This is the first in a regular series of videos about SPSS Modeler, designed to help you better understand some of the functions that are available within the package. If you’re an experienced user or you have been on one of our training courses then you’ll probably already be familiar with most of these, but if you’re a new user, you’re self-taught, or you’re currently evaluating the software then there’s likely to be a number of things in these videos that you’ll find helpful. SPSS Modeler data audit node – the Swiss army knife of data cleaning The data […]
In this four part series Jarlath Quinn demonstrates Smart Vision’s new Repeatable Application Template, Predictive Operational Analytics. This Application Template combines market leading predictive analytical software, ready to use application templates and a fully supported professional services package to ensure rapid and effective implementation and deployment. Using IBM’s flagship data science tool, SPSS Modeler, Jarlath works through a case study example where a telecoms maintenance company applied the template in order to: 1) Apply sophisticated text mining to error messages and engineer logs 2) Develop a predictive model that identified whether or not a maintenance task would result is an […]
In part two of our predictive operational analytics video series, Jarlath Quinn introduces the IBM SPSS Modeler Software before showing how to explore the example data from the case study and carry out text analytics of engineer logs to categorise the information and create key fields for further analysis. Watch part three of this series.
In part 3 of this predictive operational analytics guide Jarlath uses the results from the previous analysis stage to show how to build and assess a predictive model that identifies whether or not a part replacement will be required during a maintenance visit. We also see how to apply the model to new data so that predictions can be generated before the engineer is dispatched. Watch part four of this series.
In this final part of our predictive operational analytics video series Jarlath shows how we can use the predictive technology to go further by predicting the actual outcome of the visit including which part is likely to require replacement. In the final example we see how to build a model that predicts whether or not a site will require another unscheduled visit within 20 days.
This video introduces Smart Vision’s Predictive Promoter solution and shows how data science can be applied to net promoter scoring to help you gain a much deeper understanding of the factors driving your customer recommendation ratings and how you can take action to influence them influence them. Jarlath provides a brief history of the concept of net promoter score before introducing the case study whereby Predictive Promoter was applied to data from the guests of a major hotel chain. The hotel chain’s management team wanted to address three key questions: Understand what transactional and operational factors are most strongly related to the […]
In this video Jarlath shows how to automatically produce a model that predicts customer’s recommendation scores based upon transactional and operational data. We can also see how we can evaluate the model accuracy and browse its contents to uncover what are the key fields in the transactional and operational data that the model has chosen to make its predictions before finally examining the rules that the model has uncovered to estimate the guest’s recommendation class.
In this video Jarlath shows how easy it is to take a previously built model that has been generated based on known outcomes and apply it to customer data where the outcome is not known. Using the results from part 2 of this series, Jarlath applies the model to the hundreds of guests who have not provided a recommendation rating so that the hotel chain can estimate whether they are Detractors, Passives or Promoters. At the end of the video we see how the solution was able to identify what the hotel management referred to as ‘Persuadables’ – these were […]
In this final part of our predictive promoter video series, Jarlath Quinn tackles the project’s most ambitious task: mining the open-ended guest comments to uncover important insights. Here you can discover how we can use text analytics to extract a series of concepts and sentiments from customer comments in order to categorise the guests’ responses. By visualizing the relationships between these key concepts we are able to see what aspects of the guests experiences are most strongly correlated with positive evaluations. Moreover, by categorising the responses we are able to use the text data to build a model that reveals the […]
In this short video Jarlath Quinn demonstrates how to use the powerful simulation tools within IBM SPSS Modeler to perform What If analysis (also known as ‘Scenario Planning’). What if analysis allows business-focused analysts to go beyond simple predictive modelling to evaluate the impact of different choices and scenarios on predicted outcomes. This sophisticated, yet easy-to-use functionality allows successful organisations to find the optimal price point, discount, campaign creative or resource allocation to maximise opportunity and minimise risk.
Predicting asset failure before it happens is possible using IBM SPSS Modeler. This video shows you how organisations with substantial capital assets can use IBM SPSS Modeler to predict when asset failure is most likely. Predicting asset failure can prevent problems before they happen and enables organisations to save money, reduce asset downtime and increase efficiency.
This short video shows how you can perform a simple affinity analysis using IBM SPSS Modeler. Affinity analysis can be used to understand interconnected relationships between key factors. For example, in retail it can be used to perform basket analysis, whereby retailers can identify which products are most commonly purchased together by customers in a single transaction or over a given period time. This information can then be used as the basis for ‘next-best action’ recommendations as part of initiatives to drive increased cross-selling, new promotions, loyalty programs or new store layouts.
You will learn how to: Summarise your data fields using simple descriptive statistics such as frequency counts, percentages, means, standard deviations and so on Run different kinds of descriptive statistics for different kinds of data Display your data visually via customisable tables Examine the relationships between variables using crosstabs Select different kinds of charts and graphs to represent your data most effectively Exporting your output in other formats to distribute to people who don’t have access to IBM SPSS Statistics Using syntax to automate the process of running regular reports and analysis