The Complete Guide to Embedded Analytics

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This 66-page guide goes over everything you need to know about embedded analytics - targeted for software executives and product managers looking to build product value with embedded analytics. Learn …

This 66-page guide goes over everything you need to know about embedded analytics - targeted for software executives and product managers looking to build product value with embedded analytics. Learn more at www.logianalytics.com.

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  • 1. THE COMPLETE GUIDE TO EMBEDDED ANALYTICS
  • 2. Table of Contents Introduction.....................................................................................................................2 Part One: What is Embedded Analytics?.................................................................3 Part Two: Why is Embedded Analytics So Hot Right Now?.................................8 Part Three: Ways to Approach Embedded Analytics..........................................14 Part Four: Common Features in Embedded Analytics.........................................21 Part Five: The Business Case for Embedded Analytics.....................................26 Part Six: How to Be Successful with Embedded Analytics...............................40 Part Seven: Buying an Embedded Analytics Solutio...........................................52 Part Eight: The Future of Embedded Analytics....................................................60 Part Nine: Logi Analytics’ Approach to Embedded Analytics...........................63 Conclusion......................................................................................................................65 1
  • 3. Introduction Why Should I Read This? Every software company has one thing in common: the desire to create great products. But many software providers fail to leverage the full value of the data collected in their applications to deliver a superior user experience. The Complete Guide to Embedded Analytics is designed for software executives and product managers to answer any and all questions you have on this topic. It will show you what embedded analytics is and how it can help you build product value. It will also explain: • Why embedded analytics is so hot right now • How embedded analytics differs from business intelligence • How to build a business case and convince internal stakeholders to act • How to select the right solution • The future of embedded analytics We’ve also included worksheets that you can use to determine whether your company is ready to invest in embedded analytics and how it will help you improve user experience, attract new customers, and increase sales. We hope this guide will be your ticket to a new world of more effective, efficient, and lucrative application development. Introduction 2
  • 4. PART ONE WHAT IS EMBEDDED ANALYTICS? 3
  • 5. Embedded Analytics Defined Let’s begin with a definition. Embedded analytics is the technology that integrates analytic capabilities into software applications. When implemented successfully, embedded analytics can bring to life the data collected by your applications and provide a superior user experience. Common Analytics Capabilities within Software Applications • Dashboards and data visualizations: charts and graphs that display performance metrics • Static and interactive reports: tabular views of data with or without parameters and scheduling capabilities • Self-service analytics and ad hoc querying: enables users to ask their own questions of the data by exploring a set of data • Benchmarking: comparing performance metrics against best practices from external data • Mobile reporting: ensures interactive functionality on mobile devices and takes advantage of capabilities specific to mobile devices • Visual workflows: incorporating transactional capabilities directly within the analytic user interface, sometimes referred to as write-back Part One: What is Embedded Analytics? 4
  • 6. How is Embedded Analytics Different from Business Intelligence? It’s all about context. Business intelligence is a set of independent systems (technologies, processes, people, etc.) that aggregate data from multiple sources, prepare the data for analysis, and then provide reporting and analysis on that data from a central view point. It is most optimized for supporting management-level level decisions that require highly aggregated views of information from across a department, function, or entire organization. These systems are specifically designed for people whose sole responsibility is to perform data analysis. Embedded analytics is a set of capabilities that are tightly integrated into existing systems (like your CRM, ERP, marketing automation, and/or financial systems) that bring additional awareness, context, or analytic capability to support decision-making related to very specific tasks. These tasks may require data from multiple systems or aggregated views, but the output is not a centralized overview of information. It is targeted information to support a decision or action in the context in which that decision or action takes place. Part One: What is Embedded Analytics? Said another way, business intelligence is a map that you utilize to plan your route before a long road trip. Embedded analytics is the GPS navigation inside your car that guides your path in real time. While traditional BI has its place, the fact that BI applications and business process applications have entirely separate interfaces forces users to switch between multiple applications to derive insights and take action. Instead, embedded analytics puts intelligence inside the applications people use every day to improve the analytics experience and make users more productive by combining insight and action in the same application. 5
  • 7. Who Uses Embedded Analytics? By Industry Companies across all industries and functions (e.g. marketing, sales, finance) choose embedded analytics to help users make sense of their data so they can make better, more informed decisions. While many business applications rely on embedded analytics to differentiate their products, early adopters (as well as more advanced implementations) have primarily been consumer applications, like Amazon and Kayak. We will explore several examples in detail later in this section. Commercial vs. Internal Apps Embedded analytics is about satisfying users’ analytics needs at the exact moment they might question something within the applications they use every day. Software companies have long recognized the value of embedded analytics to their customers (and the resulting benefits to themselves), but internal application developers have been slow to integrate capabilities between analytic and operational apps. With the rise of the business user, there is mounting pressure on IT and increased expectations that more people should have access to analytic information. Particularly in large enterprises where most of the operational systems are proprietary, developers are increasingly embedding analytics into these systems to provide relevant information for knowledge workers to make better decisions. Part One: What is Embedded Analytics? 6
  • 8. Are You Ready for Embedded Analytics? Use this worksheet to determine if your company is ready for embedded analytics. For each category, select the appropriate number that corresponds with your level agreement (5=strongly agree, 1=strongly disagree). When you’re finished, tally your score to determine your results. If you scored > 35, you’re ready for embedded analytics. If you scored between 20 - 35, you’re moving in that direction. You should consider getting started with embedded analytics soon. If you scored under 20, you may not be ready for embedded analytics quite yet. But that doesn’t mean you can’t keep reading! Flip over to Part 2, and let’s get started! Factor Strongly Agree 5 4 Strongly Disagree 3 2 1 Our customers consider analytics to be an important part of our application. We need to find ways to monetize the data captured in our application. We’d like to offer more sophisticated reporting and analytics capabilities within our application. Our customers are dissatisfied with the level of reporting and analytics we offer today. We invest too much in development resources to support ad hoc reporting requests. We are losing deals to our competitors based on their reporting and analytics capabilities. We could improve our sales demos if we improved the functionality and/or look & feel of our reporting. Data drives the majority of our customers’ decisions, and we want to capture that value in our application. Our product generates (or has plans to generate) a significant level of personalized reporting for each customer. We plan to migrate to a SaaS environment and question whether our current reporting will be compatible. Part One: What is Embedded Analytics? 7
  • 9. PART TWO WHY IS EMBEDDED ANALYTICS SO HOT RIGHT NOW? 8
