So, you’ve probably heard about Data Science and now you can’t stop thinking about how it can help you improve sales, right? Well, let’s delve into this tech topic from a business perspective.

The Difficulty

Not long ago, companies used to scramble over various spreadsheets trying to make sense of immense volumes of sales data in an effort to plan for the future. They essentially made their decisions based on last year’s financial results, past marketing actions, or, in the worst case scenarios, their market competitor’s actions. The difficulty lay in having to create a plan or make a prediction based on one-dimensional data from a very small slice of the market.

Where Are You, Data?

Companies began to pursue methods to achieve better sales forecast accuracy, and began to invest in technology used to analyze data as well as reduce cost and human effort. This prompted companies to begin storing all the data generated, such as transactions, purchase orders, sales, etc. After enough data was stored, companies believed they could finally attempt to make predictions and plan sales. However, things were not as easy as they seemed to be! Even with the addition of technology, companies soon realized there was still a missing piece. So, what was it? Essentially, the data that they were collecting had two main problems: it was extremely polluted, and the volume was unfeasible for the technology of that time to process. Simply having data was not enough.

Science Over Data

The genesis of data science occurred in 1962 when mathematician John W. Tukey forecasted the consequences of modern-day electronic computing on data analysis. In the 80’s, IBM and Apple released the first personal computers, causing computing to evolve at a much faster pace and giving businesses the ability to collect data with less effort. In 2000, numerous academic journals began identifying data science as an evolving discipline, and in 2005, the National Science Board advocated for data science as a career path in order to ensure that there would be specialists who could effectively accomplish digital data analyzing.

When companies began looking beyond their frontiers and started collecting external data such as social media, alternative payment systems, and client support, the volume of data exploded into an astronomic amount that was impossible for any computer to analyze without an initial cleaning process to organize the data. Therefore, almost 80% of the time spent on a forecasting project has historically been spent on data preprocessing - required in order to determine which infrastructure should be used for big data. As a matter of fact, the definition of big data is data sets that are so large or complex that traditional methods of data processing are inadequate.

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Data Science: The Real Deal

Now that you know a bit about data science, let’s go back to the question of how data science can help you improve sales forecasting. Here are some of the many ways:

  • Sales Planning
  • -A personalized sales forecast, which means you can plan what you’re going to offer based on the purchasing behavior of your customers
    • -A very accurate and low cost maintenance of inventory allowing you to work only with what you need
    • -The ability to adjust your plan by conducting A/B tests, which allow you learn using your results
    • - Real time monitoring and projections
  • Customer Experience
    • -Personalized product recommendations
    • -Understanding your customer using social media data
    • -Integrating call center data with customer satisfaction data via the web
  • Marketing Operations
    • -Targeting efforts based on your customer's location
    • -The ability to rapidly fluctuate prices by crossing social media information with inventory and competitor’s prices

Case Studies

H&M ( $20.3 billion yearly sales): H&M has a clear goal for their product and utilizes good demand forecasting to stick to their bottom line

Zara ($14.4 billion yearly): Zara’s achievement follows the theory that if a retailer can predict demand precisely, it can implement mass production which leads to well-managed inventories, higher profitability, and more profitability for shareholders.

American Express: American Express started looking for ways to predict customer loyalty by analyzing internal and external data. The company is now able to identify 24% of the accounts that will close within 4 months.


A Promising Future

Although Data Science is not a brand new term, its application has become more and more popular due to technological advances that resulted in the explosion of data available on the web. We can consider data science as a “work in progress” that has been taken the business world by storm. When executed properly, data science has the ability to return infinite benefits to your company.


Author

Jimmy Mayal

A dedicated and creative professional. Someone who loves to be with his family, study about computers and software, and write fiction.


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