B2B intent data, or buyer intent data, is information about purchase signals from a lead or account. You can measure it through surveys, social media, and product comparison websites.
Intent data comes from digital sources mentioned above. AI models give each user’s data a score reflecting intent to buy a product or service.
You can break down intent data into first-party and third-party intent data.
First-party intent data refers to records of the pages a site visitor has reached on your website, the subsequent links they clicked on, and the amount of time they spent on your site. No name or contact information is attached to these potential leads unless users fill out contact forms. Nevertheless, companies consider their data invaluable, using it to improve their websites and CRM platforms.
Third-party data is where things get interesting. Third-party data refers to records of similar behavioral data from other websites—from blog or newspaper articles to competitor and product comparison websites. When interest in a product or service that you offer spikes, you will be able to plan accordingly.
You should expect to find data about the prospect’s activity online, such as searching, browsing, interactions, downloads, clicks and more. When it comes to keywords and search terms – there is a real potential for great insights. That’s why these digital signals are a crucial tool in a digital marketer’s arsenal. Each typed word or long-tail keyword phrase provides a strong indication of the user’s intent which can explain motivations, interests and behaviors. From a business perspective, we want to track trends in the consumption of information on topics that you write about, and segment and target those users with relevant content during the many stages of their research.
Finally, demographic data about potential customers is particularly useful. Once you have identified a population segment that is interested in your product or service, you can concentrate on reaching them.
The data helps to uncover the patterns and behaviors of your current customers. Once you see what works best for your business, you will be able to increase your growth.
You can also use this data to track leads, grow your customer base, and improve your bottom line. Intent data allows you to delve into the psyche of your customers by uncovering what content they are interested in, where they access information, and which channels they respond best to.
B2B intent data must be as accurate and complete as possible. Additionally, each industry and organization has its own goals and needs. Therefore, you must use your own organization’s current goals as the main test of your data quality.
However, there are general measures for assessing the quality of B2B intent data: accuracy, consistency, quantity, and how often data appears in a dataset. Also important is conformity: conformity to the values within a certain set of data and conformity to established database standards.
Experts in data protection also recommend the following guiding governance principles when implementing your data quality strategy:
Accountability of those responsible for ensuring data quality
Transparency in documentation measure and availability
Protection measures ensuring the data is secure
Compliance with government standards
B2B intent data can be quickly converted into actionable insights by making sure it is properly collected from diverse sources and then properly analyzed. To ensure that investments meet expectations, marketers make their intent data actionable by working with relevant teams (especially in sales) to understand the nuances of intent data and how to act on it.
Marketers using intent data report great success, raising close rates by more than 79% with Infogroup clients expecting an even higher performance due to the real-time access to the data within the company’s Data Axle platform.
Dapp development helps people, companies, and NGOs create apps with the security and speed that are characteristic of blockchain technology
As an application of blockchain technology, crowdsales are generally unregulated, allowing investments to reach startups faster and without fees. Additionally, although investors may hold onto their coins or tokens to receive dividends as if they enjoyed shares in the company, they do not have enjoy the same control over company management. Startups then enjoy a level of managerial freedom that companies previously did not have.
Finally, in addition to providing much-needed capital to startups, crowdsales provide crucial measures of the level of interest in a new project.
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.
Investors, both new and old, use blockchain-based cryptos and altcoins to effect a cryptocurrency investment strategy
Banks, credit unions, credit card companies, insurance companies, stock brokerages, investment funds, and more must report their activities to government regulatory agencies. Following financial crises in the late 2000s, regulatory compliance has become stricter and more onerous on financial services companies like those listed above.
From the stricter need for reporting and the massive amounts of data generated by financial institutions, the regtech industry has sprung up, combining regulatory reporting and big data technology.
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.
As organizations continue to adopt IoT technology, ensuring secure access to a private network becomes particularly difficult. Network access control systems, however, protect network data by requiring user authentication and authorization before every request.
These systems also proactively address security breaches, though many also integrate with anti-virus or malware systems that organizations already use.
Network segmentation refers to the act of dividing different parts of a network into separate segments or subnets. This is done either physically or technologically, usually as part of a network access control system that limits who can access what parts of the network.
Once organizations have identified subnetworks, they establish virtual fences around them using a variety of techniques, including VLANs, SDNs, and firewalls.
A large proportion of data breaches come from authorized network users. Since they have network privileges, however, this type of cybersecurity threat is extremely difficult to address. Insider threat detection comprises the methods and technologies that organizations use to identify and mitigate these insider threats.
