7 Reasons Your Team Should Be Leveraging Data Science
Regardless of where your organization sits within the business, data science has the potential to positively transform the way you do your job. A data scientist is a rare blend of computer scientist, statistician and subject matter expert. These unique individuals have the ability to manipulate big data, analyze that data and provide an understanding of how to make that data actionable. While popular wisdom often limits data science to certain business sectors like marketing or IT, successful businesses can leverage data science across all of domains. In this article we’ll share seven lessons that data science can teach your business and hopefully entice you to engage a data scientist in your network or outside of it.
1. Pinpoint Marketing With Existing Customer DataIn a modern marketing department, it's hard not to have at least a passing familiarity with data science and machine learning. By training predictive classifiers to predict which customers in your database are most at risk to stop using your goods/service or segmenting customers based on shared characteristics, businesses can optimize profitability by pinpointing likely targets for ad campaigns and new services.
Using a clustering algorithm, we can identify three distinct customer subgroups
2. Detect Fraudulent TransactionsUsing similar methods to governments looking for “needle in a haystack” online terrorist threats, businesses can use models to identify fraudulent activities within their historical clickstream or transactional databases. For instance, such a predictive model may use everything from geographic location to unusual sequences of events to calculate the risk of fraud for a given transaction based on how much it stands out from all other transactions on record
3. Forecast Costs in New Product DevelopmentProfessionals focused in product development, for instance, can identify customer preferences based on existing product sales data to predict the relative levels of demand for proposed products. In addition, they can build models using existing production data to estimate the costs for new product features, allowing decision-makers to more accurately weigh the risks versus the rewards.
4. Upgrade Overall Workforce PerformanceHuman resources departments can also successfully leverage data science to drive workforce performance. For example, one company found a patterned relationship between turnover and retention behavior of employees and pay raises: High performers tended to stay if they received high raises, whereas modest raises often pushed them to leave. By making a series of predictions using models of overall profitability and performance relative to turnover and pay raises, the company discovered that paying high performers more than the current standard raise would prevent workers from leaving, while still increasing overall profitability of the company despite the increased raises.
5. Predict Future SalesSales managers can also utilize a data science approach to forecast future sales based on past results, taking into account variables like average deal size, time and engagement. In addition, these models can help managers identify underperforming sales reps in any of these variables and coach them on how to increase their chances of closing a deal, increasing profit as well as employee performance.
Forecasting sales over a three-year period from http://mathematica.stackexchange.com.