Ecommerce has been on the rise for some time now and saw impressive growth since last year. With people locked down at home when the pandemic broke, ecommerce companies came to the rescue. Thanks to online businesses, consumers could shop for everything from groceries and medications to lifestyle products and fashion accessories from the comfort of their home. In ecommerce, the customer experience is everything. Using data analytics, ecommerce firms are implementing changes that truly matter to personalize the customer journey. Supported by data conversion service providers, big data analytics allows ecommerce firms to gain strategic insights to make smart decisions, improve their operations, personalize the customer experience, and increase sales and revenue.
Application of Data Analytics in Ecommerce
Data and information on their own cannot provide knowledge about the customer insights. The data and information has to be analyzed and interpreted and their value depends on the ability of organizations to arrive at conclusions and make key decisions.
To optimize the online shopping experience, big data analytics needs to be applied to various data segments such as demographic/geographic data, customer experience data, sales reports, and internal data.
Demographic/geographic Data: Building robust consumer profiles complete with demographic, geographic and psychographic data and analyzing is crucial. The customer’s geographical location is an important factor in retail marketing. The idea is to identify where your customers are located and use the insights about them to attract their attention. By segmenting customers based on their location and using data analytics to understand their preferences, ecommerce firms can use different marketing strategies to target them, such as location based advertisements and products to cater to their tastes. Walmart is a classic example of a retail corporation that uses location-based data for site selection and audience targeting.
Customer Experience Information: Big data analytics is used to understand customer insights to support customer experience management. Understanding customer needs, goals, and interests via big data analytics creates visibility into the buying process. With digitization, customer data is available across a wide range of channels such as transactions, mobile apps, social media platforms, online surveys, email conversations, chats, phone calls, and much more. Analyzing this data provides insight into how customers engage with brands. Measuring customer satisfaction on the basis of the feedback and using the results to improve products/services can go a long way to improve customer engagement.
Internal Data: Leveraging internal data effectively using predictive analysis is another option to drive the customer experience on an ecommerce site. This involves mining the customer database for previous sales reports, abandoned cart data, average purchase prices, web browsing history using cookies on the site, and responses to responses to direct SMS and email campaigns. Customer data analytics helps e-businesses implement tactics like reminding visitors about items left in their shopping cart, offering coupons to customers who join the mailing list before exiting the site, etc.
Strong Customer Satisfaction is the driving force behind high levels of e-business performance. Amazon is the best example of this. When e-commerce experienced an unprecedented boom during the pandemic, Amazon led the trend. Amazon makes use of big data analytics to study customer behavior as they browse on the site in order to develop and hone its recommendation engine, observes a bernardmarr.com article. As you use the site, Amazon collects data on what you look at, what you buy, your shipping address (to guess your income level), and whether you leave feedback or reviews. They then use that data to predict what you want and make recommendations to persuade you to buy that product. According to a survey conducted by the Temkin Group, Amazon has amongst the highest rates of customer loyalty in America.
Data Analytics: Importance of Data Cleansing and Conversion
Data analytics involves using specialized systems and software to take raw data and identify patterns to get valuable insights from it. When it comes to understanding their customers better, businesses need to evaluate different types of data such as:
- Basic demographic data (contact’s name, email address, phone number, job title, gender, income and location information)
- engagement or interaction data (consumer touchpoints with your brand such as pageviews, ebook downloads, social shares, email inquiries, and demo requests)
- behavioral data that provides information on the customer’s experience with your actual product or service (free trial sign ups, user account logins, and feature utilization), and
- Attitudinal data that provides you with an idea about what customers think about your company and your products/services
For data analytics and customer relationship management to work, the database must be sufficiently clean. Bad data means a customer database with missing data, typos, unorganized files, disorderly formatting, and so on. Data cleansing and data conversion services are critical to get this data under control. Experienced back office outsourcing companies that provide these services can handle these functions in a way that prevents data loss and data integrity, which are key to the success of data analytics.
