Cloud Infrastructure: Why eCommerce Companies Should Prioritize It
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As online commerce or e-commerce continues to evolve rapidly, delivering personalized experiences is no longer optional but a competitive necessity. AI and big data are essential for enabling real-time personalization that increases revenue and improves consumer experiences. According to Statista 2023, 9 of 10 businesses surveyed employed AI for personalization to grow their business.
According to McKinsey, 71% of consumers expect brands to deliver personalized interactions, whereas 76% get frustrated when there is no personalization. Research also shows that users engaged with a product had a 70% higher conversion rate during that session. Furthermore, the companies delivering personalization to consumers generate 40% more revenue than without it.
Personalization in e-commerce refers to tailoring user experiences based on individual behaviors, likes, and data patterns. Nowadays, customers expect to receive tailored, comprehensive solutions, and those businesses that do not provide them stand the chance of losing market share. According to Epsilon’s survey, about 80% of customers are more likely to make a purchase when brands utilize personalized services.
Key benefits of AI-driven personalization in e-commerce include:
Personalization in e-commerce refers to tailoring user experiences based on individual behaviors, likes, and data patterns. Nowadays, customers expect to receive tailored, comprehensive solutions, and those businesses that do not provide them stand the chance of losing market share. According to Epsilon’s survey, about 80% of customers are more likely to make a purchase when brands utilize personalized services.
Key benefits of AI-driven personalization in e-commerce include:
Achieving advanced personalization employs powerful data architecture that is capable of real-time information processing and analysis at great scope.
Individually, every data point has little value and offers less personalization. But integrated, they build a multidimensional customer profile, which gives effective personalization.
The integration of AI and Big Data makes it possible to aggregate and extract meaningful insights from voluminous datasets collected from multiple sources, including:
Big data platforms allow for the aggregation of vast amounts of structured and unstructured data from multiple sources, while the AI algorithms scan and analyze patterns within the datasets to understand what actions the user is likely to take in the future and create customized ads and suggestions for those actions.
With the help of natural language processing (NLP) and deep learning, businesses can identify user intent and provide real-time insights.
Legacy systems such as Postgres and MySQL are incapable of processing information in real time due to their design. Systems leveraging AI and NoSQL databases (e.g., MongoDB) and advanced searching tools (e.g., Elasticsearch) do offer real-time personalization on massive scales.
RBM Software’s micro-service solution enables e-commerce clients to provide users with personalized content in real time. This is critical for businesses with enormous transaction volumes and those that need to update inventories instantly.
Personalization is achieved by AI recommendation engines using collaborative filtering and content-based filtering approaches to propose to each client a unique set of products. According to McKinsey, companies like Amazon generate 35% of their revenue from AI personalized recommendations.
RBM Software combines machine learning and generative AI to personalize product searches and recommend them in ways that encourage the ever-growing customer segments. By merging analytics derived from Big Data with AI, companies can improve their recommendation systems based on behavioral and historical data across the users’ touchpoints.
Pricing models that adapt to changing market conditions, competitors’ prices, and consumer demand are now made possible because of AI. It makes sure that both profits and competition are managed with one click. According to Deloitte, revenue in the AI-driven industry can surge anywhere between 10% and 20%.
RBM Software’s e-commerce solutions allow businesses to rest easy with their budgets while employing smart pricing algorithms that are guaranteed to increase customer satisfaction with the help of advanced analytics.
AI enhances marketing through predictive analytics and segmentation. Personalized email campaigns, targeted promotions, and individualized ads based on customer behavior deliver higher engagement rates.
RBM Software helped a global client from the clothing industry boost their multilingual campaign performance with the increased customer incentive to perform international targeted promotion, leading to faster appreciation from the market.
Amazon has long been the gold standard for AI-driven personalization. According to McKinsey, their recommendation engine processes over 35% of total e-commerce sales through personalized product suggestions as “RECOMMENDED FOR YOU.”.
They use machine learning algorithms to analyze browsing and purchase history, a real-time recommendation engine, and predictive product recommendations. They saw a 56% increase in customer retention through AI personalization.
