Do you actually need big data analytics for your e-commerce platform?

E-commerce platforms generate staggeringly large volumes of data. Unfortunately, most of this data goes unutilized. Back in the day, data needed several skilled data analysts to gather and process it. Nowadays, we have solutions that can be used to run high-speed data analytics on massive amounts of data. This encourages online store owners to take advantage of these solutions and incorporate big data analytics when making business decisions.

By 2022, annual revenue from the global big data and business analytics marketis expected to reach 274.3 billion U.S. dollars.

What is big data analytics?

In short, big data as a whole refers to large datasets that are stored and analyzed with the help of advanced tools to provide useful information to the business. This process of examining, evaluating, and identifying hidden data patterns from data is known as big data analytics

With this concept, it is possible for you to access key insights like market trends, customer preferences, etc. In order for big data to work, the solution must gather enough data to be able to identify patterns in it. This data is often sourced in the following ways- 

  • Content preferences
  • Different types of interactions with certain kinds of content or ads
  • Use of certain features in the applications
  • Search requests
  • Browsing activity
  • Online purchases

How can big data analytics help your e-commerce platform?

Solutions that incorporate big data can be used in online stores in various ways. They can help improve business processes in almost every step of the online shopping process. Some of the main applications of these solutions include: 

how big data analytics help your ecommerce platform
How can big data analytics help your e-commerce platform

Supply chain management: 

Big data can help you improve the efficiency of supply chain management greatly. With it, you can manage data for your products from when they leave the warehouses to when they reach your consumers. Such solutions can also be used in inventory management, transport optimization, and delivery pricing. 

Merchant/customer fraud detection: 

Predictive analytics algorithms can be used to prevent fraud or any type of malicious activity. Most fraudulent online activities occur with the help of automated mechanisms. These mechanisms work in patterns, which can easily be extracted from data. With big data, you can detect these patterns and filter out spam. Furthermore, these solutions can detect unlawful activities with malicious intentions in a span of seconds.

Merchant analytics: 

Merchants are a key aspect of any e-commerce business. E-commerce businesses can drastically improve their own sales by promoting the growth of their merchants. Many e-commerce platforms conduct in-depth analytics for merchants so that they can explore new markets and price their products appropriately.

Recommender systems:

Recommender engines are quite common in the e-commerce industry. If you have used platforms like Amazon or Flipkart, you would have likely seen a list of suggested products that have been curated based on what you have already shopped for. These products are categorized by recommender engines, which serve as a blueprint for consumers to navigate through your e-store.  Amazon’s product recommendation engine drives 35% of cumulative company revenue. Companies that have already chosen to adopt a predictive intelligence solution have reported a 40.38% influence in revenue after just 36 months post-adoption.

The mechanics behind these engines can be seen as follows: 

  • The system will observe and analyse the preferences and needs of your consumers 
  • Based on these preferences, matches will be made through web crawling or internal search tools
  • In cases of a match, the product will be categorised in the list of suggestions

The primary kinds of user preferences that have an impact on the curation process include:

  • Product ratings and user comments
  • Interaction with web content on different sites

By analyzing a user’s activity, you can provide them with content that they will most likely be interested in.

User experience analytics: 

These platforms need to adopt a customer-centric approach if they want to establish themselves as a front-runner in the industry. As a matter of fact, this is why ensuring a superior user experience is a priority for most e-commerce platforms. So, departments focusing on user experience will often be working on developing an appropriate architecture for websites. This entails product search, ranking of products in search results, best landing pages for a particular customer, etc.

Analysis of shopping patterns: With big data, you can get a deeper insight into your customers’ shopping behavior to predict patterns. Also, it helps to improve business and marketing strategies to a large extent. Popular brands, products that are most searched for, spikes in demands, shopping seasons and more can be understood through big data.

Effective customer service:

To succeed in an online business, you need to offer top-notch customer service.  Big data can help you serve your customers better by identifying and resolving any problems that they may have. Such solutions can also improve business processes so that customers can easily execute transactions online. 

Predict future operations plans: 

Get a better understanding of the overall operations of your business. Additionally, data analytics solutions help us manage inventory, supply chains, forecast demands, and improve pricing strategies. Thus incorporating these solutions makes you think proactively. And also allow you to make the best decisions for your business.

Ease of online payments: 

Paying for purchases online is much more convenient than giving cash on delivery. However, many consumers have reservations about making payments online. This means that online stores need to make sure that their payments are secure and safe for customers. Big data can easily detect fraud-related activities and any threats to its processes. It can streamline various payment options by bringing them all onto one centralized platform, thus making the payment process easy and convenient for customers.

Customer profiling:

Creating dedicated customer profiles is a good way to provide personalized experiences to your users. Big data can help with profile development in the following ways:

  • Defining and describing customers;
  • Recognizing distinct audience segments;
  • Calculating their possible courses of actions in certain scenarios.

In other words, every piece of information that the user produces keeps some insight that helps to understand what kind of product or content he might be interested in.

When your consumers browse the internet, they inadvertently produce large volumes of data. Big data solutions help you gauge this data and categorize content based on what each customer is interested in. Moreover, by analyzing this data, information on who your customers are, which segments to focus on, and what kind of content should be targeted to which audience segments are retrieved.

Price optimization:

If you’re looking to stay competitive in a sea of rivals, then it is essential to selling your products at prices that are better than anyone else’s in the market. This is where big data comes in-you can easily monitor and analyze the activity of your competitors. With this information, it becomes easier to adjust the rates of your products accordingly. 

Most solutions follow these steps while determining the best prices: 

  • Crawler tool that checks the prices on the competitor’s marketplaces;
  • Price comparison tool which includes additional fees such as shipping and taxes;
  • Price adjustment tool that automatically changes the cost of a particular product

Personalized experience: 

Providing a personalized experience to your consumers is extremely important. This will make them feel like valued customers, which will promote brand loyalty and buyer trust. Big data solutions can help you provide a much more personalized experience to your customers. Most importantly They can execute tasks like-

  • Send emails with customized discounts and special offers to re-engage users.
  • Give personalized shopping recommendations.
  • Develop flexible or dynamic pricing, which relies on external factors such as consumer demand and competitors’ pricing. In other words, a 1% increase in price translates to 8.7% increase in profits. Walmart uses online shopping big data to determine patterns that point to higher profits. For example, a product sold on its own may not make as much profit compared to pairing it with something else.
  • Creating relevant Ad messages based on customers’ interests and the type of Ads they want to see. So you may already be using some form of big data by presenting targeted ads on your social media networks.

Should you incorporate big data analytics in your online store?

Big data can help e-commerce platforms implement high-performance data mining, text mining, predictive analytics, and various other approaches to glean insights that can lead to better decision-making. With data analytics solutions, an online store can improve existing product lines, develop the kind of products that their users actually want, improve customer retention, and gain an edge over their competition. 

Businesses that incorporate big data tend to reap excellent business benefits. Firstly, it helps to create better marketing campaigns, and explore new opportunities for enhanced revenue generation. Secondly, it helps to improve customer delivery and make operations more efficient. Thirdly, data analytics solutions can help organizations make much better business decisions. Thus making it a safe solution that is worth the effort. Above all, this makes your customers happy and will keep you one step ahead of your competitor.

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