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The success of eCommerce businesses relies mainly on how well they meet consumer needs. Today, with new trends coming in every day, these preferences fluctuate quite often. Traditional marketing strategies aren’t sufficient to anticipate such changes in trends or consumer demands.
Instead, using predictive analytics and data to anticipate eCommerce trends enables businesses to optimize their operational and marketing strategies accordingly. Predictive analytics models in eCommerce help unlock insights on consumer trends, marketing dynamics, and more, driving enhanced business growth.
In this blog, we’ll explore the role predictive analytics plays in eCommerce, the ways it improves business performance, and why every eCommerce business should consider this data-driven approach.
Predictive analytics in eCommerce involves applying statistical models, machine learning, and data mining techniques to forecast future trends and customer needs. It enables businesses to understand customer behaviors, anticipate market trends, and make informed, data-backed decisions for leading the curve.
This level of advanced insight into upcoming trends and customer needs helps eCommerce businesses improve service quality, optimize resource allocation, and most importantly, deliver personalized customer experiences.
With hundreds of different factors affecting demand, using historical data to predict upcoming consumer demand is not exactly productive. You need a more proactive approach for forecasting customer needs in eCommerce.
Predictive analytics in eCommerce helps provide the accurate insights that eCommerce businesses need. Algorithms to analyze all accessible data like seasonality, customer behavior, browsing patterns, and external factors like social trends like ARIMA, Prophet, Regression etc are now commonly used.
For instance, IKEA, the well known furniture retail giant, has built its own AI tool, Demand Sensing that leverages about 200 different data sources for forecasting demand and planning its inventories. Not only have they increased the accuracy of eCommerce predictive models and trends – saving the business from overstocking or understocking but they have also increased customer satisfaction because of higher product availability, optimized rates by quickly encapsulating dynamic changes in supply chain and even reduced their carbon footprints.
Scaling eCommerce businesses face the constant challenge of allocating resources like staff, marketing budgets, and more correctly. Relying on instincts or past evidence isn’t sufficient to make accurate decision-making. Predictive analytics in eCommerce allows businesses to interpret and convert raw data into actionable insights for optimal resource allocation with the help of techniques like Gradient Boosting and Deep Neural Networks.
For instance, Walmart uses historical data – like past sales, online searches, and page views – and future-oriented data, such as macro weather patterns, macroeconomic trends, and local demographics, within its ML platform Element to create models like Neural Networks, RNNs, LSTMs to optimize resource allocation across all their operations – from market intelligence to channel performance. Their state-of-the-art data management system, along with automated facilities, department-ready freight, Next-Gen fulfillment centers, and last-mile delivery networks leverage their state of the art deep learning AI Models to deliver one of the most seamless and gratifying holiday shopping experiences.
If you’ve ever noticed eCommerce businesses recommending the exact products that you’ve been looking for, that’s predictive models and trend analysis in play. Predictive analytics in eCommerce helps in creating personalized shopping experiences based on customer data, including past purchases, browsing history, and even predicted interests. This bodes well for businesses since 76% of consumers are more likely to make a purchase when a brand offers personalized experiences.
Sephora is known for using data to anticipate eCommerce trends and deliver personalized recommendations with the help of advanced NLP models to its users. Through a simple pantone test Sephora assigns you a color IQ number for your skin tone and gives recommendations for its in-store and online products. This coupled with its famous virtual artist feature which allows shoppers to see designs and colors like lipsticks on their facial avatars before they buy the product and share it on social media making buying products easier and more fun.
While there are creative giants like Sephora leveraging AI, even smaller businesses are adopting predictive analytics in eCommerce. A large number of eCommerce startups today leverage user-profiles and metadata to help make personalized product upselling and cross-selling suggestions using recommendation engines.
For instance, if someone purchased a face scrub and face wash, the recommendation engine using underlying models like collaborative filtering would recommend complementary products (like a face scrub) that the user would likely buy by matching the current user’s purchase with other similar users. Other common techniques like associative rule mining and regression models are also widely used to identify the most likely product to purchase given order histories and current cart.
An overexposure to advertisements has left consumers desensitized to most marketing campaigns. However, ads that really speak to the audience and connect to their problems is still something that sticks out and brings a great ROAS (return on ad spend).
Predictive analytics in eCommerce enhances marketing effectiveness by using algorithms that identify customer segments and predict what type of marketing messages will resonate with each audience. While this is mostly done by clustering algorithms, there are a lot of interesting use cases that can also come up through other techniques.
Spotify, for example, analyzes users’ listening history, preferences, and behavioral patterns to create a very rich and dynamic dataset of preferences – language, lyrics, mood and emotional state, which it uses for hyper-targeted ads and customized playlists. The integration of emotional and mood data enables Spotify to recommend music that fits not only the genre or artist preferences but also the listener’s current emotional state. For instance, if a user is feeling stressed, Spotify may recommend calming playlists, while an energetic playlist might be suggested during workouts.
This kind of personalization uses reinforcement learning and drives greater user engagement, as listeners feel understood and more connected to the platform. By refining its recommendations with a deep understanding of language, emotions, and mood, through NLP techniques – Spotify enhances its user experience, boosting retention rates and keeping users coming back for a more personalized listening journey.
Predictive models forecast customer needs in eCommerce and recommend optimal times to launch campaigns, feature new products, and determine which customer segments are most likely to respond.
At RBM, we utilize AI/ML predictive analytics tools to streamline customer needs prediction for online stores, helping companies maximize their marketing returns by reaching the right audience, at the right time, with the right offering.
Trends in eCommerce come and go like rapid fire. Which is why it’s easy to miss out and be late to hop on them. Traditional marketing methods and strategies aren’t enough to catch these trends and capitalize on these trends, leading to missed opportunities.
Using data to anticipate eCommerce trends with predictive models can turn things in your favor. Techniques like anomaly detection and unsupervised clustering models help you spot trends before they’re trending. These algorithms utilize customer data, behavioral patterns, and market dynamics to forecast customer needs and determine potential points of interest.
Identifying trends early on allows eCommerce stores to stay ahead of competitors and amplify their popularity in the market.
The diagram below illustrates customer data inputs, predictive modeling processes, generated predictions, and personalized actions that enables ecommerce brands to serve their customers better.
Consumer preferences change every other day, and predictive analytics in eCommerce gives businesses the power to forecast and capitalize on upcoming demand. It focuses on analyzing market dynamics, predicting consumer behavior, and using the data to anticipate eCommerce trends before they take up so that instead of reacting to them once it’s too late, you can proactively be prepared for them.
At RBM Software, we utilize advanced, AI/ML-driven predictive analytics solutions to forecast customer needs in eCommerce businesses. Our team of 100+ data engineers and software developers integrate predictive analytics into your eCommerce platform, amplifying your capability to understand customer preferences and deliver personalized experiences. Our advanced solutions ensure you don’t just hop on trends—but lead them.
Book a free consultation with our experts to assess your current platforms and explore how our predictive analytics solutions can scale your business productivity.
