Using Machine Learning to Predict Where Your Target Audience Would Go

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Knowing the next customer’s step is a huge advantage for any retailer, manufacturer, or service agency. Yet, the precise prediction of the audience behavior requires a lot of resources and knowledge. So, how to perform the displacement prediction using data science?

 

 

According to the IDC, an analytical agency, a big data market will reach the value of $203B in 2020. Machine learning and data science are used in many industries and areas including real estate, automobile, e-commerce, politics, and even retail business. IBM has even developed Watson, its own self-learning system for retailers that can create and test hypotheses by using data to determine possible customer behavior. Geodesists apply geodetic displacement monitoring methods and then analyze this geodata using data science to forecast landslides. In this article, we will consider displacement prediction methods and techniques that will help retailers organize their business in the way to generate more income.

 

Advanced Evaluation of Displacement Monitoring Data

 

 

Collecting the right data to predict any kind of displacements is not enough for receiving an expected result. The most important factor in information analysis is how you evaluate collected data. The evaluation process should use both qualitative and quantitative data to ensure the correctness of the future analysis. Both techniques provide significant information for evaluation and optimize the analyzing process by correct data structuring. It should also be noted that these techniques are rarely used separately.

 

Qualitative Evaluation

The qualitative evaluation includes the information that usually describes the added values, terms related to the project analysis, what and why something has happened. The analysis of qualitative data consists of examining, interpreting, and comparing particular patterns. It can include the process of reducing the data to the most meaningful and influencing points in order to facilitate data analysis and make it more efficient.

 

Pros and Cons

The main advantage of the qualitative evaluation is providing contextual information to explain various complex events, specific values, and issues. Its disadvantage is in the following: in case of the lack of qualitative data, the evaluation can become a time-consuming, expensive, and unsolvable task.

 

Quantitative Evaluation

Quantitative data is the precise information, mostly expressed in digits. It usually describes quantitative characteristics of the current research. This data can be collected through pretests, various measurements, surveys, and calculations. It often answers the questions “How many?” or “How much?” and may have a form of the statistical analysis. This data is often a basic node of the whole research while qualitative data only supplements it.

 

Pros and Cons

The main advantage of the quantitative evaluation method is that it represents collected data in a strict and comprehensive way. However, the lack of quantitative data can lead to invalid measurements and incorrect conclusions.

 

Prediction of Displacements

 

The predictive analytics is nearly as old as machine learning (ML) itself. The interest in ML-based technologies appeared in 2012 after Dr. Geoffrey Hinton had won the ImageNet image recognition contest. He used an advanced statistical modeling to ensure a precise image recognition. Now, predictive analytics can also be used for displacement prediction through different methods.

 

 

Prediction methods:

  • Extrapolation;
  • Pure prediction;
  • Exponential smoothing;
  • Extreme Learning Machine

Let’s consider each of these basic methodologies.

 

Extrapolating Prediction Method

Extrapolating prediction method is quite simple but accurate technique that allows to predict the displacement by analyzing several previous displacements at a given cross section. By applying a corresponding function to the measured displacements, it is possible to predict a final displacement using convenient calculation. The more available displacements at a given cross section there are, the more precise results will be.

 

Pure Prediction Method

This method is based on “outdated” data when new measurements are not available. Past displacements are taken from a previous cross section, therefore the prediction is made on a base of the displacements that have happened earlier. This method is not that precise as extrapolation, but it requires no new measurements.

 

Exponential Smoothing

Exponential smoothing is used when the real-time forecast is needed. Its principle is in weighting the observed time series unequally by time where the latest measurements are heavier than older ones. Exponential smoothing is calculated through the specific formula that uses such variables as displacement series, time series, and a parameter that equals within [0, 1).

 

Extreme Learning Machine

Extreme Learning Machine is a feedforward neural network that is used for various types of data processing. This network generates hidden nodes, which are independent of the training data, and analytically determines the output weights. This method is characterized by high generalization performance and learns much faster than usual gradient-based learning algorithms.

 

Customer Behavior Prediction: Case Studies

 

Many business owners learned long ago how to use machine learning to predict customer behavior. In this short paragraph, we will consider a couple of real examples how different organizations use data science to improve their marketing strategy and optimize business structure.

 

 

Restaurant Visitor Behavior Analysis

Arby’s Restaurant Group used data science to determine how visitors will behave if the restaurant is closed for remodeling. Their analysis has shown that remodeling will increase the number of visitors even taking into account that financial losses are unavoided because of the dead time. Due to this analysis, they have performed five remodels within a year to increase their annual income.

 

Store Demand Prediction

Wal-Mart is the largest retailer in the entire world. It has 20,000 stores worldwide and more than two million employees. Therefore, it is not a surprise that they are interested in big data analytics. Wal-Mart uses predictive analytics to determine the store demand. They also determine which form of checkout will be better in each store: a self-checkout or traditional one.

 

The value of data science and predictive analysis cannot be overestimated. Big data opens new horizons in front of the humanity and perspective ways of the financial growth for those who have serious expertise in data analytics. Data science can help retailers perform predictive modeling for customer behavior, qualify and prioritize leads, offer those products which customers are most likely to purchase, target potential clients with the right content and at the right time, etc. Business owners can also develop and implement improved marketing strategies based on trustworthy predictive analytics insights.

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