Wie können Sie Kundendaten mithilfe von Visualisierungstools am besten interpretieren?

Based on experience I think over the course of 15 years in my career I can definitely see the positive changes in the way topics are presented for example slides are getting shorter and smarter instead of unnecessary details. Knowing the audience and age for sure makes a big difference in the way u present and speak about. The more direct the message the better the decision made. Also the time spent to present a topic should not be long nor short Anything presented or visualised in the slides should be high level to allow the speaker explains the topics in details

One thing I have found helpful at this stage is to clearly define the questions being asked and who is doing the asking. I've also heard it put as "What problem are you trying to solve and who are the decision makers?" This lays the foundation for the decisions that follow. IE: what visualizations are needed and what tools should be used.

Some key elements to consider when choosing a data visualization: - what purpose will it serve? Who will use your data visualization “directly”. How senior are they? The more senior the less time they’ll spend on your viz. - what is the simplest datum form you can use, that is still insightful? And the most complex? Choose something in between and make sure your users are comfortable with it. This may determine the life service of your visualization. - how often will it be accessed? Can it be refreshed frequently enough? - what devices will it be accessed from? If your users are on the move and using mobile phones, a complex graph could be unusable(=useless) - is it exportable as is, and as data? The further it can be taken, the better!

Data visualisation to me is always first finding the target audience and how to keep them engaged through a storytelling, to tell the story we need to paint a picture. A picture says a thousand words, hence an story can further create interest if trending can be identified and comprehended by the audience. The key is to keep the audience looking for more!

I have found audience in choosing visualisation tool as very critical and in this regards dealing with customers is more complex as the mix of your customers will be key in considering a visualisation tool to adopt.Remember that the expectations of each stratum within your customer mix may vary, hence using the same visualisation tool across board could not provide the best experience or outcome.Again the data set is also worth considering as there are visualisation tools preferable for qualitative data presentation and analysis and same applies to quantitative data.

Choosing the right visualization tool, such as Tableau or Power BI, and implementing best practices in data interpretation are critical for logistics professionals. These tools empower users to derive actionable insights, optimize operations, and enhance customer experiences. Understanding business objectives, ensuring data quality, and fostering data literacy are crucial components of a successful visualization strategy. By combining powerful tools with thoughtful practices, logistics experts can unlock the full potential of customer data for operational excellence.

When interpreting customer data with visualization tools, selecting the right tool is crucial. Consider the nature of your data (numerical, categorical, spatial), the desired level of flexibility, and the interactivity needed for analysis. Tools like Tableau, Power BI, or Qlik Sense are suitable for in-depth exploration, providing filtering and drill-down capabilities. On the other hand, for creating and sharing elegant visuals, tools with pre-designed templates such as Canva, Infogram, or Piktochart may be preferable. Tailoring your choice based on data characteristics, audience, and goals ensures effective and impactful visualizations.

Sure thing! Think of tools like PowerBI and others as your data visualization chefs. They're great at cooking up customer data into something palatable. But here's my secret ingredient – I'm all about that custom seasoning. Picking the right tool is like choosing the perfect spice; get it right, and your data dish will be Michelin-star worthy! And don't forget to set your kitchen on autopilot. If you're manually chopping and dicing data, you're going to spend more time playing sous-chef than actually savoring the data delicacies. So, automate the prep work, or you'll end up with more chopping than chomping! 🍳🔮

The composition of your audience is crucial in determining the appropriate tool, the nature of the data visualization (dynamic or static), and even the specific filters to apply, as these elements can greatly influence the effectiveness of your presentation. For instance, leaders and executives often prefer concise executive summaries accompanied by static charts, as they typically seek high-level insights without delving into the minutiae of the data. On the other hand, individuals in functional roles may require interactive dashboards that offer the flexibility to explore and dissect the data in detail, catering to their need for a more hands-on analytical approach.

Every tool available in the market has it’s own functionalities, you need to be mindful which tool best suits for your data. In Supply Chain, we have vast customer data related to Product Planning, Inventory Management, demand forecasting, etc. we usually select the tool which can ease our desired data partitioning process without making it complex and help us in easily visualising business KPIs.

Utilize visual tools like charts and graphs to measure data effectively. Present key metrics visually for quick comprehension and analysis. As data visualization pioneer Edward Tufte said, "The greatest value of a picture is when it forces us to notice what we never expected to see."

Think of following best practices in data visualization as adhering to principles of good design. Use colors, charts, and labels thoughtfully—similar to how an artist follows principles like balance and harmony. Following best practices ensures that your visualizations are clear, accurate, and easy for the audience to interpret.

To best interpret customer data using visualization tools, it's crucial to choose the right type of visualization that matches the nature of the data, such as using bar charts for comparisons or line graphs for trends over time. Best practices include keeping the design simple and uncluttered for easy interpretation, and highlighting key insights or patterns clearly. It's also important to ensure that the visualizations are accessible and understandable to all intended audiences.

