The initial mistrust of buying on the Internet has almost disappeared and e-commerce continues its growth unstoppable. This has been possible thanks to technologies such as machine learning or location intelligence, which allow us to satisfy the needs of users of this type of commerce. Next, we analyze in what situation it is currently and what are the news that are already being implemented.
E-commerce is experiencing a good time in terms of growth, as confirmed by the data of recent years. To properly analyze and understand this phenomenon, it is convenient to know what the statistics are, what technologies are based on to achieve this success and what are the latest developments in e-commerce in retail.
The main objective of e-commerce today is to satisfy the demands of its users, ranging from a greater customization of their experience (especially through appropriate recommendations) to a solid logistics and distribution system, which allows their orders to reach your houses in the shortest time possible.
These innovations are possible thanks to technologies such as machine learning or location intelligence, which help perform tasks that would be impossible for a person, taking advantage of the processing capacity of computers.
An industry in constant growth
The consolidation of the Internet and the increasing confidence of its users to carry out online transactions have led to the popularity of e-Commerce not stop rising. Although it is still too early for it to replace traditional trade (in Spain it only means 5%), its growth is undeniable when looking at the economic data.
According to Statista data, ecommerce in Spain is expected to end the year with an increase in revenues of more than 11%, which translates into more than 15,500 million euros. The United Kingdom, France and Germany are the countries that are at the forefront of the sector. This is included in the report on B2C e-commerce.
Among the main factors that have enhanced this growth are the expansion of global markets outside the West (especially China), the increase in internet access and the penetration of smartphones, the emerging middle classes with sufficient savings capacity and Innovative technologies to improve user experiences.
Customization of the experience
One of the main drivers of innovation in the retail industry is the demands of its customers. Although currently the productions are made in large quantities, aimed at a mass audience, in terms of the user experience personalization is sought: adapt to the likes of each individually.
In fact, there are studies that ensure that 73% of customers are fed up with being presented with content that does not arouse interest.
Machine learning is the technology that allows this personalization so demanded. Within the field of Artificial Intelligence, Machine Learning refers to the capacity of learning machines based on the information provided.
As an example of the use of Machine Learning in e-commerce, it is the application that Geographica developed for OneBookShelf, a major electronic commerce of digital entertainment content in the United States. This solution analyzes the purchases of users and offers recommendations tailored to their preferences and consumption habits.
Other technologies based on Artificial Intelligence will also offer us features unimaginable until now. An example of this is that, in the not so distant future, the cameras of a store will serve for something more than to maintain the security of it. The new facial recognition techniques will even allow the buyer to send online information about products so you have shown interest in the physical store.
This analysis of information not only helps to provide a more personalized experience, increasing sales, decreasing returns and maximizing customer satisfaction, but it also allows us to extract trends that at first sight are difficult to perceive. This allows acting in a preventive way thanks to its predictive capabilities. In addition, these Artificial Intelligence systems always provide totally impartial solutions, so that human error is reduced.
Sales and dynamic prices
Traditionally traders have set the prices of their products based on various strategies (supply – demand, production costs, type of buyer, time of year, etc.). When you want to modify the prices based on the quantity available in stock, some establishments have it really difficult, since they are such dimensions that they are not able to establish with security the quantity of product they have.
The Machine Learning opens a new perspective for the establishment of prices, even in real time and considering more factors than simply the available stock. For this there are:
- Peak pricing: prices tend to be higher during peak hours. It is very common its use in the transport industry, such as aviation.
- Surge pricing: the price increase takes place at times of greatest demand, such as a specific event; very present in VTC companies.
- Segmented price: the price of the same product or service varies according to the group to which the user belongs. It is usually used to encourage the purchase to a specific type of customer. An example is when discounts are made to people over 65 years.
- Price according to the time of purchase: the price changes according to the time left for the service to occur, also very common in airlines.
New search trends
Machine Learning can also help improve search results in electronic commerce. This is done using the personal preferences of the customer and their purchase history. Instead of using traditional search methods such as keyword matching, machine learning can generate personalized search results.
In addition, new techniques are being developed, such as voice search, which has become in a consolidated trend, especially thanks to mobile assistants such as Siri or devices increasingly present in homes, such as Amazon Echo or Google Home.
This study prepared by in 2017 established that a quarter of respondents had a voice-controlled device and one in five had used voice commands to make purchases. Seeing their popularity, retailers should place greater emphasis on searches with natural language and long-tail keywords.
Augmented reality is another of those technologies that have come to revolutionize the way we buy in electronic stores. One of its major drawbacks, particularly in the fashion sector, is that the customer cannot test the garments before buying them, which causes large losses in terms of returns.
There are already several brands, such as Gap or Sephora, that use applications to see how their clothes or makeup look and make the decision with more information, making it more unlikely that the customer will return the purchased product.
If we look with a greater perspective of the future, the search for images will gain more and more ground. Proof of this is the emergence of Pinterest Lens, through its algorithm is looking online for an object that we have focused through the camera of our mobile. Another example is Adobe, which together with researchers at the University of Berkeley have developed an image editing tool that can convert sketches into usable images for use in search engines. All this is very useful with products that are difficult to describe.
These types of applications are also practical to gather very useful information about users. According to its use, brands can find out information such as what colors or types of products they like most, discovering trends in advance.
Greater importance of logistics and distribution
Electronic commerce is inherently linked to the logistics and distribution sector, so if innovations and improvements occur in one, the other must respond accordingly to meet the demands of users and not cause a slowdown throughout the industry.
Traditionally routes were established taking into account variables such as the distance between starting point and delivery. This is not always effective, since there are a variety of factors that can affect the time it takes to distribute the products (traffic, works, streets cutting for the celebration of an event, type of transport vehicle used, hours of exit, etc.).
Currently the optimization of the distribution can be improved thanks to the Location Intelligence technologies, since they work with geolocated data in real time. Thanks to them, a greater number of reference data enters the equation to define which the best route is. They even allow you to rectify the routes instantly if there has been a noticeable change in them.
Geocoding is also a tool that can be used to improve the logistics and distribution of e-commerce products. This consists of labeling all the elements that participate in the process (products, customers, suppliers …) and identifying them with spatial references, such as coordinates, name of a place or address. These locations are then defined as a geographic entity that contains all the attributes that are convenient and can be represented on interactive maps.
E-commerce in the retail sector looks to the future to find greater automation of all its processes thanks to an enormous influence from Artificial Intelligence, so much that the term A-Commerce is already used to refer to online sales platforms automated.
Another trend in the medium term for electronic commerce will be the practice of Dropshipping. Through this technique, online merchants do not need to have a store for the products, since they acquire it from a third person. In this way, all expenses derived from stock management are reduced.
In short, the growth that e-commerce is currently experiencing in retail finds one of its main causes in the use of technology. Without these technological advances, one could not understand the popularity that these services have gained or the trust that their users have placed in them.