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Personalization using Evergage

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Growing and enriching customer profiles is fundamental to business success.

In a Nutshell:

This is one of a series reviewing vendors’ personalization platforms.

Evergage is a personalization and customer data platform used for delivering personalized customer experiences. The Evergage personalization solution is comprised of testing, optimization, recommendations, analysis, segmentation and targeting across digital channels.   

Strengths:

Customer Data Platform (CDP). The breadth and depth of data collected by Evergage and imported from other apps and systems create a rich customer profile, and for B2B, account profiles as well. Evergage provides packaged integrations and APIs for exchanging data, and UI wizards for marketers to control that data exchange. 

Architecture. Evergage functions are built on the CDP rather than the unfortunately common practice of building functions around channels. The functions have been built by Evergage, resulting in unified and consistent services.

Support Resources. Evergage has a comprehensive online library which provides guidance on digital marketing activities as well as how to use Evergage. The partner network is extensive, including specialty and big four consulting firms. Customer support representatives I have spoken with are knowledgeable and experienced.

Data science and AI. Evergage has incorporated machine learning into its recommendations and analyses, one of which identifies anomalies in events in order to alert marketers to problems. The Data Science Workbench provides data scientists with the environment and tools to explore, model and enrich CDP data.

Overview of Evergage Capabilities

Evergage views personalization as the fundamental principle driving customer experience. It is not a capability that can be added to a marketing platform, it is the foundation of a marketing platform. “Personalization is not a feature of the interaction, it is the whole thing.” ——Karl Wirth, Evergage Co-Founder and CEO.

Evergage offers a platform that it believes satisfies clients’ requirements for delivering personalized customer experiences. 

Evergage categorizes its capabilities in six areas:

1. Behavior and Context Tracking and Surveying

  • Monitor engagement & time spent to determine affinities
  • Understand product & content metadata without catalog feed
  • Capture explicit data via targeted surveys

2. Segmentation and Targeting

  • Segment on data from 1st and 3rd party sources
  • Apply rules to change experiences for segments
  • Analyze segments for insights

3. Triggered Experiences

  • Display website messages based on visitor actions
  • Send one-off email messages
  • Communicate with users via push notifications

4. Algorithmic Experiences

  • Apply machine learning for 1:1 experiences
  • Recommend products, content, categories and other elements of customer experience
  • Personalize web, email, onsite search and navigation

5. A/B and Multivariate Testing

  • Conduct A/B & multivariate tests
  • Test algorithms and specific segments
  • Test email subject lines

6. Analysis and Attribution

  • Attribute against goals
  • Analyze lift over control
  • Bayesian statistical analysis
  • Utilize machine learning to guide decisions
  • Report on non-Evergage campaigns 

Market

Target Market

Evergage primarily targets large and enterprise retailers, technology providers and financial services companies, but also works with businesses in the manufacturing, travel, gaming, media and education industries.

Challenges Faced by Target Market

  • Driving more engagement and conversions. Companies spend significant time and money on top-of-the-funnel campaign activities – ads, email, SEO, etc. – designed to drive people to their websites. However, it’s wasted if those visitors immediately bounce from the site or don’t engage or convert. Businesses need to employ creative methods, including personalization and A/B testing, to optimize the experiences for their visitors and email recipients to improve engagement and conversion rates.
  • Managing customer expectations. Competition is fierce so companies today are looking for any advantage they can find or create. Increasingly, this comes from creating and delivering exceptional customer experiences – those that recognize customers, understand their unique needs/desires and make their experiences more helpful and efficient. If your company does not deliver as compelling an experience as Amazon, Facebook, and Netflix, your visitors will become increasingly frustrated and look to go elsewhere.
  • Leveraging customer data. A key challenge today for many companies is how to gather and interpret digital visitor behavioral data, how to unify disparate data sources, and how to utilize all that information to deliver relevant and consistent customer experiences. Businesses have lots of customer data but the information is often siloed across many different systems and applications, and not all information is equally valuable. Companies need a way to unify valuable customer data so that it can be used to build and improve relationships. The emergence of Customer Data Platforms (CDPs) is a result this need.
  • Cross-channel consistency. Marketing technology stacks tend to include many application-specific tools. As it applies to optimization, a typical retailer could rely on an A/B testing tool, a product recommendation solution, a mobile app messaging tool, and a geo-targeting tool, among many available tools. Each of these solutions are designed to help companies improve customer engagement, but within a limited scope of the overall relationship with a customer. To be successful, companies need to deliver consistent experiences across many different touch points, which is difficult to do when using a variety of point solutions.

Solution Strengths Against the Challenges

Against these challenges, Evergage’s strength lies in its comprehensive CDP and the architecture that centers on customers rather than channels. Evergage is a personalization and customer data platform (CDP) that can gather, unify and interpret customer data, and it is this data that drives personalization. Evergage captures details such as active time spent on page and on-page engagement like hovering and scrolling details, and also imports data from other sources. This data is evaluated against the product and content contextual data (such as color, brand, price, etc.), to provide a more accurate sense of someone’s implied preferences. In addition, Evergage also offers out-of-the-box surveying capabilities to enhance or confirm data about visitors.

