Challenges in Hospitality
Lenovo had a corporate initiative to actively solicit customer feedback at all points in the customer journey. Data was obtained, but Lenovo’s challenge was how to easily gain insights from the data, and even more importantly, how to provide end-users the ability to access the data for improved decision making. Lenovo wanted end-users to obtain actionable insights in real time from its online chat sessions, customer support sessions, and survey data logs in the North American market. The need to turn customer feedback into something valuable is why Signals™ was selected.
Lenovo is leveraging Stratifyd Signals™™ platform to automatically process textual data from its customer service online chat portal to help identify key patterns, behaviors and potential issues hampering the overall online experience in both macro and micro views.
The ability to drill down and focus on a specific day, or evaluating sessions over days, weeks or even months, allows Lenovo sales and customer support teams to quickly identify trends and issues and address them head on, while ensuring agents are following protocol and delivering top-notch service.
How Signals Helped
Signals™ enabled Lenovo end-user teams take a deep dive into the unstructured text of their internal data sets. Analyzing data in real time allows the customer service and customer experience teams to uncover potential issues and without complex IT integrations or by taxing data science teams. Signals™ has been able to quickly process all the data and present it in a way that allows Lenovo to better manage the overall customer experience.
The Stratifyd Signals™ platform processes large volumes of unstructured and structured textual data from almost any source – emails, chat sessions, text files, e.g. Word and Evernote, blog posts, app reviews, social media and more. It produces quick and actionable insights in seconds by incorporating predictive modeling, machine learning, and robust statistical Natural Language Processing (NLP) algorithms to create visualizations of behavior communities, trends, patterns and outlying themes.
Key aspects of the solution include no complex integration, an intuitive dashboard, and advanced functionality. The Signals™ dashboard offers drag-and-drop data importing and drill-down options – such as temporal trends, category overview, or buzzword and geospatial analysis.
Chat Data: customer service and sales chats were broken down into two groups: Agent and Customer, to improve agent performance, strengthen customer relationships, and generate more revenue.
Lenovo added the following customer experience data sets to the chat analysis to better understand ongoing customer demands:
Opinion Lab Data: eComm comment cards, eComm exit surveys, eSupport comment cards and exit surveys.
Survey Data: purchase experience survey, chat survey (sales and sales support), product ownership survey.
Lenovo tech support data.
Results and Return on Investment
Using the Signals™ platform, Lenovo was able to:
Expand data analytics beyond the analytics group to the end-user groups so they can affect change in the organization.
Frontline sales/support agents moved from predictive – ”what is likely to happen” to prescriptive – “what we should do”.
Strengthen relationships with customer: provide data support and agent monitoring for tactics that improve sales agent’s effectiveness in growing and retaining customers.
Increase consumption of the data: generated automatic and easy-to-consume results of sales/sales support data