You need to create a data visualization for your B2B marketing strategy. How can you find the best tools?

il est crucial de définir clairement l'objectif visé par la visualisation. Cette première étape oriente le processus de sélection de l'outil, garantissant ainsi que la visualisation finale sera alignée sur les objectifs spécifiques. Que vous souhaitiez présenter des tendances, mettre en évidence des modèles ou comparer des données, la définition précise de l'objectif facilite le choix d'un outil correspondant aux fonctionnalités nécessaires pour atteindre ces objectifs spécifiques.

Identify Needs: Tableau - Powerful and diverse data visualization capabilities. Ease of Use: Microsoft Power BI - user-friendly interface and seamless integration Customization: D3.js - Offers extensive customization options for those with coding skills. Integration Capabilities: Google Data Studio Integrates with Google services and other data sources. Advanced Analytics: Qlik Sense - advanced analytics with interactive data visualization. Scalability: Sisense - scalability, handling large datasets and complex visualizations. Interactive Data: Looker Interactive data exploration and real-time visualization features. Cost-Effectiveness: Zoho Analytics Trial Versions: Klipfolio Peer Reviews: G2 or Capterra

Defining your audience is crucial. Your executive team is going to require something different than your functional direct reports. You need a tool that is flexible, allows you create custom data views and can incorporate data from multiple platforms including channel tools like Google and LinkedIn, website analytic tools and finance.

(review your current data) focusing on lead generation MQL, Stage of sales, and pipeline review, then moving to CRM optimization and Cost Per Qualified Lead (CPQL). Define these data points or "sets," which is crucial, pending the data output. The accuracy of your source of truth and lifetime sales in the current pipeline is vital. Visualization tools then become easy if you're tracking the right metrics—business reviews, sales reviews, and meaningful conversations. (Acutual conversations and review) Incorporating continuous testing, learning from failures, and iterating are critical elements of refining and enhancing AFTER you define the metrics. (define your metrics) test (define and validate) then visualize.

Starting with a clear definition of purpose is a foundational step that reverberates throughout the entire process. Only if you get this first step right you will logically know what type of data should be included, which tools are collecting it and will need to connect to the data visualization tool you go with, and what kind of filtering will be required for swift and accurate conclusions. This proactive approach enables you to anticipate how relevant the resulting data visualization will be for your business decisions

Consider the types of data you have (e.g., sales, customer behavior, market trends) and the key insights you want to derive. Identify whether you need static charts, dynamic dashboards, or interactive reports. Next, assess your team's skill set and the tool's ease of use. Look for platforms that offer compatibility with your existing data sources and seamless integration into your workflow. One example of a versatile data visualization tool is Tableau. It allows you to create interactive and shareable dashboards with a user-friendly drag-and-drop interface. Tableau supports various data formats, making it easy to connect to different sources.

To find the best tools for data visualization in your B2B marketing strategy, start by evaluating your data. Assess the type, volume, and complexity of the data you have. Is it primarily quantitative, like sales figures, or qualitative, like customer feedback? Do you need to visualize large data sets or real-time data? Understanding the nature of your data helps in selecting a tool that can handle your specific needs effectively. From my experience, tools vary in their ability to process and visualize different types of data. Some are better suited for detailed statistical analysis, while others excel in creating more straightforward, visually appealing charts and graphs.

Data is everywhere, but can you access it, extract it, compare like with like, identify trends, correlations or causation by bringing it together and analysing it. But remember your output should be insight, rather than just more data. Ensure that your analysis is explainable, transparent, verifiable, and repeatable. Is every part of the data analysis (especially when handled by AI) understood? The best tool for synthesis and analysis may not be the right tool for visualisation.

In the beginning, and based on my experience in this field, you must first know what data you want to obtain, and then what you want to obtain after analyzing this data. In the case of working from B2B business, you must first analyze your data and then analyze the other party’s data. Perhaps one of the simplest methods is a SWOT analysis of strengths and weaknesses, and the data obtained can be represented and displayed using applications such as power BI.

Use a tool like KNIME - simply import your data into the workspace and use a data explorer to understand Your data and evaluate your data. Ensure you conduct a proper data exploratory phase to understand your data and identify where it’s lacking. Clean up, standardise and fix missing values before actually using the data to create visualisations.