  • 10. Trends Driving Embedded Analytics While reporting inside software applications isn’t anything new, it wasn’t until 2009 that Google recorded any significant traffic for the term “embedded analytics,” which has increased steadily since then. 100 Interest Over Time 80 40 20 20 13 20 12 20 11 20 10 20 09 0 20 08 In many ways, the rise of embedded analytics is a natural evolution of emerging analytic technologies and the business intelligence market. From Crystal Reports in the 90s, to OLAP multi-dimensional analysis at the turn of the millennium, we’re now entering a new wave where users expect to benefit from data analysis in every application they use on a daily basis. Their needs are more varied and require a shift in the traditional BI thinking. In Forrester’s 2013 Global Tech Market Outlook report, Forrester explains that software companies looking to deliver analytic capabilities should not rely on general business intelligence tools, since these tools often require users to create their own reports, which doesn’t help them to work smarter. Enter embedded analytics, where software companies take care of “the last mile.” 60 20 07 Natural Evolution of the BI Market Source: Google Trends Part Two: Why is Embedded Analytics So Hot Right Now? 9
  • 11. Trends Driving Embedded Analytics (Continued) Increased Data Volume, Velocity, and Variety With so much hype around big data, this one shouldn’t be a surprise. People now expect information to be available at their fingertips – whenever they want it, wherever they are. Yesterday’s fact-finding missions that started with an encyclopedia or a visit to the library have become quick Google searches on your phone to find an answer mid-conversation. Users now rely on technology to comb through massive amounts of ever-changing data to organize relevant information to solve their problems. People need answers quickly, and they choose to use technologies that optimize the analysis experience and answer questions in a visually attractive way. Data in the Hands of Novice Users This influx of non-technical analysts has forced BI and other B2B software companies to rethink their analytic capabilities and how to present information in a simple, more user-friendly way. Users expect the software to take care of the last mile instead of pulling information and mashing it together. Consumer Apps that Utilize Analytics Companies like Amazon and Kayak have mastered the art of simultaneously providing data-driven information and driving frictionless transactions in a seamless manner. These companies have embraced analytics to create a user-friendly experience that attracts more users and creates a competitive advantage. As a result, users now expect more from the business applications they use regularly to do to their jobs, which puts pressure on software providers to satisfy their needs. Data analysis is no longer relegated to a few highly trained, technical people. Ten years ago, most analytics and information delivery was taken care of by IT. In the past several years we’ve seen the rise of data discovery, which takes control from IT and puts it in the hands of power analysts. But even that falls short of today’s needs because ALL workers are now expected to use technology to gain efficiency and increase productivity. Everyone needs to be a “data expert” in their own domain so they can make intelligent decisions that drive business forward. Part Two: Why is Embedded Analytics So Hot Right Now? 10
  • 12. Embedded Analytics Drives Smart Process Applications Companies that provide embedded analytics in their applications achieve faster revenue growth. In Forrester’s 2013 Global Tech Market Outlook report, they define a new class of applications called smart process applications. These applications provide: • Embedded awareness data relevant to the business activity • Embedded analytical tools designed for the task at hand In these applications, analytics is a core capability, such that the lines are blurred between transactional capabilities and analytics capabilities. Process Applications Smart Process Applications Analytics Applications According to Forrester’s research, Smart process applications are growing at double-digit rates (18%) that are significantly faster than the overall software market (7%).” Forrester estimates double-digit growth for smart process applications from $20 billion in 2012 to $28 billion in 2014. Part Two: Why is Embedded Analytics So Hot Right Now? 11
  • 13. B2C Applications Pave the Way for Embedded Analytics Fundamentally a transactional application (selling books) Satisfies customers’ informational needs Makes it easy to initiate the right transaction Fundamentally an analytics application (flight prices) Price Trend To create a customer-friendly experience at the point of transaction. To build an entirely new business model around this concept. Amazon Kayak Amazon is the Gold Standard for providing relevant analytics to encourage on-site conversion. Fundamentally, Amazon exists to sell books. They have great processes to support the ecommerce experience – including fast shipping, low prices, and Buy Now with 1-Click. But Amazon also satisfies customers’ informational needs by providing product ratings, video reviews, and suggested products. They’ve added tremendous value by providing relevant analytic information at the point of transaction to create a superior customer experience. Another good example is Kayak. In this case, what started out as a pure analytic application (enabling users to search for flights and compare prices across multiple airlines and travel sites) launched an entirely new business model by integrating transactional capabilities at the point of analysis. So not only do you have all of the information you need to choose an itinerary, but you can also initiate the purchase within the Kayak application, without having to reenter your search criteria when you are redirected to the airline booking site. Now Kayak is able to make money by providing leads to airlines and travel sites like Expedia and Orbitz. 27.1% growth rate in 2012 (vs. 15.8% across Top 500 US ecommerce sites) 2012: Closed IPO Day with Shares Up 30%, 2013: Acquired by Priceline for $1.8 Billion Part Two: Why is Embedded Analytics So Hot Right Now? 12
  • 14. B2B Applications Lag Behind in Embedded Analytics In business applications, the analytics is typically presented in a separate tab - like the Reports and Dashboards tabs in Salesforce.com. What If I’m Behind? After seeing how Amazon and Kayak utilize embedded analytics in their applications, however, this model somewhat pales in comparison. As Amazon customers browse their site, they actually feel smarter and more confident that they’re making the right choices. That’s because analytics is a core capability, not an adjunct capability with no real connection to common workflows and overall user experience. Part Two: Why is Embedded Analytics So Hot Right Now? You might be thinking, “But wait! I don’t even have a reports tab. Can’t I take some baby steps first?” The answer is yes. In fact, we recommend evolving your analytics capabilities in phases over time. In the next section, we’ll explore the embedded analytics maturity model, which illustrates the four ways you may choose to embed analytics within another application. 13
  • 15. PART THREE WAYS TO APPROACH EMBEDDED ANALYTICS 14
  • 16. What is a Maturity Model? In this section, we’ll present the Embedded Analytics Maturity Model, which outlines the various approaches from simple to complex. To set the stage, let’s use the example of GPS in cars to illustrate the innovation model for embedded technologies. As you can see, what started out as a separate product evolved into a core car component, which created a new product category of self-driving cars. The same evolution exists for embedded analytics. Independent GPS Device Embedded GPS Navigation Integrated Functionality GPS as a Core Car Component KEY FEATURES KEY FEATURES KEY FEATURES KEY FEATURES • Bolted on your windshield - disjointed user experience • Within driving console improved user experience • Highlight gas stations when fuel is low • Create new ways of travel (e.g. self-driving car) • Auto manufacturers capture value • Route based on traffic and weather • Reach new user groups (blind, elderly) • No value for the auto manufacturer Part Three: Ways to Approach Embedded Analytics • Build new businesses 15
  • 17. Embedded Analytics Maturity Model Below are the four stages of the embedded analytics maturity model, which illustrates the progression of increasing integration and product differentiation. Along the bottom are the common elements shared by the core application and the analytics capabilities, including data source, security, UI, and workflows. Standalone Analytics Application Analytics App Your App Gateway to Analytics Analytics App Your App Inline Analytics Infused Analytics Module Your App UI Non-Embedded 8% UI Embedded 29% 47% 16% Distribution of Current Implementations (2013 State of Embedded Analytics Report) Data Source Part Three: Ways to Approach Embedded Analytics Security UI User Interface Workflows 16
  • 18. Stage 0 Standalone Analytics Application The Standalone Analytics Application is Stage 0, because the analytics are not embedded into the core application at all. Much like the standard GPS device, the data lives in the core application, and the analytics live in another application. The only integration concern is how to get the data into the analytics application. Data access is typically provisioned through an API or a data export. From a user standpoint, it is a disjointed experience because users have to work with two separate applications, which likely look and operate differently and have no security integration. A familiar example is Microsoft Excel. Users often export data from one application and export it into Excel for analysis, but all they’ve done is create a new version of the data. Once the data changes, the Excel data becomes outdated. Your App Analytics App When to Pursue This Model The most common use for a Standalone Analytics Application is when your product has no business user interface, like Google Analytics. Programmers tag their website or application with a piece of a code to track visitor activity. This visitor activity gets fed into the Google data store, but business users only access that data by logging into the Google Analytics website. Essentially it’s two separate applications, because the data store isn’t intended for business users to see. Part Three: Ways to Approach Embedded Analytics 17
