How can you use data visualization to perform customer segmentation and market basket analysis?

To get to data visualization first you need to get the required data sets using logics based on your domain knowledge or experimental approaches to help in data discovery which then you visualise and tell a story using appropriate tools and charts . This approach will help in your end objectives of customer segmentation and other analysis.

As a purchasing agent, my goal with data analytics is to improve my understanding of my trade partners (vendors), who in my case, are like my customers. While it is true, we pay them to do a job for us, I want to become their preferred builder to work for. I want to understand their needs, their backgrounds, their goals, their plans and use that to develop and improve our business relationships. The benefits will be mutual.

In AdTech, complex data in a visual format has helped identify relationships and trends that may not be apparent in raw datasets, leading to more targeted marketing strategies, effective ad stack spend, and accurate audience reach. Tools like scatter plots, histograms, box plots, and clustering analysis are used to dive into the relationships between variables to identify/confirm both the obvious and the less apparent segments. Ultimately, it's arming publishers, advertisers, and other businesses with insight into their customers' behavior and perspective for more informed decisions.

Customer Segmentation: Feature Analysis and Visualization: Visualize customer data using scatter plots, heatmaps, or parallel coordinate plots to identify patterns and relationships between various customer attributes like age, income, purchase history, and more.

Data visualisation can be done using tools like chart, graph, visually representing relations between two or more parameters. This can be represent other form of visual tools like Tableau to highlight the right trends. In customer segment, the goal is to highlight and target customer matching your product or service benefits/aspiration. data visualisation definitely helps in this. But this is not the only way. Other data analysis techniques are also necessary and should be mastered. Data visualisation is best use for story telling or selling your point to stakeholders.

With the advancement in analytics user experience design solutions, you can go above and beyond 2D charts and work on 3D models or even augmenting reality to see data come to life in real time. A good use case for this could be adding a data layer over a map to show segments by household type and then you could visualize that data onto a Google Maps solution that would highlight regional clusters. The options are growing with each advancement but this could really make it easier to narrate data stories in a real-world situation.

Given that there are a huge range of tools and portrayal preferences, we advocate and execute pushing granular-precision relationship segmentation down to the perspective of each individual sales and service representative. The reality is that over 85% of the workforce do not want to be data managers and analysts. We advocate giving those customer-interacting workforce members the precise insights they need to take tangible action. They, and their clients, value that more.

One of the simplest yet powerful visualization for customer segmentation or market segmentation is the 4 quadrant. It is applicable in situations where the intention is to build strategy using segmentation. It could be marketing strategy/sales strategy or even product development strategy.

Data visualization provides a better understanding of customer segmentation. It simplifies complex data into effortlessly readable charts like histograms and bar charts, helping you compare groups of customers. These visualizations provide valuable insights and enable companies to make informed decisions about marketing, product development and customer service. Interactive dashboards further simplify this process by allowing deeper exploration of the data. The power of data visualization lies in its ability to make data more accessible and engaging, helping to tell stories and communicate with stakeholders.In a data-rich world, data visualization serves as a guide for companies looking to improve customer understanding and decision.

To enrich customer segmentation, consider dynamic visualizations that allow interactive exploration of the data. Interactive dashboards with filters enable stakeholders to drill down into specific segments, uncovering hidden patterns in customer behavior. Incorporating temporal elements like trend lines in time-series plots can also reveal the evolution of customer segments over time. By leveraging such dynamic visual tools, businesses can not only comprehend the current landscape but also anticipate future trends, ensuring that marketing strategies remain agile and informed by real-time data insights.

Market basket analysis is a data mining technique that identifies relationships between products frequently purchased together. It's used to understand customer buying patterns, improve product recommendations, and optimize inventory and marketing strategies. The analysis generates association rules, showing which items tend to be bought in conjunction. Common algorithms include Apriori and FP-growth. Visualizations like network diagrams and heatmaps help convey these associations, aiding businesses in making data-driven decisions to enhance sales and customer satisfaction.

Based on my of Retail market basket analysis can be invaluable, By understanding which clothing items are frequently purchased together, you can create more effective promotions and enhance the shopping experience for your customers. For instance, if you notice that customers often buy jeans and T-shirts together, you can design special offers or bundles to encourage this combination. Moreover, this analysis can help you optimize store layout and inventory, ensuring that complementary items are displayed together. It ultimately leads to improved customer satisfaction and increased sales, making it an essential tool for any clothing retailer.

Market Basket Analysis: Apriori Algorithm Visualization: Implement the Apriori algorithm to discover association rules. Visualize these rules as network graphs or sankey diagrams to show item relationships.

This is a good way to create visual based user journeys for the ecommerce platforms within social networks like pinterest or etsy. This could also influence marketing campaigns with testing variations on clothing types and likelihood to convert to a buyer.

Market basket analysis is a data mining technique used in retail and e-commerce to uncover relationships between products that customers frequently purchase together. It helps in: 1. Identifying item associations. 2. Recommending related products. 3. Improving inventory management. 4. Enhancing cross-selling and upselling strategies. 5. Optimizing store layout and product placements.

Identifying time based purchasing segments can help when working to create targeted advertisement campaigns, sending notices about potential upgrade times (X product usually requires new batteries every y number of months), and help to optimize outreach and messaging based on historical customer experience.

