Exploring the Potential of Conversational Analytics for eCommerce Businesses

Exploring the Potential of Conversational Analytics for eCommerce Businesses

Artificial intelligence (AI) has empowered technology to interact with users in unprecedented ways. AI’s conversational abilities such as chatbots and virtual agents are beginning to impact global business performances with automated operations...

Artificial intelligence (AI) has empowered technology to interact with users in unprecedented ways. AI’s conversational abilities such as chatbots and virtual agents are beginning to impact global business performances with automated operations and services. As an experiential chatbot development company, Oodles AI is witnessing a radical transition of legacy businesses to conversational interfaces. eCommerce businesses are one of the earliest adopters of conversational AI development services to enhance customer engagement and experience. Conversational Analytics for eCommerce businesses is a composition of next-level AI techniques to analyze eCommerce performance metrics holistically.

Read on to find out how does Conversational analytics work and how does it automate essential eCommerce operations.

What is Conversational Analytics?
Conversational Analytics is a step ahead of AI’s conversational capabilities. The technique synthesizes all the textual and voice-based customer data generated via chatbots or virtual agents to extract key business insights. AI conversational technologies such as Natural Language Processing (NLP) and machine learning algorithms are the building blocks of conversational analytics.

The year 2020 will witness more than 25% of customer service operations using AI-powered chatbot technology or virtual agents, predicts Gartner.

The more businesses incline towards conversational interfaces, the greater role conversational analytics will play in improving critical business strategies. To examine, classify, and analyze user queries, conversational analytics combine a range of AI techniques such as-

a) Phrase Clustering- Clubbing together closely related queries and interactions to improve the effectiveness of NLP engines.

b) Intent and Entity mapping- Categorizing user queries in specific keywords and phrases to make accurate responses.

c) Automated transcription- Recognizing speech and audio files to comprehend relevant inferences.

d) Sentiment Analysis- Evaluating the emotion behind textual and voice interactions and deriving insights.

e) Anomaly detection- Raising alarms at odd conversations and typical queries that require human intervention.

f) Word Cloud- Identifying the most frequent and common user queries and utterances.

Applications of Conversational Analytics for eCommerce Businesses

  1. Tracking Purchases
    Purchase metrics are one of the key performance indicators (KPI) for eCommerce businesses. It includes stats for purchase frequency, repeated purchase rates, and order gap analysis. However, it is challenging for eCommerce businesses to track and evaluate accurate KPIs from large datasets in a timely and efficient manner.

Conversational Analytics techniques automate the recording and analysis of conversions and abandonments for eCommerce businesses. With a steady increase in AI’s impact on business sales, eCommerce businesses can effectively integrate conversational analytics in their sales funnel for-

a) Accurate assessment of user-chatbot interactions for purchases

b) Real-time identification of important user queries and keywords

c) Regular evaluation of user engaged vs users who purchased to restructure strategies.

  1. Mapping Audience Demographics
    In most cases, eCommerce business services cater to a vast audience spread across borders. For such large setups, it is difficult for marketing executives to track, record, analyze, and infer the needs of dynamic user needs and preferences. It hampers the overall customer experience and affects customer loyalty drastically.

With rapid algorithmic advancements, Conversational analytics can augment eCommerce marketing efforts with scalable data recording and processing capabilities. Underlying ML models can map user demographics across digital and social media channels to extract valuable information such as-

a) Most active user profiles

b) Audience location

c) Gender information

d) Local languages used by different user bases

e) Device compatibility

f) Most active hours, and more.

  1. Analyzing User Behavior
    In addition to visualizing audience demographics, AI can run in-depth analysis to gauge the emotional connection of eCommerce users. With sprawling social media channels, eCommerce services often become a hot debate topic among dissatisfied customers.

Conversational analytics open new opportunities for eCommerce businesses to enhance customers’ shopping journey with a deep understanding of user behavior. Technologies such as speech-to-text recognition and sentiment analysis can process complex social media posts and comments to decipher user’s outlook towards business.

Here’s a conversational chatbot built by Oodles AI in action. Customer engagement and accurate responses are key factors that determine conversions for eCommerce businesses.

Following are some most important user behavior metric that can be captured using conversational analytics-

a) Most common user interactions, intents, and utterances

b) Critical user pain points or exit messages between users and chatbots or virtual agents

c) Comprehensive visualization of user-chatbot message funnel to track break down

d) Containment reports to monitor the percentage overtime and common escalation paths

e) User behavioral flow

Optimizing AI-powered Conversational Analytics with Oodles AI
As artificial intelligence continues to transform eCommerce operations and customer services, Oodles AI explores better AI solutions to accelerate digital commerce. We bring AI-powered automation to eCommerce businesses through our multidimensional conversational solutions such as chatbots, voice-controlled virtual agents, and bot framework services.

Our chatbot development services encompass conversational applications using IBM Watson, Amazon Lex, Dialogflow, and other bot frameworks for integration across social media channels. We channelize data from these conversational chatbots to deploy industry-specific conversational analytics techniques for eCommerce, healthcare, EduTech, and other business vectors.

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