The New World of CS Automation Part 4: Real-Time Insights To Track and Mitigate Emerging Issues
Identify and address emerging issues with an unprecedented view of the entire customer service journey.
The World Already Loves AI-Powered Insights — Now It’s Time For Customer Service To Get In On The Game
In 2013 Twitter launched its “trending” feature — a list of personalized, algorithmically-determined trending hashtags. The feature was so successful that Facebook rapidly followed suit. Today, trending topics has persevered and evolved to become a staple of the social media experience.
To surface trending topics, Twitter’s algorithm polls tweets for repeated hashtags and calculates volume of those hashtags over a period of time. Consumers only see hashtags that have sharp spikes, rather than sustained growth. This means that the algorithm only surfaces unusual trends — as opposed to picking out topics that are of general interest.
Because of this, Twitter and Facebook’s ‘trending’ topics are not mere regurgitations of the news: they are unique views of real-time spikes in conversation.
This same approach to analytics can be applied to customer service. An algorithm can poll tickets in real-time and surface unusual spikes in requests. This is significantly different than traditional approaches to analytics, which give insights posthumously so that customer service managers can improve processes based on past patterns. Instead, this allows CS supervisors to identify new and unusual behavior— and act on it — thereby giving customers superior care, and giving agents insight into holistic, real-time trends across the entire customer service department.
SensAI does just that. Taking a feature that consumers have been using for years on social media, SensAI applies AI-powered ‘trending’ analytics to customer service in order to give CS supervisors an unprecedented view of customer interactions. This enables agents and managers to fix problems as they present themselves, instead of after the fact.
SensAI Insights: Completing the Circle of the Customer Service Journey
Think of customer service operations as a mailroom. Packages come in, and have to be sorted into boxes based on where they’re going — a package to New York would never be in the same box as a package to Australia. This sorting process is similar to triaging tickets based on the type of issue at hand.
Once the packages are sorted, they’re assigned to be delivered by specific postal workers. If this mailroom is located in New York, then the New York box gets handed to a courier, while the Australia box gets put on a transcontinental flight. This assignment process is also needed in customer service — tickets get assigned to agents based on different criteria. SensAI Predict automates this entire process.
To continue the package analogy, imagine that a warehouse worker goes rogue. He starts replacing parcels with the wrong item — let’s say durian (a very smelly fruit). When recipients receive something other than what they ordered— especially something offensive like durian— where do they turn? Customer service.
Suddenly, the post office has an influx of similar problems that cannot be neatly placed in a pre-existing category, since this has never happened before. The team needs to move fast to make amends, and that’s where SensAI Insights comes in.
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How Machine Learning Makes this Possible
A spike in requests related to durian is immediately identified as a deviation from the normalized ticket data. When SensAI Insights surfaces a new trend like this, it identifies this trend as a new category, and machine learning allows Insights to create a new category for durian-related issues. It gives this new label to SensAI Predict, which proceeds to read conversations and label them without any input from human agents. This rapid feedback loop ensures that emerging issues are dealt with as soon as possible and with the appropriate actions.
The tenth person to write in about receiving smelly fruit, durian, or some other related verbiage is met with an instant message, assuring the customer that the issue is being addressed along with any relevant explanations or action items — all this instead of a flood of new tickets to a perplexed and flustered agent.
SensAI Insights closes the circle.
SensAI Insights helps automated triage be more accurate in real-time, which in turn allows for successful bot-based interactions, which then normalizes unusual “trending topics”…aso that SensAI Insights can identify the next ad-hoc box of issues. It offers a tight feedback loop so that agents and managers are always on top of emerging issues, and can offer the fastest and best care possible.
Fixing The ‘Leaky Bucket’ Problem of Customer Service
SensAI’s capabilities ensure that no customer is left waiting, no ticket trend is left misunderstood, and no customer is shuffled between agents.
This tight-looped process gives customers the care they need, and agents the tools to do their jobs successfully. Leverage the power of automation and AI to get happier customers and agents with SensAI.
