Projects - TRADExperts

Data projects for all use cases...

All our data products are ongoing projects involving data acquisition, data enrichment and data updates.

Use Cases In Brief

Apply our datasets for a rapid escalation of your company’s growth and business performance

Use Cases In Detail

1 - Talent sourcing

TRADExperts’ employee database can be harnessed to discover the most suitable talents for a company, by providing up-to-date datasets of qualified global professionals. Data points such as years of experience, location, employer, job title, etc will always ensure the success of your data-driven recruitment efforts.

In a recent study of 250,000 hires by the National Bureau of Economic Research, researchers made findings showing that talent sourcing data can predict successful hires more accurately than many hiring managers and recruitment firms. Get our employee dataset of the world’s largest professional and business network here.

2 - Data Enrichment

Data enrichment or data enhancement can be defined as the process of adding more data volume and value to an original database with relevant information. This helps a company gain greater insights into their customers’ likes, interests and preferences. Combined data can be used to build the best profile of a client.

It also enables sales and marketing teams to personalize their messages when reaching out to prospects. Data enhancement is achieved though the merging of a first-party data with second or third-party sources. An enriched dataset is empowered and made more useful in developing products or used for market research.

3 - Lead Generation

Data-powered lead generation is the discovery of prospective customers based on information that is relevant to a business, products or services. Sales and marketing teams use B2B lead generation data and purchase intent data to find the right prospects they need for increasing their revenue.

Recently, U.S. marketers ranked email as their best customer outreach tool with the highest ROI for B2B lead generation. Our B2B dataset assists businesses to discover new business opportunities with the least investment; connecting with potential clients and professional contacts from local, regional or global audiences.

4 - Investment Insight

Investment intelligence is a direct result of using pre-investment analysis reports, to make informed and successful data-driven investing decisions. Most investors, financial market firms and funding institutions rely on data-driven investment strategies for research purposes before committing their funds.

According to a 2019 research by LinkedIn, 68% of investors use data on asset management firms to make financial investment decisions. Post-investment assessment is another use case for investors, to regularly evaluate the performance of companies in which they bought a stake, and for comparison with competitors.

5 - Ads Targeting

Data-based advertising is the art of reaching out to potential customers through different types of media channels, using targeted data aimed at a particular audience. Advertising that is driven by data e.g. location-based, interest-based and audience-based; eliminates the guesswork while positively increasing the ROI of a business.

A top example is LinkedIn’s advertising service which be run within the professional networking website using target audiences known as LinkedIn Matched Audiences. Facebook Ads also runs a similar service called Look-alike Audience.

6 - AI Trading

AI trading is the use of artificial intelligence, machine learning and predictive analytics, historical market and price data to build trading systems and portfolios that automate trading orders. AI traders also forecast markets with greater accuracy and efficiency than manual methods with lower risks and higher returns.

AI trading uses machine learning, market sentiment analysis and complex algorithmic predictions to analyze millions of data points and execute trades to maximize profits. The different types of AI trading are: quantitative trading, algorithmic trading, high-frequency trading and automated trading. All aim to predict future price trends.