The Evergage platform contains a customer profile record for every visitor. There are six categories of data, as follows:

  • Situational data. Geographic location, referring site, campaign source, device, browser
  • Firmographic data (for B2B). Company, industry, revenue, headcount, marketing technologies used
  • Lifecycle data. First time or returning visitor, active prospect, current customer, loyal customer
  • Affinities and intent. Content consumed, videos watched, blog posts read, feature and solution preferences, favorite brands and styles, price affinity, recency and frequency
  • Profile data. CRM/MAP data, job role if B2B, demographic information, marketing and campaign responses, offline purchase data, marketing responses
  • Account-level data: In addition to tracking insights on every visitor, Evergage tracks detailed engagement data at the account level, which is critical for B2B companies in industries like technology, retail and financial services.

Evergage has been entirely engineered in house, which means that all performance improvements are identifiable and in their control. Evergage claims to react to visitors in 20 ms, which is fast enough to support real-time.

Evergage offers three categories of machine learning capabilities:  affinity modeling which scores visitor interest, white-box recommendations which give marketers control, and machine learning anomaly detection which alerts marketers to potential problems.

The Data Science Workbench offers tools and data within a dedicated cluster that data scientists can use to explore and model CDP data. This pre-built environment, by providing access as well as computing workspace, eliminates significant barriers data scientists must overcome before beginning any value add analysis.

Evergage provides an online library to support customers’ need for new skills and guidance, including an extensive knowledgebase and playbooks that guide marketers in planning and implementing personalization campaigns. The information is rich enough to help people at any skill level figure out what to do as well as how to use Evergage to do it. The customer support representatives I have met are articulate, knowledgeable and experienced.

Collaboration

Because the effort to create, deliver, and manage personalized customer experiences engages people with many roles across many departments in a company, it is important that the personalization solution encourage collaboration. The top three requirements are support for many roles, immediately accessible learning, and enterprise controls.

Support for many roles. On average, there are six Evergage users per account. Larger organizations have upwards of twenty users. Roles and titles involved with Evergage include:

    • CMO/SVP/VP Marketing
    • Digital Marketing Demand Generation
    • Product Management
    • Customer Success
    • E-Commerce
    • Merchandising
    • Email Marketing
    • Mobile App Development
    • Information Technology
    • Data Analysis/Data Science

Evergage works with businesses across many different industries, thus end users’ titles can vary from company to company.

Immediately accessible learning. Evergage has a comprehensive library for learning how to be successful with marketing activities, using Evergage. In addition to the online library, resources include:

    • An assigned Customer Success Representative
    • eCampus courseware
    • Online knowledgebase
    • Playbooks for planning and implementing personalization campaigns
    • Webinars and events
    • Industry strategist-led planning workshops
    • Consulting partner network

Enterprise controls.  Platform administrators can assign roles to specific users (e.g., viewer, campaign editor, etc.) so their permissions adhere to company guidelines. Furthermore, for every personalization campaign built within Evergage, a company can define and then follow workflow approval processes to ensure quality assurance and appropriate management oversight.

Integration

Personalization is not achieved with a tool bolted onto your marketing environment. It is achieved with a broad range of capabilities embedded in most of what marketing does. Integration thus becomes fundamental to the value of any personalization solution. The top three requirements are customer data integration, app integration, and accessible services.

Customer data integration.   Evergage has packaged, bi-directional connectors with Oracle Eloqua, Salesforce Marketing Cloud (ExactTarget) and Marketo. Campaign, field and segment data can be passed back and forth between the Evergage platform and other systems based on your configuration. Configuration is performed via a tab in the Evergage UI, where you specify authentication and segment, field, and campaign synchronization.

App integration.  There are 25 packaged integrations for sharing of visitor and campaign details between systems available for CRM, email marketing and web analytics solutions, including Salesforce Sales Cloud (CRM) and multiple ESP/MAP solutions like Oracle Eloqua, Salesforce Marketing Cloud (ExactTarget), IBM Watson Marketing (Silverpop), Google Analytics and Marketo.

Integration with other systems is achieved with Evergage APIs. These APIs facilitate import of transactional and other customer data to enrich the CDP, and also the export of Evergage’s behavioral data.

Evergage uses server-side integration for CRM systems, including Salesforce Sales Cloud (CRM), Google Analytics, Eloqua, IBM Silverpop, Marketo and Salesforce Marketing Cloud (ExactTarget). Client-side integration is applied for ecommerce, ad network, and CMS systems, using Javascript tags, webhooks, or SFTPs. Information can be exchanged in real time, at scheduled intervals or during nightly syncs.

Accessible services.  From a single interface in the Evergage platform, a business user can build and deploy personalization campaigns across websites, mobile apps, web applications and emails.

AI and Automation

Leaders in delivering personalized customer experiences are developing the capability to personalize any part of any customer interaction. Personalization at scale is a challenge that can’t be accomplished unless AI is effective at predicting customer reactions to each step in the experience, and automatically presenting the best next step. The top three requirements are control and automation; shared insights; and real time actions.

Control and automation.   Business users can choose to recommend products, content items, categories, brands, departments, etc. Evergage’s recommendations can be controlled by creating machine learning recipes. Depending on the strategy, a recipe built by a marketer can include one or more algorithms, filters, boosters and variations. Recipes can be previewed, tested and deployed to drive recommendations on a website, in a mobile or web app, in onsite search results, and into email campaigns.