  • 19. Stage 1 Gateway to Analytics Gateway to Analytics is Stage 1, where the core application serves as a gateway to the analytics application. In this model, the analytics application is integrated with the core application at a security level. Users only need one set of login credentials, which are passed from the core application to the analytics application via single sign-on (SSO). Note that there are still two applications, but the access to analytics is embedded in the core application. It’s still a disjointed experience, however, because if users actually want to put insights to use they have to switch back to the core application. Your App Analytics App When to Pursue This Model There are three scenarios in which you might choose the Gateway model: 1. You’re working with hybrid architectures 2. Analytics is a distinct offering that customers purchase separately 3. This an intermediate step before moving on to the next stage of embedding From a development standpoint, you also have separate work streams. Obviously analytics needs access to the data, which requires security integration, but otherwise the functionality is entirely separate from the core application. Part Three: Ways to Approach Embedded Analytics 18
  • 20. Stage 2 Inline Analytics Inline Analytics is Stage 2, and the most popular choice for embedded analytics. Just like the GPS example where functionality is integrated to adjust driving directions based on traffic, analytics now appears within the core application. Beyond data access and security, analytics is now integrated at the presentation tier and shares the same look & feel as the core application UI. Note that this is the first model where we have just one application instead of two. Inline Analytics is often implemented as a Reports tab or module (as in the Salesforce.com example), but analytics are sometimes presented in other key places as well, like the home screen. Module Your App Analytics Module When to Pursue This Model Software providers choose this model when users demand easy and frequent access to analytics. From a work stream standpoint, there is more coordination involved, particularly when it comes to UI, but they can still be separate when it comes to functionality. Most third-party platforms and analytics applications can be embedded using this approach, so there are a lot of options available. Users are also very comfortable with the reports module approach, so it’s not surprising that Inline Analytics is the most common model for embedded analytics. Part Three: Ways to Approach Embedded Analytics UI 19
  • 21. Stage 3 Infused Analytics Infused Analytics is Stage 3, the holy grail of embedded analytics. Just like the GPS example where functionality is so integrated into the car that it creates new forms of driving, analytics is now embedded at the application tier within user workflows and becomes part of the overall user experience. A good way to think about this model is that if you ripped out the analytics, you wouldn’t have anything left. You can have a car without highlighting nearby gas stations or optimizing driving directions for traffic, but you can’t have a self-driving car without a GPS. With Infused Analytics, users are able to view/create analytics to derive insights, and immediately take action. As users contemplate a choice, they can view relevant information to optimize their decision. Note that this approach is the only one that qualifies as a smart process application that we discussed in Part Two. When to Pursue This Model UI Companies choose this model when their users have sophisticated analytics needs and/or the competition’s analytics implementation is mature. Keep in mind that because there is integration at the application tier, the work streams need to be more tightly integrated. Developers working on the analytics will need to coordinate with the developers responsible for the transactional capabilities. Particularly if the backend is hitting backend APIs for data updates or interacts directly with the database, the work streams must be entirely integrated. Part Three: Ways to Approach Embedded Analytics 20
  • 22. PART FOUR COMMON FEATURES IN EMBEDDED ANALYTICS 21
  • 23. Features Overview Common embedded analytics features look a lot like common “business intelligence” capabilities, but with a twist: the end user experience is integrated into the overall application. The capabilities embedded in each application vary, so we have also indicated how often we see each feature implemented: Common Sometimes Infrequent Part Four: Common Features in Embedded Analytics 22
  • 24. Information Delivery Improving how data is presented to business users is often the top reason software providers look to take on an embedded analytics project. Not only should these capabilities meet the needs of end users, but the look and feel should adhere to the style requirements of the software provider. Visualizations and Dashboards A range of visualizations are utilized for users to quickly draw conclusions and monitor key performance indicators, such as bar charts, line graphs, heat maps, and maps. They can be presented in the context of a single chart, or in a collection of visualizations in a dashboard. Mobile Data is made available to users on mobile devices, ensuring not only the accurate visual display of information, but also be compatible with mobile device features such as touch input. Reports A tabular display of data, often with numerical figures and/or listing of records within categories. Reports can be scheduled for delivery, used in conjunction with thresholds/alerts, or exported to other formats for printing or offline access. Part Four: Common Features in Embedded Analytics 23
  • 25. Analysis Software providers look to enhance the value of their offering by allowing users to perform their own analysis, creating benchmarks and applying proprietary analytics on their own data, and finding innovative ways to incorporate external data sets. Self-Service Analysis Users are given a set of data for which they can filter, create custom calculations, and create their own reports or visualizations. The data set for the end user is restricted to the particular form of analysis and their user role. Benchmarking and Data Modeling This is centered on extracting valuable insights from the existing data in hand and making these insights available in the application. For example, SaaS providers can create performance benchmarks by analyzing data across their customer base and make this data available for individual customers to compare themselves to. Another example would be applying predictive models to set expectations of future performance. Part Four: Common Features in Embedded Analytics External Data Incorporating data from external sources and delivering them into a single view or dashboard, such that the application becomes a vital hub of information. This could be in the form of third-party industry benchmarks, data feeds (such as weather and social media), and customer data from their specific data stores. 24
  • 26. Interactivity Embedding analytic capabilities inside of applications presents interesting ways for users to interact with those capabilities, as well as paves the way for a more informed and productive experience inside the application. Linking This enables the user to click on a visualization or report in order to navigate to a different analytic screen or even another part of the main application, and vice versa. In other instances, the interaction simply changes part of the screen instead of the entire screen. Personalization Users choose the visualizations or reports which are most important to them, and place them at the top of a dashboard or create bookmarks that can be accessed quickly. Workflow and Processes Beyond linking, analytics can be more tightly integrated with application functionality. Some examples: charts embedded in-context on an existing application page to guide user behavior; a report with editable data cells, which enable user to update the displayed data; visualization with selectable regions (regions of a map or area of a scatterplot) which enable the user to perform an action on the selected records.future performance. Part Four: Common Features in Embedded Analytics 25
  • 27. PART FIVE THE BUSINESS CASE FOR EMBEDDED ANALYTICS 26
  • 28. The Return on Embedded Analytics: Core Business Case To help you build the case for embedded analytics at your company, let’s first review the major benefits. Faster Revenue Growth On average, we find that software companies that embed analytics using the Infused approach have 16% higher annual revenue growth. Revenue growth comes from three distinct areas: 1. New sales – gain new customers and increase average selling price 2. Customer retention – increase retention rate and average renewal price 3. Sales and marketing efficiency – increase opportunity and win rates, lower cost per lead 65% 63% 56% 54% Create a competitive differentiator Increase customer satisfaction Give better sales demos Improve user experience 41% 36% 33% Attract new users Increase overall revenue Improve your win rate 0% 10% 20% 30% Part Five: The Business Case for Embedded Analytics 40% 50% Strong agreement 60% 70% 27
