Have you ever wished to know what products or services customers are most likely to buy from your online store? Predictive analytics is the magic wand to fulfill this wish! Relying on the most evolved analytics capabilities, sellers can analyze customer behavior, understand the maximum price for their products, manage inventory, and make well-informed decisions. In this post, we will discuss how predictive analytics in eCommerce can help boost online business revenue.
With rapid advancements in emerging technologies and changing customer expectations, eCommerce is undergoing remarkable transformations. The industry itself is taking steps towards data-driven operations. Merchants are increasingly focused on personalization, leveraging intelligent and next-gen technologies such as predictive analytics.
Staying committed to maintaining our trustworthiness as a custom eCommerce development company, we have helped dozens of leading eCommerce businesses and enterprises to scale up with predictive analytics solutions. For instance, we have helped a leading department-store chain boost sales by implementing predictive analytics in eCommerce. Personalized emails and product recommendations helped the business increase sales by 5% within three months. You, too, can use predictive analytics and take your eCommerce operations to a whole new level. Keep reading on to find out how.
Role of Predictive Analytics in Ecommerce
As an eCommerce business owner, you collect tons of data every day. But you need to utilize the data in an effective way to generate better results for your business. Predictive analytics is a rich set of cutting-edge technologies and approaches that allow you to work with data and make informed decisions. An analytics-driven solution lets you make predictions and dig out hidden patterns. For instance, an analytics solution may suggest customers add specific products to their shopping carts. With the help of data modeling, data mining, AI, and deep-learning algorithms, you can predict almost anything on your eCommerce store.
Data analytics is primarily used for marketing campaigns to make long-term predictions about product demands or analyze consumer behavior. eCommerce operators are increasingly using analytics to capitalize on valuable customer data comprising product purchases, shopping habits, location, and much more.
Utilizing all these crucial pieces of information, online retailers and merchants can predict customers’ shopping behavior, future activities, trends, and the revenue a store can make in a month. They can monitor customer behavior patterns and current market trends to predict changes and help business decision-makers make real-time and data-driven decisions.
A specialized custom eCommerce website development company, like OrangeMantra, encourages every online merchant to integrate predictive analytics solutions into their eCommerce stores to stay competitive. If you’re looking for custom eCommerce development with the advantage of predictive analytics, you can get in touch with our experts today!
Now, let’s have a look at some of the most prominent predictive analytics use cases in eCommerce.
Use Cases of Predictive Analytics in Ecommerce
Product Recommendations
Product recommendations are generally based on the purchase history of a customer. Amazon makes product recommendations based on the customer’s previous behavior, for example, the browsing and purchase history. Recommendations have, in fact, helped the eCommerce giant generate about 35% of its revenue.
By leveraging predictive analysis in eCommerce, online stores can offer relevant product recommendations to their customers.
Furthermore, an intelligent product recommendation system is exactly what made Netflix an unbeatable streaming service. Netflix’s software developers created intelligent algorithms that shifted users away from high-demand blockbusters to less popular titles, including “Bird Box” and “House of Cards.” A whopping 45,037,125 subscribers watched Bird Box within the first week after its release.
Price Setup
Predictive analysis also enables companies to switch from fixed pricing to dynamic pricing. For example, Airbnb fixes its price based on the analysis of different patterns such as the upcoming events, seasons, days of the week, holidays, etc. Based on the information, the Airbnb application suggests landlords fix the rental price of their apartments. The same goes for Uber, Ola, or other taxi aggregators. The pricing is based on the weather, traffic, and other factors.
When it comes to predictive analytics in eCommerce, retailers use ML algorithms to form prices and start selling more as these prices become suitable.
Ecommerce Sales Boost
Custom eCommerce development, combined with predictive analysis, can help you boost your eCommerce sales. You can assess the quality of each specific ad campaign and eliminate ineffective ones that drain out money unnecessarily. This helps you focus only on profitable ad campaigns.
Predictive analytics allows retailers to know individual users and subgroups and offer the most relevant products to them. That’s when customer segmentation prompts in. Also, it helps retail chains engage only with buyers who are interested in their products.
Logistics & Inventory Management
Predictive analytics in eCommerce is beneficial for both physical and online stores. For example, by checking videos from the CCTV cameras in your store, you can identify how customers walk through your store, what products they are interested in but are not in sufficient amounts to buy, what discounts attract them, etc. Based on this information, it becomes much easier to tweak the marketing strategy to achieve better outcomes.
One striking example of this is the Amazon Go chain that works without a human workforce. The system itself checks a customer’s shopping cart, the missing products on the store shelves, etc. Analytics help in estimating the need to restock, giving an accurate answer of what goods need to be reordered and what’s in surplus amount.
And it is not about eCommerce operators alone. If you are involved in custom eCommerce development, you need to make sure that you keep pace with the latest eCommerce trends to gain a competitive edge in the market. Integrating predictive analytics in eCommerce helps retailers offer personalized experiences to customers and meet the expectations of modern shoppers.
After exploring these use cases, let’s now glance over some analytics-related trends in the eCommerce market.
Case Studies of Predictive Analytics
Amazon Integrated Predictive Analytics for Personalized Product Recommendations
Amazon is a leading eCommerce brand serving both buyer and seller across the globe. They are known for their personalized eCommerce experiences by implementing predictive analytics. By adding Big Data Predictive Analytics Services, the presence of recommendation engine monitor data, including purchase history, browsing behavior, and demographic information for right product recommendations.
