solutions such as its Real-Time Scoring Engine for model activation. Its strategic partnerships with Tableau for data visualization and reporting and Lexalytics for Natural Language Processing also add valuable functionality. Plus, given its comparatively lower price point, Angoss can either work as a standalone solution or in conjunction with another customer analytics provider. Overall, client references were very satisfied with Angoss data management and modeling capabilities, as well as its overall usability. Data visualization within the tool itself could be improved one client reference said he exports results into Excel for visualization, which Angoss actually promotes to facilitate collaboration with business decision-makers. Also, client feedback 12

14 suggested that Angoss users needed some statistical background and that training in the tool is essential. We believe that Angoss is a compelling solution for firms looking for low-cost entry into customer analytics. Consider Alteryx if you re interested in location or LoB analytics at low cost. Alteryx offers a less expensive yet robust analytical tool that empowers business users with advanced analytical algorithms. It is also a strong tool for the messy business of data preparation, and makes data blending (or joining data from multiple tables) easy for users. Alteryx s partnerships are a key competitive strength. For firms looking to use geospatial data for contextual marketing, its partnership with TomTom makes it a compelling option. Strategically, its land and expand model and partnerships with companies like Tableau and Salesforce have paid off significantly and have led to dramatic growth over the past three years. Client references pointed to Alteryx s native data visualization capabilities as an area for improvement. They would also like to see the company offer more training and community building among its users, which may improve due to the recent launch of the Alteryx Community. Finally, they advised potential Alteryx clients to factor in the steep learning curve. For analysts with an Excel background, it may take three to six months to become fully ramped, but ultimately the gains in analytical productivity and maturity are worth it. Consider Manthan for retail analytics at scale. Manthan is very good at what it does. Namely, it contains 175 prebuilt reports and prides itself on its quick time to insights. It runs on Hadoop and uses Spark to process massive quantities of data in real time. It also offers a partitioned interface for marketers, data scientists, and administrators. It s what Manthan doesn t do, however, that may leave marketers wanting more. While it offers robust retail-specific analyses, it is still building its capabilities for other verticals. It provides little data preparation functionality and doesn t analyze text data. It also lacks the assortment of different algorithms of its competitors. The most limiting aspect of Manthan s current offering is its rigid architecture which can prove painful to change once it is up and running. Client references said, If you need to add an attribute, column, or anything you need to ask Manthan, which is costly and takes a lot of time. On a similar note, client references cited the ability to analyze data from emerging channels and touchpoints as a key area for improvement. Overall, however, clients were quite happy with the fact that Manthan makes it easy to onboard business users quickly and at scale. Contenders Consider AgilOne for DIFM (do it for me) data prep and analytics. AgilOne is built for marketers who do not want to deal with the messy and technical processes of extract, transform, and load (ETL) and model building. It automatically calculates 400 metrics, such as likelihood to buy and predicted customer lifetime value. It uses exact and fuzzy matching to identify the same customer across different data sets. And it has its own built-in marketing automation system in addition to integrating seamlessly with most others, thus making analytics easily and effectively actionable. As 13

15 analytics sophistication grows, we find that many marketers and CI pros want to create their own custom metrics and models, data types and structures shift over time, and changes in business priorities require analytical agility. AgilOne s do it for me solution doesn t always give these more mature marketers the flexibility to respond to these changes at the speed of business. One client reference we spoke with said AgilOne went out of their way to do things for us, but the capability that we need and their ability to deliver don t match. They advised prospective clients to make sure AgilOne fits their specific needs before purchasing it. Other clients who aren t looking to perform modeling themselves find immediate value in embedded analytics. Another client said, It was amazing to go live and have a whole book of reports. It was a quantum leap in customer analytics for us. AgilOne could easily become a Strong Performer if it manages to strike the right balance between simplicity and flexibility. 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 Ask a question related to our research; a Forrester analyst will help you put it into practice and take the next step. Schedule a 30-minute phone session with the analyst or opt for a response via . Learn more about inquiry, including tips for getting the most out of your discussion. Analyst Advisory Put research into practice with in-depth analysis of your specific business and technology challenges. Engagements include custom advisory calls, strategy days, workshops, speeches, and webinars. Learn about interactive advisory sessions and how we can support your initiatives. Supplemental Material Online Resource The online version of Figure 3 is an Excel-based vendor comparison tool that provides detailed product evaluations and customizable rankings. 14

16 Data Sources Used In This Forrester Wave Forrester used a combination of data sources to assess the strengths and weaknesses of each solution: 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. Product demos. We asked vendors to conduct demonstrations of their product s functionality. We used findings from these product demos to validate details of each vendor s product capabilities. Customer reference calls and surveys. To validate product and vendor qualifications, Forrester also conducted reference calls and surveys with 3 of each vendor s current customers. The Forrester Wave Methodology We conduct primary research to develop a list of vendors that meet our criteria to be evaluated in this market. From that initial pool of vendors, we then 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. 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 document and then score the vendors based on a clearly defined scale. These default weightings are intended only as a starting point, and we 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, go to Integrity Policy All of Forrester s research, including Forrester Wave evaluations, is conducted according to our Integrity Policy. For more information, go to 15

17 Endnotes 1 Forrester has identified the age of the customer as a 20 year business cycle in which firms must be customer obsessed in order to success. See the Winning In The Age Of The Customer Forrester report. 2 Source: Forrester s Global Business Technographics Marketing Survey, For more information on systems of insight, see the Digital Insights Are The New Currency Of Business Forrester report. 4 Marketers can no longer afford to focus solely on customer acquisition and need to manage the entire customer lifecycle. See the How Analytics Drives Customer Life-Cycle Management Forrester report. 5 Data scientists, once frequently referred to as unicorns, are now sometimes referred to as data janitors because they spend 50% to 80% of their time data wrangling or data munging. Source: Steve Lohr, For Big-Data Scientists, Janitor Work Is Key Hurdle to Insights, The New York Times, August 17, 2014 ( com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html?_r=0). 6 For more information on advanced analytics, see the Seven Advanced Analytics Must-Knows Forrester report. 7 For more information on predictive analytics solutions for Big Data, see the The Forrester Wave : Big Data Predictive Analytics Solutions, Q Forrester report. 8 Many firms struggle to bridge the insights to action gap. To do so, they need to plan for implementation well before starting to build models with individualized outputs. See the Brief: Close The Insights-To-Action Gap With A Clear Implementation Plan Forrester report. 9 For more information on contextual marketing, see the The Power Of Customer Context Forrester report. 10 For more information about predictive analytics solutions, see the The Forrester Wave : Big Data Predictive Analytics Solutions, Q Forrester report. 11 For instance, EMC built a model to predict low customer satisfaction scores and contacts customers when there is a triggering event. See the Beyond Surveys: New Tools For More Effective CX Measurement Forrester report. 12 These are estimates calculated using midpoints of ranges and are not exact. Source: Forrester s Global Business Technographics Data And Analytics Survey, SAS, IBM, Pitney Bowes, and KXEN (now part of SAP) were Leaders in the following report. See the The Forrester Wave : Customer Analytics Solutions, Q Forrester report. 14 For more information on Adobe s web analytics capabilities, see the The Forrester Wave : Web Analytics, Q Forrester report. 15 R2D2 is a beloved character from the original Star Wars films. As a droid, he understands his master s speech commands and responds in adorable beeps and squeaks. BB8 is a similar droid in Star Wars Episode VII: The Force Awakens. 16 For more information on Adobe Marketing Cloud, see the The Forrester Wave : Enterprise Marketing Software Suites, Q Forrester report. 16

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