Engagement Analytics Glossary

Engagement Analytics Glossary

When it comes to competing effectively in the increasingly digital world of the capital markets, “engagement analytics” is a current buzzword that holds enormous promise. Have you ever wondered what exactly the term means? Or, better yet, what do all the associated terms mean? Q4 understands, and we’ve got you covered. Jargon seems to come and go at warp speed these days, but engagement analytics is here to stay. In fact, it’s a practice that fundamentally elevates the next generation of capital markets connections for all participants. This engagement analytics glossary provides straightforward definitions for the most used – and least understood – terms associated with the digital transformation of the capital markets.

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Artificial Intelligence: Originating in 1950, the concept of artificial intelligence refers to the ability of computers (machines) “to mimic the problem-solving and decision-making capabilities of the human mind.” Essentially, it refers to a computer’s ability to learn as a human would and apply that learning to create insights and solve problems that don’t require human intervention.  

  • AI Applications in Investor Relations: The advancements in Artificial Intelligence (AI) technology are paving the way for more streamlined and sophisticated strategies in investor relations. Some notable applications of AI for IR professionals include:
    • Automated Reporting: Algorithms built into an AI program can generate reports based on complex financial data. These reports can cover quarterly earnings, annual results, and business forecasts. Automation helps reduce human error and improves the speed and efficiency of the reporting process.
    • Chatbots for Investor Interactions: AI-powered chatbots can interact with investors around the clock, giving them instant responses to their queries. These interactions can include questions about financial results, dividend policies, or corporate strategies. The use of chatbots ensures timely communication and can significantly enhance the investor experience.
    • Customized Investor Communication: AI can analyze the preferences and behavior of individual investors to customize communication for them. Providing an improved engagement experience offers investors the most relevant information about their specific interests and investment strategies.
    • Investor Sentiment Analysis: AI tools can analyze data from various sources like social media platforms, news outlets, and investor forums to gauge investor sentiment. Companies can use this information to proactively identify and respond to investor concerns, fostering positive investor relations.
    • Predictive Analysis: Using historical data to predict future trends, AI can help investor relations teams make more informed decisions. For instance, machine learning algorithms can analyze market conditions and previous performance to provide insights on potential stock price movements, enabling proactive investor communication.
  • AI prompts: Statements or questions used to initiate or guide the output generated by an AI program. For instance, IR professionals could give a prompt such as “Prepare a quarterly earnings report summary” or “Analyze the sentiment from the latest shareholders’ meeting.” The program would then create content based on these prompts.

Automated Targeting: The ability to automate the identification and pursuit of individuals in an organization’s target market has transformed marketing across industries. These technological advances can help IR teams identify an ideal investor through automated targeting. An AI algorithm can generate a combined probability and matching score for every firm, fund, and stock. Because of this automation, the manual work required to target the right investors is greatly reduced.  


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Bing Chat: Bing Chat is built into Microsoft Edge and integrated into Bing’s search functionality. Marketed as a “co-pilot” for the web, Bing Chat uses information directly from the web and summarizes it when answering prompts. With its access to up-to-date reporting and financial data crucial to investor relations professionals, Bing Chat can provide more current responses than ChatGPT, which is limited to data from 2021 or before.


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Capital Markets Intelligence: Capital markets intelligence typically consists of financial news, company performance data, transaction data, market insights, and sector specific data.

ChatGPT (Chat Generative Pre-Trained Transformer): ChatGPT is a generative AI program developed by OpenAI that can answer a wide range of questions and create content based on the information it has processed. It understands natural language and can have conversations with people that closely mimic how real people would respond. ChatGPT can be used for things like customer service chatbots, creating outlines, or any number of applications. 

As an example, when a user submits a question or asks it to create something, like “Write a short poem about Q4 Inc.,” you might get (as we did): 

“Q4 Inc, a name that spells success, Investor solutions that truly impress, Their expertise and tools shine bright, Empowering companies to reach new heights.” 

Content Analytics: The measurement and analysis of visitor traffic and engagement with published digital content is referred to as content analytics. It includes monitoring engagement with things like white papers, blogs, articles, checklists, podcasts, videos, press releases, and guides. Common metrics used in content analytics include click-throughs to content, page views, engagement time, acquisition sources, and social media interactions (ex., liking, commenting, sharing, retweeting). Content analytics is critical to the current and future success of IR teams because it drives a deep understanding of who engages with their content, where they come from, and what they do next. As Parse.ly explains, “Content analytics enables teams to create and optimize their content strategy and ultimately understand the value their content provides.” 

CRM/CRM System: In the broadest sense, customer relationship management refers to everything that an organization does to obtain, nurture, deepen, service, and retain its customers. Practically speaking, “CRM” is often used to refer to the software system the organization uses to gather data on customer interactions. This centralized data regarding all customer interactions, in turn, can be analyzed to improve a customer’s experience with the organization. 


