In a race to sell more products, gain more customers and stay competitive, retailers must rely on data to provide insights into every aspect of their business, from logistics to pricing to customer relationships.
The goal is to transform data into information and information into insight.
The retail industry is undergoing a fundamental shift, which Deloitte deemed “from mass to micro”: retailers are moving from stocking on goods that meet mass demand to designing hyper-personalized offerings to appease individual customers. The reason is the growing popularity of online shopping that got a powerful boost during the pandemic. Suddenly, retailers discovered that the secret to being more profitable is not in supplying the stores for the masses but rather understanding what makes each individual customer tick. What’s more, the task now is for retailers to reach customers on the other side of the digital screen – while facing fierce competition.
This is where data can make a difference. It gives retailers true visibility into everything that is going on in their businesses, taking the guesswork out of customer relationships, inventory management, logistics, and pricing. Data enables targeted customer communications, helps to prevent stockouts, provides opportunities to optimize prices based on real-time conditions, and improves customer experience.
For both in-store and online retail, data becomes the main driver of decision-making. Businesses experiment with emerging technologies, seeking to achieve hyper-personalized customer interactions, practice proactive inventory management, and implement omnichannel commerce. Technology adoption rates skyrocket – for example, Salesforce reports that 92% of retailers invest in artificial intelligence.
While giving retail businesses a huge boost in their digital modernization efforts and enabling quality decision-making, data technologies present certain challenges – integration with legacy systems, regulatory compliance, employee skill gap, to name a few. In this post, we are discussing how retailers can overcome these challenges and turn data into a powerful growth driver.
What is data-driven decision making in retail?
Data-driven retail involves leveraging customer and operational data to make strategic decisions that enhance business outcomes. There are several ways to collect and use retail data.
Collecting data from various customer touchpoints
Retailers gather vast amounts of data from multiple sources to gain an all-around understanding of consumer behavior and operational efficiency. Primarily, customer data comes from the following sources:
- Online transactions: Gathering analytical data from eCommerce platforms, retail businesses gain valuable insights into customer preferences, browsing habits, and purchasing patterns. Data from online sales, abandoned carts, and user search queries help retailers design personalized marketing strategies.
- In-store purchases: Point-of-sale (POS) systems track product sales, customer demographics, and payment preferences. POS data integration with other systems allows businesses to enhance loyalty programs and create targeted promotions.
- Mobile interactions: Retail mobile apps and mobile websites provide real-time data on customer engagement, location-based behaviors, and personalized product recommendations. Retailers can use this data to generate push notifications, plan discounts, and tailor location-based marketing.
- Social media and customer reviews: Analyzing social media engagement and customer feedback, retailers get an understanding of their brand perception, customer sentiment, and trending products. Businesses can leverage sentiment analysis to refine marketing campaigns and build data-driven retail strategies.
- IoT and smart devices: Retailers use IoT technologies, such as smart shelves and RFID tags, to monitor inventory levels and optimize stock management in real time.
Analyzing trends using AI and predictive analytics
Once data is collected, businesses need to process and analyze it to extract meaningful insights. AI-driven analytics and machine learning algorithms play a crucial role in identifying patterns, trends, and potential business opportunities.
- Customer behavior prediction: AI models analyze customers’ past shopping habits to predict what they are likely to purchase next. This data can become the basis for personalized recommendations and data-driven retail marketing solutions.
- Demand forecasting: Predictive analytics allows retailers to anticipate demand fluctuations and optimize inventory levels, reducing the risk of overstocking or stockouts. This is especially useful for seasonal products and fast-moving consumer goods.
- Market basket analysis: AI can identify frequently purchased product combinations, allowing retailers to optimize in-store layouts, find cross-selling opportunities, and generate bundled promotions.
- Fraud detection: Machine learning models detect anomalies in transactional data, preventing fraudulent activities such as payment fraud, return fraud, or unauthorized account access.
- Sentiment analysis: By analyzing customer reviews, social media comments, and online feedback, retailers can gauge customer satisfaction regarding the brand and make informed improvements to their products or services.
Implementing data-driven changes to optimize customer experience and operational efficiency
Based on the customer data analysis, businesses are empowered with enough knowledge to take action to improve both customer interactions and internal operations. The implementation of data-driven retail insights results in measurable improvements in sales, customer satisfaction, and cost efficiencies.
- Personalized customer experiences: AI-driven recommendation engines enable retailers to deliver personalized product suggestions, tailored email campaigns, and individualized promotions, enhancing customer engagement and loyalty.
- Real-time pricing adjustments: Dynamic pricing strategies use demand and competitor analysis to adjust prices in real-time, ensuring optimal pricing for both profitability and competitiveness.
- Optimized inventory management: Data-driven stocking strategies ensure that products are available when and where they are needed, reducing losses from excess inventory and preventing unfulfilled customer demand.
- Enhanced supply chain efficiency: AI-powered logistics management improves delivery times, reduces supply chain bottlenecks, and minimizes transportation costs through optimized routing and predictive demand planning.
