Explorium Guides

Navigating the data vendor landscape, Explorium ensures high-quality data discovery and integration through a meticulous process. This involves market analysis, rigorous data validation, and compliance with legal and security standards. By evaluating sources for coverage, accuracy, and freshness, Explorium guarantees that onboarded data meets the highest standards. Here, we answer common FAQs about Explorium’s comprehensive […]

This is the third installment in a series of blog posts describing how Explorium can help businesses identify their ideal clients. In this post, we will look at scoring and exporting data so it can be used efficiently by a sales team.  In our previous posts (see here and here), we have seen how Mario […]

Knowing which businesses to target for their conversion potential means knowing whether and to what degree they meet your ICP – ideal customer profile. Some CPGs (consumer packaged goods corporations) may define their ICP loosely, by referring to a handful of characteristics they believe good potential customers need to have. Others may realize that the […]

A fundamental challenge CPGs (consumer packaged goods corporations) contend with is how to best identify relevant target businesses in their TAM (total addressable market)? Is it by extracting a list of target businesses from the in-house CRM system, buying a prospecting list, using lists from external data providers generated based on several parameters relevant to […]

Do you want to quickly enrich your email data with additional information? The ENRICH_EMAIL formula in Google Sheets can help you easily retrieve a variety of information about an email address in just a few seconds. Using Explorium’s API, this powerful formula allows you to access data such as the email owner’s first and last […]

Want to gain insights into the growth of various departments at multiple companies? The ENRICH_GROWTH formula in Google Sheets can help you quickly access quarter-over-quarter (QoQ) growth data. By using a company’s website URL as input, this formula allows you to retrieve data for departments such as Engineering, Design, Marketing, Sales, Customer Service, HR, Legal, […]

Are you tired of manually searching for company information online? Fear not, because we have a solution for you. In this post, we’ll introduce you to a powerful formula in Google Sheets that can enrich any company name with its firmographic data in just a few seconds. Using Explorium’s API, this formula allows you to […]

This is the second installment in a series of blog posts describing how Explorium helps businesses find their ideal clients. In the first post, we saw how entering basic search terms and modifiers produced a large and targeted set of potential customers. When we last left Mario and Luigi, the owners of SM Software, they […]

Developers often need to validate emails. This helps ensuring they are reliable and up-to-date. It also assists in determining if an email address is worth mailing. Regex validation is not enough, though. Regex will check the validity of the email format but not its real online presence. That’s why Explorium created a super simple API […]

If you’re dealing with data, you know that data quality is key to any successful project. Data deduplication is one of the most essential steps in ensuring data quality. In this blog post, I’ll show you how I used Explorium’s API to deduplicate company names in 7 lines of Python code. Explorium’s API returns a […]

In today’s ultra-competitive marketplace, companies are searching for ways to quickly grow their businesses. Many organizations adopt a data-driven approach that attempts to extract maximum value from their data resources. A problem they often face is obtaining viable external data that can be used productively to further business objectives. An illustrative example of Explorium’s power […]

“Data standardization” means different things in different branches of the machine learning and data engineering world. We define data standardization as the process of transforming different representations of the same data into a single representation.  For instance, let’s imagine a customer’s dataset about various companies, and the dataset includes information about which country each company […]

Apache Spark is a very popular engine for running complex distributed data pipelines. Sometimes when using Spark, we need to tune our logic in order to get the best performance. That process sometimes reveals Spark’s “inner workings.” At Explorium, we learned about Spark’s EXPAND command while investigating a query over 1 billion records that failed […]

The core workflow for marketing and sales teams is to generate awareness and leads, which convert to sales opportunities, and ultimately revenue for the company. These are the key metrics to track marketing efforts and sales performance. Many businesses use Salesforce CRM to manage this process, the most popular CRM platform (customer relationship management) by […]

Have you ever found yourself developing PySpark inside EMR notebooks? Have you ever found yourself debugging PySpark locally, but wanting to run it over a real and big data set and couldn’t because your computer lacked resources?   On one of my projects at Explorium, I was implementing a scalable entity resolution process to automatically […]

In the business of external data enrichment for data science, the main focus is on the ability to provide a fast and scalable way to aggregate, join and match large datasets received from data providers with the customer’s internal data. Enriched datasets derive more features, which are leveraged by customer data science teams, resulting in higher AUC […]

Let’s say you own a factory that makes computers. You need to have a steady pipeline of parts and raw materials. You can approach this necessity in two ways. The first way is to simply look at what you used during the last batch and make a new order every time. The second is to […]

With so much data in your own stores, it’s tempting to think you have all you need to start producing great predictive insights. This might be true initially, but you’ll quickly run into one (or more) problems. The reason? Your internal data is only looking at your past performance, and not accounting for the broader […]