Intelligencia’s work in the gaming industry includes work with one of India's largest gaming organization, building complex CX models. We recognize this as a huge and growing business and expect to increase our customer count shortly.  

How A.I. is used in the Gaming industry

We seem to be living in the age of A.I. Everywhere you look, companies are touting their most recent AI and machine learning (ML) breakthroughs, even when they are far short of anything that could be called a “breakthrough.” “A.I.” has probably superseded “Blockchain”, “Crypto”, and/or “ICO” as the buzzwords of today. Indeed, one of the best ways to raise VC funding is to stick ‘A.I.’ or ‘ML’ at the front of your prospectus and a “.ai” at the end of your website. Separating A.I. fact from fiction is one of the main goals of this article; the other is to help gaming and esports executives utilize A.I. in ways that are simplistic, complex and, hopefully, rather ingenious.  


Once a mostly academic area of study, twenty-first century A.I. enables a plethora of mainstream technologies that are having a substantial impact on everyday lives. Computer vision and A.I. planning, for example, drive the video games that are now a bigger entertainment industry than Hollywood.

As shown above, machine learning is a subset of A.I., and deep learning is a subset of machine learning. According to Wikipedia, ML is the sub-field of computer science that “explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model based on inputs and using that to make predictions or decisions, rather than following only explicitly programmed instructions.”

ML “evolved from the study of pattern recognition and computational learning theory in artificial intelligence.” It “explores the study and construction of algorithms that can learn from and make predictions on data—such algorithms overcome following strictly static program instructions by making data driven predictions or decisions, through building a model from sample inputs.”

As per Wikipedia, ML can be broken down into the following three categories:

1.       Supervised learning: The computer is presented with example inputs and their desired outputs, given by a “teacher”, and the goal is to learn a general rule that maps inputs to outputs.

2.       Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).

3.       Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle), without a teacher explicitly telling it whether it has come close to its goal or not. Another example is learning to play a game by playing against an opponent.

Gaming companies can tap into five prominent segments of A.I.—sound, time series, text, image, and video. Areas such as CRM, customer loyalty, marketing automation, social marketing and social listening should all be radically affected by A.I. and ML.

There are so many use cases for ML and deep learning for marketing departments that it is impossible to create an exhaustive list here, but it is particularly useful for marketing personalization, customer recommendations, spam filtering, network security, optical character recognition (OCR), voice recognition, computer vision, fraud detection, predictive asset maintenance, optimization, language translations, sentiment analysis, and online search.

The table below shows the general use cases for AI broken down by industry. This is a generalized list and many of these use cases can be utilized by the gaming industry as well.




Voice recognition

UX/UI, Automotive, Security, IoT

Voice search

Handset maker, Telecoms

Sentiment analysis

CRM for most industries

Flaw detection

Automotive, Aviation

Fraud detection

Finance, Credit cards

Time Series

Log analysis/Risk detection

Data centers, Security, Finance

Enterprise resource planning

Manufacturing, Auto, Supply Chain

Predictive analytics using sensor data

IoT, Smart home, Hardware manufacturing

Business and Economic analytics

Finance, Accounting, Government

Recommendation engine

E-Commerce, Media, Social Networks


Sentiment analysis

CRM, Social Media, Reputation mgmt.

Augmented search, Theme detection


Threat detection

Social Media, Government

Fraud detection

Insurance, Finance


Facial recognition

Multiple industries

Image search

Social Media

Machine vision

Automotive, Aviation

Photo clustering

Telecom, Handset makers


Motion detection

Gaming, UX, UI

Real-time threat detection

Security, Airports


ML can also be used to spot credit card or transaction fraud while it is happening. ML can build predictive models of credit card transactions based on their likelihood of being fraudulent and the system can compare real-time transactions against these models. When the system spots potential fraud it can alert either the bank or retail outlet where the transaction occurred. This is exceptionally important for business with online retail presences because online fraud is on the rise and this could be an additional security layer that ensures purchases made are purchases paid. 