Technology Solution
- Purchase History Analysis: To serve customers with right products, predictive analytics algorithms monitor old records to identify patterns and preferences. By knowing customers product history, Amazon can recommend similar or complementary products.
- Browsing Behavior Insights: Next, comes the tracking of customers’ browsing behavior. This starts with products they’ve accessed, or added to cart. By analyzing browsing behavior using Data Analytics Services, target products can be recommended for engagement and sales.
Revenue Impact
The personalized recommendation helped Amazon to grow their revenue by bringing personalized shopping experience. By leveraging advanced Big Data Predictive Analytics Services, upsell and cross-sell products has seen a major surge, increasing average order value and revenue. As per reports, personalized recommendations has a major role in Amazon’s total revenue, highlighting how positively predictive analytics impacted eCommerce success.
Netflix Implemented Predictive Analytics for Personalized Content Recommendations
Netflix a leader in content streaming industry is known across the globe to deliver personalized content recommendations. Subscribers love exploring Netflix because of personal content recommendation, significantly boosting engagement and retention rates. By leveraging advanced digital transformation services, Netflix analyzes a large data sets comprising viewing history, user preferences, and interactions.
Technology Solution
- Viewing History Analysis: The predictive analytics models analyze viewing history data to keep a check on the individual preferences. By knowing users interest, watch list, and skip, insights can be derived for content preferences, which suggest future recommendations.
- User Preferences and Ratings: Another method that machine learning development company suggested to Netflix is consideration of user ratings and preferences for better recommendations. Having prior information about user rating, Netflix identified patterns and suggest content as per viewer taste.
Revenue Impact
Personalized content recommendations helps to drive higher engagement and customer retention. Both are important parameter for the subscription-based business model. Knowing and serving users with content that meets their preferences, Netflix increases the likelihood of users identifying new content. This ended into extended viewing times and higher satisfaction rate. The effectiveness of predictive analytics in maintaining and growing overall subscriber base underscores the value of investing in AI-driven Data Science Consulting Company and advanced analytics technologies.
Steps to Implement Predictive Analytics in Ecommerce
- Define Clear Business Objectives: Knowing specific goals and objectives for the predictive analytics establishment, including increasing sales, improving customer retention, or optimizing overall pricing strategies.
- Invest in the Right Tools and Technologies: Identify right predictive analytics tools and platforms to meet your business needs and goals. Look for factors like scalability, flexibility, and smooth integration process.
- Build a Strong Data Infrastructure: Invest in robust data infrastructure and architecture to support predictive analytics initiatives. Ensure data accessibility, quality, and security to drive actionable insights.
- Train Employees on Data Literacy and Analytics Tools: Giving training and resources to the employees to enhance overall data literacy and analytical skills. Perform cross-functional collaboration and knowledge sharing to refine the outcome of predictive analytics.
- Continuously Monitor and Refine Predictive Models: Being an iterative process, predictive analytics requires ongoing monitoring and refinement. Continuously keep a check on the model performance, include feedback, and update algorithms to refine accuracy rate and effectiveness over time.
Latest Ecommerce Market Trends
It’s a fact that modern customers expect personalized shopping experiences. So, if you’re opting for custom eCommerce development, you must make sure that your store offers a personalized shopping experience.
- 71% of customers get annoyed with an impersonal shopping experience. (Segment)
- 80% of customers prefer to purchase from stores that offer personalized shopping experiences. (Epsilon)
- 83% of customers willingly shared personal data for a more customized shopping experience. (Accenture)
Online retailers are now prioritizing personalization and have reported an increase in business revenue.
- 9% of eCommerce businesses are investing in personalization tools, which exceeds other sectors. (SmarterHQ)
- Businesses implementing advanced personalization have reported receiving a return of $20 for every $1 spent. (Clickz)
- 88% of US marketers have witnessed significant improvements because of personalization, with over half of them reporting to gain a 10% increase. (Instapage)
Considering all these eCommerce trends, we see that online retailers use AI and ML-based technologies to automate analytics, study customers, and enhance customer experiences. Predictive analytics is one of the leading intelligent automation technologies used for this purpose.
Final Note
Predictive analytics development is a complex and long-term process that demands expertise in data science and analytics. Professionals that work with prediction models require in-depth knowledge and expertise in artificial intelligence, machine learning, and big data. In most cases, it’s a custom eCommerce development company that handles the custom eCommerce development. Most of these tech requirements cost considerable money.
However, it does not mean small and medium enterprises can afford predictive analytics. SMEs and startups can either deploy ready-made external solutions or outsource the predictive analytics project to another country. If you are looking to implement predictive analytics in your business, reach out to us.
FAQs
Q.1. How does Amazon use predictive analytics?
Ans. Amazon uses predictive analytics to increase eCommerce sales and profits, thus increasing delivery times and overall costs. It uses to predict the products customers are likely to purchase, where they can purchase them, and where the goods might be needed.
Q.2. What is predictive commerce?
Ans. Predictive analytics provides retailers with an in-depth understanding of customer shopping habits and preferences. As every customer is unique and their shopping behavior differs based on individual tastes and preferences, predictive analytics helps companies to study different variable elements in a customer’s behavior.
Q.3. Do you provide post-development support and maintenance?
Ans. Yes, we provide extended support and maintenance services for all the solutions we deliver to our clients; however, they are chargeable.
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