Mobile commerce (m-commerce) is part of the broad spectrum of digital commerce covering sale and purchase of products as well as services. The use of mobile and other handheld devices has already reached greater heights and moving further as it gives people independence to browse and shop from any place at any time. Advancement of technology, ubiquity, flexibility and mobility are the essential features of mobile commerce that has led to an evolution in consumer behavior thus benefiting consumer behavior.
Think of industries like healthcare, real estate, pharmacy, car rentals, hospitality and a host of other services other than retail businesses that have released mobile commerce platforms to keep up with the buying boom. In the process competition has become stiff and more and more companies are employing data analysis and interpretation of e-commerce to target potential customers. Analytics help digital commerce players get an accurate prediction of customer product demands helping in them manage their inventory and coordinate sales. This apart, it also plays a huge role in augmenting user experience while browsing and looking for the right product/service.
The entire process is carried out with the help of complex machine learning and deep learning components. These keep a track record of customers’ online habits and analyze patterns to suggest products/services based on them. This is the reason why each time you look up any mobile app for a product/service, your social media account is sure to get flooded with promoted ads of the same category. Also, while browsing, the app is likely to give you suggestions on similar products/services that the platform has to offer.
Such suggestions are made by recommendation engines that also pop up discounts against items that customers are browsing. This is the most lucrative part of the process as customers are most likely to fall prey to a discounted item of their choice. The entire process involves a lot of data filtering and analysis by machine learning algorithms that give their feed to the recommendation engines integrated with the mobile app.
A data analytics tool that has been in use by retailers for some time now, the concept is based on customer buying behavior. If a customer buys a particular item, it is most likely that she/he is likely to group in a couple of related products with it. Online retail customer analysis shows that customers are most likely to carry out impulsive shopping and market basket analysis predicts such behavior and makes buying suggestions based on them. Much like search recommendations, market basket analysis is based on machine learning and deep learning algorithms.
Analytics used for trading is related to strategies meant to boost sales and promote products. Specific channels are targeted by the data analytical tools whose algorithms go through data sets and pick up insights about customer habits keeping in mind trends, relevance and seasonality. Historical data is analyzed to form predictive patterns. The algorithms collect, classify and group data about customer preferences, recent buying patterns and related behavioral inputs. Interdependencies between customers’ characteristics and preferences are also analyzed and suggestions given to them on relevant channels where they frequent.
Selling the product/service to the customer at the right price is crucial for both the buyer and the seller. Factors involves here are costs, profit margins, customer affordability and competitors’ pricing. All of these factors are well-calculated by the machine learning algorithms and come up with optimal pricing tags. This part of data analytics is extremely important to all retailers and service providers helping them stay in business at parity with the rest of the industry and also gain a competitive edge.
Retail inventory management will always have to be well-maintained for mobile commerce to go on smoothly. Managing sudden spike in demand will totally depend on effective inventory management involving supply chain analysis and stock taking. Use of powerful machine learning algorithms analyze data in great details and detect patterns to correlate with current purchase and those made in the past. The analysis them helps come up with strategies to help increase sales while maintaining regular stock along with timely delivery.
The process uses machine learning algorithms that analyze customer reviews and their sentiments towards a brand or a product. The feedback is crucial to improving product and subsequent business. The data is mostly extracted out of social media which is the platform where customers most readily share their sentiments. Positive or negative words are picked up by these algorithm tools and saved for further action by the company. What was earlier carried out manually is done by the data analytic tools integrated with mobile commerce apps!
This is a location based service that allows retailers and service providers offer mobile users products and services that are more popular in the precise region they are in. All of this is done with the help of GPS and specialized RFIDs software to provide a high-quality service to mobile commerce customers. The process ensures that personalized messages are sent target mobile customers sending them relevant offers time to time.
Augmented reality associate the data taken from the physical world to those that are created with the help of digital tools. Incorporating Augmented Reality during the purchasing process enables visualization and animation of the product via mobile devices of consumer. This results in consumers spending a significant amount of time interacting with the product on their mobile devices.
Data analytics is fast encompassing all aspects of human life and living trying to make it better and easier. Coming to the area of mobile commerce, it is playing a crucial role for sellers and service providers in identifying potential customers and getting their attention in a hyper-personalized way. Machine learning and deep learning algorithms identify a range of data from different relevant platforms, store and analyze them comparing with past data to give predictive models that distinct aid in improving sales and turnover.