Shared insights.   Evergage Guardian is a feature of the platform that uses machine learning to monitor a company’s analytics data and then surface any anomalies, either positive or negative, that deviate from an AI-predicted pattern.

AI decisions powered by Evergage can be fed to call centers and in-store or in-branch systems to provide customer-facing staff with real-time, relevant suggestions and recommendations they can offer on the spot to individual customers/prospects. 

Evergage-driven recommendations can also be incorporated into websites, onsite search, mobile apps, web applications, and email.

Evergage’s affinity modeling uses machine learning to evaluate and score a visitor’s interest in product/content items based on the visitor’s profile and behavior, and the many attributes that are associated with each item. This information is available to apps via the CDP.

Evergage’s Data Science Workbench provides tools for data scientists to explore CDP data, create visualizations, execute data transformations, and run numerical simulations and statistical models. Output from these analyses and models can, in turn, be brought back into Evergage as profile attributes so those insights can be used for improving real-time personalization efforts. Users of the Data Science Workbench are provided with a dedicated cluster, where they can access Evergage’s data through a safe and secure read-only proxy. The cluster is pre-installed with a suite of familiar tools which run on top of Apache Spark. Apache Zeppelin provides a notebook in which Python, R and Scala can be used together, sharing data across languages. Additionally, libraries allow for data to be pulled from Evergage into Spark DataFrames in a clear and well-documented manner.

Real time actions.   Evergage models are updated in real time.

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Personalization using Adobe Target and Adobe Experience Clo

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Personalization is not necessarily unique content for every visitor. Even people who seem to share the same context have unique experiences, and may be pursuing unique outcomes. 

In a Nutshell:

This is one of a series reviewing vendors’ personalization platforms.

Adobe is the market leader in enterprise marketing solutions, offering integrated suites of services for content creation, marketing, advertising, analytics and commerce. These suites are Adobe Experience Cloud, Adobe Document Cloud, and Adobe Creative Cloud.

Adobe Experience Cloud is comprised of Adobe Target, Adobe Analytics, Adobe Audience Manager, Adobe Campaign, Adobe Experience Manager, Adobe Advertising Cloud, Magento Commerce Cloud, and Marketo Engagement Platform. These all share the core and AI services of Adobe Experience Platform. Adobe Target is Adobe’s personalization solution, and the focus of this report.

Strengths:

Vision. Adobe’s vision and strategic planning are prime contributors to its success. The vision has been marketed as digital transformation, data driven marketing, and most recently as the experience business. Adobe invests its own marketing resources in guiding its customers and prospects in how (and why) to be an Experience Business. Customer Experience Management is Adobe’s moniker for the convergence of myriad separate categories, such as personalization, loyalty, customer data platforms, and cross channel campaign management, into a single broad category. Adobe of course dominates this category it has defined. Adobe’s participation in the Open Data Initiative, aimed at easing the management and use of a comprehensive and unified customer profile, is a recent demonstration of Adobe’s vision for customer experience management.

Accessible AI Services. Adobe Experience Platform provides Adobe Sensei AI services to all Adobe solutions. Within Adobe Target, AI drives decisions for traffic allocation, best experience, best offer, and product and content recommendations. Adobe’s Personalization Insights reports surface which visitor attributes the machine learning model found most influential in making decisions, and the audiences the model identified as important when determining the best experience or offer to deliver. Finally, the custom criteria feature allows marketers to apply the results of their own data modeling to Adobe Target Recommendations.

Automation. Adobe Target is able to automatically route traffic to the best-performing experience; end A/B tests; and deliver the best available experience or offer.

Best Practices. For a decade or more Adobe has been investing in identifying, understanding, documenting, and teaching best practices for optimization and personalization.

Breadth. Adobe’s digital marketing offerings include content and asset management; cross-channel campaign management; optimization and personalization; analytics; segmentation and audiences; customer data management; programmatic advertising management; eCommerce management; and a unified customer profile.

Overview of Adobe Capabilities

Adobe Experience Cloud provides personalization capabilities that include the following:

  • A/B and multivariate testing to audiences
  • Auto-Target experience personalization
  • Automated Personalization for offers

Rules-based personalization (Experience Targeting)

Personalization Insights report

  • Product, content, and media recommendations
  • Personalized search
  • In email personalization
  • Mobile site and app personalization
  • IoT personalization
  • Voice assistant personalization
  • Single page application (SPA) personalization
  • Integration with asset (i.e. content) libraries, audience analysis, analytics

Market

Target Market

Adobe Target is used in personalization programs in Automotive, Retail, Financial Services, Media, Healthcare, Travel and Hospitality, Telecommunications, Consumer Packaged Goods, Technology, Government, Education, and more; by personalization newcomers and sophisticated industry veterans; in firms ranging from small businesses to multinational enterprises; and for both B2C and B2B applications.

