5 Ways Brands use eCommerce Data to Improve Their Customer Service

eCommerce data

Data can wield tremendous power in the eCommerce race. With more players than ever, securing data about your customers, competitors, and the market can be decisive.

Since people and markets are always changing, data should be updated continuously. Constantly searching for new data can improve strategic business decisions, understand customers, and reduce marketing costs. In addition, data can improve customer service and customer experience to enhance brand loyalty.

What is eCommerce data?

Data is a popular buzzword, but it is important to remember that not all data is created equal. The kind of data that will drive a successful eCommerce strategy includes customer analytics, which measures the number of visitors, click-through rate, amount of time spent on the webpage, and conversion rates. Reviews and social media shares also provide insight into how your brand is viewed on the web. Collecting a variety of data is essential for a successful eCommerce strategy.

How Can eCommerce Data Improve Customer Experience (CX)?

The customer experience your company provides to customers can’t simply be quantified in the number of sales, but can be measured at all phases of the customer experience, from browsing through your web pages to chatting with a customer service representative to leaving a review on an eCommerce platform.

1.  Analyze CX Relevant Data

When analyzing data for effective CX, it is essential to consider whether your company attracts the right customers, visits are being converted to sales and subscriptions, and if CX is strong. Some important indicators include customer satisfaction scores, number of referrals, the speed at which customer service queries are handled through calls or live chat, and checkout ease. Measuring these factors can provide a picture of CX and help pinpoint what to improve.

2.  Pay Attention to Customer Reviews

Looking at reviews on product pages and comments on social media allows you to see verbatim how customers feel about your products. Analyzing a large number of reviews provides insight on certain themes that keep recurring and can give you significant direction on how to revamp your products or improve CX. Reviews provide an advantage over numerical metrics since they give depth and context to customer approval or criticism.

However, one challenge reviews and comments pose over numbers is they take some time to read. Textual analysis tools can shorten the time it takes to get through reviews while still allowing you to glean insights from customer feedback. Web scraping makes retrieving texts, such as reviews and comments, easier.

3.  Personalize CX

Receiving eCommerce data, such as visits, conversions, and clicks can help personalize CX. Tracking online behavior provides useful information on how to communicate with your customer on their next visit. Data be placed into segments to influence campaigns that will appeal to certain types of customers, or specific data will help create product suggestions.

Also, customer service interactions give clues on how to reach out to the same customer in the future. Specific customers may prefer certain channels, and data can target a strategy to reach customers where they like to browse and make purchases.

4.  Customize Unique Offers

With data on consumer behavior, you can organize your customers into personas, or those with similar demographics, interests, professions, hobbies, and lifestyles. This information can be valuable for designing promotional campaigns and making special offers. Data informs which offer will appeal to which customer persona and can help you create multi-tiered campaigns with broad appeal to increase customer loyalty.

5.  Predict Future Behavior

It is a common principle in psychology that the best predictor of future behavior is past behavior. This is also true in marketing. It isn’t enough to satisfy customers today and predict what they are going to do tomorrow. In a market that keeps changing, and an eCommerce environment that becomes increasingly competitive, leveraging data to predict future behavior is essential.

Creating a predictive Consumer Behavior Analysis model is one step towards deciding which direction to take with marketing campaigns and product development. Looking at shopping history, rated and liked products, views, purchases, and subscriptions can help you determine what to focus time and resources on.

Besides, data from competitors and information about the market can form the basis of predictions that will create a smarter eCommerce strategy. Using residential IPs to collect data from competitors’ websites can provide data that can indicate the direction of future trends.

Getting to Know Your Customers

Connecting with your customers is the key to understanding what they want and providing them with the products and services they will buy. Data about customer behavior online and reviews have predictive value and can help you fine-tune CX. Personalizing marketing and promotions and presenting your products on the platforms your customers like to use can increase brand awareness and customer loyalty.

About the Author

Alon GhelberAlon Ghelber is the Chief Marketing Officer at Revuze, an AI-enabled technology analyzing millions of customer reviews, online opinions, and offline pieces delivering experience-related insights and intelligence for companies to optimize decision-making.

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