  • 29. Benefits of Embedded Analytics Create a Competitive Differentiator Reduce Ad Hoc Support Requests One of the major benefits of embedded analytics is to help you differentiate your product. Many software providers only deliver basic reporting capabilities through their applications, particularly in the business application market. There is still a huge opportunity to create a competitive differentiator by offering more advanced and/or easier-to-use analytics in your application, which provides real value to end users. In a recent survey, we found that the most cited benefit of implementing embedded analytics was creating a competitive differentiator, and 93% of respondents agreed they had realized this result. How many times have you been asked to create a custom report for a customer? And once you deliver that report, the customer comes back asking if you can change this or that, and before you know it you’ve spent a significant number of man hours supporting this request. Multiply that over hundreds of customers and you’re no longer a software company – you’re a development shop. Embedded analytics helps you to bridge the gap between what exists in your application today, and what your customers are asking for, so you can focus on more important things. Improve User Experience When implemented properly, embedded analytics can have a profound impact on your user experience. By enabling users to solve problems quickly within your application, as opposed to fumbling around in standalone reports trying to piece together what’s happening, or forcing them to export data to slice and dice in Excel, you make their lives easier. At the end of the day, it’s about minimizing the time and effort that exists between insight and action. The easier you make it for your users to do something about insights they find in your application, the more value you can capture. Part Five: The Business Case for Embedded Analytics 28
  • 30. Competitive Differentiation from Embedded Analytics Deeper integration of analytics into your software application enables greater ability to create a competitive differentiation. Overall, 65% of software providers strongly agree that embedded analytics is a source of competitive advantage, and that increased to 91% of software providers who choose the Infused Analytics Model. This is great news if you’re thinking about adding new analytics capabilities, but also if you’re looking to increase the value of your existing analytics by integrating them deeper into your application. Competitive Differentiator by Maturity Level 91% 65% 29% Standalone Analytics Application 46% Gateway to Analytics Inline Analytics Infused Analytics Source: 2013 State of Embedded Analytics report Part Five: The Business Case for Embedded Analytics 29
  • 31. Building Product Value Through Embedded Analytics Not surprisingly, as you embed analytics more deeply within your application, the more valuable analytics become as a percentage of the overall application. Note that there is virtually no difference in the value of analytics between the Standalone and Gateway models, but the value more than doubles as soon as you bring analytics into the core user interface. Value of Analytics (Relative to Overall Application) by Maturity Model 51% 34% 15% 15% Standalone Analytics Application Gateway to Analytics Inline Analytics Infused Analytics Software providers with Infused Analytics also grow faster than other software companies. The 2013 State of Embedded Analytics study found that companies with Infused Analytics report 16% higher annual revenue growth than average. Part Five: The Business Case for Embedded Analytics 30
  • 32. Investment and Costs Once you estimate the returns from embedded analytics, you need to develop the other side of the business case by understanding the investment required. There are various costs associated with developing/implementing embedded analytics. If you decide to build, your costs will primarily consist of initial development resources and ongoing maintenance and enhancement costs. If you buy a business intelligence platform or integrate with an embedded platform, then your investment will consist of the following: software, development, consulting/training, support, etc. See page 49 for a further discussion around the build vs. buy question for embedded analytics. Part Five: The Business Case for Embedded Analytics 31
  • 33. The Cost of Embedded Analytics Choosing an analytics software provider to extend the capabilities in your application is a way to get to market faster and reduce resources over the long run. These providers may offer a variety of different pricing models. CUSTOMER-BASED PRICING assigns a cost to each of your customers. This is the most common pricing model since it is easy for companies to predict how many customers they will have in year 1, year 2, and year 3. USER-BASED PRICING assigns a cost to each end user of your application. This is a good model for companies with very stable growth, but can be challenging for early stage software companies and high growth software companies since it’s hard to predict how many end users you’ll have in three years, particularly as you start expanding your user base at each customer. Part Five: The Business Case for Embedded Analytics SERVER-BASED PRICING assigns a cost to each server. This can be challenging because you don’t want to be limited in your analytics capabilities, or face a steep price increase if one of your customers grows. Ideally, you want to find a vendor who is flexible enough to match their pricing to your business model, so that your analytics capabilities (and costs) are in line with your customer base and revenue forecasts. Other Investment Factors Most embedded analytics vendors offer add-ons on top of the software licensing costs—including new customer enablement, premium support packages, consulting services, and instructor-led education and training. The purpose is to accelerate ROI and time to value, and costs tend not to exceed 20% of the software licensing costs. You should also take headcount and hardware costs into consideration, both current state and anticipated costs in the future. The best results come from looking beyond only the software. When you OEM software and make it part of your application, you should consider who offers the best package as a long-term business partner. 32
  • 34. Bring It All Together Now it’s time to combine the improvement in analytics and the anticipated costs to calculate your return on investment. The key steps are: Gather baseline metrics such as number of current customers, average selling price, customer retention, and average renewal price Estimate how embedded analytics will improve your baseline metrics Estimate the implementation costs, including software, support, consulting, and development costs Calculate how improved baseline metrics will translate into revenue growth and lower development costs Calculating ROI Contact salesteam@logianalytics.com to build a personalized business case for your company using our Embedded Analytics ROI Calculator. Part Five: The Business Case for Embedded Analytics 33
  • 35. Selling Embedded Analytics Internally In some cases, not everyone will immediately see the value of an investment in embedded analytics. To convince them, you’ll need to understand their priorities, and what challenges stand in their way. Then you can connect the dots for them and position embedded analytics to address their specific concerns. When you’re trying to gain internal support for an embedded analytics purchase, position it as a potential solution to the current problems your business faces, and how it will impact your customers and the bottom line. That way your organization will see it as a priority instead of putting it on the back burner indefinitely. Your ability to deliver a compelling business case for embedded analytics hinges on how well you structure your argument to resolve the chief challenges and priorities of your audience. No matter which executive you want to convince, make sure to manage expectations about the time to value and effort required. Don’t overpromise and under-deliver. Embedded analytics is not something you just turn on and see instant results. Getting value takes a strong plan, time, and effort. Start small, and evolve your analytics capabilities over time as you and your end users become more sophisticated. Part Five: The Business Case for Embedded Analytics 34
  • 36. 5 Tips to Sell Embedded Analytics Internally Understand their goals so you can present a case that meets their needs. Create a financial case that proves how embedded analytics is the key to accomplishing those goals. If revenue is the main objective, make your case based on revenue. Discuss, don’t present. Start by asking questions to understand what they are looking to achieve. Then transition by saying, “If I could show you how to meet those objectives efficiently and effectively, would you be interested in learning more?” Support your case with real-life case studies, particularly from companies that are similar to yours. Enhancing our product through new analytics has assisted us in upselling into the marketplace to win deals with larger enterprises. During the economic downturn, we’ve been growing at 25 percent and replacing competitors with larger market share at Fortune 500 companies.” – Mike Mercadante, CTO, VPI Be ready if they say yes. Have your high-level plan ready, an overview of the next steps, timeframes, and required resources. Part Five: The Business Case for Embedded Analytics 35