There are various types of insider threats, not all intentional or malicious. Pawns, for example, are simply victims of phishing or other social engineering traps while Goofs lost confidential data due to ignorant or arrogant flaunting of security policies. The malicious types of insider threats come from Collaborators and Lone Wolves, who are rarely encountered.
Taking the place of an in-person managers, a financial robo-advisor provides personalized investment and wealth management services to individuals as well as small and medium-sized businesses.
Robo-advisors use deep learning and other artificial intelligence techniques to offer advice and even automate trades in a variety of industries and account types, from individuals with retirement accounts to small businesses with equity finance plans.
VR therapy is the use of virtual reality equipment and programs within a therapeutic setting, sometimes as the primary therapy used, to address both mental illness and physical injury. In many cases, VR headsets are used in combination with biometric sensors like heartbeat or electrodermal activity sensors.
CCBT stands for Computerized Cognitive Behavioral Therapy and refers to the use of apps or other programs to do CBT therapy on a personal basis.
Cognitive behavioral therapy is, essentially, a method of training people to recognize fallacious beliefs or self-destructive behavior in themselves and then working with them to change their beliefs and behaviors to more healthy, productive ones. For example, someone prone to catastrophizing (assuming the worst outcome of a situation) may need to learn how to identify more likely outcomes.
CBT works best with mild to moderate emotional disturbances, especially depression and anxiety. Patients may use these programs in concert with their therapists or general practitioners, but cCBT has also proven itself very effective when used on its own.
Machine learning models help identify, analyze, and predict stress in individuals and larger populations. Stress management programs use these models to help people improve their responses to stressors and thus reduce their overall stress levels.
With the expanding online fitness and wearable health device industries, there is an increased interest in health-related apps and devices; stress management programs that integrate with these apps and devices should continue to grow in size and number.
Precision agriculture refers to the management of farms with satellite and other data and technologies. The resulting maps provide highly detailed information about the topology and condition of farms, down to the individual plants.
Other common terms for precision agriculture include satellite farming and site-specific crop management.
A sea-faring vessel using a variety of sensors to manage its regular processes and navigation is a smart ship. Uses of this IoT technology range from single vessel to fleet management. However, even management of a single ship involves using massive amounts of data.
Companies of all sizes use payroll automation to deliver payments faster while cutting down on errors.
Robotic process automation is a commonly-used term, reflecting the fact that this system takes simple payroll rules and acts on them. However, not all systems are purely robotic; many incorporate machine learning to check for anomalies.
An e-learning course enables people to learn material at their own pace, with no instructor present. Machine learning programs optimize learning with supervised, unsupervised, semi-unsupervised, and reinforcement methods.
While individuals use these courses the most, the highest-paying customers include higher education institutions and corporations.
Of note, although the term is often used interchangeably with “distance learning” or “virtual classroom,” these terms refer to a learning environment overseen by a professional educator. E-learning is self-directed.
A distance learning course refers to online education method where students learn at their own pace but also have the benefit of teacher instruction.
Interest in distance learning has spiked massively over the past year, due to worldwide lockdowns forcing in-person educators to change tactics, so there is more data than ever on this subject. Often, the term is used interchangeably with “e-learning” and “virtual classroom” although there are important differences. E-learning, for example, refers to self-paced study without the instruction of a teacher or set study times. A virtual classroom, on the other hand, has a teacher overseeing students and a set class time.
A cybersecurity posture is essentially the total procedures, policies, and services that an organization uses to defend itself from cyber attack. Companies evaluate their security risk then develop policies, employee training procedures, and, if they have the resources, malware, virus, and other threat detection services, focusing on their most vulnerable assets.
In short, a cybersecurity posture describes an organization’s cyber threat readiness and identifies areas where this readiness can be improved.
Crop Disease and Pest Identification helps large-scale and subsistence farmers protect their crops and increase yields and health
Sensor-enabled vehicles use an autonomous navigation system to move through the atmosphere or down roads and terrain without direct, continuous human direction. Vehicles that use autonomous navigation systems include robots, spacecraft, ships, airplanes, and cars.
Naturally, there is a lot of overlap between this use case and remote navigation systems.
Employee onboarding refers to the process of welcoming and training new employees. This includes setting up their details in the HR system, providing them with security clearances, training them in the company’s policies and the duties, and integrating them into the social life of the company.