Deep learning has made speech-understanding practical on our phones and in our kitchens, and its algorithms can be applied widely to an array of applications that rely on pattern recognition,” the Artificial Intelligence and Life in 2030 study adds.

Google Duplex has shown that AI bots can do things like make reservations at hair dressing salons and restaurants and this is one of the deep learning futures. Businesses need to develop voice and speech understanding technology or risk being left behind by their competition. Voice, in particular, is a technology waiting for mass use. We communicate through voice as much as any other sense and the companies that win the battle in voice will win the battle for the 21st Century consumer. 

Business who want to stay ahead of the SEO curve need to look at voice as it can give them a big leg-up on the competition. Tomorrow's leaders in voice are shaping the landscape today and this could be a technology that leaves secondary players in the dust. 



Natural language refers to language that is spoken and written by people, and natural language processing (NLP) attempts to extract information from the spoken and written word using algorithms. NLP can be used to help the gaming company's call center, as well as be the basis for creating chatbots. NLP can be used for sentiment analysis and social media listening, as per below:


A chatbot is a computer program or an artificial intelligence which conducts a conversation via auditory or textual methods. Such programs are often designed to convincingly simulate how a human would behave as a conversational partner. Although chatbots are cheaper than handling customer service inquiries over the phone, there is a catch as chatbots can only deliver highly personalized and contextual assistance if they have access to universal consumer profiles that are populated by real-time data. This means, done correctly, developing chatbots is an expensive upfront investment, it is an investment that should be done company-wide, not siloed by just the marketing or customer service department as information that chatbots tap into are useful throughout the organization.

Why Choose A.I.?

So why choose to go down the complex A.I. road? Well, in the article Artificial intelligence Unlocks the True Power of Analytics, Adobe explains the vast difference between doing things in a rules-based analytics way and an A.I.-powered way, including:

·       Provide warnings whenever a company activity is falls outside the norm. The difference:

o   Rules-based analytics: You set a threshold for activity (e.g., “200–275 orders per hour”) and then manually investigate whether each alert is important.

o   A.I.-powered analytics: The A.I. analytics tool automatically and determines that the event is worthy of an alert, then fires it off automatically. 

·       Conduct a root cause analysis and recommend action. The difference:

o   Rules-based analytics: You manually investigate why an event may have happened and consider possible actions.

o   A.I.-powered analytics: Your tool automatically evaluates what factors contributed to the event and suggests a cause and action. 

·       Evaluate campaign effectiveness:

o   Rules-based analytics: The business manually sets rules and weights to attribute the value of each touch that led to a conversion.

o   A.I.-powered analytics: The AI analytics tool automatically weights and reports the factors that led to each successful outcome and attributes credit to each campaign element or step accordingly. 

·       Identify customers who are at risk of defecting:

o   Rules-based analytics: You manually study reports on groups of customers that have defected and try to see patterns.

o   A.I.-powered analytics: Your tool automatically Identifies which segments are at greatest risk of defection.

·       Select segments that will be the most responsive to an upcoming campaign:

o   Rules-based analytics: You manually consider and hypothesise about the attributes of customers that might prove to be predictive of their response.

o   A.I.-powered analytics: Your tool automatically creates segments based on attributes that currently drive the desired response. 

·       Find your best customers:

o   Rules-based analytics: You manually analyse segments in order to understand what makes high-quality customers different.

o   A.I.-powered analytics: Your tool automatically identifies statistically significant attributes that high-performing customers have in common and creates segments with these customers for you to take action on.”


Knowing all this, why wouldn't you choose A.I.?


Gaming - BI

According to Gartner’s Magic Quadrant for Business Intelligence Platforms, modern analytics and business intelligence platforms represent mainstream buying, with deployments increasingly cloud-based. Data and analytics leaders are upgrading traditional solutions as well as expanding portfolios with new vendors as the market innovates on ease of use and augmented analytics.

By 2020, augmented analytics — a paradigm that includes natural language query and narration, augmented data preparation, automated advanced analytics and visual-based data discovery capabilities — will be a dominant driver of new purchases of business intelligence, analytics and data science and machine learning platforms and of embedded analytics.