Challenges Faced by Target Market

As consumers demand more relevant and consistent experiences, brands must keep up with an ever-escalating competitive environment. Adobe sees the personalization engine market evolving to address key challenges customers are facing in the following areas:   

  • Intelligent and always on. Personalization engines must enable scale by applying Artificial Intelligence and machine learning for faster and smarter testing and personalization. AI is the critical component to expanding personalization programs beyond manual rules. To support this growth, the AI must be demystified to make machine learning decisions defensible and understandable by all. A growing number of companies have in-house AI research and their own data science. The personalization engine must also support a bring-your-own approach that enables ingesting of data science to augment, experiment with, or potentially replace vendor-powered AI. 
  • Adapting to and evolving with changes in technology. As the technical environments continue to evolve and shift, personalization engines must grow to offer solutions for emerging technology trends such as single page apps and accelerated mobile pages, while being extensible and modular to support growing headless content management system (CMS) use cases. Personalization engines must continue to embed with content pipeline platforms, analytics and visualization tools, orchestration layers, and content and experience management tools for a holistic, unified view and management of customer experience.  
  • Everywhere consumers engage. Personalization has to move beyond the browser to support the ever-growing number of devices in consumers’ digital lives. The engine has to expand beyond web to app, IOT, living rooms, kiosks, brick and mortar, e-mail, call centers, voice assistants, and more. Adobe expects growth in unified orchestration layers for both inbound and outbound consumer interactions wherever they may occur. 
  • Connected across devices and embedded across solutions. Existing everywhere consumers engage will not be enough for the personalization engines moving forward to compete. Personalization engines must also support a unified, cross-device concept of identity or profile with consistent, coordinated, and seamless experiences across critical interactions.  
  • Intuitive to use for every user and scalable for every business. Even the best technology is useless if it’s too hard to use. The personalization engine must be accessible to a wide variety of audiences – from marketers to developers, product owners, and UX professionals. The features and functionality must be relevant for each audience and provide enterprise-level governance with permission structures that support local and global scale.

Solution Strengths Against the Challenges

Against these challenges, Adobe’s strength lies in the architecture and breadth of its services. Adobe solutions share a common set of services via Adobe Experience Platform that increasingly provide consistent data and functions across the Adobe portfolio.

Adobe’s broad range of marketing capabilities, and its broad partner program, create a personalization environment that collaborates with content pipeline platforms, analytics, visualization, orchestration layers, and content and experience management tools.  

Adobe’s marketing suite applies artificial intelligence via Adobe Sensei, the AI layer of Adobe Experience Platform. Adobe Sensei services are used across all Adobe solutions and are also open to users who can add algorithms and models. Adobe demystifies AI by providing explanations of the algorithms used, how they work, and how to interpret the results. The data and insights captured within Adobe Target are to some extent available to users’ data science teams. In particular, the decisions made by the machine learning models in Adobe Target on what to show customers and how they responded is immediately available. Additionally, Adobe Experience Cloud can apply user developed algorithms and models to personalization decisions.

Collaboration

Because the effort to create, deliver, and manage personalized customer experiences engages people with many roles across many departments in a company, it is important that the personalization solution encourage collaboration. The top three requirements are support for many roles, immediately accessible learning, and enterprise controls.

Support for many roles. The average number of Adobe Target users in a company is around 10, with many more in organizations that are larger, global, or have mature practices. User roles include marketers, product managers, IT and developers, data scientists, and content producers. Adobe Target specifically provides workflows aimed at marketers, developers, and product owners, especially those working with data science.

Immediately accessible learning. Adobe Digital Learning Services provides online training and learning opportunities with online tutorials, skills assessment, learning paths, e-learning online courses, and custom learning opportunities.

Adobe provides a variety of free and fee methods for acquiring knowledge and skills including:

  • On-demand and self-guided training
  • Instructor-led training and in-person training
  • Digital marketing skills assessment 
  • Certification programs with both in-person and remote proctoring available
  • Personalization Thursdays Webinars
  • Adobe Target Basics Webinars
  • Adobe Target Tutorials, covering topics such as composing experiences and creating tests
  • Adobe Target Online User Guide
  • Robust help documentation
  • Adobe Target Blog
  • Adobe Experience League with Experience League for Target (online community of peers and experts and learning resources)

The knowledge bases and online learning programs make it possible for people to make use of Adobe Target capabilities as their roles and challenges change. The knowledge provided is oriented to how to use Adobe Target, but also includes playbooks on how to execute basic marketing functions such as creating and managing campaigns. If you are suddenly handed an assignment outside your experience, the self-learning environment makes it possible to tackle it.

Enterprise controls. Adobe Experience Cloud is an enterprise level solution, meaning it is designed for the safe use by multiple people and groups, including protecting data, access, and boundaries. Workflows support enterprise governance practices

Integration

Personalization is not achieved with a tool bolted onto your marketing environment. It is achieved with a broad range of capabilities embedded in most of what marketing does. Integration thus becomes fundamental to the value of any personalization solution. The top three requirements are customer data integration, app integration, and accessible services.

Customer Data Integration. Customer experience information can be immediately accessed by other applications, including what content was seen and what was clicked on, via the response tokens on web pages. If server-side integration is in place, this information is available via server calls.

All Adobe Experience Cloud applications can ingest third-party and CRM data to augment the customer data profile. There are limits to the number and type of attributes imported and how they can be used, but marketers have considerable flexibility to use the data that is important to them. Profiles can be queried and downloaded to third party apps as audiences or segments. Adobe recognizes the challenges marketers face in creating accurate and comprehensive profiles. In September 2018, Adobe, Microsoft, and SAP announced the Open Data Initiative, intended to eliminate data silos and enable a single view of the customer, through a common data model. The data model will provide for the use of a common data lake service on Microsoft Azure. This may ease some of the data cleansing and integration tasks marketing organizations routinely face.

