A report by Research and Markets states that the retail analytics market worldwide is expected to grow at 18% between 2019 and 2025. This means that soon the market will be valued at $9.5 billion.
The number while uber impressive begs the question “How”? How a technology that was not even present five decades ago bringing such a massive impact in an industry that has been around since 10,000 years. A simple answer to this can be the fact that it fits. Data analytics – as a capability – has been everything it takes to change the face of inventory optimization and retail as a whole.
What is big data in retail?
Big data and analytics for retail is the application of data-driven tools used for articulating business patterns and performance. On a higher level, big data or data science in retail is the mobilization of business analytics processes in the retail economy.
Retailers make use of business intelligence and big data analytics to gain key insights that can be used for decision making and improving inventory management, operational efficiency, sales, and overall customer experience.
This information that big data in retail generates enables retailers to:
- Find the target personas
- Create customer behavior and buying patterns
- Compare the customer preferences
- Identify location-based and seasonal trends
While this was about the role of big data in retail, let us refine our focus on inventory management.
The current state of inventory management analytics and the role of data analytics
Projected to reach USD 3.82 Billion by 2028, the inventory management solution market is a fast expanding one. Powered by intense demand for competitive-level efficiency, the present-day inventory management capabilities go beyond accurate inventory and automation of business processes. Smart data discovery, data mining and analytics have the key to providing business insights that help with making data-backed decisions needed for better profitability and productivity.
Efficient inventory optimization solutions can analyze a vast portion of past sales and anticipate the inventory future demand by adding in seasonality and lead times. Moreover, in the age of big data, inventory optimization techniques can give you insights into customer preferences, product performance, and the channel performance.
The inclusion of big data in inventory optimization solutions can help answer questions like:
- What is the amount of inventory needed to meet the demand while keeping the stock levels down?
- How can stock management be optimized?
- How can product recalls be reduced?
- How can cross-selling be enabled for improving the performance of slow-moving stocks?
While it is important to know the use cases of data analytics in inventory management, it is equally crucial to answer the how.
How does data analytics smoothen the inventory management process? Through the use of its 4 models.
1. Descriptive analytics: It gives retailers the summary of the inventory performance – movement of items, speed of replenishment, etc.
2. Diagnostic analytics: It answers the why. Why did the items stocked out? Why did the customer leave a bad review? Etc.
3. Predictive analytics: It helps anticipate trends and shopper behavior on the basis of the inventory management history.
4. Prescriptive analytics: It helps retailers make gradual adjustments in the anticipation of change in consumer emotion, supply shocks, demand, etc.
Now that we have looked into the high level benefits of big data and analytics for retail and inventory optimization, let us dive into the details.
What are the benefits of data analytics for inventory optimization?
Identifying ways to better inventory management is a key role for any retailer. The adoption of big data and analytics for retail makes it easier. Here are the many ways data analytics help to improve inventory management.