Survey after survey shows that effective onboarding is crucial to employee retention and company reputation, but few organizations set up a well-planned, data-based system.
At heart, a virtual classroom is simply a class that meets virtually. In comparison, distance learning and e-learning classrooms are self-paced, requiring no set meeting times.
While virtual learning sounds simple, it does come with a number of unique challenges, especially for children.
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.
Companies post content to their websites, newsletters, and social media accounts based on social media metrics that indicate when posts receive the most engagement. This engagement varies by social media site, industry, content type (promotion, article, etc.), and the time of posting: post engagement varies by weekday and even time of day. A content calendar, then, is the scheduling of content publication to increase engagement and conversion.
Companies also schedule site maintenance on these calendars, to ensure they only occur during times of least engagement.
Other terms for content calendar include editorial calendar, social media posting schedule, and other variations of these. An editorial calendar, however, focuses on content planned for company-managed websites plus social media accounts. Social media content schedules, on the other hand, focus on social media posts, as the name indicates.
Life insurance underwriting is the act of accepting liability under a life insurance policy. Insurers increasingly use machine learning to identify risk categories and recommend policies, faster and more accurately than humans alone.
In these times of lockdowns, these programs become especially important as people are more interested in life insurance but may only be reached remotely.
Remote employee management refers to the methods of managing remote workers. Whether they are seasoned in-field professionals or office workers newly adjusting to working from home, the approaches may be different but the data collection and analysis will be similar.
In these fraught times, especially, managers must take special care to account for their employees’ mental states.
Brand repositioning refers to the process of changing the associations that people have with a brand.
More than a superficial change like a logo redesign, a repositioning strategy represents a radical change in a company’s marketing and identity. Rarely do companies reposition themselves without great need.
A predictive maintenance model uses sensors and IoT technology to monitor the condition of equipment and predict when it will need repairs or replacements. This equipment may be anything from personal cars to large factory machines.
The machine learning programs used to run these models tend to take either the regression approach or the classification approach. Regression produces more accurate results but relies on more data; classification best predicts equipment failure within a set time period.
The online fitness industry consists of videos, video-based classes, personalized coaching, wearable technology, and more. An online fitness program uses data generated by users themselves to create a custom fitness coaching experience.
Online fitness has experienced a massive growth over the past year with lockdowns and is likely to stay very popular.
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.
In order to enter into a new market, businesses must have an effective product adaptation strategy. This strategy is essentially the knowledge of what part of a product or service must be changed in order to appeal to local customers.
Adaptation differs from localization in that the former changes only surface-level factors.
More than merely translation material into a target language, marketing localization consists of adapting your approach to the local culture. This includes website translation, currency conversion, and relationship building with local banks or distributors. Even product adaptation—making changes to the products you sell—can be part of a broader localization strategy.
Machine learning models are increasingly used to adapt material to local cultures. Translation, especially, lends itself to machine learning solutions.
Content, whether in the form of a video, a newsletter, an ad, or anything else, needs to reach the target audience at the right time to make an impression. A good content distribution strategy, then, finds the best ways to provide content to audiences.
Content distribution involves using at least one of a number of distribution channels: paid, owned, or earned. Paid distribution channels include advertisements; owned channels include company newsletters or websites; earned channels include shares and reviews.
Mobile app development entails more than just crafting an attractive and simple user interface (UI) for established customers; it involves designing a secure app that uses very little RAM yet is compatible with different devices. A particularly well-developed app should also be capable of scaling to meet customer demand as your company grows.
MFA stands for multi-factor authentication. It refers to the use of more than one means of identification to access a secure software system. Usually, MFA security uses a combination of traditional security measures, like keycards and passwords, and biometric measures, like retinal scans.
A subset of MFA security, which uses two, three, or more authentication measures, 2FA security uses two.
Smart building technology arms a building’s component systems with the ability to communicate with each other and with management. Security systems, plumbing, electricity, waste management, temperature control, and more work exchange data constantly in order to improve the comfort and safety of people living or working inside the buildings.
Smart building technology works for both residential and commercial buildings.
Smart factory technology allows factory sensors to communicate with each other, with employee mobile devices, and with management’s software platform. This enables employees to monitor equipment 24/7 and receive immediate alerts whenever there is a problem. The technology also enables the equipment itself to automatically adjust operations in order to keep the factory running smoothly.
