Are you tired of running your store blind? Retail analytics uses real data to boost sales. Learn more & take control with DataBrain.
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Running a retail business can take time and effort. Without precise data, you might be:
These issues can hold your business back from reaching its full potential.
Retail analytics can address these problems using customer data and sales information and help you make informed decisions. With retail analytics, you can:
Retail analytics is collecting and analyzing data about your business to improve operations and decision-making. This data can come from various sources, including:
By analyzing this data, you can gain valuable insights into your business. For example, you can see:
See it in actionRetail Management Dashboards
Retail analytics thrives on the information you gather about various aspects of your business. Here are three critical types of data analyzed in retail:
Retail analytics cuts through the noise, giving retailers the power to know their customers truly. It translates to a more relevant shopping journey, building stronger relationships and boosting sales.
Define business goals and objectives: Let's say your goal is to reduce out-of-stock situations for popular clothing items by 50% within the next six months. To achieve this, you'll need a three-step approach:
1. Data collection and cleaning:
2. Identify relevant data:
You need to identify clothing items with consistently high sales figures, their stock levels, and how quickly they sell.
It is difficult to identify these items manually, which brings us to the next point - choosing a BI tool.
3. Choose the right analytics tool:
While retail businesses often lack the technical expertise for creating data visualizations, a BI tool can help transform the raw data into a suitable visual format.
But, not all BI tools are created equal. For retail businesses, choosing a tool with a user-friendly interface and drag-and-drop functionality is beneficial.
DataBrain, Tableau, and Power BI are a few BI tools available in the market.
Let's take DataBrain, for example; along with the features mentioned above, it uses AI to answer your queries with charts or graphs in seconds, saving you time and effort.
This can help you forecast demand based on historical sales data and current inventory levels.
4. Data analysis and interpretation:
Use the BI tool to analyze sales inventory and look for trends:
For example, you might discover that a particular style of jeans consistently sells out within a week of being restocked.
Based on the findings, develop specific actions:
Monitor and measure results:
Once you've implemented changes based on data insights, track critical metrics:
Monitor performance and adapt your inventory management strategy based on new data insights.
Following these steps, you can leverage data analytics to gain valuable insights into customer demand for clothing items. It allows you to optimize your inventory management, reduce out-of-stock situations, and ultimately improve customer satisfaction within your retail store.
Here's a quick breakdown:
You can use these powerful tools for various applications in retail analytics, including:
By incorporating AI and ML, retailers can gain deeper customer insights, automate tasks, and optimize their operations for a smarter and more profitable business.
See it in actionRetail Management Dashboards
Profit vs. loss geographically is a technique used in business to understand how profitable a company is in different regions or locations. This analysis helps companies identify areas where they excel and areas that require improvement.
Benefits
See it in actionRetail Management Dashboards
Let's say you run a bakery. You want to know two things:
Combining these two questions into one big picture helps the bakery understand the relationship between how you acquire customers and the money those customers bring in.
Here's an example:
The bakery spends a lot of money on online ads (high customer acquisition cost), but those who come from those ads buy only small-value products (low revenue).
This metric would show the bakery that there are more effective ways than online ads to get high-spending customers. They might consider other ways, like offering in-store promotions, to acquire customers.
By understanding this metric, the bakery can make smarter decisions about attracting customers who will bring in more money, making their business more successful.
By looking at this chart, you can quickly see which product is the most expensive (the tallest colored bar) and which is the cheapest (the shortest colored bar). You can also easily compare prices between products of similar types (e.g., different varieties of apples) by looking at the heights of their colored bars.
This is a way of presenting data (unit price) clearly and visually appealingly (using a multi-colored chart) to make comparisons easier.
Retail analytics gives you the tools to make winning decisions based on real customer data. You'll understand your customers better, keep them coming back, and see your sales soar. Start using retail analytics today and watch your business flourish!
If you understand the power of retail analytics, sign up for DataBrain to save time, understand customers better, and improve sales.
See it in actionRetail Management Dashboards