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."
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.
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.
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 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.
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.
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.
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.