Predictive analytics in retail: use cases, examples & adoption guide

August 22, 2023

Home
Predictive analytics
Predictive analytics in retail

Head of AI/ML Center of Excellence

Due to its ability to anticipate consumer behavior and timely respond to ever-changing market conditions, predictive analytics has emerged as an invaluable tool for improving retail operations. Today, retailers are increasingly applying predictive analytics solutions to conquer the market.

Predictive analytics has emerged as an invaluable tool for retailers. The ability to anticipate consumer behavior and timely respond to ever-changing market conditions is one of the main competitive advantages in retail. With data analytics becoming more accessible, retailers heavily rely on predictive analytics services to conquer the market.

The role of predictive analytics in retail

In 2022, predictive analytics-powered programmatic advertising and market forecasting accounted for a 33.1% share of the global AI in retail market revenue.

Scheme title: Global AI in retail market revenue share, by application, 2022
Data source: gminsights.com — Artificial Intelligence in retail market

24%

projected CAGR of the global predictive analytics in retail market from 2023 to 2032

Global Market Insights

40%

more revenue is generated by companies that excel at personalization

McKinsey

Top 8 use cases of predictive analytics in retail

With the help of predictive analytics, retailers can solve various business challenges and gain considerable competitive advantage. Let’s explore the top 8 use cases of predictive analytics in retail:

Optimizing inventory management

Predictive analytics help retailers accurately forecast inventory needs and stock levels by leveraging past purchase patterns. Insights into customer demand and inventory levels allow companies to plan and optimize their supply chain more efficiently.

sales increase when making data-driven decisions around stock and store optimization

McKinsey

Benefit

Streamlined supply chain management leads to quicker delivery times, reduced costs associated with overstocking or under-stocking, and fewer disruptions.

Personalizing customer experiences

By analyzing customer data, predictive analytics provide retailers with data-driven insights into customer preferences and buying behaviors. Therefore, businesses can understand their customers, deliver tailored customer experiences, and better personalize their offerings.

of leading retailers consider customer loyalty and LTV as top priorities

Deloitte

Benefit

Predictive analytics helps companies to increase customer engagement and retention rate and convert one-time buyers into lifelong customers.

Developing targeted marketing campaigns

Predictive analytics can help retailers create more effective marketing campaigns by providing valuable insights about future customer preferences and buying behaviors. Intelligent data analytics allows businesses to target their ads more accurately and tailor messages to specific customers.

increase in product sales for retailers that use data-driven targeting

Deloitte

Benefit

As a result, retailers can maximize the effectiveness of ads and promotions by improved targeting, resulting in higher ROI from their strategies.

Creating pricing strategies

Predictive analytics help retailers analyze market conditions and customer data for product pricing optimization, helping them increase profits and gain a competitive advantage. Predictive analytics can also identify and analyze customer segments for common e-commerce trends or customer behavior patterns. This allows businesses to tailor their products, services, and their pricing accordingly, increasing sales.

immediate margin performance improvement due to data-driven pricing management

Deloitte

Benefit

Data-driven dynamic pricing strategies respond to the ever-changing market trends, competitor prices, seasonality, and consumer habits and preferences, maximizing revenues.

Enabling smart upselling & cross-selling

Predictive analytics can help to identify customer needs and suggest related or complementary products. For example, by analyzing past customer purchases, a retailer can recommend relevant items to customers when they are shopping on their website and target them with personalized discounts or promotions. With AI-powered predictive models, retailers can detect hidden patterns in their customer data and forecast trends or predict customer preferences.

sales increase for fashion companies that use data-powered product offering

McKinsey

Benefit

Improved awareness leads to smarter marketing and sales decisions, resulting in higher sales and profits.

Automating customer service

Based on past purchase patterns, customer demographics, and real-time sentiment analysis, predictive analytics can help customer service agents to better tailor their communication strategies and substantially increase customer satisfaction.

acceptance rate of artificial intelligence chatbots in retail (higher than any other industry)

Invesp

Benefit

By leveraging AI-powered forecasting, conversational chatbots, and automated marketing campaigns, retailers can improve the efficiency of their operations and reduce manual labor costs.

