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17 | 03 | 2025

How to choose your next martech solution wisely

Choosing a martech tool is a strategic decision for any team, as it impacts productivity, process automation, and business scalability. However, the selection process is not straightforward. In this article, we explore key considerations before acquiring a marketing technology solution, current purchasing trends, and best practices to ensure an effective choice. Considerations for choosing a martech 1. Define needs and objectives Before making a decision, companies should evaluate multiple factors to ensure the solution fits their needs and objectives. The first step in choosing a marketing platform is to be clear about the problems they want to solve. According to Gartner, 54% of marketing teams seek to improve operational efficiency and 44% consider reducing costs. Therefore, it's important to understand how this purchase aligns with a long-term strategy. 2. Understand the category that defines the platform If all marketing platforms say the same thing, it's necessary to consider the category. Given the high degree of specialization among platforms, being an agency, adtech, or martech doesn't mean the same thing. For example, although we can all provide a solution for "media optimization," talking about media auditing isn't the same as talking about media management: while the former refers to a Data Analysis category, the latter refers to an agency or adtech category. 3. Evaluate the reputation of the technology Customer reviews and ratings are a decisive factor when purchasing software. Evaluating awards, recognition, and badges is essential to understand whether a technology is keeping pace with the industry it operates in. According to Gartner, four in ten buyers rely on user reviews before making a decision. In this regard, Bunker DB is positioned as the only Latin American martech recognized by Software Advice in the data analysis category. 4. Prioritize ease of implementation A marketing platform may seem ideal in terms of functionality. However, its ease of implementation will dictate its adoption. What good is having the best solution on the market if it's so complex that we barely get any use out of it? Would you prefer to use 10% of a complex solution or 100% of a simpler one? At this point, it's important to evaluate the estimated implementation time and the estimated learning curve. 5. Prioritize integration capacity Likewise, becoming independent of an IT team requires simple and high-quality integration with other platforms. For example, if the data structure is stored in AWS, Google Cloud, or Microsoft Azure, having that export is essential. If my most used platforms in marketing are Google Ads, Facebook Ads, and TikTok Ads, those integrations will also be required. At this point, it's important to evaluate integration with native connectors. 6. Look for onboarding and technical support Understanding the quality of technical support is also essential. Will it be self-managed, or will there be professionals available to assist me? Will it be in my local language, or are there language limitations I should consider? How many professionals and teams does the platform provide for successful onboarding? A faster onboarding process will allow for a more immediate time-to-value for that solution. 7. Cost as a decisive factor The price of a platform isn't the only cost to consider. When calculating the total cost, you should consider subscription or license costs, one-time implementation and configuration costs, and the human costs of training, technical support, and consulting. For example, does the platform charge per user per month? Does the platform offer discounts for multiple brands or for purchasing a solution package? Why is there hesitation when choosing a martech? Currently, it is estimated that more than 14,000 companies make up the martech ecosystem. Unlike a decade ago, the problem today is not a lack of options, but an overabundance of offerings. With so many alternatives, choosing the right martech has become a challenge for brand managers and growth marketing teams. Among the main difficulties in effectively filtering a handful of relevant options, we find the following: Lack of relevant content. Increasingly, all companies say the same thing or something similar, making it difficult to conduct an in-depth evaluation. All companies may promise to "maximize ROI," "optimize time," or "automate processes," but there are significant differences in how each does it. Without reliable and detailed content, it is difficult to know who to trust. More stakeholders involved. The technology selection process involves more teams within a company. The teams evaluating marketing technology include IT, Marketing, Operations, and Sales, each with their own timeframes and demands. This adds complexity and lengthens decision-making times. Perceived risk and shorter contracts. The cost of a martech leads companies to prefer shorter contracts to reduce risk in case the project doesn't perform as expected. It's very common today to find annual contracts with termination clauses of six months or less. Trends in martech purchasing As if that weren't enough, the martech ecosystem is changing, and certain trends are consolidating when evaluating technology selection. Among them, we identify: Less dependence on IT teams. Given the multiplicity of stakeholders in this decision-making process, there is an explicit focus on mitigating IT department decision-making times, prioritizing business teams to have greater influence in decision-making. In fact, today, 60% of marketing technology buyers are marketing, sales, and operations leaders. This reinforces a trend toward more accessible solutions and user-friendly interfaces. A gap in marketing performance maturity. According to a study by McKinsey, 41% of marketing leaders surveyed consider their marketing performance measurement method to be mature, and only 30% acknowledge that they dynamically adjust their investment. Purchases based on real-life demonstrations or tests. Buyers are no longer satisfied with sales presentations. 40% of buyers seek personalized demonstrations before purchasing. Increasing prioritization of AI. Artificial intelligence tools are helping companies analyze performance data and compare solutions more objectively. 35% of buyers use AI to compare prices. slice1 Final recommendations for choosing the best solution The process of purchasing a martech platform has evolved, and companies must adapt to new strategies to make more informed decisions. Define goals and needs from the start. Prioritize highly rated solutions on comparison platforms. Consider solutions with a good reputation among their customers. Evaluate compatibility and integration with your current tools. Take advantage of trends like AI, live demos, and personalized recommendations. Evaluate ease of implementation. Evaluate the quality of onboarding and the customer support team. Consider the total cost, not just the initial price. Making the right decision can accelerate your company's growth and improve operational efficiency. Make sure you choose wisely!