App Integration. Adobe Experience Cloud provides hundreds of APIs via Adobe.IO, Adobe’s API gateway. The Adobe Experience Cloud Exchange is a single destination where customers can look for and get digital marketing extensions via apps, such as data connectors, custom configurations to Adobe’s core products, third-party applications, and reports. Adobe Target supports emerging standards including single page apps, accelerated mobile pages, and headless CMS use cases. Adobe Target can be implemented both client-side and server-side. This makes it possible to connect to devices that don’t involve a browser, such as gaming consoles and IoT devices. Adobe’s mobile visual design tools also put Adobe everywhere marketers engage customers. A marketer can set up an Adobe Target activity, including designing a new page or setting rules for selecting content, anytime he or she can use a phone.

Accessible services. Core services are available to all Adobe Experience Cloud Solutions via Adobe Experience Platform and a single UI. Adobe Experience Cloud solutions all share a single user profile, a single set of APIs, services, client-side and server-side implementation methods, and SDKs. Adobe Target integration with other Experience Cloud solutions allows the same data, audiences, attributes and metrics to be shared.

Adobe Experience Cloud has a unified customer profile that spans devices, time, and applications, including non-web devices such as IoT and gaming platforms. The profile data is accessible to all Adobe solutions via the People Core Service, as well as to user applications and analysis. It is constantly augmented with information on what has been shown a customer, and how the customer reacted. This information can then be used for targeting, audience analysis, and segmentation.

AI and Automation

Leaders in delivering personalized customer experiences are developing the capability to personalize any part of any customer interaction. Personalization is a challenge that can’t be accomplished at scale unless AI is effective at predicting customer reactions to each step in the experience, and automatically presenting the best next step. The top three requirements are control and automation; shared insights; and real time actions.

Control and automation. Adobe Target can automatically route traffic during tests; end A/B tests; create audiences and deliver the best available experience; and automatically divert more traffic to the winning experience during a test. As needed, marketers can create static or dynamic business rules to influence or control decisions made by the AI, such as global recommendations exclusions, boost/bury weightings, and filters. Offers can be further targeted and constrained by eligibility rules or brand and marketing guidelines.  Customer profile attributes can be compared against static values (e.g., inventory is greater than 5) or by dynamic values (e.g., propensity to apply for mortgage is higher than propensity to apply for auto loan).

Shared insights.  Adobe Experience Platform provides AI services to all Adobe solutions. Within Adobe Target, AI drives decisions for traffic allocation, best experiences and offers, and product and content recommendations. Adobe Target content and product recommendations and next-best offer/next-best action decisions are enabled by machine learning algorithms that consider a user’s omnichannel Adobe Experience Cloud profile.  These decisions can be made in any channel including web, app, voice, OTT, and IOT surfaces and consumer interaction points.

Adobe offers two Personalization Insights reports that analyze AI decisions and describe them in terms that are useful to marketers. The Automated Segments report presents  different segments that the machine learning models assembled automatically and determined were important when deciding which offers or experiences to deliver in an AI-driven personalization activity. It also shows you how many visitors were included in these automated segments and how they responded to a given offer or experience. The Important Attributes report shows the top visitor attributes of a visitor that influenced the model, along with their relative importance. These attributes could come from any first-, second-, or third-party data source being shared with Adobe Target—including data management platform (DMP) or customer relationship management (CRM) data. Adobe has patented an algorithm that generates human understandable insights from the output of the AI-driven activities in Auto-Target and Automated Personalization.

Adobe customers who have invested in building their own data models, such as propensity score models, can export the output of these models for use by Adobe Target Recommendations. These custom criteria can be used for real-time filtering rules, weightings, and customizations.

Real time actions. Models are updated hourly, with decisions happening in real time for every visitor on every visit.

Questions for Vendors of Personalization Solutions

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The path to delivering personalization is well-worn, but still brings adventure. 

I am evaluating personalization solutions using a brief questionnaire that I think gets to the heart of solution value and differentiation. There are two inputs to my choice of questions: what leaders in personalization have demonstrated to be effective, and the requirements revealed by the top user scenarios. My evaluations of vendor solutions are published in this blog.

You might find the questions useful for your own evaluation.

General

  • Describe your target market.
  • What are the top challenges your target market struggles with?
  • Which aspects of those challenges does your solution address?
  • What are the strengths of your solution in addressing those challenges?

Collaboration

Because the effort to create, deliver, and manage personalized customer experiences engages people with many roles across many departments in a company, it is important that the personalization solution encourage collaboration. The top three requirements are support for many roles, immediately accessible learning, and enterprise controls.

  • What is the average number of users at an account?
  • What roles are represented?
  • How would new participants establish the skills and resources for their testing, targeting, or optimization projects? E.G., learning what to do and how to do it; creating reports or dashboards; etc.
  • Describe user access controls and workflow support.

Integration

Personalization is not achieved with a tool bolted onto your marketing environment. It is achieved with a broad range of capabilities embedded in most of what marketing does. Integration thus becomes fundamental to the value of any personalization solution. The top three requirements are customer data integration, app integration, and accessible services.

