- November 11, 2020
- Posted by: Vaibhavi Tamizhkumaran
- Category: Text Analytics
The new brand is the Customer Experience (CX)!
Businesses cannot afford to treat it as an option. Irrespective of the product`s or service`s niche, the opinion of your customers is a strategic factor.
The retail business is concerned with both products & services where products are consumed, and services are to be experienced. Providing the best customer service is the way to win loyal customers. A 5% Customer retention can ensure a 25%-95% increase in revenue without a doubt.
Good reviews and brand endorsements can carry new leads to the flock while maintaining established loyalty. But a bad man could cost you more than a fortune and leave your out of business.
Text feedback is the best way to communicate one-to-one with any customer. Such feedback makes it possible for them to be free and accessible. Customers may convey what they prefer from the product/service, what they care about, and why without delay. Although an increase in the number of surveys and responses may result in a large volume of collected data. Processing them is challenging and time consuming.
Text analytics, a method that extracts meaning from text uses natural language algorithms to identify patterns and common themes. This will help to take meaningful steps on the basis of intuition. It can quantify parameters such as customer opinions, user inputs, product feedback, reviews, survey results, etc…
Text analytics solutions can address key questions about how a company or organisation is performing based on certain metrics. This is largely termed as ‘Voice of the Customer- VoC’. Acting in accordance with the customer reviews can help generating better customer experiences and customer loyalty towards a firm`s product/service.
The NPS- Net Promoter Score plays a vital role in determining the overall review of a product/service. The NPS is the Difference between the percentage of promoters and the percentage of Detractors. This score makes it easy to distinguish between the good and bad reviewers.
Everyone in the business has a similar collection of concepts from which to function. As businesses recognise the NPS problem and started researching it as a key metric, that helps them channel their customer service efforts and increase sales through referrals and upselling.
The Retail Crisis
A sports goods retail organisation faced a challenge of redressing its customers based on the reviews they got about their products. It became challenging and time consuming for the organisation to identify the Voice of its customers.
What went wrong?
The firm was lacking analytical solutions for improvising their customer experiences.
- The firm wanted an extraction pipeline to extract all their unstructured reviews to obtain some valuable insights for improving their customer`s experience with their products.
- The firm was also in need for a text classification and text summarization function to bring together all the reviews into a compact single structured document.
- The firm looked out for generating a user-specific analysis based on its business objectives.
The firm immediately implemented the thought of resolving the issue by partnering with a text analytics tool.
teX.ai to the Rescue
teX.ai helped solve the text extraction, classification and summarization needs of the firm.
It’s personalization and scalable feature helped the firm to analyse the local sentiments of customers who had reviewed their products.
- The voice of the customers was analysed by the processes of text extraction, text classification and text summarization of all the many reviews they had got about their products.
- Customer grievances were redressed by the store managers faster than usual after the VoC analysis.
- The firm was able to increase its NPS (Net promoter score) by 50% at the end of this process.
- Data storage and retrieval was made secure and safe.
Story Behind the Success
Firstly, a CDC logic was setup using Python codes to streamline the extraction of the unstructured customer reviews. This extracted data was stored Elasticsearch Data warehousing tool.
A complete VoC analysis was performed by teX.ai through a series of clustering, tokenization, key phrase identification, sematic search, sentiment analysis, and other advanced NLP- Natural Language Processing techniques to bring out a summarized sentiment of the reviews extracted from the pipeline.
Top three key words/ sentiments/ categories were identified, and the data was classified or clustered under them. The text summarization module of teX.ai summarized the data in every category to bring out an overall sentiment for the product.
tex.ai can provide actionable insights to your business by automating the entire process of extraction, classification and summarization, making the process 3x faster and cheaper.
Other applications of teX.ai include product categorization, lead generation, financial trading analysis, business intelligence and many other applications that cover almost all the domains in the market.
Some of the domains include BFSI, Ecommerce, healthcare, logistics, manufacturing, utilities, and BPOs.