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12 | 05 | 2025

Visualization 101: 9 essential principles of data storytelling

The second language of business Being able to read, analyze, work with, and communicate data is no longer optional—it's a key skill. So much so, that Gartner calls it “the second language of business.” According to the latest report from The Data Literacy Project, data literacy will be the most in-demand skill by 2030. In fact, 85% of leaders already consider it as essential as knowing how to use a computer. As marketing professionals, our job is to turn data into actionable insights to drive business decisions. Therefore, delivering clear and useful information is essential to our role. In this article, we’ll share the principles we use every day to turn data into results. Data visualization as a strategic form of communication As marketers, any data we present—beyond a raw list of numbers—is a form of visualization. And just as a picture is worth a thousand words, a powerful visualization can enhance data comprehension and support our communication goals. Whether it’s a structured table, a simple chart, a detailed infographic, or a complex 3D model, our job is to help the audience understand the data. “An editorial approach to visualization design requires us to take responsibility for filtering out the noise and identifying the most valuable, impactful, or relevant dimensions of the topic.” —Andy Kirk Data visualization is not about making pretty charts—it’s about communicating clearly. If the data isn’t clearly communicated, it’s useless. And if the story isn’t remembered shortly after it’s presented, it wasn’t the right story to tell. Every decision we make—about typography, color, visual weight, chart type, layout, scale, labels, or supporting text—impacts how our audience interprets the data. The three real ingredients to communicate with data Communicating with data is not just about having the right information. What matters is how you show it. At Bunker DB, we experience this every day: data is useful only if it helps you understand and decide faster. Here are the three essential ingredients to make that happen: Data that speaks. It doesn't have to be just numbers. It can be user comments, qualitative insights, or standard metrics. What matters is that it makes sense for your goal and context. Visuals that show. A good chart is worth more than 20 rows in Excel. A dashboard that highlights key points helps you spot patterns instantly. That’s what turns data into action. A story that connects. This is the heart. Without a narrative, data is scattered. The story provides structure, context, and explains the "why" and "what for." And most importantly, it engages your team or client. When you combine clear data, powerful visuals, and a compelling story, you unlock those moments of clarity that transform decision-making. Use these nine principles to connect those three elements effectively: 1. Data alone doesn't tell a story Don’t assume that a chart or data table automatically conveys your message. Think carefully: Is this visualization necessary? What essential part of the data am I showing? How could it be interpreted? Are there alternative readings? As marketing professionals, we should never let the audience apply their own editorial judgment to our data. Every data point should support our perspective, and our focus should be on building and clearly communicating that point of view. That’s why the connection between the data and its visualization must be direct and unambiguous. If the data backs up your argument, the visualization should make it even more obvious. 2. Not all data needs to be visualized Not every data point requires a visual. Often, our instinct leads us to create a chart just to communicate a number. Avoid overloading what's already obvious. A chart isn’t always necessary to make something meaningful. For example, in a monthly report, using a pie chart to show an 80% vs 20% split is unnecessary—just state, “80% of sales came from Product A.” If the data is already clear, don’t overcomplicate it with unnecessary visuals. 