  • How, and in which of your apps, can third party data and CRM data be used?
  • What are your solutions’ capabilities for updating CRM databases?
  • How and when can your apps exchange audience/segment information and decisions? What data can apps share about audiences, and how is it shared? 
  • Are the core services of your solution extendable and usable in other channels?

AI and Automation

Leaders in delivering personalized customer experiences are developing the capability to personalize any part of any customer interaction. Personalization at scale is a challenge can’t be accomplished at scale unless AI is effective at predicting customer reactions to each step in the experience, and automatically presenting the best next step. The top three requirements are control and automation; shared insights; and real time actions.

Which apps can make automated real time actions based on predictions and recommendations, in what circumstances?

  • What aspects of AI decisions can be constrained or controlled by users?
  • How frequently are models updated?
  • Under what circumstances can your AI services be extended to other apps or channels?
  • To what extent can AI decisions and actions be communicated in human terms? 

Understanding Requirements for a Personalization Solution

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Fitting the customer experience to the customer — at scale — has been the long-running goal, and challenge, of personalization solutions. 

Lessons from Leaders in Personalization

A recent study, Forrester’s Business Technographics Global Data and Analytics Survey 2018, determined that only 7% of companies have figured out how to compete effectively on experiences. These companies are in hyper-growth mode, and Forrester estimates they will drive $1.8T in revenue by 2021. These are the leaders the rest of us – more than half of us, according to the survey — should emulate.

Our research into achievements in personalization have identified three broad lessons to be learned from today’s leading marketers.

  1. Leaders achieve broad engagement and collaboration among all stakeholders.
  2. Leaders are passionate for insights into customer behaviors and motivations.
  3. Leaders create a culture of experimentation.

We see three categories of requirements that are driven by how these leaders have made strides in personalization.

  • Collaboration
  • Integration
  • AI and Automation

Collaboration Requirements

Because the effort to create, deliver, and manage personalized customer experiences engages people with many roles across many departments in a company, it is important that the personalization solution encourage collaboration. The top three requirements are support for many roles, immediately accessible learning, and enterprise controls.

  1. Support for many roles. User interfaces should be oriented around roles, such as campaign manager, and marketing tasks, such as monitoring and optimizing campaigns. The supported roles should include CMO, customer success, customer loyalty, merchandising, channel marketing, ecommerce, IT, mobile app development, administrator, demand generation, lead generation, and product management, among others.   
  2. Immediately accessible learning. New participants in personalization, or those shifting to new areas, must quickly and independently establish the skills. The solution should offer online learning on how to use the solution to accomplish common tasks. Ideally, it will also offer higher level guidance on how to be effective in personalizing the customer experience.
  3. Enterprise controls. A solution used by a number of people, especially across roles and departments, needs built-in controls to ensure they are not making conflicting changes. Workflow for changes, campaigns, and tests is a big help in this environment.

Integration Requirements

Personalization is not achieved with a tool bolted onto your marketing environment. It is achieved with a broad range of capabilities embedded in most of what marketing does. Integration thus becomes fundamental to the value of any personalization solution. The top three requirements are customer data integration, app integration, and accessible services.

  1. Customer data integration. Personalization requires a broad range of data, including data generated during an interaction,  the customer data that typically resides in several sources within a company, as well as data supplied by third parties. A personalization solution must be able to use all of this data in a systematic and consistent way, add to it, derive insights from it, and share those insights with other customer-touching systems.
  2. App Integration. The personalization solution should have published APIs that suit at least your immediate requirements; server-side and client-side integration; and pre-built connectors for common marketing and sales solutions such as Salesforce and Google Analytics.
  3. Accessible services. Ideally, a personalization solution allows its core services to be used in other apps and channels, via a consistent platform, API, and UI. These core services should allow marketers to request recommendations decisions, request and also share audience definitions, request personalization decisions, use customer attributes, and add to customer attributes.

AI and Automation Requirements

Leaders in delivering personalized customer experiences are developing the capability to personalize any part of any customer interaction. Personalization at scale is a challenge can’t be accomplished at scale unless AI is effective at predicting customer reactions to each step in the experience, and automatically presenting the best next step. The top three requirements are control and automation; shared insights; and real time actions.

  1. Control and Automation. At times you will want to control AI’s decisions and actions, so a personalization solution needs to offer constraints such rules and filters.
  2. Shared insights. ideally, marketers should have insight into what ai has learned about their customers – turning ai internals into recognizable attributes, communicated in human terms. AI decisions should be available to other apps in order to promote more consistent customer experience, either via requests for decisions and predictions, or via data such as audiences or customer profiles.
  3. Real time actions. Automatically delivering the best possible experience within each interaction requires real time decisions, predictions, and actions. AI models should be capable of real time or at least hourly update.

Method: Scenario-based Requirements

Technology is always acquired to improve our processes and results. When we gather requirements for technology solutions, we look at what how we have worked in the past, and what we need now and in the near future to do the same work more effectively.  But technology changes the way we do things, in ways that are not always possible to imagine. As a result, we prioritize requirements that will soon have little value while missing those that will soon seem critical. For example, rather than insist it be easier to cut and paste from this screen to that one, insist that the data transfer be automatic.

A inherent tension in the standard requirements process is simplification vs. context. Managing requirements almost demands reducing each to a bullet or headline entry in a checklist. But getting what you need demands that you retain the context. For example, “Salesforce integration” is useful shorthand, as long as you don’t lose sight of the real requirement, such as “Exchange customer profile data with Salesforce in real time”.

