The Key to Developing an Efficacious Escalation Prediction Model
In today’s cut‑throat market, support organizations can gain a competitive edge only by providing top‑notch customer service. Firms are seeking ways to meet customer demand and enhance their satisfaction. And, one way to achieve this is by having a definite answer to how long it takes to fix a problem or resolve a query.
Assessing the probability of support ticket escalation is one crucial element that needs to be considered for hitting this nail right on the head. However, hybridization of support has increased the complexity of agent interactions. That is where an efficacious escalation prediction model works wonders.
By tapping into the ocean of customer data and harnessing the power of real AI, predictive models identify the likelihood of an incoming support ticket getting escalated, thereby helping you work out an estimated time of completion early on and working backward to boost customer satisfaction.
How Does an Effectual Escalation Prediction Model Work?
Usually, escalation models undertake the following four steps to successfully predict and prevent ticket escalations:
Step 1: Input Data
ML and NLP models act upon the raw data to capture a support ticket’s context and generate actionable insights. Therefore, the first step is to select the platform where your support tickets are stored and select the time period for which you want to train your ML algorithms.
Step 2: Feature Engineering = Feature Extraction + Feature Selection
The raw data now needs to be processed before being fed to the selected ML model. This is where feature engineering kicks in. It taps into enterprise knowledge to find useful features that correlate with the target class (escalations in this case).
Often, the raw data is not in a form that is receptive to learning, but you can extract features out of it.
Once the features have been extracted, they need to be analyzed both in isolation and combinations so that you can identify the most useful ones from the pool.
By performing dimensionality reduction (or minimizing the number of input variables in training data), both feature extraction and feature selection help lower the complexity of cases and deliver a certain degree of precision during feature categorization. This way, you can minimize redundancy and maximize relevance in the feature set.
Step 3: Scoring Data
Once the features are finalized, the ML models take over. They’ll evaluate the relevance of each feature and measure its impact on the incoming support tickets. Based on the scores, you can ascertain the probability of a case leading to escalation and identify those that require immediate attention.
Step 4: Escalation Prediction
Last but not least, predictive analytics unfold to help you anticipate escalations in real-time. It monitors your complaint profiles to analyze the nature and intensity of complaints. Then, it notifies your front-line staff in real-time, thus enabling a timely and early intervention.
From Expert’s Desk
Things do not always go as planned; hence it is recommended to retrain the escalation prediction model every now and then to keep it in good shape. Also, consider model retraining whenever major changes are incorporated or there’s a heavy inflow of support tickets.
For example, your predictive model is converted into five‑tiered (very low – low – medium – high – very high) from three‑tiered (low – medium-high). Consider retraining it immediately to maintain a high degree of relevance. Else, data distribution is likely to drift, taking a toll on customer satisfaction and agent performance.
Want to Witness the Magic of Such a Predictive Model in Your Workflow?
Instead of weaving the magic of AI and predictive models from scratch, get your hands on our Escalation Predictor. SearchUnify’s end‑to‑end support suite includes an Escalation Predictor that preemptively manages escalations to curtail customer churn. Reach out to your CSM for more information.
Assessing the probability of support ticket escalation is one crucial element that needs to be considered for hitting this nail right on the head. However, hybridization of support has increased the complexity of agent interactions. That is where an efficacious escalation prediction model works wonders.
By tapping into the ocean of customer data and harnessing the power of real AI, predictive models identify the likelihood of an incoming support ticket getting escalated, thereby helping you work out an estimated time of completion early on and working backward to boost customer satisfaction.
How Does an Effectual Escalation Prediction Model Work?
Usually, escalation models undertake the following four steps to successfully predict and prevent ticket escalations:
Step 1: Input Data
ML and NLP models act upon the raw data to capture a support ticket’s context and generate actionable insights. Therefore, the first step is to select the platform where your support tickets are stored and select the time period for which you want to train your ML algorithms.
Step 2: Feature Engineering = Feature Extraction + Feature Selection
The raw data now needs to be processed before being fed to the selected ML model. This is where feature engineering kicks in. It taps into enterprise knowledge to find useful features that correlate with the target class (escalations in this case).
Often, the raw data is not in a form that is receptive to learning, but you can extract features out of it.
Once the features have been extracted, they need to be analyzed both in isolation and combinations so that you can identify the most useful ones from the pool.
By performing dimensionality reduction (or minimizing the number of input variables in training data), both feature extraction and feature selection help lower the complexity of cases and deliver a certain degree of precision during feature categorization. This way, you can minimize redundancy and maximize relevance in the feature set.
Step 3: Scoring Data
Once the features are finalized, the ML models take over. They’ll evaluate the relevance of each feature and measure its impact on the incoming support tickets. Based on the scores, you can ascertain the probability of a case leading to escalation and identify those that require immediate attention.
Step 4: Escalation Prediction
Last but not least, predictive analytics unfold to help you anticipate escalations in real-time. It monitors your complaint profiles to analyze the nature and intensity of complaints. Then, it notifies your front-line staff in real-time, thus enabling a timely and early intervention.
From Expert’s Desk
Things do not always go as planned; hence it is recommended to retrain the escalation prediction model every now and then to keep it in good shape. Also, consider model retraining whenever major changes are incorporated or there’s a heavy inflow of support tickets.
For example, your predictive model is converted into five‑tiered (very low – low – medium – high – very high) from three‑tiered (low – medium-high). Consider retraining it immediately to maintain a high degree of relevance. Else, data distribution is likely to drift, taking a toll on customer satisfaction and agent performance.
Want to Witness the Magic of Such a Predictive Model in Your Workflow?
Instead of weaving the magic of AI and predictive models from scratch, get your hands on our Escalation Predictor. SearchUnify’s end‑to‑end support suite includes an Escalation Predictor that preemptively manages escalations to curtail customer churn. Reach out to your CSM for more information.
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