The Key to Developing an Efficacious Escalation Prediction Model

The Key to Developing an Efficacious Escalation Prediction Model

A question that always pops up in our head while raising a support ticket is, ‘when will it be resolved?’

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 being able to answer how long it will take to fix a problem or resolve a query.

Assessing the probability of support ticket escalation is one crucial element that needs to be taken into consideration to hit this nail on the head. However, hybridization of support has increased the complexity of agent interactions. This 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 leading to escalation, thereby helping you work out the estimated time of completion in the early stages of the customer interaction and working backward to boost customer satisfaction.

Key Components of an Effectual Escalation Prediction Model

It is paramount to engineer an exemplary predictive model to attain your support goals. You can infuse it with unsupervised learning and real AI to provide a tailored solution with the growing support data. After all, training predictive models for every support request is insurmountable. Here are a few key ingredients that’ll come in handy while designing a potent predictive model:

1. Context Capture

As the adage goes, ‘If content is king, then context is the kingdom.’ That is why it is crucial to capture the context of a support ticket to monitor its real progress.

Multiple parameters, including dynamic (how a case evolves over time) and pre‑contextual (prior customer relationship with the organization), are taken into account; and then combined with text‑based information that is extracted from the ticket, harnessing the power of NLP.

2. Case Comment Analysis

Comment streams are a rich source of analysis that reflect how customers think about your brand. By closely examining both inbound (from customer to support) and outbound (from support to customer) comment streams, you can pin down the root cause of escalations and ascertain the intensity of a support case.

For example, the steady increase of response time in comment streams could indicate growing customer frustration. Capturing such information contributes to building better escalation models.

3. Technical Content Extraction

When you are up against an endless queue of support tickets, you need to remember that not all of them are created equal. Some demand prompt attention while some can wait a little. This is where technical content extraction comes into play.

An ideal escalation predictor leverages cutting-edge natural language processing to decipher the technicalities of a support ticket and gauge its complexity from comment text. It also taps into sentiment analysis to capture real customer emotion. This way, you can identify cases with high emotional content and treat them as high‑priority and keep customer dissatisfaction at bay.

4. Feature Engineering & Feature Selection

Once features in the abovementioned steps are extracted, they need to be analyzed both in isolation and combinations so that you can identify the most useful ones from the pool. This is where feature engineering and feature selection kick in.

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.

5. Model Retraining

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 a good shape. Also, consider model retraining whenever major changes are incorporated in certain features 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 Live in Action?

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. To know how, request a demo today!

Moreover, our biggest release of the year, Mamba ‘22, is almost at the door. It will bring in a whole flurry of features and enhancements along with a revamped suite of support apps. It will roll out on November 30th. Watch out this space to discover what’s coming your way to step up customer support and self‑service.

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