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DALL-E (stylized as DALL·E) and DALL-E 2: Developed by OpenAI, the company that created ChatGPT, DALL-E, and DALL-E2 generate digital images from prompts. DALL-E can be an instrumental tool in creating visual aids and presentations. For instance, when preparing for an investor meeting or presentation, rather than spending time and resources on graphic design, an investor relations professional can simply describe the type of chart, infographic, or other visual aid they need, and DALL-E could generate it.

Dashboard: A dashboard is a visual display of data. Think of it as the presentation layer. The dashboard is not synonymous with the data itself. Rather, data and data sets, potentially from multiple data systems, feed the display you see on the dashboard. While dashboard technology can be provided by a stand alone software package, the newest data systems that take the place of legacy and/or siloed data systems are making great strides in including dashboard functionality within their platforms. 

Data, Data Sets, and Data Systems: A data set is a structured collection or grouping of data. Structured data is more easily optimized and therefore usable in the context of engagement analytics. The data set contains many individual data points. Multiple data sets are often stored within a single database or data system. 

Data Analytics, Business Intelligence, and Engagement Analytics: Different from the data itself, how it is collected, where it is stored (data systems), and how it is visualized (dashboards), data analytics is a catch all phrase covering the process of synthesizing and analyzing raw data to make conclusions from it. Originating in the 50’s at the start of the digital revolution, the practice of data analytics has been in place for several decades. As our systems and technology have improved, both the power and language associated with the concept have proliferated.  

The phrase business intelligence gained widespread popularity in the 21st century, and the two terms are often used synonymously. However, business intelligence emphasizes converting raw data into meaningful information (analysis) to drive profitable business decisions and actions.  

In its most promising form to date, we often refer to data analytics as engagement analytics. As our data systems and analytical tools have become more sophisticated and integrated, engagement analytics has come to refer to analyzing the proliferation of data associated with a dizzying array of human, online behavior and digital phenomena to extract insight previously unattainable.  

Data Hygiene, Data Scrubbing, Data Quality, “Clean” Data: Data hygiene or data scrubbing refers to the processes performed to ensure that an organization’s data is “clean.” Clean data is relatively free of error and inaccuracies and is complete.This ensures that its “quality” (i.e., its usability) and hence its value to the organization, is high. 

Data Modeling: Data modeling is the practice of visually representing an organization’s data elements and the intended connections between them. Data models are meant to graphically illustrate the relationships among data elements, sets, and systems that are required for valuable analytics. They are an invaluable tool for an organization beginning its engagement analytics journey. 

Data Optimization: Data optimization refers to collecting, storing, managing, combining, and manipulating the wide range of an organization’s data in the most efficient, effective manner possible. Since optimized data is required in order to analyze or make meaning from its combination, it is a prerequisite for engagement analytics. By necessity, data that is functionally optimized does not live in siloed systems.

Digital Footprint: An individual’s digital footprint refers to the trail of data they leave when using the internet. It is essentially a record of a person’s online behavior. Digital footprints are an essential input to engagement analytics, as it is focused on aggregating data regarding online behavior and phenomena to create previously unattainable analysis.


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Engagement Benchmarking: Engagement Benchmarking takes an aggregate view of User Engagement. It provides an overall view of how an entire audience interacts with your digital content, events, and web pages in order to establish engagement norms (“benchmarks”) by which to measure your performance. 



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Generative AI: When content, like existing text, images, or audio, is submitted to a generative AI program, it can analyze that information and create new content based on it. Suppose a company wants to make a report for its investors about its financial performance for the past year. Instead of having the IR team read through all the finance data, the company could submit last year’s financials to a generative AI program which could then create a summary for the IR team to use to write the report.  

Google Bard: Google Bard is a conversational AI chatbot designed to compete with ChatGPT. Trained on Google’s search data, Bard can access and process real-time information from the Google search engine. This means it can create content derived from the latest updates on a company’s financial performance, market trends, and investor sentiment, a feature not available in ChatGPT, which can only access information from 2021 or earlier. 



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Investor Targeting: Investor targeting refers to the process of identifying and attracting investors who will bring the most value to your company based on your financial and business goals. In short, successful targeting allows you to engage the “right” kind of investors at the “right” time, those whose participation aligns with your company messaging and helps to maximize your company’s value. Investor targeting is inherently tied to engagement analytics. It requires access, synthesis, and analysis of a host of investor, potential investor, and market data.   

IP Address: An IP address is a unique string of numbers (for example, that identifies a specific device on the internet or on a local network. An IP address contains local information related to the device and makes devices accessible for communication with one another. IP addresses are a critical weapon in the IR team’s arsenal because they are most closely associated with an individual’s digital footprint. Tracking an individual IP address across web pages, events, and email engagement, for example, allows Investor Relations to identify high-value potential investors, identify potential threats from activists, and understand the information and engagement needs of its current investors. 