- Improved employee productivity: Data analytics tools help retailers allocate staff more efficiently based on foot traffic patterns and sales trends and ensure optimal in-store assistance during peak hours.
- Better in-store experience: Data-driven retail solutions such as advanced POS systems, automated checkouts, and indoor mapping apps give a powerful boost to customer engagement and loyalty.
Major trends shaping retail
As consumer expectations grow, the retail industry is rapidly transforming and adjusting. The main focus remains on creating ever-improving customer experience (Gartner’s 2024 report shows that it is a priority for 86% of retail businesses.) On this journey, retailers rely on technology to provide opportunities for building customer-centric shopping environments.
The main trends in today’s retail highlight the pivotal role of data in ensuring competitiveness and growth by placing the customer in focus.
Implementation challenges and solutions
For retail businesses implementing pioneering technologies and solutions to enable data-driven decision-making and boost growth, the adoption path is not always easy. However, with the right approach to planning, a choice of effective transformation strategy, and collaboration with a trusted technology partner, these challenges become opportunities.
| Data fragmentation across multiple channels | Challenge: In an omnichannel commerce system, data coming from various physical and digital touchpoints (online and brick-and-mortar stores, social media, mobile applications) is often siloed and disconnected. This prevents consistent insights and affects decision-making. |
|---|---|
| Solution: Opt for the unified commerce approach and build a single system integrating all customer and operational data from all channels, creating a single source of truth. | |
| Legacy system integration | Challenge: Outdated POS, ERP, and CRM systems are often poorly compatible with advanced data technologies. |
| Solution: Choose cloud-based platforms supporting flexible API-driven integrations with legacy tools and systems that allow the use of modern AI-powered analytical solutions without major overhaul. | |
| Data privacy and compliance | Challenge: The retail industry is subject to multiple regulations such as GDPR, PCI DSS, or HIPAA (for retailers in the healthcare sector) that require strict data safety and security measures. |
| Solution: Use end-to-end data encryption, role-based access control, and compliance-oriented data management practices. | |
| Skill gap | Challenge: There may be insufficient in-house expertise in data science and analytics, AI, ML and other emerging technologies to ensure actionable insights. |
| Solution: Invest in employee training or partner with a reputable technology company that has proven expertise in retail data solutions. | |
| High implementation costs | Challenge: The cost of new technologies and uncertain ROI can discourage retailers from adopting data solutions. |
| Solution: Use a phased implementation strategy, focusing first on high-impact areas and measuring the outcomes. Adjust your investment plan based on the results. |
Intellias: implementing emerging tech in retail
As an experienced provider of retail software development services, Intellias has helped many businesses on the path to adopting pioneering technologies and shifting to data-driven decision-making in retail.
Data warehouse migration
For our long-term partner, a beauty and medical products retailer, we designed a cloud-based infrastructure to host their data warehousing resources. Switching to a cloud platform significantly improved the performance and availability of the company’s data processing infrastructure.
The migration, performed using the lift and shift approach, ensured uninterrupted operation of the retailer’s environment and minimized errors. After the migration, the company obtained consolidated storage capacities ensuring integrity of data received from various sources and transparent accessibility. The solution also brings the added benefit of using a dynamic pay-as-you-go pricing model allowing the company to optimize cloud infrastructure costs.
Automated order cancelation solution
An IT solutions retailer with a 250,000+ customer audience needed to improve order management efficiency. The existing manual method of processing order cancellation requests created an excessive workload on the personnel and considerably slowed the supply chain process.
Our team of data engineers designed and implemented an automated order cancellation solution that expedites the process 10 times and minimizes manual work. As a result, the administrative workload on warehouse employees was greatly reduced, the rate of human-induced errors decreased, and the overall process became user-friendly and transparent.
Intelligent solution for retail analytics and data-driven insights
An international automotive retailer was looking for a way to gain better visibility in its sales and marketing information and get insights that help them discover growth opportunities. The company’s extensive branch network produced about 1TB of data on a daily basis, which required proper consolidation, processing, and analysis.
We delivered a cloud-based big data analytics platform that handles all data processing functions, from ingestion of various types of data to generation of human-readable insights. With this solution, the company now benefits from a centralized data processing infrastructure that represents a single source of truth and improves the value of insights. The platform’s analytical capabilities enable sales and marketing predictions that allow the business to optimize costs and adjust marketing efforts dynamically.
Ensure growth with advanced data-driven analytics in retail
Today, data is the lifeblood of any industry, retail being no exception. The key is to know how to use it and understand it. Luckily, retailers are not alone on this journey – information technology providers support them with powerful analytical tools that turn raw data into insights and a basis for decision-making.
For an established retail business, embracing data may be a leap of faith. However, in collaboration with a professional retail software development company, it becomes a well-planned and organized transition to a digital space of AI-driven hyper-personalization, efficient inventory management, and unified commerce approach.
Contact us, and let’s work together to empower your retail business with data.