  • 37. How to Position Embedded Analytics for Each Executive CEO CTO Chief Concerns: Chief Concerns: • Grow revenue and “hit the number” • Manage costs, meet shareholder expectations for profitability • Ensure that the product(s) function appropriately from an engineering standpoint over the long term • Improve products continually to stay competitive • Attract and retain talent • Build a better user experience • Innovate and out-execute the competition • Manage and leverage third-party technologies to deliver required capabilities • Build and align the organization to enhance collaboration • Manage risk Connect the Dots: Contextualize embedded analytics as key to business growth. Position it as a solution to create a competitive differentiator so you can become a leader in your space. Discuss how embedded analytics can enhance sales effectiveness through better demos, shorter sales cycles, etc. Show how embedded analytics improves the user experience of your product to increase customer satisfaction, and drive new revenue. Connect the Dots: Educate them on the available technologies and architectures to show how embedded analytics platforms enable long-term growth. Discuss how embedded analytics can ease the burden on development to deliver analytics enhancements on time. Discuss options of working with an embedded analytics partner who is familiar with security, white labeling, and UI/UX requirements so the implementation goes more smoothly and allow their team to maintain their focus on core functionality. Discuss how an embedded analytics platform enables you to go to market faster with new analytics capabilities without sucking up development resources. Part Five: The Business Case for Embedded Analytics 36
  • 38. How to Position Embedded Analytics for Each Executive (Continued) CFO Head of Sales Product Manager Chief Concerns: Chief Concerns: Chief Concerns: • Manage expenses and cash flow • Make quota • Contain risk • Get an accurate forecast • Enable profitable growth • Beat the competition • Plan for the future • Expand market share • Success of the product line in the market • Deliver features and functionality customers are asking for Connect the Dots: • Make customers successful • Deliver a superior user experience Do the math. Present your developed business case. Don’t worry too much about the fact that you’re making estimates, provided that they’re clearly labeled. You’ll build credibility simply by walking in the door with a spreadsheet of numbers, showing you can speak the CFO’s language. • Develop the sales force • Maximize customer satisfaction Discuss the notion that embedded analytics helps you save time and resources fielding ad hoc reporting requests from current customers. Connect the Dots: • Meet launch deadlines Remember that sales is on the front lines and often drives product direction based on what they hear from their prospects. Explain how embedded analytics can deliver the analytics capabilities your customers are asking for quickly so their team can start selling it. • Beat the competition Discuss how embedded analytics helps their team to deliver better sales demos, decrease sales cycles, box out the competition, and drive new revenue. Discuss the value of embedded analytics to end users, which drives increased renewal rates, exposes new opportunities to sell more product, and possibly drives new revenue streams (depending on your packaging model). Part Five: The Business Case for Embedded Analytics Connect the Dots: Build the vision of how the product will be enhanced through embedded analytics – what will their users be able to accomplish and what is the value of solving those problems? Discuss how embedded analytics is a “need to have” and how it can create a competitive differentiator as well as enhances sales effectiveness through better sales demos, short sales cycles, and increased revenue. Educate them about the options in the market to add analytics quickly while meeting launch deadlines and maintaining control over UI/UX. 37
  • 39. The Cost of Delaying Here are some common reasons why people may delay investing in embedded analytics platform, and how you can overcome them. OBJECTION RESPONSE I don’t need to use a third-party tool because I can build this all internally. That’s true. With enough time and energy, you can build everything an embedded analytics platform can offer, but do you really want to spend years building analytics? A third-party application allows you to develop faster and save money so you can maintain your focus on advancing your core functionality. Embedded analytics has evolved to the point where hundreds of companies spend 100% of their time trying to make analytics better, faster, and easier for end users. By utilizing a third-party platform, you can do what you do best, while relying on analytics experts to make your product better. Let’s just give our customers access to the data and let them do what they want. You’re leaving value on the table, and instead of enabling end users to do their jobs more intelligently, you’ve settled for becoming a data collection tool. Besides, data exports are no longer going to cut it – your customers expect more. By only providing exports, you’re inviting your customers to come back frustrated with custom reporting requests that eat into your time. We don’t have the resources necessary to do this successfully. All the more reason to integrate with a third-party platform, which brings the domain expertise necessary to implement embedded analytics successfully, including how-to guides, best practices, and in-person consultations. You can start small, or look for tools that conform to your architecture and your development process. The days of Big BI are over - there are some great options that will enable you to add real value to your applications without taking over everything. Part Five: The Business Case for Embedded Analytics 38
  • 40. OBJECTION RESPONSE We know our customers want something but we don’t know exactly what it is they want. If your customers are communicating some sort of pain, you need to investigate what’s driving that frustration. A good embedded platform will support you through a structured evaluation process where you build a proof of concept together, which you can use to validate your direction with your customers. In this case, it’s important to choose a platform that is easy to use and supports rapid prototyping so you can pivot appropriately. You can also rely on embedded analytics platforms to guide you in how to package an analytics offering for your customers based on their requirements. Embedded analytics is a nice to have, not a need to have for us. Educate them on the evolution of embedded analytics within B2C and B2B applications, and how it is most definitely a need to have. Show how embedded analytics builds product value and enables your users to work more productively with your application. Present findings from the 2013 State of Embedded Analytics study, which outlines the clear benefits of embedded analytics, including creating a competitive differentiator, improving sales demos and conversion rate, and faster revenue growth, to support your case. Bottom line: there’s never a perfect time to roll out new software or start a new project. Don’t let the delay go on too long. You’ll always be busy. Once you’ve determined you’ll benefit from the investment, the longer you wait to implement embedded analytics, the longer you’ll wait to see a positive impact. Part Five: The Business Case for Embedded Analytics 39
  • 41. PART SIX HOW TO BE SUCCESSFUL WITH EMBEDDED ANALYTICS 40
  • 42. The Embedded Analytics Go-to-Market Plan Being successful with embedded analytics requires more than great technology. While there are important differences between embedded analytics platforms, success is primarily determined by your strategy and ability to execute. Here is your go-to-market plan to implement embedded analytics successfully. User Experience Implementation Market Delivery Business Value Packaging & Pricing Promotion User Profiles Agile Development Sales Enablement Capabilities Map Build vs. Buy Education Part Six: How to Be Successful with Embedded Analytics 41
  • 43. Step 1 - Define the User Experience: Creating User Profiles Repeat this mantra: I create value in my software by solving customer problems. The power of data is your customers can leverage your application to: • Derive insights faster • Draw accurate conclusions • Increase efficiencies and productivity • Reduce costs Part Six: How to Be Successful with Embedded Analytics 42
  • 44. Creating User Profiles (Continued) When planning to implement embedded analytics within your application, you’ll need to determine: • What problems do your users need to solve? • What information do they need access to? • What data sources do they need to connect to? • How often do they need access to analytics? These questions can help you flesh out the user roles for your product, which will inform your technical and functional requirements. Use the template below to define your user roles and their pain points, their transactional/analytical needs, and how your product addresses them. • Will static output be sufficient, or will they need to be able to explore data, create their own reports, etc? • Are they interested in benchmarking performance against their peers? • Where and how will they access the analytics (e.g. mobile devices while on the road)? • Can you increase adoption or attract new user types by adding specific analytics capabilities? USER 1 USER 2 USER 3 USER 4 Role Behaviors Attitudes Motivations Barriers How We Help Part Six: How to Be Successful with Embedded Analytics 43
