Estimated reading time: 4 minutes
Historically, analyzing customer feedback has been riddled with issues pertaining to efficiency and effectiveness. This drove us to create a solution for the laundry list of problems enterprises face, making customer analytics easier and more insightful.
Here are 10 issues that can be solved by using our platform:
1. Uncovering the Most Popular Discussion Topics
Stratifyd’s AI-powered platform leads with Natural Language Understanding (NLU) and implements topic modeling to analyze both structured and unstructured data and break down the most talked about topics within customer feedback.
In one instance, a Fortune 500 Auto Manufacturer was using our platform and noticed that some customers were complaining of a burning smell. Upon further inspection, the manufacturer found that there was a production error and these particular customers had vehicles with missing resonator caps. Our platform led analysts to pinpoint the exact model and class of car affected and the location of the production error, allowing the company to save millions by recalling only a small amount of vehicles instead of having to issue a mass recall.
2. Identifying Key Themes
We take feedback from forums, chats, contact center calls, app reviews, product reviews, surveys, and many other sources and extract key features from the recurring topics to give you more comprehensive insights.
Kimberly-Clark Corporation uncovered a recurring topic of confusion when using our platform to gather insights about a new type of toilet paper – lacking a cardboard tube in the center of the roll. Customers said it wasn’t clear they were buying tubeless toilet paper, and many didn’t like the new design. So even though the product was selling well, the majority of reviews said that customers wouldn’t buy it again. KCC cancelled a huge inventory order to restock this product and saved significantly.
3. Categorizing Complaint Versus Feedback
Data can sometimes be difficult to categorize given the use of both positive and negative words, and the nature of the product or service. Our platform’s NLU and sentiment scoring capabilities accurately categorize unstructured data into complaint versus feedback categories in minutes.
For example, when Prudential Financial used our platform to analyze feedback about disability and life insurance claims, positive reviews would often include words like "terrible" because they discussed situations involving injuries or deaths and how the company helped them through those experiences. Analysts gained 20 weeks of work back to focus on mission-critical tasks since they didn't have to manually read and categorize that data.
4. Customers Not Filling Out Surveys
When customers don’t respond to satisfaction surveys, you’re missing out on critical insights. Stratifyd solves this problem by using predictive intelligence to analyze call and chat transcripts and accurately predict the CSAT scores those customers would've left, as well as the reasoning behind them.
One of our clients is a Fortune 500 Financial Services company, which used this functionality to predict CSAT scores for the 95% of its customer base who chose not to participate in surveys. This information helped the company increase customer satisfaction to 92%.
When it comes to data analytics, reporting the findings to leadership and executive teams is a crucial part of the process. However, when reporting to multiple teams, each one could require the data to be presented differently, and creating these reports is time-consuming for analysts.
Stratifyd provides various visualizations – word clouds, sentiment meters, graphs, excel spreadsheets, etc. – of structured and unstructured data on one easy to use dashboard and creates reports for you.
6. Limited Data Sets
Well-done customer analysis requires looking at the entire picture, not just data from a handful of sources. Our platform ingests data from an unlimited amount of commonly used connectors (think: Amazon reviews, social media, and surveys), and we create new connectors for our clients based on their needs. This solves the problem of siloed data and helps surface unknown insights.
7. Speed – Or Lack Thereof
One of the biggest problems with customer analytics is speed. To manually ingest, analyze, categorize, and visualize data from thousands of customer reviews takes months. Stratifyd does all of that and more in mere minutes and allows for true self-service. Analysts can quickly and iteratively play with the data, allowing for better comprehension.
8. Insights That Can Only Be Understood by Data Scientists
One of Stratifyd’s core principles is to empower every employee to contribute data-driven decisions, so our platform allows analysts and data scientists alike to choose topics from the visualizations and drill down into them. Democratizing the data gives everyone the ability to click any visualization on our dashboard and see verbatim reviews to truly make sense of the data and identify areas of improvement.
9. IT-Led Analytics
In the past, customer analytics had to be led by IT teams to build the proper functionality and update it every time other teams requested additional features. Our platform democratizes AI so every employee has access to NLU, neural sentiments, and predictive models to uncover the most impactful insights and avoid the unnecessary back-and-forth between teams.
10. Workflow Inefficiency
Traditional customer analytics required data to be moved throughout teams like an assembly line to be properly explored in order to find meaningful insights. Our platform’s automation and case management functionality allow clients to track data as it’s coming in and take manual components out of the equation. This means data-driven decisions can be made sooner to improve the business, products or services, and CX.
If you’d like to see how Stratifyd can help you solve your customer analytics problems, schedule a demo with us.
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