Given the volume of data, it’s no wonder that so many companies are investing in algorithm development. From personalization to search to recommendation engines, algorithms collectively store and analyze vast amounts of data. Regardless of the size of your organization or the scope of your business, it is likely that you use an algorithm at some point each day.

This article will discuss the landscape of frequently used algorithms and explore both pros and cons associated with each type. It will also discuss best practices for creating and maintaining an effective algorithm strategy within your company.

What is an Algorithm?

An algorithm, also called a rule-based system, is a formulaic approach to solving problems. It leverages data to make inferences, often through logic, and applies a series of steps to create a solution. Typically, algorithms are structured and repeatable, which makes them ideal for machine learning.

Algorithms are used to make decisions and solve problems, but they’re not always accurate or perfect. Regardless of their limitations, algorithms are an essential tool in the digital strategy because they’re able to collect and generate large volumes of data in order to create unique content and offer personalized experiences.

Best Practices for Optimizing a Company’s Algorithm

Optimizing a company’s algorithm is critical to long-term success. You can’t just throw money at an algorithm problem and expect it to produce results. There must be a strategic approach to optimize your data and algorithm to ensure the best possible experience for your users.

First, evaluate your data to identify which variables are important to your decision. In some cases, this could be a limited list such as product name, category, or popularity. In other cases, your decisions will be more diverse such as product images, price, reviews, and other metadata related to your content.

Next, determine what impact your decisions are having. Are users getting the optimal experience based on your data? Are they being served ads that are targeting them correctly? Are they being sent to websites that display content that is helpful and relevant to them?

The Next Level of Algorithm Development

Beyond optimizing your data and data-driven decisions, you can also try to go a step further and create your own algorithms to optimize your search engine results. Search engine optimization (SEO) is the process of improving the visibility of your content on search engines by applying a variety of techniques, including alt tags and internal linking.

The most common SEO techniques include optimization of the title and meta-descriptions as well as linking to your website from other sites. Admittedly, these techniques aren’t perfect and may not produce all of the results you desire. The best SEO practices rely on thorough content analysis, understanding your audience and the issues they might be facing, and using natural language processing to understand the language and search history of your users.

Search Engine Optimization (SEO)

Most companies focus their SEO efforts on optimizing their content for popular search engines like Google, Bing, and Yahoo! However, your website’s ranking on other search engines also plays a role in your SEO. To maximize your ranking on other search engines, you’ll want to make sure it: paginates well (i.e. it loads fast and is easily scrollable) is in good physical condition (i.e. it doesn’t have a broken link or error-filled contents) is properly structured with valid headings and subheadings


Search engine algorithms constantly evolve to meet new challenges and optimize the experience for their users. While there is no guarantee that your current strategy will lead to higher rankings, you do want to make sure that your data is up-to-date so that it reflects the most recent content ideas and decisions you’ve made.