By 2020, the number of users of modern business intelligence and analytics platforms that are differentiated by augmented data discovery capabilities will grow at twice the rate — and deliver twice the business value — of those that are not. By 2020, natural-language generation and artificial intelligence will be a standard feature of 90% of modern business intelligence platforms. By 2020, 50% of analytical queries will be generated via search, natural-language processing or voice, or will be automatically generated. By 2020, organizations that offer users access to a curated catalog of internal and external data will derive twice as much business value from analytics investments as those that do not.

Visual-based data discovery is a defining feature of the modern analytics and business intelligence (BI) platform. This wave of disruption began in around 2004, and has since transformed the market and new buying trends away from IT-centric system of record (SOR) reporting to business-centric agile analytics with self-service. Modern analytics and BI platforms are characterized by easy-to-use tools that support a full range of analytic workflow capabilities. They do not require significant involvement from IT to predefine data models upfront as a prerequisite to analysis, and in some cases, will automatically generate a reusable data model.

A self-contained in-memory columnar engine facilitates exploration, but also rapid prototyping. Modern analytics and BI platforms may optionally source from traditional IT-modeled data structures to promote governance and re-usability across the entire organization. Many organizations may start their modernization efforts by extending IT-modeled structures in an agile manner and combining them with new and multi-structured data sources. Other organizations, meanwhile, may use the analytic engine within the modern analytics and BI platform as an alternative to a traditional data warehouse. This approach is usually only appropriate for small or midsize organizations with relatively clean data from a limited number of source systems. The rise in the use of data lakes and the logical data warehouse also dovetail with the capabilities of a modern analytics and BI platform that can ingest these less-modeled data sources (see "Derive Value From Data Lakes Using Analytics Design Patterns").


Gaming CX

As Forbes sees it, "Customer experience can include a lot of elements, but it really boils down to the perception the customer has of your brand. Even if you think your brand and customer experience is one thing, if the customer perceives it as something different, that is what the actual customer experience is. You may think you have high-quality products and a strong customer experience, but if a customer gets a broken product that isn’t fixed, their perception of your company as lower quality then becomes the reality."

This is as true for the cruise line industry as any other industry, perhaps even more so because the product that a cruise line offers differ little from one cruise line to another. It's the experience and the emotional sense and perception of that experience that truly counts; this is where CX thrives. Above all else, the ultimate goal of a CX system is to drive customer loyalty. 

Loyalty is so important for a cruise line because customers become more profitable over time; increased profits can be derived from increased purchases, reduced operating costs, profits from customer referrals, as well as profits from price premiums as long-term customers are more comfortable paying regular prices for services rather than being tempted into using a businesses’ lower profit products and/or services.

One of the best ways to increase customer loyalty is for cruise lines to implement Customer Relationship Management (CRM) systems. CRM is a strategy used to learn more about a customer’s needs and behaviors in order to develop a stronger relationship with them, thereby creating a value exchange on both sides. From a customer perspective, well-implemented CRM systems can offer a unified customer interface that delivers customization and personalization. At each transaction point, such relevant patron data as a customer's personal preferences, as well as his or her overall past history transactions are available to the clerk serving the customer, giving them valuable information about how to interact with the person.

In assessing CRM solutions, Intelligencia follows the Forrester and Gartner rankings closely. We understand the current CRM offerings, each vendor's unique strategy and market presence, and we can help our clients find a solution that is tailored to their specific needs. 

Another important element to a strong customer experience solution is a powerful Multichannel Marketing Hub (MMH). Gartner defines the MMH as a technology that orchestrates a company's communications with and offers to customer segments across multiple channels. These include websites, mobile, social, direct mail, call centers, paid media and email. MMH capabilities also may extend to integrating marketing offers/leads with sales for execution in both B2B and B2C environments.

A Campaign Management solution can enable a cruise line to develop and manage personalized customer communications strategies and the timely delivery of offers to its patrons. It allows users to rapidly create, modify and manage multi-channel, multi-wave marketing campaigns that integrate easily with any fulfillment channel, automatically producing outbound (contact) and inbound (response) communication history.