A smart stadium uses IoT to collect, analyze, and act on data within a stadium. This data may come from the stadium itself or from the mobile phones of visitors. Some stadiums also collect data on surrounding areas, like parking lots.
Smart farming refers to the use of connected technology and artificial intelligence in the field of agriculture. It is also referred to as smart agriculture, agritech, agrotech, or even agro technology. However, the terms agritech and agrotech include the fields of horticulture and aquaculture.
Chatbots answer customer visitor questions or requests. While many rely on command-based functions, the better AI chatbots use artificial intelligence, especially NLP (natural language processing), and sentiment analysis.
Occasionally people refer to these bots as AI assistants, conversational interfaces, conversational agents, or even chatterbots.
Retail AR refers to the use of augmented reality (AR) and even virtual reality (VR) to improve retail sales. With this technology, customers can do things like try on clothes without undressing or visit stores while in quarantine. Aside from the attraction of trying the newest technology, AR and VR can meet customers wherever they are.
Warehouse management systems run machine learning software to maintain a warehouse or distribution center (or a fleet of them).
Athletic performance data measures an athlete’s performance as well as their overall health. This includes their history of injuries, current mood, stress levels, and more.
Fair trade certification is the confirmation from an official organization that a product was produced and sold according to fair trade principles: mainly, fair compensation to farmers for their labor.
Additional goals of fair trade networks include environmental sustainability and women’s empowerment.
The process of economic forecasting is at heart the process of attempting to predict the future of an economy. Generally this refers to the economy of a country, but it may also refer to municipalities within a state.
A power grid resilience system monitors the status of a power grid and the threats to its function.
Cities produce large amounts of waste, from litter to sewage; managing this waste is a constant challenge for municipal authorities. Waste management as part of the smart city model involves the use of IoT technology and machine learning to run and to optimize city waste management.
Traffic management, as part of a smart city model, enables private individuals and municipal officials to monitor and manage the flow of traffic.
Share of voice (SOV) refers to the amount of engagement your brand receives within your target market. It also specifies the sentiment behind the engagement—that is, whether people feel positively or negatively about your company.
A smart city is a city whose infrastructure and services have the ability to collect and share data. Smart cities data, therefore, refers to the data that make up or are generated by smart cities.
The system for maintaining a healthy body weight, human or animal, is weight management.
Fleet management uses IoT and other technology to monitor the movement and function of a fleet of vehicles. These vehicles may be trucks, cars, or ships.
From crop or livestock production to the consumer, agricultural waste is generated. However, a good agricultural waste management system enables people to prevent, reduce, or reuse this waste.
Whether for vehicles, industrial equipment, or mental health conditions, diagnosis is crucial to resolving problems but very difficult to do remotely. Thankfully, however, new advances in technology and artificial intelligence have made remote diagnostics possible.
The translation of text between languages comes with a wealth of challenges, even for deep learning AI programs. However, machine translation has made great strides in this area of linguistics.
V2X Technology uses wireless devices in vehicles and other objects to integrate internal and external data in real time via machine learning programs to improve road safety
Usage-based insurance refers to the collection of driver behavior and environmental data to underwrite car insurance plans. It is also called Pay-As-You-Drive or Pay-How-You-Drive insurance.
More than a static budget sheet in Excel, an AI financial planning program helps users set budgets and analyze their spending behavior. With a good ML program, company and personal budgets perform better—faster than via traditional methods.
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.
Competitor analysis is the process of understanding your competition in order to succeed in the marketplace. With a proper competitor analysis model, you can benchmark your progress against more established competitors, identify your target market, and even anticipate new products or services from your competition.
Influencer marketing is the practice of hiring influencers—prominent social media users with active engagement on their posts—to sell a product or service.
Crisis management describes the means by which a company avoids or survives a PR crisis with minimal damage to their reputation.
Trend forecasting is the act of identifying market trends before they come into existence so you can you monitor your market and take advantage of opportunities to meet business goals.
A good marketing campaign strategy is the process by which companies realize a marketing goal. In most cases, this means sales and conversions, but it may also refer to collecting customer feedback or raising awareness of a new product.
In-store performance prediction is sales forecasting for products in brick-and-mortar stores.
Brand affiliation measures people’s identification with a certain brand. More than recognizing a brand or liking their products, affiliation refers to a personal connection, where customers feel like the brand represents them.
Drug discovery covers the whole field of the drug development process, from identifying chemical compounds that could become useful drugs to the clinical trial phase. The drug discovery process is long and complicated, with every stage ripe for implementation of artificial intelligence models.
