Depending on the task, data for customer analysis can be obtained through various sources. Managers can utilize their historical sales transactions data collected over time. Transaction data covers detailed information on the time/date/location of the transaction, product attribute information, payment type, marketing mix information (pricing, promotions, discounts), and other purchase-related information. Another way to understand consumer behavior is to utilize household purchase data (also known as panel data), where households continuously disclose their purchasing habits. This data is very rich because it includes not only product and transaction information but also detailed demographic (i.e., education level, number of children, profession, marital status, etc.) and attitudinal information (i.e., feelings, beliefs, opinions) and spans across all product categories consumed by participating households. Such data is typically available through third-party data collection agencies. Additionally, data can be scraped on the web targeting specific information (e.g., online reviews, Twitter posts, mentions, etc.).

The simplest use of this data is descriptive analytics, which examines seasonal trends, purchasing patterns, correlations, outliers, growth rates, day-to-day changes, and so on. This type of analysis answers the question, “What happened?”. For example, one can detect a dip in historical sales or a spike in prior demand due to an external event that took place during that time frame. Data visualization tools can include charts, plots, graphs, and diagrams, and descriptive statistics can cover frequency distributions, central tendency measurements, variation, and percentiles. Numerous descriptive analytics tools exist on the market today. All statistical programs that provide descriptive statistics, such as R, Stata, Python, SAS, or Mathlab, also have excellent visual capabilities. Programs like Power BI, Tableau, and Alteryx, just to name a few, provide state-of-the-art visualizations, including highly interactive dashboards, graphs, and reports.

In contrast to descriptive analytics, predictive analytics takes a step further and beyond describing what has happened in the past and makes advanced predictions about what could potentially happen in the future. This type of analysis finds answers to the question,” What might happen?” Building an appropriate statistical model and using historical data to predict an outcome based on the model can be done in various powerful statistical analysis tools available using regression analysis, machine learning, and artificial intelligence algorithms. If data is not available, one can use a simulation model instead.

Descriptive analytics

 What happened?

 Dashboards, reports, bar charts, line graphs, scatter plots

 Tableau, Alteryx, Power BI, Stata, Python, R, SAS

Predictive analytics

 What might happen?

 Regression analysis, simulation

 Stata, Python, R, Matlab

Prescriptive analytics

 What should happen?

 Optimization

 Gurobi, SAS, Matlab, Mathematica

Finally, prescriptive analytics offers a specific solution/recommendation based on the insights obtained in predictive analytics. This type of analysis answers the question, “What should happen?” A decision maker feeds predictive analytics values, business rules, and constraint information into an optimization model and, depending on the goal (e.g., maximize profits or minimize costs), determines the exact course of action to achieve this goal.

At NumberSci, we have developed a proprietary approach for collecting information quickly, optimizing and developing a solution that is the best fit for your company’s needs but also benchmarked against your competitors, utilizing the latest market and academic research to gain descriptive, prescriptive, and predictive insights into your business using advanced analytics tools.