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Lead Generation: At its most basic, lead generation refers to the process of identifying and pursuing potential customers for your business through engaging their interest. It’s typically achieved through a combination of marketing activity that can precede the involvement of your sales team. As the capital markets continue to become more digital, lead generation is implicitly coming to refer specifically to marketing technologies that attract the online interest of customers and potential customers, electronically capturing data relevant to validate and prioritize (“score”) their interest, calculating your next best marketing or sales activity, and contacting them via the most appropriate channels. 

Lead Generation Waterfall: The lead generation waterfall refers to a marketing process with the goal of generating as many qualified leads as efficiently as possible. The waterfall begins with marketing-driven digital interactions with your brand and progresses through the delivery of more valuable, gated content. When a marketing automation system is in place, the process allows you to easily capture the data required to deliver a subset of marketing qualified leads (MQLs) and ultimately a smaller, more refined subset of sales qualified leads (SQLs).  

Legacy (Data) Systems: Legacy data systems are outdated technologies or software (“systems”) that continue to house any variety of an organization’s data sets (groups of data). They are still in use and perform their original functions. However, they lack additional functionality offered by newer systems. The limiting impact of legacy systems has been particularly profound over the last several years as advancements in engagement analytics have proliferated and these older systems often limit an organization’s ability to connect disparate sets of data required for valuable insight creation. 


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Machine Learning: Machine learning is a subset within the broader concept of artificial intelligence. IBM explains that it refers specifically to a machine or computer’s “use of data and algorithms to imitate the way humans learn, gradually improving its accuracy.” Machine learning is a transformative force in the digital evolution of the capital markets. It is the foundation of engagement analytics.  

Machine learning is a subset within the broader concept of artificial intelligence. IBM explains that it refers specifically to a machine or computer’s “use of data and algorithms to imitate the way humans learn, gradually improving its accuracy.” Machine learning is a transformative force in the digital evolution of the capital markets. It is the foundation of engagement analytics.  

Marketing Automation: A marketing automation system is intended to automate as much of the lead generation function as possible when it occurs across multiple online channels like email, social media, web content and sites, digital events, etc. Since marketing automation combines and analyzes multiple sets of online behavioral data, it also functions as the underlying driver for effective lead generation and nurturing as well as the creation, execution, and tracking of omni-channel marketing strategies. 

Multiplier Effect: The Multiplier Effect refers to the potential for artificial intelligence to significantly increase the efficiency and impact of Investor Relations functions without needing to expand the IR team. This effect is achieved by the AI system’s ability to quickly digest, synthesize, and make meaningful inferences from various data sources; public capital market data, proprietary engagement data from numerous investor interactions, and data from individual IR programs. 




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Platform: A technology platform functions as the central nucleus that drives many of the applications, activities, services, and programs defined in this glossary. From a technology perspective, this is the core environment for building and running various software systems.   

Predictive Analysis / Analytics: This automated process uses computer algorithms to analyze data from the past and figure out what might happen next. It’s like having a crystal ball that tells you what will likely occur. This can help businesses that use predictive analytics decide which customers to target or what future strategies could be successful based on previous results.



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Siloed (Data) Systems: Many systems, particularly legacy systems, are often “siloed,” meaning that the older technology employed by those systems won’t let them interact with other data systems and newer functionality. This limitation, in turn, blocks the ability of an organization to easily bring the disparate data sets that live in various siloed systems together for analysis. The presence of siloed systems is a common reflection of the software limitations that existed when these systems were implemented. However, it can be a particularly dangerous roadblock right now, as current and future advances in data science such as automated intelligence and machine learning require system connectivity. 

Structured/Unstructured Data: Structured data has been entered and organized into a formatted repository, usually a database and often into data cells. While those cells can exist in any variety of formats and data systems, the simplest example is a cell in an Excel spreadsheet. Structured data fields have predefined selections to maintain the uniformity required for effective, efficient search, aggregation, and analysis. Unstructured data can exist in a multitude of formats such as audio, video, and social media postings. It has historically been very difficult to  search, aggregate, and analyze.  


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Technology Stack: This is the list of technologies used to build and run a single application. In other words, it’s the technology infrastructure that supports and houses the complex data ecosystem required to activate many of the programs identified in the glossary – engagement and behavioral analytics, marketing automation, artificial intelligence, and machine learning. 


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User Engagement: Sometimes referred to as Customer Engagement (though its relevance goes far beyond current customers), user engagement collects and measures a person’s response to, and engagement with, a digital location, asset, or publication. User Engagement is important because it allows you to identify those who may be interested in your stock and/or your company’s product or service.   


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Virtual Event

A virtual event is one that happens online. People interact in real time during the event, but they do so digitally. Webinars, webcasts, and digital conferences (with multiple meeting rooms and collaboration experiences) have become more commonplace over the last couple of years.   

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