Product Insight Machine Learning or Natural Language Processing (NLP) helps in driving changes in customer experience. Understanding how your customers behave and what they think about your Brand helps to identify opportunities. These in turn improve customer experience by gathering insights that help in developing strategies and tactics that optimize interaction across all touchpoints.

NLP and customer experience

NLP drives change in Customer experience by –

• Effectively measuring customer experience across channels

• Understanding the why behind customer sentiment – beyond likes and volume

• Translating brand awareness into loyalty

• Taking market share by tracking competitors

• Identifying, prioritizing, and implementing changes with the greatest impact on your organization

To build the model

Sprinklr starts to build the model by collecting unclassified raw data from various channels. Then Sprinklr classifies the raw data into unsupervised clusters. These clusters are then used to train the category model manually to ensure accuracy. Once the machine is trained, it finds natural patterns in data and generates insights.

Below are the steps in detail –

• Collect Raw Data: Sprinklr collects unclassified raw data like comments, mentions, tags, etc. from various channels.

• Automate Classification: The raw data is classified and are grouped together into unsupervised clusters.

• Category Model and Training Guidelines: Category model is built based on automated classification and matching guidelines to drive annotation.

• Human Annotation: Annotation and classification of data is done manually to ensure a highly accurate model.

• Machine Learning Model: Machine learning models find natural patterns in data and then generates insight and ensures a highly accurate model.

• Actionable Intelligence: You will get highly accurate data classified against a customer and industry-specific model.

How does Sprinklr find Insights?

Sprinklr's NLP finds insights in every document. Finding Insight starts with pre-processing for analysis by NLP like tokens, sentences, parts of speech. For example, if a comment says – "I really love the smell of the shampoo but the multipacks are way too expensive”, then the sentence is split into two sentences –

"I really love the smell of the shampoo. The multipacks are way too expensive."

Then each sentence is analyzed alone, in context with each other, and with historical training to identify category and sentiment. For example, in the first sentence –

"I really love the smell of the shampoo"

• The Subject is smell.

• The Attribute is love.

• The category for smell is the smell of the product and the category for love is the quality of the product.

In the same way, in the second sentence –

"The multipacks are way too expensive",

• The Subject is multipacks.

• The Attribute is too expensive.

• The category for multipacks is the product and the category for "too expensive" is the pricing and value of the product.

To make NLP more accurate, selected messages and insights are viewed and are corrected manually by the QA Team. NLP learns from these corrections.

How are insights scored?

After identifying the insights in each document by their subject, attribute, and category, the severity of language is analyzed. Then each insight is assigned to an experience score. These scores are then fetched by widgets and are displayed in the dashboards.