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Chatmeter today announced enhancements to its text analysis tool, Pulse, and its custom reporting and data analysis offering, Chatmeter Analytics Studio.
Pulse, launched last December, uses AI to analyze the “tone of the sentences” in customer reviews and discern whether a review is negative, positive or neutral. This reduces the time it takes businesses to sift through customer feedback posted online.
Topics are color-coded and categorized so businesses can easily flag potentially negative reviews based on specific categories (e.g., service vs. parking). This enables business owners to be more responsive and timely when addressing customer complaints or specific service issues.
Over 60% of customers expect personalization from the businesses they interact with, but delivering personalized customer experiences at scale can be a challenge. This is where Pulse can offer real value by enabling businesses to understand what areas—and at what locations—they need to focus on to improve customer experience.
Real-time insights made possible with AI
Chatmeter’s enhancement to Pulse, their AI-driven text analysis tool, promises accuracy of nearly 90% when reviewing customer information, 18% above the industry-average accuracy.
Achieving such high accuracy in real-time equals better responsiveness to customer concerns and gives brands the ability to quickly focus on areas that need improvement. AI-driven analysis also means that fewer resources are needed to uncover actionable insights provided by the tool.
In short, it’s not enough to collect all your external customer review data into one vast database—you also need a way to quickly review and analyze this data based on location and uncover trends and issues quickly.
Pulse, using Chatmeter’s built-in AI functionality and combined with the Analytics Studio, does this via the following approach.
- Enterprise data visualization on a local level: A robust data warehouse enables teams to visualize and share local data using custom reports and location snapshots.
- Trend analysis: Teams can review historical data over time to gain detailed insights about their customers’ experience and engagement.
- Actionable insights: The tool allows companies to analyze customer reviews across all providers and locations, providing an understanding of a brand at the national level which is informed by local-level insights.
- Manage data at scale: Pulse’s AI uses machine-learning and natural language processing to do the bulk of the work, controlling unstructured data at scale so businesses can put their robust database of customer feedback to good use.
Customer reviews and user-generated content
Online customer reviews affect nearly every business from small local boutiques to large national or multi-national brands.
Nearly 95% of shoppers read online reviews before making a purchase and over 90% of customers use reviews to help determine the quality of a local business. For businesses with multiple locations, it can be particularly difficult to monitor and address customer reviews in a timely manner, much less discern actionable insights from trends that may affect more than one location.
Online reputation management agencies and technologies have sprung up to address the growing need of managing this type of user-generated content. This service has typically been the purview of search marketing agencies since it involves ensuring that local search listings for a given company are accurate and positive.
Chatmeter focuses specifically on multi-location brands, providing tools to help them manage the online and offline customer journey by ensuring that online reputation, business listing and SEO rankings at the local level are accurate.
The tool analyzes billions of customer reviews, social media mentions, and feedback from hundreds of sources across over forty industries including retail, healthcare, finance, real estate and food services.
“Smart businesses have invested so much in martech stacks and solutions” said Sridhar Nagarajan, Vice President of Product at Chatmeter. “What many haven’t yet done, though, is up-level their systems to increase the accuracy of text or photo analytics to eliminate error and relate to customers in a consistent and human way. That next level is made easier by these enhancements.”