Retail Store Clustering with just one click

Retail Store Clustering: Optimize Your Store Strategy

Retail store clustering is a powerful data analysis technique that groups similar stores based on various characteristics. This allows retailers to better understand local market trends and tailor their product offerings and marketing strategies to local demand. Key benefits include:

• Better understanding of market needs
• Improved store planning and operations
• More efficient allocation of resources
• Enhanced customer experience
• Competitive advantage

Our clustering solution lets you customize inputs, assign weights to each feature, analyze the results, and iterate until you're satisfied. In this article, we’ll guide you through how to leverage your data using this solution.

Getting Started with the Clustering Solution

Before you begin, make sure to download the project and sample data:

• Click here to Download the Project from Official Knime Page
• Click here to download the Sample Data

Step-by-Step Guide

Step 1: Input Setup
Start by setting the location of the data file, specifying the number of clusters, and assigning weights to each feature in the model.

Step 2: Data Loading and Merging
Next, load your data into KNIME and merge the relevant sheets. Ensure that your dataset uses the same sheet names as the sample data, as the sheet name parameter is passed to the sample.

Step 3: Exclude Outliers
You can optionally exclude certain stores that may be outliers based on their features. This helps refine the model’s accuracy.

   

Step 4: Filter by Store Capacity
This step allows you to filter and group stores by capacity. For example, you can focus only on department stores, excluding others. This is useful when store size is a key factor in your distribution strategy.

  

Step 5: Apply User Weights
User weights are applied to the features, and some transformation steps are executed before sending the resulting table to the next step. To assign weights to each product category column, use the Column List Loop Node. This node enables you to change the names of each category column to anonymous columns and apply user-defined weights. It accommodates any number of product category columns.

Note: If your dataset contains more features than the sample dataset, some nodes may require adjustment. If so, please leave a comment or contact us.

Step 6: Run the Model
You can use both K-means and hierarchical models by feeding your data into each model. After this, run the model to create groups for use in the results panel.

The result dashboards, contains : 

• Cluster Store Count Distribution
• Store Count by Cluster and Store Capacity Group
• Cluster - Store Details
• Cluster Averages

Additionally, you can visualize the cluster store distribution on the map using the OSM Map View Node. Customize the map tooltip within the model using the column filter node at the top of the metanode.

Performance Comparison
You can compare the performances of two models by checking their silhouette scores.

Exporting Results
Finally, export the results to Excel using the Excel Writer Node for further analysis.

Conclusion
In conclusion, retail store clustering is an essential tool for retailers to gain a competitive advantage and improve their overall performance. Our solution allows you to easily cluster your stores in just a few clicks.

If you liked this project, leave a comment below and share it on social networks.

Share:

Read More

How to create Date Table using Knime

 

In this article, we'll show you how to use KNIME to create a date table with user-specified start and end dates.

Click Here to Download the Workflow from Official Knime Page



Date tables are an important part of any data analysis project because they allow accurate and efficient queries over data that spans long periods of time. This table is generally used in combination with a table of facts containing digital data analyzed. The dates table contains dates and other information, such as a month, a quarter and a year, which are used for filtering and a set of data in the table of facts. 



One of the main advantages of using the date table is that it can easily filter data using a specific date. For example, if you want to display sales data in a specific month, you can easily filter the data on the monthly table corresponding to the date of the date. This is much more efficient than filtering the data in a fact table by individual dates, which would require more complex queries..

Date tables also make it easier to forecast and analyze trends. This allows you to easily calculate trends, moving averages, and other important metrics. Having a separate table for dates makes it much easier to query data within a specific period of time, and also allows you to perform time-based calculations, such as calculating yearly or monthly growth.

The KNIME date table generator allows you to create your own custom date table with all the key metadata included. Additionally, you can enrich your date table by adding your own metadata fields using the column formula node*. In conclusion, using a date table in data analysis is a best practice that can significantly improve the efficiency, accuracy, and flexibility of your data analysis. It allows you to easily filter and aggregate data and provides powerful trend analysis and forecasting. It also ensures data quality by providing a consistent format for date and time information.

Share:

Read More

How We Automated Our Twitter Promotion with Knime

In this blog, we’ll walk you through how we automated our Twitter promotions using a Knime workflow, making our social media efforts more efficient and hands-free

Click here to download the Workflow from Official Knime Page

The workflow starts by connecting to the RSS feed of our blog, pulling essential information such as the blog title, publication date, and URL. This data becomes the foundation for our promotional tweets.

Next, the workflow connects to our Twitter Developer Account using the Knime Twitter API. It automatically posts a tweet for each new blog entry, incorporating the blog title, URL, and custom hashtags that we define.

Here is the breakdown of the workflow...

1. Extracting Blog Information:

We begin with the Table Creator node, where we input the URL for our blog’s RSS feed. Using the RSS Feed Reader node, we read the feed to extract the blog title, publication date, and URL. (Note: You may need to install the RSS Feed Reader extension in Knime.)

2. Filtering for Latest Posts:

To ensure we only promote the most recent posts, we filter the published date to match today’s date. We created a "today" variable inside a metanode to handle this, making it dynamic and adaptable for daily use.

3. Preparing Data for Twitter:

Next, we clean up the data by removing unnecessary columns, keeping only the blog title and URL. These two columns are then transformed into variables that can be used later in the workflow. The Group Loop node allows us to cycle through each blog post’s title and URL, which are then passed on to the Twitter Post Tweet node.

4. Posting on Twitter:

Using the Twitter Post Tweet node, Knime sends a tweet for each blog post, automatically incorporating the title, link, and hashtags we’ve pre-set. The tweet can be fully customized, allowing us to add additional hashtags, links, or specific text to fit our social media strategy.

We can customize the text, add more hashtags or links using this node.

With this workflow, we’ve eliminated the need for manually promoting our blog posts on Twitter. Once set up, it runs in the background, ensuring that every new blog is promoted with minimal effort on our part.

Thanks to Knime’s flexibility, we’ve saved countless hours while still maintaining an active presence on social media.

If you found this helpful, don’t forget to share this blog and leave a comment below!

Share:

Read More