We use a scenario-based approach to requirements because in our experience it is your best hope for success with the requirements process, focusing on what you need in future rather than past aggravations you’ve suffered; and capturing the context — the why, who and when —of the requirement.

Here’s how you use the scenario approach: Your team talks through business activities and goals, with the aim of creating a narrative of ideal scenarios. For a moment, forget the constraints and limitations of today’s tools. What are the most frequent, and what are the most important, outcomes that you pursue? What roles are involved in pursuing those outcomes? In a perfect world, how would your team accomplish those goals? In the ideal, you are not wasting time making up for your tools’s shortcomings, such as trying to reconcile segment definitions or revenue reports, importing or exporting data, inputting results, studying dashboards to identify anomalies and their causes.

Use your scenarios as the basis of requirements, and even more importantly, of vendor demo evaluation. Insist that the demo show us how we would accomplish our goals and do our jobs with the vendor’s tools. Don’t accept a canned demo that is organized around the features of the vendor’s tools.

We recommend starting with your top 3 scenarios. The Table presents a generalized scenario that you can use as a starting point for your own.

Scenario: Director of Marketing is overseeing launch of new product category

Task or Event

What is Success

Requirements to Achieve Success

Test the idea with current customers to find the target audience

Identify target market without negative impact on other results

Testing optimization: MVT testing that also segments audiences, predicts responses, and routes traffic to best performing experience

Identify the attributes of the target segment

Begin to understand how target market compares to other segments

Machine-discovered segment is described with human-meaningful attributes

Machine-discovered segment definition is available/useful to analytics and other marketing tools

Seek more customers to add to segment

Quantify the size of target market

Explore possible similar or related segments

AI/ML analysis that identifies similarities within a group that are predictive of behavior

Based on analysis of customer response and target market, decide to launch the category

Feel comfortable that the range and likelihood of outcomes is clear

AI analysis that quantifies outcomes, probabilities, and confidence intervals

Prepare content for target customer journeys

Customers respond to content by taking the hoped-for actions

Analysis identifies similar segments, which marketing uses to ideate content and paths for target segment

Use the target segment definition in campaigns, both web and email

Meet campaign and launch goals

Testing optimization predicts most effective content and paths for visitors and routes them accordingly

Campaigns can use both user-defined segments and observed segments in matching customers with paths and content

Deploy increasingly personalized campaigns to learn more about target customers and create richer profiles

Positive impact on business results

Improved target segment definitions

Understand how to motivate behaviors of customers

Apply third party and company data in analysis, prediction, traffic routing, segmentation

Real time prediction and analysis

Automated deployment of best experience

Of course, your standard process for requirements gathering can’t be ignored. We recommend the scenario approach as an addition to your requirements process, a way to organize your evaluation, and the best way to focus on what is most important to your future success. 

Until AI Automates the World

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We’re all on the AI bus careening rapidly toward the End of Human Work.

Until we get there, we all still have a lot of work to do.

We talk about the transformation as if the goal is autonomous AI, automatically doing all our work. But I think there is no question that the most valuable results will be achieved by the AI-assisted super-human, producing work never before possible – or imagined. The most valuable applications of AI will be human-machine collaboration, where AI augments human jobs and humans augment AI tasks.

We are in for a long period of working with and around intelligent machines.

To date, we have mostly experienced master-slave relationships with our machines. We feed in scads of data, and machines pour out the orders, invoices, payments, computations, and categorizations we rely on to keep business going. Or, machines feed tasks to workers such as warehouse pickers who have no other motivation/control over what to do next.

Today, for the most part it is humans who see the information, have the insights, make the decisions, and take the actions. Nevertheless, in a few arenas machines are producing better results than humans. These are limited cases where algorithms are tested enough data is clean enough, systems are integrated enough, the problem is clearly bounded and context is sufficiently indicated. Sadly, these conditions are rare: The majority of today’s business systems suffer greatly from poorly integrated, context-poor, messy data deployed in support of poorly articulated strategies and goals. Improving those conditions is a gargantuan effort, an effort already underway for decades.

For decades to come, then, AI will augment most of our jobs, and automate very few. We are not even on the cusp of understanding how to do that. We need to learn how to effectively collaborate. We need new design patterns, methods, and metaphors for this new shared work.

Technology may be ready for an AI-automated world in my lifetime, but corporations, systems, and people will be struggling to catch up.

A few questions we urgently need to answer:

  • How does an organization learn to assess opportunities to apply AI, experiment, and measure the resulting impact?
  • How do organizations acquire the skills needed to be effective users of AI?
  • What are the design methods for sharing work with a semi-autonomous agent?
  • What are the design patterns for collaborating with a machine?
  • How do we design interfaces that encourage trust? Given that ML won’t make perfect decisions in every case, how do we make people comfortable enough to use systems?
  • How do we design interfaces that involve people at the right time, in the right way?
  • How do experience designers develop sufficiently deep understanding of ML to know which behavior and context information is essential to improving ML?
  • How do we design interfaces that evolve as machines learn over time, and yet feel consistent and reliable?

Practically Personal

How personal should you make the customer experiences you deliver? How personal can you make customer experiences?

