In a nutshell:
- Predictive analytics can help retailers manage inventory, optimize customer retention, and increase sales.
- AI is used in various retail areas, such as inventory management, assortment planning, and promotions.
- Predictive analytics can transform retail businesses by improving inventory management, reducing supply chain risk, and reducing customer churn.
- It can also optimize lifetime value, enhance marketing efforts, and support targeted expansion.
- Predictive analytics will boost profit margins and operational efficiency as retailers collect more data.
Predictive retail analytics can provide valuable insights into pricing decisions, allowing retailers to optimize their pricing strategies based on historical data, emerging trends, and customer behavior. By leveraging predictive analytics, retailers can forecast trends and anticipate future events, enabling them to make better pricing decisions that align with their business objectives.
But perhaps the biggest business benefit of predictive analytics is that it can be easily adopted by retailers already collecting large amounts of data. Through machine learning models, predictive analytics can extract highly valuable insights from the information companies gather on customer purchases, procurement volume, sales trends, supply chain operations, inventory status, and more.
To be fair, most retailers are getting insights from this kind of data. However, the standard analytics typically used to process this information is backward-looking, meant to study the past to improve operations for the future. Predictive analytics differs from this approach by using current data to project future outcomes for right-now decisions.
Retailers are getting on board with predictive analytics, too. By 2026, AI spend in the industry will top $20 billion, for a CAGR of 39% since 2019.
How is AI Used for Retail Predictions?
Predictive analytics is used in several retail areas, including inventory management, marketing, and promotions. It’s also instrumental in gathering data on customer journeys and potential sales channels. Predictive analytics can provide data-driven insights into the customer journey by analyzing data from various touchpoints, such as online behavior and interactions with retail stores. This information can help retailers personalize their offers and create targeted marketing strategies for specific customers, increasing buying likelihood and driving better results.
This technology can also play a crucial role in optimizing the performance of online shops. By analyzing online behavior and customer data, predictive analytics can help retailers identify emerging trends and anticipate customer needs. This approach allows retailers to implement targeted marketing strategies, such as personalized recommendations and customized shopping experiences, to enhance the online shopping journey and increase customer engagement.
Additionally, predictive analytics transforms the way retailers approach personalized marketing. By leveraging predictive data analytics, retailers can gather valuable information about their customers, including their preferences, purchase history, and likelihood of responding to specific offers. This data-driven insight enables retailers to create personalized offers and special promotions tailored to individual customers, enhancing customer satisfaction and driving sales.
Customer relationship management strategies also stand to benefit from predictive analytics, particularly strategies related to customer retention. Using predictive analytics, retailers can better target loyal customers with rewards and offer targeted promotions to retain disengaged customers. Machine learning technology can be used to refine these proven tactics that reduce customer churn.
6 Examples of How Predictive Analytics Can Transform Your Retail Business
While empowering retail business functions like inventory management and customer engagement sounds excellent, it’s also important to understand that predictive analytics can produce real results and transform a retail business into a more successful enterprise through established use cases.
1. Better inventory management. One significant benefit of predictive analytics in retail is reduced stockouts and overstocks, plus lower operational costs connected to storage and increased logistical demands. Procurement teams can use predictive demand models to make adjustments and ensure that inventories are kept at optimal levels.
- A well-managed inventory also provides customers with a better experience, as they can buy the products they came for and are more likely to receive orders on time — supporting customer satisfaction and brand value.
- Predictive analytics can optimize inventory in the short term, avoiding overstock or stockout situations. However, it can also reduce longer-term risks, such as low inventory due to natural disasters or disease outbreaks.
- Predictive analytics can also indicate optimal changes to inventory based on shifting customer sentiment toward products, raw materials, places of origin, brands, regional politics, and other similar factors.
2. Lower supply chain risk. Predictive analytics can also help retailers avoid supply chain disruptions by supporting better logistics demand forecasting — which projects the demand for logistics services. Most retailers rely on a highly complex supply chain prone to disruptions.
- Predictive demand forecasting can help minimize these disruptions and maintain stock levels by anticipating demand within the supply chain, even with many shippers and suppliers in an organization’s logistics network.
- Predictive models can analyze regional weather, traffic patterns, shipping lanes, flight data, and other factors to anticipate demands on an organization’s supply chain.
- This predictive capacity allows an organization to shift strategies with predictive inventory management, marketing campaigns, shipping, and other business functions to meet customers’ demands and expectations.
3. Reduce customer churn. Many retail companies recognize the immense value of offering subscription services to their customers. By leveraging the power of predictive analytics, these companies can now analyze user activity to accurately predict customer churn before it even happens. This method is particularly crucial for subscribers, as they often exhibit telltale signs that indicate their potential to cancel their subscription.