3. Keep your visualizations simple No one wants to decode a chart just to understand the point. Focus on what matters. If you're trying to support an idea or drive a decision, make sure the visualization is clear and direct. Don’t fall in love with a pretty chart—make sure it actually communicates what you need it to. Don’t expect your audience to have the time or desire to do complex analysis. In fact, overly complex visuals can mislead. Avoid confusing elements like broken axes, lack of context, or 3D charts that distort meaning. 4. Decluttering: Less is more This is a golden rule for clarity. Data visualization is more like telling a short story than writing a novel—you need to remove anything that’s not essential. If your report has a beautiful visual with three fonts, six colors, and trend lines reinforcing the obvious, chances are no one will get the message. Decluttering isn’t just aesthetic—it’s critical. Remove anything that doesn’t support your story: gridlines, borders, 3D effects, shadows, decorative elements. When in doubt, leave it out. 5. Your message defines visual hierarchy Not everything deserves equal visual weight. Visualizing data means choosing where to focus attention. If you want to highlight a campaign’s jump in performance after optimizations, don’t bury it in a full historical series. Sometimes all you need is to guide the viewer’s eye. Use color sparingly to emphasize key data—one or two colors used effectively are more powerful than a rainbow without meaning. 6. Use simple structures to tell your story Your audience doesn’t want to see everything—they want to understand what matters. Don’t overload a presentation with 15 charts if you only have 15 minutes. Don’t write a long email if you only have 3 minutes of your reader’s attention. Use simple storytelling structures to engage, contextualize, and drive decisions: Situation: Where are we? Complication: What challenge emerged? Resolution: What did we do? What was the impact? 7. Design for the user, not for yourself A common mistake in BI teams is designing visuals like final artwork. But good visualizations are built for the viewer, not the creator. Structure your dashboards so they’re interactive and easy to filter by brand, channel, date, or key variables. This way, insights become actionable tools—not just static reports. Engage your audience, ask questions, and build interactivity wherever possible. 8. Give meaning to the numbers A key metric (KPI) is not automatically understood. A number without context is just a number. If you show 10,000 sales and someone asks, “Is that good or bad?”, you missed the mark. Always provide context: Comparisons: “+23% vs last month” Relevance: “2x industry average” Impact: “Generated 1,500 qualified leads” 9. Pay attention to color, labels, and order Effective data visualization isn’t just about data—it’s about design. Use color to create meaning—green for positive, red for negative. Ensure all labels are easy to read. Organize data logically—by value, alphabetically, or chronologically. A good test: show your chart to a colleague. If they don’t understand it within a few seconds, it needs refinement. Conclusions Data visualization is not just a technical skill—it's a strategic tool to communicate, influence, and drive smarter decisions. That’s why mastering these principles is key to turning numbers into concrete actions: Are your visualizations telling the right story? Does your team speak a common visual language? Are your data framed within your area’s storytelling? Are your dashboards easy to read, navigate, and act on? A great visualization doesn’t just inform—it inspires smart decisions. Apply these principles and take your data communication to the next level. References Qlik & Accenture. (2022). The seven principles of data literacy. The Data Literacy Project. https://thedataliteracyproject.org/wp-content/uploads/2022/11/The-Seven-Principles-of-Data-Literacy-by-Qlik-and-Accenture.pdf Duncan, D. (2018). Lies, damn lies and data visualization. CIOReview. https://data-visualization.cioreview.com/cioviewpoint/lies-damn-lies-and-data-visualization-nid-27117-cid-163.html