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.
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
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.
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
- 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.
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.
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.