  • 45. Step 1 – Define the User Experience: Using a Capabilities Map Once you’ve identified your distinct user types, use a capabilities map to match them with relevant analytics capabilities required in your application. You may find that there is a lot of overlap in terms of the analytics capabilities required by your user types. That makes your job easy – simply focus on delivering those capabilities first. However, you may find that there’s no overlap at all, and that you have distinct user types that all want different things. Usually you’ll land somewhere in the middle, where you have some users who need mostly static dashboards and reports (like executives), and other users who need the ability to slice and dice data, create their own reports, etc. (like power users). Sample Capabilities Map Write backs Visual Workflow Creating Reports Customizable Reports Customizable Dashboards Creating Visualizations Drilling/linking Interactive Dashboards Scheduling/Exports Interactive Reports Static Dashboards Individual Charts / Graphs Static Reports Reports USER TYPE 2 USER TYPE 1 Visualizations Part Six: How to Be Successful with Embedded Analytics Benchmarking USER TYPE 3 User Data MultiDimensional Third-Party Data Data Analysis and Exploration Cross-Module Data Joins Data Exports Mobile Self-Service Other Capabilities 44
  • 46. Step 2 – Implementation: Packaging Options for Embedded Analytics Now that you’ve identified your users and their analytics needs, you need to determine how to package and price your offering. There are three common packaging models employed for embedded analytics: • Tiered model – multiple editions, each with increasing analytics capabilities (see example below) • Single, separate module – all analytics capabilities in one offering, separate from the core application • All-inclusive model – standard core offering includes infused analytics capabilities Sample Capabilities Map with Tiered Packages Write backs Visual Workflow Creating Reports Benchmarking Customizable Reports Customizable Dashboards Creating Visualizations User Data Drilling/linking Interactive Dashboards MultiDimensional Third-Party Data Static Dashboards Data Analysis and Exploration Cross-Module Data Joins Data Exports Mobile Self-Service Other Capabilities Scheduling/Exports Interactive Reports Static Reports Individual Charts / Graphs Reports Visualizations Gold Platinum Silver Part Six: How to Be Successful with Embedded Analytics 45
  • 47. Packaging Options for Embedded Analytics (Continued) Tiered Model All-Inclusive Model • Base offering with limited functionality, sometimes lends itself to a freemium model • Analytics capabilities are positioned as core to the overall application value • More capabilities are available at higher tiers, analytics can be distinct options by themselves or bundled with other application functions in each tier • Typically not charged for separately • Price for each tier can be relative to the base price and structure (usage model) or a fixed dollar amount The tiered model is appropriate when you have distinct user types with different analytics needs, or specific analytics capabilities that are inherently more valuable than others. In general, you can define the tiers by 1) business value – base set of report/data access vs. expanded set, 2) user type – report reader vs. report writer, or business/functional role, or 3) capabilities – reports vs. dashboards vs. self service analytics. • Value is built in The all-inclusive model is becoming increasingly common as embedded analytics trends towards the Infused model where it is a core component of the overall application. In this case, software providers typically CAN justify a price increase for their application because the value is higher. If you are in a particularly competitive and/or sophisticated market, however, the value of embedded analytics often manifests itself in a higher win rate and/or higher retention rate instead of a price increase. Customers don’t want to pay more for it because everyone has it, but they’ll choose your solution over others because the value is still there. Single, Separate Module • No tiers, just one optional analytics module • Typically charged as a separate line item • Single addition option for a given price The single, separate module is less common today than six or seven years ago. Now, it’s really only appropriate when you have a very homogeneous group of users with only basic reporting needs. Part Six: How to Be Successful with Embedded Analytics 46
  • 48. Step 2 – Implementation: Tips for a Successful Embedded Analytics Project Once you’ve decided how to package and price your analytics offering, here are three tips to completing a successful implementation. Have a Vision, but Build in Phases When you start building specific analytic capabilities into your application, it can sometimes become overwhelming as you see how far you have to go to complete your vision, particularly as new ideas surface along the way. That’s okay. Before you try to bite off the whole ocean, remember to start small and build from your successes. Start with one user type, one problem, one report. Get feedback and move forward. Requirements shift and evolve over time as users start to see what’s possible, so it’s important to stay agile and approach embedded analytics in many iterations and phases. Where should you start? • By business problem • By role of customer • By capabilities Keep in mind that you may have to decide between implementing the most valuable items vs. the most widely utilized features first. Bottom line: Walk before you run. Part Six: How to Be Successful with Embedded Analytics 47
  • 49. Tips for a Successful Embedded Analtyics Project (Continued) Involve Internal and External Stakeholders There’s nothing more frustrating than building out a really cool feature that no one uses. To avoid this, be sure to get regular feedback from internal and external stakeholders as you build specific analytics capabilities into your application. Utilize screen mockups early in the process and review them with current customers to validate your direction. Ask what they like, what they don’t like, how they would use it, and what suggestions they have to make the product better. This will help you to stay focused on solving real user problems with embedded analytics, and also enables participants to become advocates when the capabilities become generally available. Part Six: How to Be Successful with Embedded Analytics Perform a Usability Study to Identify Gaps With select customers, you should consider conducting on-site usability studies to see how they actually use the application and identify remaining gaps. The point of conducting a usability study is to find out in advance what problems will bother your users, so be prepared for users to complain when they are lost or frustrated – that’s exactly the kind of feedback you’re looking for. Avoid helping users to get to the right answer. Instead, ask them to complete specific tasks and learn how they expect to navigate your application to accomplish them. Ask them to rate certain aspects of the application, and prioritize enhancement requests. Ask openended questions to get the most feedback, not questions that can be answered with yes or no. 48
  • 50. Step 2 – Implementation: Build vs. Buy The build vs. buy argument is increasingly irrelevant for embedded analytics as user expectations become more sophisticated. There are many companies in the analytics space, whose sole mission it is to make analytics better, faster, and easier to use. For this reason, software companies are increasingly more likely to integrate with a third-party embedded platform, but regardless, here are the three options available to you. Option 1: Build Often the first option companies choose because their requirements seem simple. • Code-intensive approach Option 2: Integrate a BI Application The best option if your requirements truly are simple, you are okay with a disjointed UX, and your deadline is very tight. Option 3: Embedded Platform The Goldilocks approach…most of the functionality you need, without being too restrictive. • Flexibility to create UI/UX • Development with third-party charting libraries, or open source code • Integration-centric approach Pros Pros Pros • Flexibility to create the desired UX • Faster to acquire out-of-the-box functionality • Accelerated time to market • Low cost for low-complexity projects • Less coding of functionality • Flexibility to create desired UI/UX Cons Cons Cons • Cost of development, support, and maintenance over time • Need for specialized BI skill sets for development and integration • Need strong partner as value is wrapped up in a third-party tool • Time to market poor • Less ability to craft your own UI/UX • Usually a code-intensive process • Harder to support advanced workflows for your end users • Bolt-on third-party application Part Six: How to Be Successful with Embedded Analytics • Quickly acquire and configure functionality • Utilize existing resources and skills sets with minimal coding required 49
