Big Data & Analytics

How Predictive Analytics Can Take Your Company to the Next Level

Analytics

Introduction

Companies are in search for a way to decrease expenses and increase revenue, as global competition is continuing shrinking profit margins. However, at the same time, companies are overwhelmed with data, which is getting generated through operations and actions. As rapid surge in information is creating new challenges for some companies, other companies are using the same information to get higher profits into their business. These smart companies are using predictive analytics to gain a competitive advantage by turning data into knowledge.

The predictive intelligence is achieved by business users using statistics, text analytics along with data mining and this is accomplished by unveiling relationships and patterns from unstructured and structured data. The structured data generally has relational data model and it is about real –world objects, whereas un-structured data is generally opposite, as it doesn’t have pre-defined data model, as it is usually text. To deal with unstructured data usually involve text- analysis and sentiment analysis. [1]

Why Use Predictive Analytics?

Predictive Analytics (PA) can be used in any industry including marketing, financial, workforce, healthcare, and manufacturing. It is mainly used for customer management (customer acquisitions and customer retention), fraud and risk management to increase revenue, improve current operations and reduce associated risks. Almost every industry makes profits through selling goods and services. Credit cards industry has been using models since decades, who predict the response to a low-rate offer. Nowadays, due to sudden growth in e-commerce, companies are also using online behavior and customer profile information to promote offers to the customers.


Response-Purchase-PA-Model

Figure – 1. Response/Purchase PA model

The figure given above borrowed from article referenced as [5] depicts response/purchase PA model. This model represents the customer lifecycle starting from old customer/former customer, established customer, and new customer/prospect customer. The scores derived using these models can be used expand the customer acquisition ratio or lower the expenses, or can be used for both. The below given are the real world instances where response/purchase PA model is used currently in day to day business decision making process.

Banking

Many banks are using PA to foresee the probability of fraud transaction before they get authorised and PA provides answer within 40 milliseconds of the transaction commencement.

Retail

One of the best office supply retailer uses PA to determine which products to stock, when to execute promotional events and which offers are most suitable for consumers and doing so 137 % of surge in ROI was observed.

Manufacturing

One of top notch computer manufacturer who has used PA to predict the warranty claims associated with computers and its peripherals and using so it has been able to bring down 10% to 15% warranty cost to the company.

Talent Acquisition & Resource Management

According to the survey conducted by Radius, start-up companies such as Gilds, Entelos, and many others are using PA to find out the right candidates suitable for the job. The selection criteria of candidates using keywords for job descriptions and search is not only restricted to LinkedIn, but they are also targeting blog posts and forums that includes candidate’s skills. In some instance, finding candidate for a particular skill is hard, such as master of new programming language, and in such cases a PA approach can help in discovering candidates; candidates having skills closely related to the requirement. There are PA algorithms that can even predict when a hopeful candidate (candidate who is already employed), is likelihood to change the job and become available. [3]

Predictive Analytics Process

Predictive Analysis Process

Figure – 2. Predictive Analytics Process

A typical predictive analytics process can be depicted as shown in the figure given above which was borrowed from article referenced as [6] and only the main stages of the process are briefly outlined here:

  1. Define Project: In this step project outcomes, deliverables, scope of the project, business objectives are defined and data sets which are going to be used for analysis are identified.
  1. Data Collection: The data for PA is generally collected from multiple sources in order to perform PA. It provides a complete view of the customer interactions to the user.
  1. Data Analysis: This is the vital and critical step of PA process, as in this step data is analysed to identify the trends, imputation, outlier detection, and identifying meaningful variables etc. to discover the information which can help business users take right decisions and arrive at conclusions.
  1. Statistics: In PA process, the statistical analysis facilitates to validate the assumptions and test those assumptions using standard statistical models which include analysis of variance, chi-squared test, correlation, factor analysis, regression analysis, time series analysis muti- variates, co-variates and many more techniques.
  1. Modelling: Using predictive modelling, user is given ability to automatically create accurate predictive models about future. For predictive modelling, mainly machine learning, artificial intelligence, and statistics are used. The model for predictive modelling is chosen based on testing, validation and evaluation using the detection theory to assume the probability of a result for given set of input data.
  1. Deployment: This is the phase where actually, user is given a preference to deploy the analytical results into everyday decision making process and to automate the decision making process. Depending on the requirements, this phase can be very simple as generating a report or it can be complex as implementing data mining process.PA model can be deployed in offline/online mode depending on the data availability and decision making requirements. It generally assists a user to make informed decisions.

Predictive Model Implementation

In this blog, we will target business problem associated with the retail industry to learn more about how exactly PA works. SportDirect (a fictitious company) is an online sports retailer and wants to come up with the strategy for selling more sports equipments to existing customers to increase revenue in total. To achieve the same, company tried several different marketing campaigning programs. However, it resulted in waste of time and money. Store didn’t receive any outcome out of these programs. The store has now become very keen to identify information that, which customers are eager to buy more sports equipments, what products they are most likely to buy and what effort would be required to make them purchase sports equipment’s and products. Based on these insights, marketing team needs to project their next customer offer. The store has eventually stored several years of data including sales and customer data online, which will play vital role. Store has decided to put into action an IBM SPSS predictive- analytics solution.