This goes without saying and many of us do this extensively. Critical thing to note is to do comparison between products of the same category i.e to do an apple to apple comparison. Otherwise it will be difficult for us to evaluate. Also we should come up with a set of standard feature sets that we need to evaluate across platforms. Without that we cannot just gaze around the product and select because of a few monetary reasons.

Going back to the definition of a tool - it makes a specific task easier or more efficient. If it looks like you're about to birth a small industry in data handling, stop and think. Keep asking if there is a simpler way of doing it. Sometimes PowerPoint or Excel can handle the task (formatting idiosyncrasies notwithstanding). Also, consider the next step in the value chain. Do your audiences need to be able to further manipulate the data (filter, time series, export)? You may benefit from discussing dashboard options with stakeholders to consolidate efforts and do less data handling in the long run.

As an Agency, we've spent a ridiculous amount of time and money on data visualization tools and learned these lesson the hard way... It's critical to not only test how the tool fits into your current tech-stack, workflow and reporting lines, but ALSO to make sure it's serving the original purpose you've defined. Don't get distracted by shiny objects, the data is only ever as good as how and what it's being used for.

Utilize actual data from your marketing campaigns to see how each tool handles and presents it. Focus on the tool's ability to handle complex data sets and its flexibility in showcasing different types of data visualizations. Consider ease of use: how intuitive is the tool for team members who may not have extensive data analysis backgrounds? Also, assess the level of customization available – can you tailor the visualizations to align with your brand's visual identity? Getting feedback from various team members, especially those who regularly interact with data, can provide valuable insights into the tool's practicality and effectiveness in your business context.

Begin with a comprehensive online search to identify popular tools, considering factors such as user-friendliness, features, and integration capabilities. Next, check reviews and testimonials from other businesses to gain insights into their experiences with different tools. Look for platforms that align with your marketing goals and have positive feedback regarding reliability and customer support. Once you've narrowed down your choices, conduct a hands-on evaluation by using trial versions or free plans offered by the tools.

Que el proceso desde la data sea innovadora y de mejora continua, es para mí el atractivo del valor y que da el conocimiento para una toma de decisión pensando en él hoy inmediato y en la prospección del futuro, sabiendo que ese futuro cuando sea hoy el dato nos permitirá corroborar o ajustar ??

There will no doubt be a growing suite of AI-based tools with incredible specificity in the types of data, analysis, and visualisation they can handle. Make sure they support human insight and decision-making, rather than obscure important information and disintermediate people who should retain accountability and credibility. Transparent | Explainable | Verifiable

1.Tableau: Best for: Complex data sets and powerful analytics. 2.Microsoft Power BI: Best for: Integration with Microsoft products and ease of use. 3.Google Data Studio: Best for: Easy sharing and accessibility. 4.QlikView/Qlik Sense: Best for: Data discovery and business intelligence. 5.Looker: Best for: Big data and custom-built applications. 6.Domo: Best for: Cloud-based business intelligence. 7.Sisense: Best for: Scalability and data integration. 8.Infogram: Best for: Interactive online charts and infographics.

It's also really important to have a network that you can rely on. There are so many choices in the MarTech space, that having a trusted network to share their experiences and help you narrow down the field of choices can be a great timesaver.

Getting referral customers or users is important. This will help us understand the product better and also some of the intangible aspects like customer service, response time, etc.,

Track how and when the output is used in reviewing performance, identifying challenges and potential solutions, and influencing decision-making on future campaigns or strategic choices. If it is underutilised, seek to understand the reasons. If the value isn't there, don't be afraid to make changes.

Select a tool that can visualize the executive summary for those you report to, but also go deep into the nitty gritty for marketing teams to monitor performance and optimize. Make sure you map all the conversions that are high priority, as well as any micro conversions that are worth reporting, and strategic touchpoints, to map the most prominent journeys and funnel stages efficiently.

Without a self-reporting attribution, you lose valuable data insights, even if you use the best tools. Indeed, it’s not a one-time project, as the market continues to go up and down, and many trendy topics today may become older a few days later.