This post offers a brief introduction to three important predictive analytics application areas – customer analytics, operational analytics, and threat and fraud analytics. Further, the four key steps of the predictive analytics process are outlined.
- Three important application areas of predictive analytics
- The predictive analytics process (4 steps)
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Three important application areas of predictive analytics
This section outlines three important application areas of predictive analytics: customer analytics, operational analytics, and threat and fraud analytics (see Hill, 2013).
1. Customer analytics
Understand your customers and what they are likely to do:
1.1. ACQUIRE customers more efficiently: Understand who are your best customers; connect with them effectively; take actions to maximize sales.
1.2. GROW the value of existing customers through personalized up-sell/cross-sell: Understand the best mix of products/services your customers need; maximize revenue from customers; optimize interaction strategy with customers across digital media platforms.
1.3. RETAIN customers for a long time: Understand what value your customers seek/gain from your products/services; keep your best customers happy (vs. the value/experience they seek); take action to retain customer loyalty (build relationships).
2. Operational analytics
Manufacturing, supply chain, service industry–people, processes, assets:
2.1. PLAN operations: Allocate expenditures effectively; manage inventory; identify the economic and social impact.
2.2. MANAGE daily operations: Understand causes of failure; improve employee performance (productivity); reduce upkeep cost.
2.3. MAXIMIZE operational performance: Improve asset (machine) performance; reduce operational costs; drive operational excellence in procurement, development, availability, distribution.
3. Threat and fraud analytics
Detect and mitigate threat/fraud/suspicious/anomalous transactions:
3.1. MONITOR system/organizational environment to identify fraudulent patterns: Identify data/asset leaks; enforce compliance; leverage insights in critical business functions.
3.2. DETECT suspicious activity/threat: Identify fraudulent patterns; identify collusive merchants and employees; identify unexpected or unusual transactions.
3.3. CONTROL outcomes to deliver the best response to reduce exposure or loss and maximize the impact of remedial actions: Take action in real time to prevent abuse; reduce claim processing time; alert customers about fraudulent transactions.
The predictive analytics process (4 steps)
This section outlines the steps to build a predictive analytics model to predict the propensity of a customer to churn, as an example.
1. DEFINE targeted outcome/dependent variable.
Output data: targeted variable (the likelihood to churn).
2. CAPTURE/collect customer data required to train the predictive model.
Input data: predictors or independent variables.
Demographics (descriptive) data: age, gender, recent activity, satisfaction, marital status.
Transactional (behavioral) data: # orders.
3. PREDICT outcome/output.
Split the data set into training set (~80%) and testing set (~20%).
Training set: apply an algorithm to the training data to construct a set of rules that predict the value of the churn (target) attribute (predicted value or dependent variable).
Testing set: deploy model (scoring).
Assess the accuracy of the model (ratio of correct predictions to actual outcomes).
4. ACT on insights/predictions.
Key references
Hill, Brad. [bradhill14]. (2013, Jul 9). What is Predictive Analytics? [Video]. YouTube. https://www.youtube.com/watch?v=0KXME_y-3QA
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