To develop the accuracy of analysis and prediction the store is required to build and deploy a predictive model. This model will provide suggestions for offers to be given on special products to the set of clients. To build this model and deploy it, thorough participation from an administrator, a data architect and an analyst will be required. The administrator will configure, manage and control access to the analytic environment, the data architect will provide the data, and the analyst will use the data to create the model itself.

For the first step, the team of an analyst, an administrator and an architect would discover and locate all the required information. The significant subset of the store’s chronological (Historical) sales and customer information will be used to build model. However, building model from historical data doesn’t provide store a comprehensive view of its existing customers. Thus, business analyst provides suggestions to survey preferences and opinions regarding sports equipment of existing customers. The store would use IBM SPSS Data Collection to pull together the additional data by creating a customer survey, gathering information from completed surveys and managing the resulting data. To determine customer buying habits, patterns and preferences related to the sports equipment the survey data will be inserted into the model and associated with historical data.

SportsDirect would use IBM SPSS Text Analytics software to analyse and identify the valuable customer sentiment and product feedback which could lie within the text inform of thousands of blog entries and email customer have sent to its service center. This information can be used to gain insights into customer buying patterns, habits and opinions about products. Hence, this information can be used to feed into the model and figure given below borrowed from article referenced as [1] demonstrates the steps required to build a predictive model.

Steps for building predictive model

Figure – 3. Steps for building Predictive Model

To generate a model, an algorithm and complete set of data are required, where in algorithm is used to mine data, and identify trends & patterns that leads to predicting outcomes. The analyst will perform market-basket analysis using association algorithm, and this algorithm will automatically discover the combinations of products that are sold well together and will provide suggestion for providing specific offers to the distinct clients.

The next step is building, training and testing model based on the collected data and algorithm and IBM SPSS Modeler workbench is used for the same. Now, PA includes information that can be used to real-world customer to determine buying behaviours, predicting future buying patterns and identify the best marketing offer for each customer resulting in increase in sales. This modelling process provides an output which is called as ‘scoring.’ The sales and marketing team managers uses this score as an input to their respective marketing campaign and decision-making process. This scoring output generally contains the list of clients who are most likely to purchase a certain type of products. In special cases, special discount is also offered to attract classified set of customer to act swiftly.

To better understand the scoring output of PA modeller, let’s consider tennis players as a customer for this conjectural findings and recommended actions. Tennis players are the customers who have purchased a tennis racquet from SprotsDirect in the past. These tennis players live in hot-region and due to the hot weather, they purchase three times as many racquet grips in given time-period compared to players from other regions. However, same customers are buying limited number of cans of tennis balls to those of residing in other regions. Based upon this discovery, hot-weather customers will be given an email offer of 25 % discount on the next order, if customer would purchase racquet grips and tennis balls together. This also provides many other recommendations targeting different types of customers and sports. This can also help in pricing policies, reducing price at the end of buying season for a particular product line, generally when demand is quite low. [2]

Summary

The PA focuses on finding and identifying hidden patterns in the data using predictive models and these models can be used to predict future outcomes. It has been acknowledged that predictive models are built automatically. However, for overall success of the business, it actually requires exceptional marketing strategies and powerful team as James Taylor in [4] states that “Value comes only when insights gained from analysis are used to drive to improve decision making process.” PA can make real difference, by optimising resources to make better decisions and take actions for the future.

The predictive analytics is currently used in retail, insurance, banking, marketing, financial services, oil & gas, healthcare, travel, pharmaceuticals and other industries. If applied correctly and successfully, predictive analytics can definitely take your company to the next level as there are many reasons including,

  • It can help your organization to work with own strengths and taking full advantage of areas where competitors are falling.
  • It can help your company to limit the distribution of offers and distribution codes only to the audience who are about to leave.
  • It can help your company to grow beyond increasing sales and it provides insights through which company can improve its core offerings.
  • It can help your company to grow existing base and acquire new customer base by enabling positive customer experience.

References

[1] Imanuel, What is deployment of predictive models?   [Online]. Available: http://www.predictiveanalyticstoday.com/deployment-predictive-models/ [Accessed: Nov. 16, 2016].
[2] Beth L. Hoffman, “Predictive analytics turns insight into action”, Nov. 2011, [Online]. Available: http://www.ibmsystemsmag.com/CMSTemplates/IBMSystemsMag/Print.aspx?path=/power/businessstrategy/BI-and-Analytics/magic_8_ball  [Accessed: Dec. 8, 2016].
[3] Gareth Jarman, “Future of the Global Workplace: The Changing World of Recruiting”, Sep. 2015 [Online]. Available: http://www.radiusworldwide.com/blog/2015/12/future-global-workplace-changing-world-recruiting [Accessed: Dec. 12, 2016].
[4] Kaitlin Noe, “7 reasons why you need predictive analytics today”, Jul. 2015[Online]. Available: http://www.ibmbigdatahub.com/blog/7-reasons-why-you-need-predictive-analytics-today [Accessed: Dec. 14, 2016].
[5] Olivia Parr-Rud, “Drive Your Business with Predictive Analytics” [Online]. Available:  http://www.sas.com/en_in/whitepapers/drive-your-business-with-predictive-analytics-105620.html [Accessed: Dec. 14, 2016].
[6] Imanuel, “What is Predictive Analytics?”, Sep 2014, [Online]. Available: http://www.predictiveanalyticstoday.com/what-is-predictive-analytics/ [Accessed: Oct. 14, 2016].