  • 51. Step 3 – Delivering Embedded Analytics to Your Market You’ve embedded analytics within your application…now what? Here are some tips for getting the most out of your investment. Promotion Best Practices Be Visual Analytics are inherently visual, so the best way to showcase your new capabilities is to utilize compelling visuals. Use screenshots liberally on your website and sales decks, and use webinars and videos to guide users through your new features. Consider creating a Visual Gallery with topical or customer examples. Leverage Customer Stories To convince prospects of the value of your application, nothing is more convincing than customer stories. Reach out to your customers regularly to solicit feedback, and ask if you can quote them in a case study, webinar, press release, and/or testimonial on your website. Consider creating a Testimonials section and/or Customer Success Gallery to present all of your happy customers in one place. Part Six: How to Be Successful with Embedded Analytics 50
  • 52. Delivering Embedded Analytics to Your Market (Continued) Sales Enablement As you roll out new analytics capabilities, be sure that your sales team knows what’s coming and how to sell it effectively. You’ll need to craft new messaging, determine the new value propositions for each of your user types, and train the sales team before launch. Remember to share new use cases and teach the sales reps to identify when and how to target new user types. Create compelling demonstrations that highlight the most valuable capabilities, and how your product is different from competitors. Prepare interesting demo data to simulate a rich production environment. Once you get rolling, remember that sales is on the front lines and is capturing a ton of feedback from prospects, so be sure to close the loop so that feedback informs future development plans. Think Education Today’s selling process has transformed into an education process. Your prospects need to recognize your company as a thought leader that understands their frustrations and challenges before they’ll agree to a sales pitch. Educate your potential customers through compelling content aligned with each stage in the buying process, including white papers, solution briefs, topical webinars, buyer’s guides, RFP templates, and demos. Part Six: How to Be Successful with Embedded Analytics Tip for capturing testimonials: While you’re on the phone with customers, ask if they would be willing to provide a testimonial. If they say yes, send them an iPad in the mail and have them record a short video on their experience with your product via FaceTime. Then let them keep the iPad as thanks for their effort! It’s a win-win. 51
  • 53. PART SEVEN BUYING AN EMBEDDED ANALYTICS SOLUTION 52
  • 54. Buying Process Great! You’ve decided to buy an embedded analytics platform. Now you need to select the right solution. Of course, in our (admittedly biased) opinion, Logi Analytics is almost always the best solution. But here’s an unbiased process you can follow to buy the embedded analytics solution that is right for you. Determine Your Goals & Timeline To get where you want to go, write it down. Statistically speaking, you increase your likelihood for success simply by putting your goals on paper. Hard metrics may be: • Decrease sales cycle • Improve win rate • Improve customer retention Soft metrics may be: • Create a competitive differentiator • Give better sales demos • Improve user experience Part Seven: Buying an Embedded Analytics Solution 53
  • 55. Buying Process (Continued) Identify the steps you’ll take to get where you want to go. You aren’t ever “done” with embedded analytics, so build time to evolve and learn from your customers. Ask yourself, “When do I want to…” • Start the selection process? • Have detailed vendor presentations and demos? • Finish a proof of concept? • Make my final decision? • Start development? • Go to market? Identify Technical Requirements Review your administrative, integration, and technical requirements. Research your competitors and talk to your customers to develop a firm understanding of the capabilities you want to add to your application. What other technologies do you have that the analytics will need to work with? What data sources will you need to access? What level of integration are you looking for with your core application? Reference the functionality checklist in Part Four to certify you’ll get what you need today and in the future. The Importance of Easy It should be easy to get started quickly, so you can bring your product to market sooner. Get all of your major questions answered during the structured evaluation so you are confident in your choice. It should be easy to develop analytics, so you can meet all of your capability requirements while maintaining focus on advancing your core functionality. It should be easy for anyone on your development staff to use, not just a select few. Go with a platform that utilizes open standards and offers an intuitive development experience. Consider who will use the platform to create analytics and their current skill set. How important is ease of use? What level of additional services, training, and support will you need? Turn the requirements into functional scenarios. Describe how you want your users to interact with your application once you’ve embedded analytics, and what they’ll be able to accomplish. Part Seven: Buying an Embedded Analytics Solution 54
  • 56. Buying Process (Continued) Assemble the Team Determine the stakeholders that need to be involved. Who is going to care about embedded analytics internally (executive team, product management, lead developers) and externally (key customers, customer advisory board)? Be sure to review the technical requirements with them upfront, and build the business case collectively to get buy-in to move forward. Research Potential Vendors Assign a point person to research potential vendors and evaluate whether their functionality matches your requirements. Look at industry sources like the Gartner Magic Quadrant for BI Platforms to create your initial list and pay attention to platforms that specialize in the OEM market. Get demos from each one and confirm a basic fit. Discuss your requirements and ask each one to demonstrate how they would deliver your specific processes and scenarios. Ask tough questions and make sure the vendor actually shows you that they have what they say they have in terms of functionality. Confirm ballpark pricing to move forward. Avoid a feature bake-off. Instead, focus on the requirements you identified in step 2, and try not to be dazzled by features that don’t deliver on your criteria. Of course during your search process you may update your goals as you learn what’s possible, but stick to what you can envision yourself using that will provide value to your customers. Complete Structured Evaluations with Selected Vendors Narrow down your list to the top 2-3 vendors and begin a structured evaluation process with each one. This is where you’ll define a proof of concept and establish clear cut guidelines for what you want to accomplish within, say, 30 days. The amount of interaction you have with each vendor is based on your preference – ranging from an assisted trial where support is generally available if you run into issues, to a true structured evaluation where you and the vendor are building a proof of concept together. At the end of the evaluation, present the output back to the stakeholders to get feedback and validate your direction. Evaluate each vendor’s ability to make you successful during the implementation process through access to best practices, community, consulting, support, and training. Part Seven: Buying an Embedded Analytics Solution 55
  • 57. Buying Process (Continued) Talk to References Now it’s time to find out if your vendor can actually make customers like you successful. The Importance of Flexibility Ask your vendors for references. Solicit others from your personal and social networks. Look for references that are similar to your organization (size, industry, use case, etc.). You know best how your users prefer to work with your application, so you should absolutely maintain control over the UI/UX. Choose a platform that fits into your overall product vision, not the other way around. Find out whether your situation is similar to theirs. Don’t just ask whether they’re happy with the vendor, drill into what functionality they’ve delivered, what is support and training like, how long was implementation, what roadblocks did they run into, how did the vendor handle any problems or issues? You don’t want to invest a lot of time and energy with a platform, only to rip it out a year later. While you don’t want unnecessary complexity, you don’t want to outgrow the solution either. Going too small or cheap—without aligning to your future requirements—is a clear path to failure. When you integrate third-party technology into your application, the licensing needs to make sense. Look for vendors who can tailor their licensing to match your business model. Part Seven: Buying an Embedded Analytics Solution 56