Optimizing merchandising strategies

In tandem with computer vision, predictive analytics can help optimize merchandising strategies by analyzing shoppers in-store movement and historical data on past purchases. Knowing the purchase history, merchandisers identify key metrics for improvement and develop hypotheses for improving UI accordingly to boost sales and profits.

of retailers in Deloitte's study scored themselves as mature or leading in product recommendations

Deloitte

Benefit

Advanced analytics-powered merchandising helps manage product layout to effectively recommend related products and increase the average check size.

Reducing customer churn

By analyzing customer data, audience demographics, and social media preferences, predictive analytics can identify customers likely to stop using a product or service and develop strategies for preventing it. Predictive analytics enables building models to reveal the customers most likely to attrite, determine the drivers of attrition, and preemptively intervene to win the customer back.

Benefit

As a result, AI-driven data analytics helps retailers retain existing customers and save on customer acquisition costs.

Our predictive analytics services

Itransition offers a suite of predictive analytics services for retailers, developing tailored solutions to help your business take full advantage of the power of retail predictive analytics.

We provide expert consulting services that enable retailers to effectively identify areas where predictive analytics can bring the most value and develop optimal strategies for leveraging it.
We help organizations define their goals and objectives, build effective data pipelines, and select the right technologies for their needs.
We build predictive analytics models and algorithms that are tailored to each retailer’s unique needs.
We use the latest machine learning techniques to develop advanced solutions that enable retailers to leverage real-time insights for better decision-making.

Ready to unleash the power of predictive analytics for your retail business?

Real-life predictive analytics examples in retail

Many retail industry leaders use predictive analytics to improve operational efficiency and customer experience. Some notable examples include:

Farfetch & Talkdesk

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.

Image title: Agent Assist in action
Data source: talkdesk.com — Talkdesk Agent Assist, empowering agents to support customers

Farfetch turned to Talkdesk, a cloud contact center company, to find much-needed help in the form of AI and predictive analytics. As a result, one of Talkdesk’s signature services, Agent Assist, now provides customer support agents with real-time tips during conversations with customers. For example, with the help of NLP and predictive analytics, Agent Assist can transcribe calls in real time, automatically generate call summaries, suggest the next best actions, and reveal relevant articles from Farfetch’s existing knowledge base to provide customers with quick answers.

25%

increase in customer satisfaction

50%

decrease in resolution times

Belk & antuit.ai

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 Sporting Goods & Adobe

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.

Steve Miller

Senior Vice President, Strategy, eCommerce & Analytics, DICK’S Sporting Goods

2X

more homepage visitors get a personalized experience

10%

more spent by visitors receiving a personalized experience than those who do not

Skullcandy & Sisense

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.

Mark Hopkins

Chief Information Officer, Skullcandy

Predictive analytics implementation roadmap

Here’s how you can start leveraging predictive analytics for your retail business:

1

Problem definition

In the first stage, we define the key business objectives, assess your technical readiness, and define the best way to proceed, considering your goals and current capabilities.

2

Data analysis

After we agree on the objectives, we move to data discovery and analysis. This means assessing the viability of both your existing data and external databases.

3

Design

In this phase, we design the solution's architecture, set the project's timeline and budget, and define the implementation strategy and optimal technology stack.

4

Implementation

We start implementation with data cleaning, labeling, and transformation. Then we define the solution's evaluation criteria and start building the system.

5

Deployment

At the deployment stage, we launch the solution into operation and ensure it seamlessly integrates with the rest of your infrastructure.

6

Support and maintenance

After your solution is up and running, we use user feedback and new data to continuously fine-tune your predictive analytics system.

Top 5 predictive analytics platforms for retail

Microsoft Power BI

Microsoft BI is one of the most recognizable business intelligence platforms. Users can create their own predictive models, validate them and make them a part of their day-to-day operations. Power BI’s AI visualizations can help surface insights and understand how various factors influence a particular metric.

Pros

Highly customizable
Built-in data cleaning capabilities

Cons

Expensive for small businesses
Steep learning curve

Pricing

Pro version is
$13.70 user/month

Premium version
is $27.50 user/month, and $6,858.10 capacity/month

Tableau

Tableau can fit retailers that want to get insights from their data quickly. It provides a wide range of visualization options, AI-driven recommendations, and a user-friendly drag-and-drop interface, which makes it easy to find hidden patterns in your customer and sales data.