While primarily a streaming platform, Netflix’s personalization strategies offer crucial insights for e-commerce. Around 75% of content watched on Netflix comes from the personalized recommendation system, which means the AI algorithms guide the viewers toward their next favorite movie or TV show. Netflix’s personalization approach consists of:
Netflix has more than 1300 “recommendation clusters,” which are built using viewers’ preferences through personalization. In 2024, Netflix generated $39 billion in revenue, which is a 15.7% increase year-on-year.
Alibaba uses AI to personalize product recommendations across its e-commerce platforms, Tmall and Taobao. According to a 2021 case study published by Alibaba, the use of AI recommendation systems resulted in a 20% increase in sales during the last quarter of 2020 on their e-commerce platforms.
Sephora, a prominent global beauty retailer, adeptly leverages artificial intelligence and augmented reality to reimagine customer experience. The commitment of Sephora to personalization is illustrated by the leading use of AI technology through Virtual Artist, launched in 2016, with the feature of trying products virtually before actually taking them, hence making online purchases more appealing.
That initiative increased Sephora’s e-commerce net sales significantly, surging from $580 million in 2016 to over $3 billion in 2022, more than four times as high in six years.
AI algorithms can analyze millions of data points to make product recommendations that dramatically outperform traditional “customers also bought” suggestions. These recommendation systems are also user-specific as they learn and adapt with each user interaction in a bid to become more accurate.
Large Language Models (LLMs) can generate comprehensive customer categories by analyzing data from past purchases. By inputting the model with past transaction data, you can create subgroups according to frequency, preferences, purchase patterns, etc.
AI-powered pricing systems can now adjust prices in real time based on factors like competitors’ prices, demand patterns, and the unique price each customer is willing to pay.
A McKinsey study found that retailers implementing AI-driven dynamic pricing saw revenue increases of 5-10% within the first year.
Although search remains the most important function of an e-commerce site, many retailers still offer one-size-fits-all search experiences. AI-enhanced search can:
Delivering complex, personalized search experiences usually brings 15-25% additional sales and revenue from search terms.
Customers now interact across multiple brand touchpoints before they spend money, so they have the potential to be more invested. With AI and big data, it’s easier to provide the same tailored service across these:
This ensures a connected experience for customers, reinforcing the brand at every interaction point.
Maintenance of AI models is essential after deployment to guarantee continuous efficacy and dependability. Over time, models can experience “model drift,” where their performance degrades due to changes in data patterns.
To solve this problem, retraining and continuous monitoring are crucial. Ignoring maintenance can reduce accuracy and undermine confidence in AI systems.
Enterprise retailers typically have poorly integrated information on several disparate systems:
Without a unified data strategy, personalization efforts remain limited by incomplete customer views.
AI systems are prone to magnifying existing biases in the training data. A recommendation system, for example, could encourage users to accept predefined notions of gender roles, or certain customers may be inadvertently discriminated against if appropriate attention is not paid to system design and governance algorithms.
Implementing ethical AI practices requires both technical expertise and ongoing governance.
Personalization inherently involves collecting and analyzing consumer data, which presents significant privacy issues. Companies have an obligation to their consumers to fully comply with current data protection laws and to be transparent about how they use consumer data.
There is an escalating transformation in the global regulatory environment, with GDPR in Europe and the CCPA in California, and so much similar legislation cropping up elsewhere.
These regulations:
Advanced personalization must operate within these constraints while still delivering value.
The key to sustainable personalization is maintaining the logical balance between how much to deliver customization and concern about privacy:
Implementing AI and big data can require a lot of resources. An effective solution for this is offshore development, which offers specialized knowledge at a significantly lower cost without sacrificing quality.
RBM Software’s offshore model allows e-commerce businesses to:
The cutting-edge personalization will be the result of customer data platform (CDP) adoption and the use of advanced AI models, such as generative AI (GenAI). Businesses must make the switch to:
According to Gartner, e-commerce AI utilization will touch 80% by 2025. This puts all customer interactions under AI’s domain. Brands that are first to implement personalized marketing stand to gain the compounded returns of the next e-commerce boom.
RBM Software delivers cutting-edge AI and big data solutions tailored for modern e-commerce challenges. Our expertise includes:
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