Users can define target segments, prioritize selection rules, prioritize offers across multiple campaigns and channels, select communication channels, schedule and execute campaigns, and perform advanced analyses to predict and evaluate the success of customer communications. Most of today’s solutions provide the capability to represent complex data structures in a format appealing and comprehensible for business users in order for them to make business decisions. These capabilities provides a business metadata layer shielding users from the complexity of data structures and guarantee operational efficiencies when dealing with customer data for campaign purposes.

We have worked with the best-in-breed CRM and MMH solution providers and we can show you how to best utilize them for your own specific IT needs. 

Gaming DI + DV

Today, Enterprise Data Warehouses (EDWs) are expanding to support higher scalability, higher performance, deeper Hadoop integration, more automation, and realtime streaming technology. Large EDW vendors have started to offer more scalable platforms that use solid-state drives and offer distributed in-memory technologies to process large amounts of data faster, delivering both predictive and prescriptive analytics that can increase optimization and deliver a strong ROI.  

Today’s EDWs enable gaming organizations to deliver actionable, timely, and trustworthy intelligence to a wide range of business users and operational systems. An EDW can organize and aggregate historical analytical data from functional domains, such as customer, finance, and human resources, that align with key processes, applications, and roles.

An EDW serves as the repository for a substantial amount of an organization’s operational history. It offers in-database analytics, predictive models, and embedded business algorithms to drive business decisions. An EDW is a robust, secure, and proven ecosystem that supports integration with data models and security frameworks, real-time analytics, automation, and a broad range of business intelligence (BI) and visualization tools. It is the foundation for BI to support timely reports, ad hoc queries, and dashboards, as well as supply other analytics applications with trusted and integrated data.

Figure 1

The next generation of EDW is expanding to support higher scalability, higher performance, deeper integration, real-time analytics, stronger security, and more automation (see Figure 1). Recent innovations include:

  • Integrating with in-memory architectures – data stored in-memory can be accessed orders of magnitude faster than that stored on disk.
  • Leveraging Hadoop to support larger and more complex data sets as well as unstructured data, such as social media analytics data. 
  • Integrating with data virtualization platforms to simplify ingestion.
  • Integrating with real-time stream processing services.
  • Supporting advanced data compression to manage larger data sets more efficiently.
  • Enabling in-database analytics to process complex functions rapidly.

The Enterprise Data Warehouse should:

  • Leverage data to better drive marketing, customer service and other corporate decision making.
  • Leverage existing gaming architecture to better understand corporate businesses. 
  • Build up over time to contain necessary historical data stores that will support long range forecasting and patron activity tracking.
  • Provide easy access to frequently needed data.
  • Create a collective view by a group of users.
  • Improve end-user response time.

These capabilities will be used to support the following business objectives:

  • Enhance the patron experience.
  • By accessing data in any format from virtually any source, existing investments in enterprise resource planning and operational systems can be extended, and enterprise data integration can be streamlined.
  • Easily create marketing campaigns.
  • Improve marketing performance.
  • Increase the number of active reward card members.
  • Increase the gaming and non-gaming revenue from these reward card members.
  • Increase cost effectiveness of marketing activities.

The data integration tool market has established a focus on transformational technologies and approaches demanded by data and analytics leaders. The presence of legacy, resilient systems and innovation all in the market together requires robust, consistent delivery of highly developed practices.

Data Virtualization

Data virtualization provides an agile data platform to support new and emerging business use cases. It delivers a data services layer that integrates data and content on-demand from disparate sources in real-time, near real-time, streaming, and batch to support a wide range of business processes. Automated processes can update, transform, or cleanse data provided through the data services layer. A critical component of data virtualization is the metadata catalog, which keeps track of all data, its location, availability, and state and ensures trusted and timely availability of data.

Data virtualization also supports transactions that write back to the original data sources, whether online or offline, on-premise, or cloud. EA pros like its automation and self-service capabilities for data integration, access, and management, which reduce time and effort to support new business use cases. They have been expanding beyond customer analytics to support analytics for social media, the internet of things (IoT), fraud detection, and integrated insights.