Demographic Data

Since most cryptocurrencies are not yet regulated by a central authority, crypto owners cannot store their currencies in traditional bank accounts. Thus, cryptocurrency wallets (or crypto wallets) were developed.

These wallets generally come in the form of either software or hardware—also called hot or cold wallets. Hot wallets connect to the internet and allow the owner to both receive and send crypto tokens. Cold wallets, on the other hand, do not connect to the internet. For this reason, hot wallets have more security features than cold wallets.

Language revitalization refers to the process of bringing a dead or dying language back into common usage. It is also called language revival, although many believe the term “revival” should only apply to dead languages and “revitalization” to dying ones.

Language reclamation is a related concept and frequently occurs together with revitalization/revival. However, it refers only to the process of a population reasserting its right to speak its language.

Artificial intelligence has opened new language acquisition pathways for learners and teachers. These programs allow any learner, anywhere, to receive personalized lesson plans and feedback. When part of a classroom (virtual, physical, or mixed), AI language learning programs help teachers assess student progress.

Price segmentation is the practice of offering the same products or services for different prices based on who is paying. Examples of a price segmentation strategy include loyalty cards, coupons, and student or senior discounts.

Posting authentic online content from your brand or employees has proven to be an effective customer engagement and marketing strategy. Further, there is a wealth of data to show you how to reach customers with personable content.

Influencer marketing is the practice of hiring influencers—prominent social media users with active engagement on their posts—to sell a product or service.

Machine learning models, referred to as EHRs (electronic health records) or EMRs (electronic medical records), help clinicians better manage patient health records through population management, diagnostics and smart documentation. Clinicians also use these models to perform other clinical tasks.

Historically, insurers have relied on linear regression of a small number of risk factors, largely reported by the policy-holder on a trust basis, to determine an insurance premium. However, a good prediction model of individual future insurance costs is becoming a business essential as competition in the insurance industry and low customer switching costs have become key drivers for insurers to build a pricing structure which covers their incurred costs.

Everyone wants more leads, but the more we are able to generate, the harder it becomes to identify which of them are actually worth the time and effort spent in order to try to convert them. Lead scoring models let you automatically rank your leads in order of the perceived value each lead represents to your company. Resources for marketing and sales can then be distributed by the priority determined by lead scoring.

Customer lifetime value (CLTV) is the expected profit that a single customer brings to a company over the course of their lifetime. CLTV represents a shift in emphasis from quarterly or annual profits to the long-term relationships with customers.

An online recommendation engine is software that analyzes available user data to generate suggestions for something the user may also be interested in. Such engines are used in an advertising capacity to promote products or services or for the purpose of recommending similar content to the content the user has consumed in the past, such as in streaming services and online shopping services.

Traditionally, personalization was focused on a set of rules based on existing data. Firms used to collect data in advance without any consideration for real time data but now better and much faster results can be obtained by using AI that allows businesses to conduct profiling and real-time analysis to optimize each conversion. This process is defined as Predictive Personalization and is driven by Machine Learning.

Nowadays, the abundance of data and the advances in Machine Learning and big data applications reduce the need for top-down segmentation of customers. Smart customer clustering based on many commonalities help companies better address customer needs to provide the right experience and divide resources efficiently.