In a previous post we described how a guy who doesn’t ski reacts to images on an ecommerce site of a woman skiing.  He believes he’d respond more if shown something he can relate to.

If we somehow knew (and that’s an issue for a future post) that this guy bicycles, we could show images of bicyclists. The question for today is, what images do we need in our library to satisfy our visitors and our customer experience goals? Do we have to address, say, 6 possible biking interests (mountain, commuting, BMX, racing, family recreation, camping); 2 genders; at least 3 age groups (child, young adult, senior); and perhaps 6 environments (urban, rural, forested, plains, mountains, coastal). That’s 17 attributes, and 216 images to satisfy all combinations. If all you sell is bikes, perhaps you can afford that. If you cover all sports, or if sports is but one of your categories, how can you possibly?

Most likely, you don’t need 216 or even 17 images to be effective with this guy who bicycles. Maybe you only need 3 images. Which ones? How many? Who knows.

The only way to know is to “test” out the impact of having a few variations. I wish I could believe that there is one answer to the question of what will make our bicyclist happy. I fear that it depends on his current context, and therefore the customer experience must be variable as well.

In this realm, “test” bears no resemblance to A/B testing. Rather, it describes a an automated, data-driven prediction of what will have the greatest impact at this moment in time. Machines can make the predictions and deliver the customer experience. The marketing team has to decide how much to invest in content variations, and which variations are most likely to be important to visitors. Automated customer experience delivery and content planning are two programs that most companies have as yet to perfect, or many have as yet to attempt.

The Right Questions for Personalization Success

path to personalization

“I’m a guy, and I don’t ski. Why are you showing me pictures of a woman skiing?”

I wish I could remember the name of the man who said this, because it is a great summary of the customer perspective of personalization. The implication is, he’d be more responsive to offers that featured guys doing his sport – whatever that might be.

His complaint surfaces what I call the 5 Introductory Personalization Questions:

  1. How can we know enough about our visitor?
  2. How do we use that knowledge to select the best experience for this moment?
  3. How do we have the right content on hand?
  4. What is the mechanism for retrieving and delivering the best content to this customer at this moment?
  5. How do we know we delivered the best experience possible?

These questions are signposts for your personalization journey, and during the journey you will ask and answer many more.

You have almost certainly talked to people who want to answer these questions with technology. Technology is unquestionably necessary, but in my experience the culture and process concerns are far more challenging. Every organization that is struggling to deliver personalized customer experience describes issues with strategy, commitment, alignment, and workflows. Any time you fool with customer experience, the ripples reach every part of the company. Somehow, that is a lesson that never gets old but must be learned and learned again.

People don’t anticipate the breadth of what they are taking on when they begin their personalization journey. As a result, they start in the middle without the provisions, collaboration or roadmap they need. With a little more knowledge of what the journey entails, progress is more certain and less expensive Here’s my [You Won’t Be] Lonely Planet Guide to help you anticipate and overcome the barriers.

Program or Task

Culture

Process

Tooling

Acquiring Customer Knowledge

What knowledge do we think is valuable?

What are we willing to collect?

What sources are acceptable?

How much resource are we willing to devote to the process?

Who owns the information?

Who owns the policies and processes?

Who will collect and analyze information to create knowledge?

Who will establish, who will manage, third party relationships for data collection?

Who is responsible for budget and planning?

Who is responsible to distribute and protect the information and knowledge?

How is customer information captured, ingested, and stored?

How is knowledge extracted from information?

In what manner is the knowledge stored?

Applying Customer Knowledge to Creating Customer Experience

What is the strategy for customer experience?

How does customer experience strategy align with business strategy?

Who owns the customer experience strategy?

What aspects of customer experience should be influenced by customer knowledge?

Who decides?

What degree of automation and what degree of explicit control is acceptable?

Who/what can use the information, for what purposes, in what circumstances?

How do various customer segmentation tactics apply to knowledge-driven customer experience?

Who designs and manages the variable customer experience?

What is the mechanism for knowing the customer?

How is the best experience for each customer

identified?

How are the elements of the experience delivered during the experience?

Provisioning Customer Experience Content

How many variations of content are we willing to fund and manage?

What sources are acceptable?

What degree of quality and consistency are required?

How doe we reconcile variations with our brand?

Who creates and tracks the content strategy and plans?

How is the content tagged, stored, improved, and replaced?

Who decides which variations will be shown in each customer experience?

What is the time frame in which the decision is made?

How is content tagged and formatted for use in various experiences, across various devices?

How is content use and impact tracked and reported?

Evaluating Results

Who is responsible for the quality of customer experience?

What is the goal for quality of customer experience?

How is quality of customer experience measured and reported?

How is the value of the experience to the customer measured?

How is the quality of content measured?

How is quality of customer experience measured and reported?

How do we measure the impact of customer experience improvements on business results by period?

How do we measure the impact on value delivered to customers?

What are the mechanisms for measuring, evaluating and communicating the quality of customer experience; and the value to our business and to customers?

Optimizing Results

Who is responsible for improving customer experience?

What are the goals for improvement?

Are the goals differentiated by customer segments, product categories, or sales region?

How is improvement measured and tracked?

How do we track progress toward goals?

How do we identify and prioritize efforts to improve customer experience?

What are the mechanisms for predicting, delivering, measuring, and evaluating what makes the best experience for each customer at each moment?

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