However, predicting customer churn isn’t relevant only for subscription services. It’s also useful for analyzing all your customers’ behavior for signs of potential churn. With the help of predictive analytics, retailers can project churn based on historical data and proactively identify and prevent churn once the initial signs have been revealed. This approach empowers retailers to take timely and targeted actions to retain these valuable customers, ultimately optimizing their lifetime value.
- Predictive analytics enables retailers to understand their subscribers’ preferences and needs comprehensively. Predictive models highlight meaningful customer behavior patterns and analyze data points such as purchase history, browsing activity, and customer feedback.
- Retailers can personalize their offerings, tailor their marketing strategies, and provide an exceptional customer experience that fosters loyalty and engagement.
- Retailers can take proactive measures to resolve customer dissatisfaction, enhance their products or services, and ultimately strengthen customer loyalty by identifying and addressing issues that may relate to predicted churn.
- Retail competition is fierce, and customer acquisition costs are high. Reducing customer churn through predictive analytics helps retailers retain their existing customer base and allows them to allocate their resources more efficiently toward acquiring new customers.
4. Optimize lifetime value. Predictive analytics is a powerful tool for retailers to increase their revenue from existing customers and maximize each customer’s lifetime value. By leveraging data and advanced algorithms, retailers can gain valuable insights into customer behavior and preferences, allowing them to develop targeted strategies to enhance customer loyalty and drive long-term profitability.
- Predictive analytics can optimize lifetime value by identifying upsell and cross-sell opportunities. Retailers can accurately predict which products or services will most likely appeal to individual customers.
- Retailers can offer personalized recommendations and promotions, increasing the likelihood of additional purchases and higher average order values.
- Retailers can also optimize their marketing and communication strategies to target existing customers effectively. This optimization increases the chances of customer engagement and strengthens the relationship between the retailer and the customer, leading to long-term loyalty and repeat purchases.
5. Better marketing. Retailers are awash in customer data, from online tracking cookies to customer loyalty programs. Conventional data analysis can provide some marketing-related insights, but predictive analytics for retail can support more targeted marketing efforts. A survey conducted by Epsilon and GBH Insights revealed that customers genuinely want retailers to provide personalization, with 80% expressing an expectation for customized experiences.
- By putting to work all of the data an organization can collect on its customers, predictive models allow marketing departments to target specific segments of consumers better.
- Predictive models help develop targeted promotions, identify upsell and cross-sell opportunities, and help retain valuable customers before they churn.
- Retailers can perform better-targeted marketing on e-commerce platforms with optimized cross-selling and upselling opportunities through data and automation.
6. Targeted expansion. Comprehensive planning is necessary when a brick-and-mortar retailer is considering opening a new location, and predictive analytics can provide valuable insights for targeted expansion.
- By analyzing historical data, market trends, and key performance indicators, predictive analytics can predict future sales and inform retailers’ expansion strategies. This data-driven approach minimizes risks and increases the likelihood of success for new retail locations.
- A predictive model can use geographic data on past sales, demand, commercial property values, and other factors to predict future sales and inform an expansion strategy.
These are just a handful of use cases for predictive analytics in the retail industry, and the number of use cases will likely expand.
Concerns about predictive analytics in retail
Using consumer data for predictions
As the use of AI in retail expands, some businesses and individuals may be concerned about using customer transaction data. These concerns usually revolve around:
- Invasion of privacy: Worries regarding collecting and analyzing personal information without explicit consent, which may violate individuals’ right to privacy.
- Data protection: Concerns about data breaches or leaks that could expose sensitive personal information, resulting in identity theft or fraudulent activities.
- Personalized advertising: Irritation caused by highly tailored ads that give consumers the impression of constant surveillance and manipulation.
While these are legitimate concerns, many consumers are willing to exchange their information for relevant offers. According to Salesforce research, 57% of consumers would share personal data in exchange for personalized offers or discounts, and 62% are OK with companies sending customized outreach based on their purchase history. Data security can be addressed by selecting platforms and partners with strong security practices.
Keeping models current with rapidly changing market conditions and customer behavior
Given the volatile, changeable times we live in, this is also a perfectly reasonable concern. Indeed, traditional methods of building predictive analytics models could make it hard to keep up with customer preferences, shopping patterns, and product demand. Rapid changes in consumer behavior can result from various factors, including economic shifts, cultural trends, or unforeseen events, which historical data might not capture effectively.
However, retailers with more adaptable predictive models are better positioned to react quickly to shifts in the market, capturing new opportunities and mitigating risks. Retailers may also need to adjust their data sources and data collection methods to capture emerging trends and shifting consumer preferences.
Fortunately, this is where an automated predictive analytics platform like Pecan shows its value; models and data sources can be quickly and easily updated and adjusted to accommodate changing environments.
Ready to get started with predictive analytics for your retail business?
As retailers access more data, predictive analytics will help them further boost profit margins through increased sales and greater operational efficiency. We’re here to help you and your marketing team take the next steps toward AI adoption. Get in touch for a quick, easy use case consultation.