How brands apply text analytics to improve online shopping experience?

Successful eCommerce companies have one thing in common. They listen to their customers’ feedback and take proper steps to enhance their online shopping experience continuously.
Online shopping and technologyHowever, with more than 80-90% of unstructured data in the system, it is challenging to gather actionable information without text analytics.

What is Text Analytics?

Text analytics refers to analyzing unstructured data to gain insights, identify patterns, and determine consumer trends to enhance the customer experience.

Text analytics uses artificial intelligence, machine learning, and natural learning processing to cut the noise through unstructured data to provide the information you need.

Here are the seven essential functions of text analytics:

# Language identification
# Tokenization
# Sentence breaking
# Part of speech tagging
# Chunking
# Syntax parsing
# Sentence chaining

Each function is achieved on a spectrum between machine learning and software rules.

Using Text Analytics To Optimize Shopping Experience

Applying tech for consumer data collection is great, but if you are not using it to enhance your shoppers’ experience, then you are at risk of losing your customers to your competitors.

Text analytics helps you uncover your customers’ thoughts about your eCommerce brand as they express them online. Here’s how you can use text analytics to improve the online shopping experience.

1- Curate Data in One Place

The first step towards improving the online shopping experience is curating the data in one place. You need to determine the type of information that impacts the user experience of your eCommerce store.

Some of the insights (related to text analytics) include:

# Positive and negative keywords
# Customer feedback
Sentiment analysis for the shopping experience
Sentiment analysis for customer support during shopping
Missing features on the website

Make sure to customize the type of data you are curating over time to improve your understanding of how customers shop from your online store and what their experience is.

2- Categorize Data Into Concepts, Sentiment, and Behaviors

Text analytics uses natural language processing (NLP) to understand the sentiment and emotions behind the feedback.

Classifying the data into three categories: concepts, sentiment, and behaviors, text analytics can help you understand what customers think of your brand.

Besides, text analytics can deliver valuable, often-missed insights about the customer’s experience. You can then use the information to improve the overall online shopping experience.

For example, if you see a decline in positive customer sentiment, you should look deeper into negative keywords to understand the issues your customers are facing. With those insights in hand, you can fix the problem instantly.

While it isn’t practically possible to make each of your site visitors happy, make sure most of them are. Even if the overall customer sentiment (related to shopping experience) is positive, find the unsatisfied shoppers, discover their issues, and take relevant action to make them happy.

3- Enhance Customer Service Interactions

Text analytics is extremely helpful in determining the reason behind customers’ issues. It can dig through transcripts from customer call center conversations, emails, and live chat to detect the common reasons behind customer complaints.

For example, let’s say you sell smartwatches. By using text analytics, you can minimize returns by identifying keywords like “returns” and “smartwatch” in all the conversations (across channels). Text analytics can also distinguish the top terms associated with smartwatch returns.

You can then easily determine if the smartwatch is being returned due to connectivity issues or poor battery. Use the feedback to improve your product or pass it on to the manufacturer.

Leverage text analytics in a similar way for all your products to address customer frustration and prevent future problems.

4- Prioritize Customer’s Issues

Each customer has a different opinion about shopping in your eCommerce store. It could be difficult to determine which issues need immediate action.

However, with text analytics, you can visualize both the mentions certain complaints are getting and their emotional impact on the overall online shopping experience.

You can then prioritize the issues accordingly to deal with them in a way that will have a maximum positive impact.

This will also help you determine what new features your customers expect on your website (e.g., live chat, more in-depth filters, increased return window, etc.).

Once you have made the required changes, use text analytics to discover how they impact the overall shopping experience.

5- Implement One on One Personalization

Personalization helps you connect with customers on a personal level and increases their satisfaction level.

Text analytics helps identify the products a customer is most interested in buying by monitoring their reaction on social networks and the search terms they use on your website. This enables you to analyze and act on real-time data to seize opportunities as they arise.

With text analytics, you can also personalize your website visitor’s experience by recommending products that they are most likely to buy. Powered by machine learning and AI, text analytics can suggest relevant products through text-based filtering.

Final Thoughts

The online shopping experience is one of the key drivers of sales for eCommerce stores. To compete with major online retailers and emerge as a leader in your industry, you need to offer the best possible shopping experience.

Text analytics is the best way to understand what your customers feel and think about buying from your website. Use the five tips mentioned above to improve the online shopping experience and increase sales. Happy Selling!
Alon Ghelber, CMO at Revuze
By Alon Ghelber, CMO at Revuze
Alon Ghelber’s LinkedIn profile:

Alon is a Tel Aviv-based chief marketing officer who supports B2B tech startups in capturing customers’ (and VCs’) attention through marketing based on data-driven storytelling.

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