Pros

Drag and drop interface
Mobile-friendly
Supports many data sources

Cons

Inflexible pricing
Poor versioning
Requires SQL knowledge in many cases

Pricing

From $15 to $70 per month per user

Qlik Sense

Qlik Sense is suitable for users who want to get high-value insights from big data. Its unique associative engine lets users quickly and easily access the real-time data they need, while its AI-driven natural language query capabilities make it easy to create sophisticated reports.

Pros

Allows for complex data analysis
Supports self-service

Cons

Computationally expensive
Poor integration with other software

Pricing

Quote-based

TIBCO Data Science

TIBCO Data Science incorporates various tools that optimize machine learning model deployment and monitoring. It empowers retailers to make data-driven business decisions and maximize customer engagement, profitability, and sales.

Pros

Highly secure
Comprehensive analytics capabilities
Seamless integration with other services

Cons

Steep learning curve
Lack of documentation

Pricing

Upon request

Alteryx

Alteryx is a unified solution built to cover companies’ data science needs. From data preparation to extensive predictive analytics, Alteryx provides a reliable workflow to make the most out of your data daily.

Pros

Intuitive drag-and-drop interface
Supports various data types
Excellent data cleansing capabilities

Cons

Limited visualization
Limited data storage options

Pricing

From $2,300 to $5,195 per user per year

Start using predictive analytics to boost your sales and improve customer experience

Get in touch

Adoption challenges

Some of the most common challenges associated with the implementation of predictive analytics in retail include the following:

Challenge/Description

Challenge

Solution

Description

Solution

Outdated team structure

Siloed team structure can impede retail companies from using the full potential of predictive analytics. Removing data science teams from other departments could result in poor alignment between them.

Streamline the analytics team structure by creating cross-functional teams with clear objectives, responsibilities, and authority that also facilitates experimentation and puts data-driven decision-making at the center stage.

Lack of talent

Inability to use predictive analytics tools effectively because of the lack of relevant expertise and skills.

Include initiatives such as establishing data labs that give employees hands-on experience with cutting-edge analytics tools and platforms, training programs to upskill staff in machine learning and artificial intelligence areas, and hiring specialists from outside organizations who can provide advice when needed.

Inflexible legacy systems

Outdated IT systems could prevent retail organizations from leveraging new data-driven technologies.

Modernize the existing architectures by leveraging microservices and APIs to reduce the complexity of integrating new analytics-driven solutions.

Siloed data

Retailers’ crucial data sets are often large and unstructured, undermining the potential of analytics solutions.

Revamp the data governance approach, invest in a data lake and a cloud-based data platform, and enable data interoperability to gain the most value from predictive analytics.

Join the retail leaders with predictive analytics

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.

Ecommerce personalization:
tactics, examples, and adoption guide

Insights

Ecommerce personalization: tactics, examples, and adoption guide

Personalization in ecommerce is indispensable today. Explore powerful tactics for creating unique customer experiences and get practical implementation tips.

Business intelligence in the retail sector:
main use cases, benefits & platforms

Insights

Business intelligence in the retail sector: main use cases, benefits & platforms

Discover how your retail company can benefit from business intelligence software by exploring its key features, common integrations, and best platforms.

How voice commerce transforms online retail

Insights

How voice commerce transforms online retail

This article explains the concept of voice commerce, covers its use cases and implementation challenges, and provides some adoption tips.

Virtual reality in retail:
11 use cases, benefits, and adoption practices

Insights

Virtual reality in retail: 11 use cases, benefits, and adoption practices

Explore the concept of virtual reality in retail, its benefits, use cases, and the best VR adoption practices

Ecommerce architecture: which one should you choose?

Insights

Ecommerce architecture: which one should you choose?

Selecting a suitable ecommerce architecture isn’t easy, so in this article we cover main ecommerce architecture types and provide tips on choosing between them.

Machine learning in retail: 10 ways to upgrade your store

Insights

Machine learning in retail: 10 ways to upgrade your store

Explore the top use cases of machine learning in retail and find out what benefits this technology can bring to your business.

MACH architecture: components, benefits, and implementation tips

Insights

MACH architecture: components, benefits, and implementation tips

Explore the concept of MACH architecture, its advantages, pitfalls, examples of successful adoption, and implementation best practices.

A guide to making your supply chain smart

Insights

A guide to making your supply chain smart

Learn what a smart supply chain is and what its key components are and discover how it can transform modern businesses.