When you have a lot of data to present to a customer, you need to know how to do so in a way that is compelling and satisfying to them. Big data is overwhelming when not broken down a bit into the relevant information for the client you are presenting to. There are a few ways to learn what sort of information you need to showcase, these are called business analytics and the top three include descriptive analytics (information from historical data), predictive analytics (trends and behavior patterns), and prescriptive analytics (solution-focused and dependent on a specific situation) By using these effectively you can present a ‘full package’ to your client which shows past experiences, future prediction, and strategies for implementation.
Descriptive analytics focuses mainly on analyzing past events and data to clarify what happened, and how it could be changed or repeated. Data mining is used to see patterns, trends, and discover the variables which may have contributed to success or failure in the past. Examples of this in everyday life are sales and marketing reports which usually talk about the past before leading into ideas for building a successful future. This type of analysis quantifies relationships which occur in data and group the relevant information together. It does not predict what customers will do but shows what they have already done. For example, product preferences, life stages, and income.
Predictive analytics works by taking information from historical, or existing data and analyzing patterns in order to predict future trends and outcomes. It can help your client forecast scenarios which may occur in the future such as trends, popularity, and other factors. It also provides valuable information for the outcome of ‘what if’ scenarios and a good view of the risk assessment of the scenarios. Future probabilities can be forecast from current information and historical information. This can help your client understand their customers better, see which products remain popular over time, and also identify risk areas. Some techniques for this type of analysis include data mining, machine learning, and statistical modeling.
Predictive analysis works well with large amounts of data. Large amounts of data will give better results than smaller amounts. More subtle patterns and trends can be found and predicted based on having more information to analyze. One example of this is a store loyalty program — companies can take the information about past buying habits and tailor promotions that each customer will be the most interested in taking advantage of. It can also be used by monitoring website browsing habits.
Prescriptive analysis is usually the last step in the stages of analysis because it is intended to enhance decision making, or show your customer the best actions to take for maximum profit and growth. This is the most advanced form of analytics and is useful for large business goals like increasing service levels or profit. It does not focus on just one product or one action. It goes beyond the method of forecasting and shows a future plan for your customer’s business. It also eliminates the unnecessary information and helps clients to prioritize how to implement their new plan. The main tools of this technique are simulation and optimization. One example of simulation in the predictive analysis model is video object tracking. This is used in the design stage to determine the best ways of presenting objects in a video in order to ensure that they get noticed and stay in the customer’s mental focus. It is where many test subject trials may be conducted. This way the simulation is under control and specific scenarios can be run to see which work best. Optimization is a supportive analysis which uses linear programming to find the best outcome for a business.
By: Rick Delgado
Bio: I’ve been blessed to have a successful career and have recently taken a step back to pursue my passion of writing. I love to write about new technologies and how it can help us and our planet.