Data analytics is now essential to ecommerce success. Targeting the right audience through advertising platforms is highly necessary to boost online sales as customers only want to look at relevant products or items they need.

Artificial intelligence (AI), with the assistance of machine learning (ML), helps determine the target audience based on customer preferences and past browsing data, which helps bring potential buyers and score inbound sales. 

Similarly, suggesting the right products to customers on a platform also helps bring in more sales. Ecommerce services like Amazon and Alibaba use data science to power predictive recommendations which help in suggesting various products that users will like. 

For advertising products on platforms like Facebook and Google that act as mediums through which ecommerce companies can run ads, there is heavy dependency on data science to show relevant ads to potential buyers. For instance, when users search for specific products on Google, it shows relevant ads for the same product from different companies.

The accuracy of AI in determining potential buyers for specific products goes a long way in suggesting to them the product they would need immediately, resulting in immediate predicted sales. Without this, the chances of buyers stumbling upon the product they would like and buy are relatively lower unless they are actively looking for a product.

Data Analytics in ecommerce

Data analytics powers predictive forecasting using various data sources, such as historical data on sales, economic shifts, customer behaviour, and searches. This empowers e-commerce companies by promoting relevant products to potential buyers. 

Machine learning (ML) and artificial intelligence (AI) make it possible to provide shoppers with predictions based on what they like even before deciding to look for a product or if they need something in particular.

ML and AI get this done by analysing the behavioural trends of customers and creating a relation between past purchases. Customer sentiment analysis plays a significant role in identifying future sales prospects and the target audience, enabling direct marketing tactics and sales promotions.

Data analytics plays a significant role in investigating trends and discovering patterns in customer behaviour and brand sentiments.

Analysts can use data science to analyse purchase patterns and develop strategies to increase sales and effectively stock the inventory. Businesses can further utilise data analytics to predict sales and demand, which helps companies make better decisions to advertise or stock up on specific products.

There are many ways in which data science is boosting sales in the e-commerce domain. These are: 

Recommendation Systems:

Data analytics powers recommendation systems that are entirely based on the past data of users alongside the heavy use of ML and AI to help e-commerce services give more relevant and accurate recommendations.

This works like a charm and seems almost to recommend products that users will always wish to buy or at least show interest in. This translates to increased sales by producing the right product in front of the right buyer.

Recommendation systems are personalized according to customers and modelled with the help of user information, such as products a user is buying and pages a user is clicking on. Amazon’s recommendation system and Amazon Personalize have helped improve sales; both are an integral part of Amazon’s armoury, which now controls 40% of total US e-commerce revenues.  Notably, according to Barilliance, product recommendations account for up to 31% of ecommerce site revenues.

Customer Feedback Analysis:

Data science allows e-commerce companies to work on their shortcomings by collecting the relevant feedback for each product or service and then taking action based on collective analytics. Methods such as sentiment analysis and brand image analytics help companies understand what a customer or the target audience requires, increasing sales significantly.

Ecommerce giants and startups use NLP or natural language processing, text analysis, text analytics, and computational linguistics to power analytics of this kind.

Inventory Management:

Data analytics allows established ecommerce companies and startups to manage their inventory more effectively. This also indirectly helps them not waste capital on unpopular products which are not selling well and have no need for restocking. Since ecommerce companies work with tons of customers and thousands of products daily, advanced data science is highly necessary to conduct accurate inventory management and predictive forecasting for future requirements.

Room and Board used predictive analysis to get around 2900% return on investment.

Customer Experience and Customer Service:

Data science helps ease and improve customer experience by automating a lot of functionalities and making regular things hassle-free with the help of feedback and analytics. These implementations can range from automated experiences to easier navigation.

As per reports, around 80% of customers think that customer experience is also important and helps them come back to a specific site. In addition, determining preferences via social media can also improve customer service, and recommendations as many millennials and Gen Z have discovered products via social media platforms like Instagram.

ML is especially useful in customer service as it leads to better IVR and chatbot services which help solve customer issues more effectively with time.

Tools like Sentiment Analysis are quite good at understanding customer experience and helping companies retain them. 

Does data analytics help ecommerce companies advertise better?

It helps in advertising analytics as well. Also, advertising platforms run on AI and ML, using data analytics to perform various functions like audience targeting through behaviour and other factors, such as demographics. Notably, it allows you to run relevant advertising campaigns. 

How is machine learning used in online sales?

Machine learning promotes online sales in various ways, from virtual assistants to personalised recommendation engines. For example, ML helps convert more browsers or prospects into immediate buyers with the help of customised recommendations increasing the chances of conversion. Also, it helps in gathering new customers based on historical data. 

Hyper-personalisation comes of age.

One of the most sophisticated opportunities in ecommerce marketing for the application of data analytics is in hyper-personalisation software of which the distinctions are significant. This identifies exactly what each consumer is most likely to buy next, and often, when, and then emails details of those items to the consumer at the ideal time.

By its very nature, this is wholly autonomous software, a purpose-driven marketing app, that requires zero staff whatever at any time. In hard-pressed times, this means you can let your entire email team go, and yet are reassured that each consumer continues to receive personalised emails perpetually updating with every visit and purchase. The most unappreciated benefit of AI hyper-personalisation is that almost entirely obliterates your RoR rate.

If you offer promotions and incentives through your marketing and those products sound brilliant at first glance to your consumers (if you are good at your job), you still know you will have a hike in your rate of returns. When you include hyper-personalisation into your customer care and marketing, it identifies which products each individual is most likely to buy, and keep, next.