Simply put, an algorithm is a process or set of rules to be followed in order to achieve a particular goal. The number of operations or set of rules within that algorithm is important to data scientists because the most efficient algorithms optimize an organization’s workflow.

Most computer and smartphone users are blissfully aware of the algorithms that are hard at work behind the simplest of operations. A Google search, or a search on Amazon or a social media platform, uses complex algorithms to deliver the right results. But how does Google or Amazon know what to program in its algorithms? What factors come into consideration?

What Makes for a “Good” Data Science Algorithm?

As mentioned above, the algorithm’s goal is to optimize a workflow and inject efficiency into an organization’s operation.

At its core, an algorithm should introduce two experiences: speed and conversion.

Speed makes sense. If a website or app is taking too long to deliver results, users will simply close out of it. If it’s a new website or app, that user might not return.

Conversion is the other important desired deliverable of an algorithm. As for search, users want the right results: answers to their questions, the items they wish to purchase (at the right price), or the information they need to know right now.

Of course, Google wants to deliver the right advertisements, too: those that are natural and unobtrusive so as not to distract from the searching experience, but relevant and contextual in the event that the searcher might just click on it — satisfying both parties in the process.

As content and apps on the internet proliferate, and as datasets grow larger, “good” or efficient algorithms become vital to the success of an organization selling digital products. Consumers and even advertisers might not be aware of how it all works, but algorithms affect the bottom line.

“The way that we write our code influences the speed at which our data is analyzed and conclusions can be reached accordingly,” notes data scientist Paula Zheng in Towards Data Science.

Data Science Algorithms for Marketing and Customer Onboarding

Aside from algorithms utilized by consumer-facing applications, marketers use algorithms behind the scenes to inform decisions related to ad spend, email sends, social media publishing and even which landing pages prospective customers might see.

Marketing performance is “trained,” and data science algorithms are created using machine learning, deep learning and other crucial data to automate activities (i.e., send another email, alert a salesperson to contact a prospect) based on a particular set of rules.

As customers move through the funnel and convert from prospects to leads, financial institutions must have processes in place to securely onboard new customers to limit their losses from potential fraud. This is where Instnt comes in. We use advanced artificial intelligence data science algorithms that investigate geographic location, device intelligence, user behavior, among others, to ensure that your customers are who they say they are. Then, users are successfully onboarded, meeting the goal of the algorithm.

Why use Instnt?

Organizations attempt to capture as much information as they can about their prospective customers, but sometimes fraudsters manage to sneak through.

Leaning on the performance of hundreds of thousands of customer sign-ups in the financial services industry, Instnt is the first fully managed digital customer onboarding service, uniquely offering up to $100MM annually in fraud loss insurance. Get started today.