One can harness the power of visualization tools such as Tableau, Power BI, and Google Data Studio. These tools empower teams to craft compelling dashboards, charts, and graphs, providing a clear lens into customer data. By translating trends and preferences visually, these tools elevate decision-making, fostering operational efficiency and bolstering overall customer satisfaction.

1. Define Clear Objectives: Clearly outline the goals of your visualization – whether it's identifying patient trends, assessing treatment efficacy, or optimizing resource allocation. 2. Understand Your Audience: Tailor visualizations to your audience, whether it's healthcare professionals, administrators, or researchers. 3. Data Cleaning and Preprocessing: Ensure data accuracy by cleaning and preprocessing it before visualization. 4. Choose Appropriate Visualizations: Select the right chart types based on the nature of your data. For instance, use line charts for time-series data and bar charts for comparisons. 5. Use Color and Annotations Wisely: Employ color strategically to highlight important information.

Testing and refining are to be treated as litmus tests when it comes to the final rollout of the dashboards. Taking feedback from the management is of utmost priority in growth stage organizations. Reason being , the leadership and management will have the most current insight of the market and clients associated. Their need for current metrics can change based in order of their strategy. So the tool should also be built keeping in mind what the leadership wants from it alongside tracking customer progress in different aspects. Testing with more variables that can point to the future preference of a customer is also important as it helps the firm to prepare in advance for a swing in customer mood, choice , strategy & execution.

This one step has helped me tremendously in my data and analytics career so far. Iterative testing is essential for refining visualizations. One must gather feedback from your audience, analyze how well the visualizations convey the intended message, and make necessary adjustments. Continuous refinement ensures that visualizations remain relevant and effective as your data evolves or as you gain a deeper understanding of your audience’s needs.

Solicitation of feedback is key; and it’s important to utilize outside voices who weren’t part of the project. A ‘fresh set of eyes’ on the data, if you will. This will help to ensure the data can be interpreted clearly and concisely by your target audience

My family members are the victims of my visuals! Many times I used to show them my presentations to get their feedback. Because I paid a lot of effort in preparing the visuals (graphs, tables and figures which I always prepare by myself), and spent too much time in revising the manuscript, sometimes I miss something. Like when your eyes are used to read certain article you have prepared, you may miss a word, a spelling that had been there from the beginning. But when one of your colleagues ask you to have a look at his/her piece, your eyes quickly catch the issue 😉. It's because we are accustomed to what we do for long, thus it is advisable to take a break then to come back and refine your visuals. Or, to ask for the opinions of others.

In my experience I have used Excel, SQL & Tableau to view same data. I have noticed that data cleaning has taken the most time taking process. It is important not to be distracted by single set of findings. I have tested them in different dimensions to ensure my analysis is accurate. The biggest problem is data is more often missing or incomplete. Hence refined analysis will depend on refined data.

One thing I always do when starting with any dashboard is, first study the use case scenario, business context of the dashboard & what user is going to analyze from that dashboard. Often I ask end-user terminology used in the data, visualization, or any connected thing with it. This helps me prevent developing illogical calculations. Also, this sometimes help me suggesting end-user alternative visuals or dashboard presentation with better analytical possibility. End-user generally prefer developer to understand their use case, which helps communicating better.

In this most important step is cleaning the data. Whenever I want to present the information in the form of graphs, I make sure that that data is properly cleaned. After cleaning the data, give some time to understand data by making sure that how you are going to represent the data in more interactive way so you grab audience attention. This are some initial checks to be done before starting with Visualisation tools.

Before interpreting any data into visualisation, data integrity needs to be ensured as if they were sourced from right sources or not. Data purity should be checked if they pertain to the right information else the interpretation becomes meaningless.

I will recommend to follow these steps to make your customer data more informative. 1. Select visualization tools like Tableau based on your data. 2.Use charts for demographics, track purchase behavior with line charts, segment customers visually, and map their journey with flowcharts. 3.Showcase popular products and gauge satisfaction with visual surveys. Incorporate interactive dashboards for dynamic exploration. 4.Keep visuals clear and concise to align with your analysis goals and regularly update for ongoing insights.

To interpret customer data using visualization tools, you can follow these steps: -Identify the data you want to analyze, such as customer demographics, purchase history, website traffic, etc. -Choose the right visualization tool based on the type of data you are analyzing and the insights you want to gain. -Create the visualization by selecting the appropriate data points and creating a visual representation of the data. -Analyze the data to gain insights, such as identifying trends, patterns, and outliers. -Communicate the insights you have gained from the data by creating a report, presenting the data to stakeholders, or sharing the insights with your team.