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

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

August 22, 2023

Aleksandr Ahramovich
by Aleksandr Ahramovich, Head of AI/ML Center of Excellence
Predictive analytics in retail is the use of advanced statistical modeling and machine learning to identify emerging patterns in customer behavior. It provides retailers with the insights needed to make informed decisions about inventory management, marketing strategies, and pricing.

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.

Table of contents

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 consumer 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.

Our predictive analytics services

Being a pioneer in big data analytics and machine learning solutions, Itransition offers a suite of predictive analytics services for retailers. We can develop comprehensive strategies 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?

Contact us

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 and Talkdesk

    Farfetch and 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.

    Farfetch and Talkdesk

    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

    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 BI 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

    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

    Solution

    Outdated team structure

    Challenge

    Description

    Solution

    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

    Challenge

    Description

    Solution

    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

    Challenge

    Description

    Solution

    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

    Challenge

    Description

    Solution

    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 industry. Start your journey today and join the leaders in the industry.

    Related cases

    Ecommerce personalization: tactics, examples, and adoption guide

    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: key features, benefits, and platforms

    Business intelligence in the retail sector: key features, benefits, and platforms

    Learn how business intelligence solutions transform the retail sector and explore their key features, integrations, best software, cost factors, and benefits.

    How voice commerce transforms online retail

    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

    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?

    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

    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

    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

    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.