To visualize market baskets, you can use techniques like association rule visualization, network diagrams, and Sankey diagrams. Association rules show products frequently bought together, with metrics like support, confidence, and lift. Network diagrams illustrate product relationships. Sankey diagrams display the flow of products through purchases, revealing item combinations and sequences. These visuals help businesses understand buying patterns and optimize recommendations, inventory, and promotions.

We need to decouple segmentation and visualization first, and look at them individually for their strengths and then bring them together logically. CUST & MARKET seg : It is a business decision, based on revenue (ability to pay), reachability (addressable market),market research/customer preferences (product variations needed). VISUALIZATION: It is a tool.Any visual way that conveys the essence with minimum cognitive load is the best. From the "low end" excel, to "high end" clickable, data pipelines of Tableau, Power BI, spatial data and now with predictive data analytics and AI/ML usage, the crux remains the same. Each visualization should cater to one or more KPIs with control limits for action, which in turn is tied to a business need.

I have found powerbi to be a very effective tool in visualizations. For a certain feature set, it is free to use. Microsoft has done a fantastic job in democratizing visualizations and simple data preparation/analytics via PowerBI. You can use various training, often free, offered by Microsoft, to learn PowerBI visualizations and get started.

For data mapping you should use a visual tool such as Visio or Lucid chart. Something that allows you to link data together.

There are multiple visualization tools available, catering to company preferences, needs, experience levels, and budget constraints. Excel or Google Sheets allow users to create bar charts, line graphs, pie charts, pivot tables, and other basic visualizations. Many widely used business programs, such as Salesforce, Google Analytics, and Netsuite, have built-in data visualization functionality. For more advanced requirements, Business Intelligence Software like Power BI, Tableau, or DOMO are great options. These tools are used to combine data from multiple sources to produce impactful visualizations. Additionally, tools utilizing programming languages such as Python or R can be explored.

I think visualizations can really be created in any tool and it depends on what the person has available to them. Visualizations don't need to be super complicated. Picking the best tool will always be decided by the individual creating the graphs and their preferences. We don't often have access to whatever tools we want where we work. If the company can't afford something like Tableau or Domo, Looker Studio from Google, is free, and does some pretty decent visualizations. It also offers a bunch of built in data connectors. It also depends on how often the visualization will be used and what internal/client adoption rate will look like per platform. If the audience is non-technical it's important to pick a tool that's easy to learn

To get the best visualizations, you need to have the ability to ask the right questions. It's necessary to analyze the data, and combine it with your intuition to build compelling visualizations

Ask for feedback from the people consuming the graphs. I don't believe that there's any single approach to creating a visualizations. It completely depends on the situation, the individual and the audience that will be consuming the visuals. Getting better at creating visualizations will only come by getting good at taking feedback and always finding ways to take complex data and ideas and make it simple for non-technical people. If a chart requires a lot of explanation it may be hard for people to learn and could lead to a lack of adoption. Be careful who to ask for feedback from though, and learn what kind of feedback will actually be helpful to take.

To enhance data visualization skills: 1. Learn from experts: Study visualizations created by professionals. 2. Practice: Create your own charts, graphs, and dashboards. 3. Master tools: Familiarize yourself with visualization software like Tableau or D3.js. 4. Seek feedback: Share your work for constructive critique. 5. Stay updated: Keep up with design trends and data storytelling techniques.

Study strong examples. One of the best ways to improve your data visualization skills is to study the work of others. Look for data visualizations that are clear, informative, and engaging. Pay attention to the design elements that are used, such as color, typography, and layout. Use a variety of chart types. There is no one-size-fits-all chart type for data visualization. The best chart type to use will depend on the specific data you're working with and the message you're trying to convey. There are many different types of charts available, such as bar charts, line charts, pie charts, and scatter plots. Experiment with different chart types to see which ones work best for your data and audience.

Practicing with Real Data The more we work with real-world data, the more proficient we become at creating relevant and insightful visualizations. Learning Design Principles Good data visualization is not just about conveying information; it's also about aesthetics and user experience. Understanding design principles is crucial for creating visually appealing and effective visualizations. Explore Data Storytelling Data storytelling combines data visualization with narrative to convey a compelling message. It's about using data to tell a story that resonates with your audience. Asking the right questions to understand the viz needs is also very important in knowing the best viz strategy. Seeking feedback it is a gift for improvement

To be fair, these two use cases are rarely needed in a sophisticated visual form. 1. Customer segmentation or cohort analysis: A matrix (i.e. table) would do it most of the times. Time on X axis and some other variable used for the segmentation on the Y axis. 2. Basket Analysis: Similar, products on both Y and X axis and values can be "times bought together", you can then order by that.

Anything with data analysis and visualizations will always be focused on ensuring that the end user/audience can understand what's being shared. A lot of projects aren't done with the end users in mind and then the analysis and insights learned won't really matter because they won't be implemented or understood.

Data visualization is a powerful tool for customer segmentation and market basket analysis. It simplifies the process of understanding customer behavior and item associations by providing visual representations that can be easily interpreted. This, in turn, helps businesses make data-driven decisions and formulate effective strategies to enhance customer satisfaction and drive revenue.

Something I do when preparing a report to all the various stakeholders in an organization is to understand what their needs and preferences are, so I can adapt my segmentation for them. For example: To the executive team, I need to be able to explain segmented data to them, without them being an expert in my function. To the organization I give more narrative and simpler details. My team will require more details on the specifics in context to the broader picture.