The movement of goods on the Internet includes complex supply chains and partner networks that have to be managed with the help of extensive IT systems. Such IT architectures process millions of orders every hour. One of the biggest problems for online retailers is that order processes can be lost in these complicated systems. This not only affects sales, but also customer satisfaction and the brand's reputation. In order to be able to master the masses of data, e-commerce companies need sophisticated analysis solutions.
The benefits of accessing and analyzing data in e-commerce are endless.
However, it is crucial to understand the perceptions of customer behavior and their interaction with the website to be successful. By collecting data, customer service is improved and a personalized experience is provided, allowing to increase sales, optimize the price of the product and even influence the location of a new store.
Technology, in continuous evolution, is made available to everyone and the world of commerce is no exception. Today's retailers can access a wide range of data and analytics tools, and brands are increasingly turning to different ways to use artificial intelligence for their e-commerce.
The other side of this coin is that more and more companies have customer behavior data. That is why competition is tightening in this regard and it is crucial to have the best tools.
Having the data at your fingertips is one thing, but understanding it, designing a strategy, and implementing concrete improvements based on that data is another. At Hyperlink InfoSystem, we understand the particularities of this industry, and, in that sense, we will now analyze the uses of data science in electronic commerce and why it is vital not to be left out.
Why you should consider data science for your business
• Recommendation and customization system
As its name suggests, it is a system that filters information and predicts user preferences while browsing the Internet. Analyze people's previous searches and their purchases to find relevant products. There are three main recommendation techniques:
1. Collaborative filtering: This is the most popular technique used in e-commerce. It collects data and find similarities between the activities and interests of different users.
2. Content-based filtering: This technique finds recommendations based on product descriptions of items that users like.
3. Hybrid recommendation filtering: It uses the 2 techniques described above and combine their results or use the results of one technique as input for the other.
• Customer Lifetime Value
It is a prediction of how much a client can contribute to the income of a company during its lifetime. It is calculated by the buyer's previous purchases and their interaction with a specific e-commerce site. Calculating the lifetime of a customer is done with a simple formula: (Average order value) x (Number of repeat orders) x (Average customer life)
• Improvements in customer service
One of the keys to e-commerce success is customer service.
Data science helps companies improve it, pulling ratings and comments, not just from the website but also from social media and call center archives. After extraction, it is possible to segregate them and do a sentiment analysis to better understand why a product or service was given bad reviews. This allows companies to analyze customer behavior and market insights.
Advantages of applying data science in your ecommerce
At this point you may be wondering how all this relates to your online store, right? The answer is: a lot. The large amount of data available and its subsequent analysis allows ecommerce that make use of Data Science not only to improve the focus of their business, but also:
• Have an efficient database
Data Science allows you to more easily track and understand the information that comes from different sources, such as stock control sheets, results of campaigns in Ads, email marketing statistics, among others.
• Have more control over the store's stock
One of the most important data that Data Science provides is knowing what users buy and, in this case, this can help to better control the stock and always have that product that customers prefer.
• Make decisions based exclusively on data
This is essential because, unlike “hunches”, decisions made based on specific information reduce a business's margins of error and avoid pointless expenses.
For example, if an ecommerce analyzes certain data and concludes that it has some products that users visit but do not buy, then it can determine that it is a product to promote until its stock is eliminated.
• Personalize the customer experience
The objective of applying data science in an ecommerce
is to be one step ahead of users. And the more personalized the actions you implement, the more conversion opportunities you will have.
For example, to personalize the experience of your customers in your store, you can make recommendations for products similar to those they visited, advise products that complement their purchase or even show offers for products they have browsed before.
• Improve customer service
In this case, the data analysis serves to help identify a problem (complaint about a product) that is being generated in a specific channel (online store, social network, etc.) and provide an immediate solution.
It also helps to identify if it is the same user who made a query by mail and then replied to it on Facebook or Instagram. In this case, help can be offered quickly and effectively.
• Be aware of what the competition is doing
With some top solutions, real-time data on prices and products of a certain sector in the ecommerce world can be analyzed. In this way, you avoid losing customers by offering them price and quality.
Today, consumers are looking for more personalized experiences and each brand must be able to predict what users are looking for on its e-commerce platform. Each customer interacts with a website in a different way and has individual preferences. As in recommendation systems, predictive analytics analyzes the purchasing patterns of consumers and their interaction with the site, providing access to all the ideas. E-commerce businesses can offer a better customer experience and decide the minimum and maximum prices for their products.
Consumers demand personalization, elasticity and greater adaptation to their needs. It is the challenge for e-commerce companies to understand this need and turn to Data Science and Artificial Intelligence to transform it into optimal results and higher profits.