  • 58. Questions to Ask During a Reference Call Success Criteria & Selection • What were the key business processes and goals you set for this embedded analytics project? How well has the system delivered on those goals? • Were you the decision maker responsible for purchasing this solution? • What made you choose the solution you selected? Implementation & Ramp-up • Tell me about your implementation – what was better than expected and where did you run into challenges? • How long did it take you to learn basic functions, like creating a dashboard or report? • How complete is the integration between the analytics and your core application (including security, white labeling, etc.)? How hard was it to set up and maintain? • What has your experience with training and support been like? • How proactive has the vendor been to make sure you are successful? Results • Have you deployed your analytics solution yet? If so, what was the reaction like from your customers and prospects? • Have you seen any specific benefits – like time to market, competitive differentiator, better sales demos, increase in customers, and/or increase in revenue? • What do you love about this platform, and what do you hate? • Beyond the licensing, what other costs did you incur during implementation? • If you were to do things over again, would you make the same vendor decision? Would you do anything else differently? Part Seven: Buying an Embedded Analytics Solution 57
  • 59. Buying Process (Continued) Select a Vendor and Get Started It’s go time! Choose the vendor that you feel most confident in as a partner to reach the goals you identified earlier in the process. Of course you’ll have to compare and negotiate terms and conditions, but look beyond software for the vendor who gives you the highest chance of success. Make sure your vendor has the resources to help you, even if you don’t need the help today. Later on, you’ll appreciate being able to test ideas and leverage best practices as your needs evolve. Get training for those who will be using the platform to create analytics. Create your first set of reports. Work with your vendor’s enablement and consulting teams for best practices. Monitor, Adapt, and Optimize There’s a lot that could be said here, given how endless your possibilities are when using embedded analytics. But for the purpose of time and space, here’s an overview of how to approach this phase of your process. Customer reactions have been astounding. Customers are particularly interested in this portion during the sales process. Additionally, we find that they engage a lot more with the reporting because they can control it and investigate what they’re seeing. The new dashboards and reports have enabled them to see much quicker results.” – Andy Madge, Head of Technical Services, Creative Virtual • Invest in the training you need to be successful. • After three to six months, do a check-up, and consider reengaging with your vendor’s services. Evaluate additional services that could take you to the next level. • Engage with your vendor’s community to learn and share best practices. Suggest ideas for new features while you’re at it. Part Seven: Buying an Embedded Analytics Solution 58
  • 60. Additional Factors to Consider Beyond the features and functionality previously discussed, here is a list of additional factors to consider when selecting an embedded analytics vendor. Implementation & Ramp-up • How long does it take to get live at a basic level? A medium level? An advanced level? • Do you provide training live or on-demand? • What skill sets will I need on my team to be successful? Service & Support • What’s your service-level agreement for technical support? • How will you help expose our users to new ideas and best practices? • How active is your online community? How quickly will I get answers? • Who are your key service partners? Who provides strategy and process design, change management, and technical integration support? Likelihood for Long-Term Success • What kind of ongoing costs and resources should I expect? • Is your company viable in the long run? What funding and capital do you have? What’s your growth rate? • How well does your product roadmap align with my future needs? Part Seven: Buying an Embedded Analytics Solution 59
  • 61. PART EIGHT THE FUTURE OF EMBEDDED ANALYTICS 60
  • 62. The Future of Embedded Analytics Embedded analytics is a new and dynamic industry, and we’re seeing new capabilities all the time. Here are the three key trends that will drive this evolution over the next few years. Infused Analytics Becomes the Standard As we discussed in Part Three, we are seeing a trend towards the Infused Analytics model, where analytics becomes a core component of the parent application within a seamless user experience, such that it’s impossible to tell the difference between the two. Embedded analytics enables workflow so users can derive new insights, draw accurate conclusions, and identify ways to increase productivity and decrease costs. Just as B2C applications have evolved from bolt-approaches to more infused implementations, B2B applications will follow suit so users are able to access analytic information and initiate transactions without leaving the core application. Part Eight: The Future of Embedded Analytics 61
  • 63. The Future of Embedded Analytics (Continued) Analytics Everywhere, for Everybody We’ve already started to see the increased need for self-service capabilities. Related to this trend, we anticipate more non-technical users to start demanding access to analytics within the software applications they use on a daily basis. This has repercussions on the user experience required for those applications, as analytics will need to be extremely easy to use, with built-in controls so even novice users can arrive at accurate conclusions. The days are gone that a few power users do 100% of the analysis and share the results with the rest of the team. Now all team members will want direct access so they can do their jobs more efficiently. You’ll likely still want to provide basic reports for the majority of your users, but prepare for more users wanting control over the output. Your product management and developers will be more challenged by self-service than they ever were by managed reporting. It’s harder to enable your users to ask their own questions than it is to provide dashboards that answer a few key questions. Eventually you will need to deliver both. More Sophisticated Analytics Capabilities Hopefully it’s obvious by now that embedded analytics is here to stay and that it is a need to have for all software applications. Your customers expect it, and you need to meet their expectations to stay competitive. However, the sophistication of analytics is still pretty basic across the board. If you think about the range of analytics from least to most advanced, you might have: Part Eight: The Future of Embedded Analytics • Descriptive analytics – describe what’s happening with the data (e.g. sales are going up, and here’s a chartthat’s showing that) • Diagnostic analytics – no longer just describing, now diagnosing what the issues are (e.g. West Coast sales have plummeted; this is something you need to address) • Predictive analytics – here’s the data, here’s what it means, and here’s what the next quarter is going to look like • Prescriptive analytics – here’s what’s happening, here’s why, here’s what the future looks like, here’s what you should do about it While 93% of software companies provide some level of analytics within their applications, few are delivering prescriptive analytics, where they not only present what’s happening, why, and what the future looks like, but also offer direct guidance to solve the problem. If you think about it from an end-user perspective, that provides a lot of value. And in the end, isn’t that what it’s all about? 62
  • 64. PART NINE LOGI ANALYTICS’ APPROACH TO EMBEDDED ANALYTICS 63
  • 65. Logi Analytics’ Approach to Embedded Analytics Why Logi Analytics? Seamless. Fast. Complete. Architecture Made for Embedding Interactive Dashboards and Reports Logi Analytics offers the best platform to quickly create, integrate, and iterate upon embedded analytics with minimal impact on development resources and infrastructure. Deliver superior data visualizations, charts, and graphics Fastest Time to Market Our development approach enables you to quickly assemble pre-built elements to create even complex data visualizations and analytics with minimal coding. Your Partner for Success Logi Analytics has a fully dedicated OEM team and over 10 years of experience helping hundreds of software companies to embed reporting, dashboards, and analytics. From visual design to feature selection and integration strategies, we’ve developed best practices for every situation and will work with you to make them part of your application. Self-Service Analytics Enable power users to create their own reports and explore data Mobile-Ready Development Deliver content on mobile apps through HTML and HTML5 Seamless User Experience Your users will never know Logi is running behind the scenes Elemental Design Approach Use pre-built elements to create analytics with minimal coding Security that Matches Your Application Flexible and highly granular security layer ensures proper access for all users Cloud or On-Premise Support Including single-instance, multi-tenant SaaS environments Extensible Platform Create a uniquely tailored user experience for your application Licensing to Match Your Business Customized licensing aligned to your business Visit www.logianalytics.com/oem-solutions to learn more. Part Nine: Logi Analytics’ Approach to Embedded Analytics 64
  • 66. Conclusion So there you have it: everything you ever wanted to know and more about embedded analytics. Whether you’re building your first product or revamping an existing one, embedded analytics can help you solve real customer problems, which builds product value and creates a competitive differentiator to propel your business forward. HAPPY DEVELOPMENT! Contact Logi Analytics North America: +1 703-752-9700 Website: www.logianalytics.com Europe: +44 118 935 7256 Blog: www.logianalytics.com/blog Email: salesteam@logianalytics.com Twitter: @logianalytics #CG2EA 65