Most employees do not feel engaged or motivated at work. To deal with this issue, many companies are turning to data-driven solutions like AI machine learning models.
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.
A store’s location directly impacts supply, profits, marketing, and almost every other aspect of store performance. Much can be learned from the performance of different types of retail stores in different neighborhoods, countries, or even climate areas. Therefore, retailers use geospatial data machine learning models to plan store locations and predict profits.
Hedge fund investments use several different strategies to achieve returns in both domestic and international markets. They are often aggressively managed and trade in land, real estate, stocks, derivatives, currencies, etc. markets.
The most lucrative hedge funds also have a diverse portfolio—which means investors need to analyze large amounts of data to make investment decisions.
Insurance claims management is the process of managing a claim from reception to settlement. The insurance claim process is particularly suited to machine learning solutions as much can be done to cut time and costs, leading to speedier resolution of claims to the satisfaction of both insurer and insured.
Insurance fraud can be committed by either the buyer or the seller of an insurance policy.
The seller may offer policies from non-existent companies, fail to submit premiums, and churn policies to create more commissions. The buyer exaggerate claims, falsify medical history, post-date policies, sell their policy to for cash when they are diagnosed with a terminal disease, or fake their death or kidnapping. We will focus on the buyer insurance fraud in this post.
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.
Most if not all companies and organizations seek to establish themselves in social media platforms such as Facebook, Twitter, and Instagram in addition to their official website. Online and social media performance tracking is meant to analyze the effectiveness of a company’s social media presence. Are they generating new leads? Are they promoting brand awareness? These questions are important to assess the efficiency of the use of these social media platforms.
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.
As opposed to customer segmentation which divides the market into groups based on demographics and purchase history, persona detection and segmentation creates more personal profiles in order to better understand potential customers using information about behavior, attitude and personal journeys.
Price optimization is the mathematical analysis by a company determines the response of potential buyers to different prices for its products and services. The aim is to meet a company’s objectives of maximizing profits and growing and retaining a customer base.
Product performance forecasting predicts future sales volumes—at what price, during which time, and in which market. It enables businesses to make informed decisions in both the short term and the long term. Nowadays, companies rely on machine learning software for product performance forecasting as these programs continuously improve, providing businesses with faster and more reliable estimates all the time.
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.
Promotional planning is the process of optimizing marketing tools, strategies, and resources to promote a product or service to generate demand and meet set objectives. Artificial intelligence (AI) can be used to effectively plan promotional events, measuring their outcomes, and adjusting as necessary to achieve growth.
Property value and mortgage value assessment is the estimation of the future value of a particular asset for purchase, insurance, and more.
Unlike most other purchases, real estate tends to rise in value over time. This rise is influenced by economic and social trends, environmental conditions, and on governmental controls and regulations. These large-scale trends directly affect the general demand for property ownership, the scarcity of property for purchase, the utility of the property for potential owners, and the ease at which property rights can be transferred.
The abundance of job seekers nowadays leads many companies to use machine learning throughout their recruitment process. Some examples of this can be found in the fields of talent management, talent attraction, and candidate selection.
Talent management entails understanding the best fields, positions, and backgrounds to set talent up for success. Talent attraction selects the top outlets and channels for attracting the most suitable employees. Finally, candidate selection use neuro-linguistic programming (NLP) tools on CV’s to find suitable candidates.
Remarketing campaigns show ads to people who have visited a business’s website or downloaded one of its apps. Remarketing identifies people who have shown interest in a company in order to prompt them to recall the business, increasing the odds of them converting.
Apartment rental prices are influenced by various factors. The aim of a house or apartment rent prediction model is to analyze the different features of an apartment and its surroundings to generate the most suitable price for rent.
The shelf is a dynamic environment, where shoppers select items purposefully and on impulse, where store owners showcase products to entice customers in the store and online.
Inventory management does not just entail having the right stock but also ensuring said stock was effectively sourced, stored, and sold at the right price, at the right time.
Organizations must satisfy the unique demands of their customers. The good old days of mass marketing and predictions on limited mixture of products in one location are gone. Conversely, the use of artificial intelligence in inventory generates huge dividends to companies willing to reshape their supply chain orders.
This technological development results from the availability of massive amounts of real-time data now routinely generated by enterprise software systems and smart products. By collecting this data, organizing it, and interpreting it, artificial intelligence and machine learning have introduced an entirely new level of data processing leading to deeper business insights.
