March 12, 2025
With solid experience in predictive analytics consulting services, Itransition helps retail companies build scalable, future-proof analytics solutions that drive innovation and strengthen their competitive edge.
projected global retail analytics market size by 2034
more revenue is generated by companies that excel at personalization
The adoption of predictive analytics enables retailers to overcome key business challenges, from personalizing customer interactions to balancing customer demand with inventory, while securing a competitive advantage.
sales increase when making data-driven decisions around stock and store optimization
Streamlined supply chain management leads to quicker delivery times, reduced costs associated with overstocking or under-stocking, and fewer disruptions.
of leading retailers consider customer loyalty and LTV as top priorities
Predictive analytics helps companies increase customer engagement and retention rates and convert one-time buyers into lifelong customers.
increase in product sales for retailers that use data-driven targeting
As a result, retailers can maximize the effectiveness of ads and promotions by improved targeting, resulting in higher ROI from their strategies.
immediate margin performance improvement due to data-driven pricing management
Data-driven dynamic pricing strategies respond to the ever-changing market trends, competitor prices, seasonality, and consumer habits and preferences, maximizing revenues.
sales increase for fashion companies that use data-powered product offering
Improved awareness leads to smarter marketing and sales decisions, resulting in higher sales and profits.
acceptance rate of artificial intelligence chatbots in retail (higher than any other industry)
By leveraging AI-powered chatbots, and automated marketing campaigns, retailers can improve the efficiency of their operations and reduce manual labor costs.
Predictive analytics solutions, when used alongside computer vision software, assist retail companies in optimizing their merchandising strategies. By analyzing shoppers' in-store movement, historical data on past purchases, and seasonal trends, merchandisers can identify the optimal placement for each product and determine which products should be placed together, enhancing the overall shopping experience and driving sales.
of retailers in Deloitte's study scored themselves as mature or leading in product recommendations
Deloitte
Advanced analytics-powered merchandising helps manage product layout to effectively recommend related products and increase the average check size.
As a result, AI-driven data analytics helps retailers retain existing customers and save on customer acquisition costs.
Itransition offers comprehensive predictive analytics services that help retailers adopt advanced analytics solutions and navigate a highly volatile market with confidence.
Many retail industry leaders use predictive analytics to improve operational efficiency and customer experience. Some notable examples include:
Farfetch is one of the leading global platforms for the fashion industry, with a presence in over 190 countries worldwide. However, an internal audit revealed that Farfetch’s contact center faced many service and quality issues due to rapid international expansion. As the company opened offices and contact centers across the globe, new contact center agents were overwhelmed with information in the first 30 days on the job. To tackle this issue, the company needed an efficient solution that would cater to the needs of both veteran employees and newbies.
increase in customer satisfaction
decrease in resolution times
Belk is a California-based fashion retailer that operates nearly 300 retail stores across the US. Belk’s store managers were struggling to understand why some physical stores significantly outperformed others. Basing their sales forecasts on historical buying patterns rather than future trend analysis wasn’t effective. Realizing that a comprehensive data analytics solution is essential to solving such issues, Belk invested $130 million in a technological transformation. Predictive analytics-powered sales forecasting was Belk’s top priority, so the company partnered with antuit.ai to develop a custom end-to-end demand forecasting platform. Antuit’s bespoke platform incorporated AI-enabled analysis of seasonality, promotions, events, and many other factors to produce accurate forecasts. As a result, each store’s manager now has a much deeper insight into inventory management, which drastically increases their chances of accurately predicting product demand.
DICK’S is one of the largest sports goods retail chains with over 150 million customers across 850 US stores. With such a massive number of customers, the ecommerce program manager at DICK’S realized that the only way to deliver personalization at scale is big data analytics. Therefore, DICK’S turned to Adobe to provide customers with tailored experiences across every channel. For example, the Adobe Customer Journey Analytics platform consolidates all the data in one place, allowing DICK’S to determine how exactly certain customer activities at different touchpoints impact their purchase decisions. With the help of Adobe Customer Journey Analytics and a stack of other Adobe tools, DICK’S can quickly decipher customer intent based on myriad factors, including customer interests, location-relevant events, and past purchases, to enable real-time personalization.
We know within milliseconds if someone is browsing a particular brand’s footwear on the website, that they are an athlete who would benefit from engaging more with that brand while they’re online.
more homepage visitors get a personalized experience
more spent by visitors receiving a personalized experience than those who do not
Skullcandy is a US-based company that produces consumer-grade audio equipment, including headphones, earbuds, and speakers. The company’s product development team required a more detailed understanding of their customers’ needs and sentiments about existing products to come up with better-performing new products. By feeding Sisense’s bespoke predictive analytics engine with historical warranty costs, claims, product attributes, and attributes of potential new products, Skullcandy developed a reliable predictive data model to detect what impacts the warranty costs of a new product before it hits the market. On top of that, Sisense helped Skullcandy to leverage its BI platform, custom NLP engine, and Amazon Comprehend to granularly understand customer sentiment. Currently, Skullcandy can seamlessly correlate positive and negative sentiments to a particular product performance, which yields compelling insights for future product development.
Here at Skullcandy, we’re happy to report that “dropping in” to the predictive and sentiment analytics game was worth the initial uncertainty.
Here’s how you can start leveraging predictive analytics for your retail business:
Problem definition
Data analysis
Design
Implementation
Deployment
Support & maintenance
Some of the most common challenges associated with the implementation of predictive analytics in retail include the following:
Predictive analytics has become essential for retail companies looking to strengthen their competitive edge, improve customer experience, and increase efficiency. By leveraging advanced data-driven technologies such as machine learning and artificial intelligence, retailers can gain actionable insights and unlock new market opportunities. With over two decades of expertise in cloud technologies and advanced data analytics solutions, Itransition provides full-cycle predictive analytics services tailored to the retail sector’s needs and specifics. Start your journey today and join the leaders in the industry.
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