Algorithms in Tech and Business

How do companies apply algorithms to leverage data, save costs and increase revenue?

Algorithms are everywhere. Automotive anti-lock braking, Amazon’s recommendation engine, dynamic pricing for airlines, predicting the success of upcoming Bollywood blockbusters, credit card fraud detection, and Uber matching drivers with passengers, all of this has been made possible because of data-driven algorithms.

Data has become arguably an important valuable resource. It has ended up a “mystery currency” which has helped a few of the world’s largest Exponential Organizations scale past their wildest dreams. Companies such as Facebook, Google, and Netflix are just some of the many leveraging this “mystery currency”.

When computers advanced to a level of performance where they could compute more complex algorithms, two exciting fields emerge Machine Learning and Deep Learning. Both of these have become the doorway into a brand-new era of Artificial Intelligence (AI) which is changing the game for business success. This is where algorithms come in, they help organizations make sense of huge amounts of data and apply it to scale their operations and profits. In simple terms, machine learning is when algorithms parse data, learn from that data, and then apply their learnings to make informed decisions. A machine learning model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from those data.

The 4 types of models used in businesses are:

1. Lead/Opportunity Conversions Model: The lifeblood of every business is new leads and opportunities. This model predicts where you’re more likely to convert those leads can be an effective guide to growth.

2. Attrition/Customer Retention Model: Once you have a customer in your ecosystem, it’s in your best interest to keep that customer for the long haul. This model can tell you who has a high propensity to churn, so you can market to your existing base effectively.

3. Lifetime Value Model: Increasing the lifetime value of your customers or clients is critical. This model offers behavior-driven insight that will help you keep your customers in your pipeline longer.

4. Employee Retention Model: Losing top talent is a huge cost to organizations. With a machine learning model in place, you can determine which team members have a higher propensity to churn.

Implementing these models provide many benefits to the business growth such as Real-Time Business Decision Making, Eliminating Manual Tasks, Enhancing Security and Network Performance, Improved Business Models and Services Reducing Operating Expense

We conclude that with sufficient data sources, algorithms will be able to predict and offer quantifiable, actionable results — and in real-time.

Read more:

https://www.riverlogic.com/blog/algorithmic-business-is-changing-the-way-companies-operate

We thank Anushree Deshpande for her valuable insights for this article.