AI has been used to improve supply chain and demand forecasting for a couple of decades. Demand planning applications use data driven algorithms to take historical data and use it to forecast. Demand forecasting can include promotion planning or stock and sales forecasting. The machine looks at the forecast, compares it to actual shipments, and suggests alternatives and optimization options. Over time, many companies started doing more specific forecasting for specific regions, products or stores or for more granular points in time. Both retailers and their suppliers (CPG companies or 3rd party shipping and supply chain companies) use their data to help conduct automated decision making within the supply chain.
Targeted marketing is a marketing strategy which identifies an audience that is most likely to buy a product or service and subsequently creates a marketing campaign designed specifically to advertise said product or service to the target audience, using advertisements and promotional messages. This allows different companies to hone in on certain market segments, creating a “specialty” and possibly lowering competition with similar companies.
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.
Algorithmic stock trading—aka “algo-trading”—uses machine learning algorithms to make stock market trades faster than a human could, calculating the best time, price, and amount to trade in an instant.
Clinical Decision Support (CDS) provides clinicians, staff, and patients with patient information, usually at the point of care. Clinical Decision Support takes over many routine tasks from clinicians and flags potential problems and solutions for the medical team and patient.
Any account management, customer success and support team has one key goal — to reduce and minimize churn. Churn prediction is the process of identifying segments or specific customers that are at risk of churning, i.e discontinuing their business, within a short amount of time in order to deal with the customer health issue as much in advance as possible.
Case management is the optimization of customer care through the construction of a network of formal and informal activities, services, and supports.
Brand awareness is the degree to which a customer is able to recall and recognize your brand. Brand awareness is critical for promotion and marketing, especially for a young company. High brand awareness can result in a company becoming very popular or “trending.”
B2C fraud includes insurance fraud, payment fraud, identity theft, etc. and reviewing claims is so time-consuming and difficult that many insurers do little more than a cursory review on small claims. Fraudsters know this and will file—and win—small claims for losses that didn’t really occur.
Credit scoring is a statistical analysis performed by lenders and financial institutions to assess a person’s creditworthiness for mortgages, credit cards, and private loans. Credit scoring is used by lenders to decide whether to extend or deny credit.
Traditionally, a person’s credit score determined by credit bureaus is a number between 300 and 850 with 850 being the highest credit rating possible. As new types of lenders and insurers emerge, however, the traditional credit score becomes just one parameter joined with a large variety of alternative data that helps determine a person’s creditworthiness.
Fighting money laundering is a complicated task with substantial costs and risks, including—but not limited to—regulatory, reputational, and financial crime risks. Money laundering can be difficult to track, with many false alerts making detection even more challenging. But new technology, such as artificial intelligence (AI) and big data, can increase detection rates and keep your firm safe.
Behavioral targeting is the process of marketing to people who are most likely to use your products or services based on their demonstrated behavior. This behavior may be online (for example, keywords searched, websites visited) or offline (location tracking data, for example).
Portfolio management is the management of investments to meet long-term financial objectives.
Today, machine learning models and external data are used in order to help companies and individuals better manage, diversify, and maintain their assets and take on less risk for higher reward.
Today, machine learning models and external data are used in order to help companies and individuals better manage, diversify, and maintain their assets and take on less risk for higher reward. Portfolio management models and use cases include any collection of investment instruments like shares, mutual funds, bonds, FDs and other cash equivalents, etc.
Fraud between companies can interrupt the flow of business and destroy their reputations and it is becoming increasingly difficult to identify and stop criminals from committing fraud: PYMNTS.com’s 2019 yearly report, “Securing B2B Payments,” relates that global markets lost $4.2 trillion in 2019 alone due to fraud. However, machine learning can identify fraud accurately before it has occurred.
In today’s global economy, supply chains include numerous partners, with services and sourcing managed across several organizations and in jurisdictions across the world. Corporations are increasing their use of third-party suppliers in the execution of key strategic imperatives and these third-party operations are becoming larger and more complicated as time goes on. Businesses should upgrade their risk management framework if they don’t want to miss potential profits and saved costs.
Many companies supply goods, loans, and services based on business and trade credit, either invoicing customers for payment at a later date or providing B2B loans. Business credit risk management assists companies with lending decisions based on a client’s financial health as well as other parameters that may indicate how likely they are to pay on time. Providing the right amount of credit will reduce the risk of late payments or defaults, which expose the vendor to financial risk.
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