In this digital age, massive amounts of data have never been more readily and publicly available, and data is now recognized as one of the world’s most valuable assets. To promote continued growth and profitability, a company or organization needs to evolve from a Data Collecting Company to a Data Driven Company. A decade ago, when the U.S. General Services Administration (GSA) initially launched the site data.gov, it consisted of only 47 datasets. Data.gov now provides public access to over 200,000 government datasets including federal agencies, states, counties, and cities. This type of publicly available data is being utilized for analytics on national and state level trends for citizens across the country.
There’s a good chance that your company also collects large amounts of data including employee, consumer, and product data. But all this data is only valuable if you learn from it and implement business decisions through a data-driven strategy. Each type of data you collect offers unique opportunities to inform business decisions and ongoing strategy.
Ensuring that your employees are satisfied and feeling fulfilled is crucial to maintaining a steady and well-equipped team. One practical application of data science that comes into play in this regard is referred to as churn modeling. Churn modeling involves collecting and analyzing data related to employees that both stay and leave your company. The outcomes and goals of this practice have two facets:
- Determine which factors are most likely to lead to employees leaving their career. Understanding these factors allows you to implement institutional level changes to reduce the commonalities that cause employees to leave.
- Use churn modeling in combination with predictive analytics based on Big Data to identify the likelihood for a given employee to leave and implement measures to reduce this likelihood.
These insights can give your company a competitive advantage in reducing employee churn.
Learning what your customers like and what you can improve upon in their eyes is key to the growth of your company. Here’s another great use of churn modeling focusing on what keeps return customers around and what’s causing other customers to look for greener pastures.
The way to put this method into practice involves Machine Learning or Artificial Intelligence and utilizing Big Data collected from current customers as well as exit surveys from former customers. The approach here involves implementing a multivariate analysis upon, for example, a survey of past customers who no longer work with your business. This analysis would work on assigning weights to each of those variables in order to determine which ones play the most prominent role in customers leaving. From the resulting predictive churn model, your company can work on institutional changes to reduce the public perception of the qualities that cause customers to cease working with your company.
This is likely the easiest aspect of data collection, as typically you have more control over the collection of data on the products or services your company sells. The base level analysis of product data is routine; determining how long products are expected to last comes into play in a variety of roles such as warranties, pricing, etc. However, the real value of collecting data related to the products or services your organization sells is how that data might inform business decisions related to your consumer data.
For example, looking at what products have the strongest correlation with satisfaction of return customers. Perhaps “Product A” might not have been our best-selling item last year, but it displays a strong correlation with satisfaction of return customers. This might indicate that rather than watching this product sit in the middle of your sales figures, it’s time to increase advertising and boost sales.
Shifting to Data Driven Company and Business Decisions
Now that we’ve discussed the various ways data can influence your company, let’s get back to our initial question. How can you guide your company in its evolution from a data collecting company to a company that is truly driven by its data? We’ve all made decisions in our lives based on a gut feeling and our life’s experience; however, in the age of machine learning and artificial intelligence, we stand on the brink of eliminating human error in our decision making.
Think back to our earlier example of “Product A.” In the days before machine learning and artificial intelligence, the below average initial sales of Product A could have led you to discontinue the product. Yet, as we discussed earlier, there may be an unknown relationship between Product A and the above average customer satisfaction associated with the product.
Advanced analysis allows you to more readily surface less obvious and often overlooked relationships between the products or services you sell and the customers who purchase them. As a Data Driven Company, you will use these previously unknown insights to improve the way your company develops, markets, and sells products, as well as how it attracts, rewards and retains valuable employees. With the technology available today, Data Driven Decision Making has never been easier to integrate into your business practices.