How AI-Powered Consumer Insights Is Reinventing eCommerce Customer Experience
In this article, we will discuss how AI-powered consumer insights are reinventing the way eCommerce businesses are offering tailored shopping experiences.
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Big data is definitely the future of eCommerce. Traditional businesses are struggling to keep up with technology-savvy newer generations of eCommerce businesses.
The most significant advantage that eCommerce offers to customers is improved experience. You can measure the customer experience of any business using digital platforms.
As per Microsoft, more than 90% of customer interactions will happen through channels leveraging artificial intelligence. Hence, consumer insights are essential to creating a genuinely excellent eCommerce experience.
Machine learning and AI are playing crucial roles to deliver personalized and unique experiences. In this article, we will discuss how AI-powered consumer insights are reinventing the way eCommerce businesses are offering tailored shopping experiences.
Traditional Approaches No Longer a Viable Option to Collect Data
The traditional methods of collecting data like surveys and focus groups can no longer be trusted.
Surveys are not capable of collecting genuine feedback, and results are transient. Survey respondents are self-selected, and they give a skewed picture of the problem.
Similarly, focus groups are biased because the opinion of the participants might get brainwashed with the moderator's personal belief.
The best way to collect customer insights is to gather data from multiple sources like eCommerce reviews, social media mentions, and UGC data.
AI automatically turns the unstructured data into valuable consumer insights. The need to hire expensive analysts is avoided. Hence, AI saves money and offers an improved eCommerce customer experience.
Emotional Analysis Using Deep Learning
Sentiment analysis is the study of positive, negative, and neutral feelings from the text. It aims to recognize the type of emotions that your customers have towards your brand.
There are three approaches to calculate customer emotions like rule-based, statistic-based and hybrid.
Out of these three approaches, the hybrid system is the best because it leverages statistics and real-time data to identify the emotions of the customers. When you know the sentiments your users have for your brand or your competitor’s brand, you can take appropriate steps to optimize them.
Optimized customer experiences lead to higher customer retention rate and lower churn rate resulting in increased profits.
AI for Video Analysis
Eye-tracking technology is useful in collecting data related to the eye movement of the viewers. The most and least used data points are identified to decide the product placement strategy in eCommerce.
Tools like Karna AI are excellent in gathering data that shows the distribution of consumer attention with color-coded heatmap. The gaze spot shows the exact areas in the videos where the user attention is the maximum. The collected data is analyzed further with the help of AI, and the landing pages or videos are designed carefully to help the user.
Social Listening to Improve Response Rates
You need to listen to what your customers are saying about your business on social media. Social listening improves your response rate and lets you convert negative comments into positive ones.
You get notified when there is a rise in negative comments, and you can effectively take measures to fix the issues.
Pro-active social listening results in a higher customer retention rate. Moreover, you can compare your business with your competitors within seconds and set personalized alerts.
For example, you know when your competitor’s update the pricing of their product so that you can adjust the prices of your products accordingly.
Consumer Behavior Insights Using Location Data
Location data helps to understand how people move and behave in the real-world. The contextual user information gathered via smartphones, and mobile devices is a valuable resource for marketers.
Most eCommerce companies used to track IP location data. However, as the world is moving to a mobile-first approach, eCommerce businesses are using smartphone data to uncover relevant consumer insights.
Location is a great way to identify consumer behavior. When you have behavioral insights, you can personalize your messaging for better conversion rates. AI and big data technologies can be used to build highly complex location intelligence algorithms with great accuracy.
Scalable Data Unification
Data unification is the process of combining all the data into a single source. Parsing, deduplication, and schema integrations are all components of successful data unification.
When consumer insights are gathered, they are mapped together to identify the key entities and attributes.
After that, the entities are matched together to prepare a unified customer profile. Lastly, the conflicting attributes are merged. AI helps to conduct data unification at scale.
Both AI and machine learning are being used to gather relevant and real-time consumer insights to streamline audience targeting. Due to rising competition, the eCommerce space is overcrowded. Businesses need to think of new and exciting ways to acquire new customers and most importantly, retain the existing ones. Consumer insights powered by AI offer crucial data on user behavior that businesses should use to optimize their offerings. Consumer insights and AI help you reach your business goals and beat your existing competition.
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