Data-Driven Communication Tactics

Crafting a Data and Analytics Strategy That Really Resonates For many organizations, articulating the tangible value of a data strategy can be a significant challenge. It's common to default to a technology-centric approach, leading to skepticism about solving a "problem" with a "hammer". 🔵 Strategy First, Technology Second Gaining buy-in for your data and analytics vision before diving into the technical details of the operating model. This prevents stakeholders from questioning the need for proposed technology solutions. Communication is key, and it must be segmented based on your audience – whether you're educating or informing (sideways; business partners), persuading (upwards; sponsors), or instructing (downwards; D&A teams). Each approach demands different content, length, and emphasis in your presentations. 🔵 Concise, Outcome-Led Vision Your vision statement should be remarkably concise, ideally 20-40 words, deliverable as an "elevator pitch". It should clearly state how your data and analytics team contributes to the top three organizational goals, identifies the specific stakeholders you aim to help, and outlines three mechanisms for delivering value. This also includes explicitly stating what you won't focus on, ensuring clarity and preventing dilution of effort. 🔵 Align with Business Transformations and Culture To ensure relevance, your strategy must connect with ongoing major business transformations within the organization. Furthermore, addressing cultural barriers to data-driven decision-making is paramount. I suggest framing the culture as "outcome-led" / "value-driven" and "decision-centric" rather than merely "data-driven". 🔵 Broaden The Appeal and Resonate, Wider Incorporate contemporary drivers and trends (e.g. how DA& teams are responding to Generative and Agentic AI), categorizing them as technology, internal, or market/societal factors, to demonstrate your strategy's forward-looking nature. 🔵 Defining Value and Measurable Impact Prioritize your primary stakeholders (ideally three), and for each, define the top three goals your team will help them achieve. For each goal, identify three measurable metrics, creating a "metrics tree" that clearly tracks your contribution to their success. Gartner defines three core value propositions for data and analytics: 1️⃣ Utility: Providing enterprise reporting as a service for common questions. Central team, allocated budget, data warehouse, etc. 2️⃣ Enabler: Facilitating business outcomes through self-service analytics, coaching, and projects based on business cases. 3️⃣ Innovation: Driving new initiatives like AI for decision making and prescriptive analytics. Each value prop requires a different delivery model, from service desks for utility to portfolio management for innovation, and these should be aligned. Collaborating with leaders like CIO, CISO, CAIO is also crucial for innovation efforts. Develop a D&A strategy that demonstrates tangible business value.

You were just put in charge of the data team at a 2500-person company. And guess what? On day one, the business has already asked about AI and new dashboards. It might be tempting to simply tell your stakeholders "No" or maybe start techno-dumping on why you currently can't implement AI. But that wall of techno babble will simply make their eyes glaze over. You're confusing and not providing clarity. So if you're looking to better to communicate here are a few techniques I use to help get everyone on the same page. 1. Analogies ✅ Do this: Use familiar analogies tailored to their world(do they like to golf, garden, etc) . "AI without reliable data is like building without foundation and on top of sand." ❌ Not that: Don't rattle off system dependencies or mention Kafka, dbt, and data contracts in your first meeting. 2. Impact Framing ✅ Do this: Translate everything into outcomes. "Right now, we can't confidently say which campaigns are actually driving qualified leads, fixing this could help us avoid wasting 100k on a campaign like we did last month." ❌ Not that: "Our data warehouse isn't set up to handle multi-touch attribution at the moment."(ok but why do they care?) 3. Cost of Inaction ✅ Do this: Quantify the downside, "If we skip the groundwork, we risk burning $200K on a model that breaks in production." ❌ Not that: Don't assume vague warnings like "this isn't scalable" will motivate change. 4. Maturity Models ✅ Do this: Show where you are on a crawl-walk-run spectrum, "Right now, we're barely in the 'descriptive' phase; if you ask a question like "How many subscribers did we lose last month due because they had credit cards expire, we wouldn't be able to tell you." ❌ Not that: Don't just say "we're not ready" without context, it sounds like you're saying "We can't" instead of "Here's what comes first." 5. Real-Life Examples ✅ Do this: Share stories of companies that wasted time or money chasing AI too soon. ❌ I guess I don't really know what the opposite is here… Hopefully this was helpful, and let me know if you've used any of these or other techniques to help get on the same page with the business!

One of the key pillars of a successful demand generation strategy is a diversified marketing mix. Recently, I had the opportunity to work with a client who initially relied heavily on just two channels—SEO and Paid Ads. Within 6 months, we transformed their approach from a two-legged strategy into a well-rounded marketing mix that now drives revenues from multiple sources. And we’re just getting started! How did we achieve this? ➡ Holistic Data-Driven Analysis: We began with a comprehensive audit of their current marketing efforts, identifying gaps and opportunities across various channels. A significant part of this was convincing the C-suite why relying on just two channels is a dangerous strategy. ➡ Targeted Channel Expansion: Instead of relying solely on SEO and Paid Ads, we expanded into Email Marketing, Social Media, and Referral Programs. Each channel was carefully selected based on the client’s audience and business goals. For email marketing, we created custom flows for both current customers and prospects, building an engaged audience through just-in-time, educational, and transactional emails. ➡ Consistent Messaging & Cross-Channel Synergies: I'm a firm believer in Ogilvy's "The medium is the message," so we ensured the brand message remained consistent across all channels. This created a seamless experience for the audience and strengthened the brand’s presence. We also ensured that channels like email and social media reinforced one another, driving stronger brand presence and conversions. ➡ Data-Driven Adjustments: Linear attribution by channel is outdated, so we had to first "sell" the idea of assisted attribution to the client. In our omni-channel world, it was crucial to analyze data and make campaign adjustments based on those insights. By closely monitoring performance metrics, we quickly optimized our strategies for the best ROI across all channels. ➡ Collaboration and Buy-In: As marketers, our real "selling" begins after onboarding a client, as we're constantly pitching new ways to drive demand. Achieving this transformation required strong collaboration with the client’s internal team and stakeholders. Together, we aligned on goals, brand positioning, and data insights to drive initiatives forward. Looking back, we could’ve taken the safer route of only managing the client’s paid media and organic search efforts, but that would’ve been short-sighted. Instead, we took a slightly riskier approach by launching new demand generation initiatives that might have got us fired, but it was in the best interest of the business. This strategy not only diversified their revenue streams but also made their marketing efforts more resilient and adaptable to changing market conditions. Would love to hear your thoughts....what are your greatest challenges with demand generation marketing?

Data-Driven Email Subject Lines: Maximizing Engagement in Crowded Inboxes Link in bio. Email marketing remains a high-ROI digital channel, with subject lines serving as the critical gateway to engagement. Compelling subject lines directly impact open rates and overall campaign success. Key Data Insights: Open Rate Influence: 47% of recipients open emails based solely on the subject line. Spam Detection: 68% of emails are flagged as spam based on the subject line alone. Personalization Impact: Personalized subject lines increase open rates by 19% on average. Optimal Length: Subject lines between 6-10 words generally yield the highest performance. Strategic Subject Line Crafting: Curiosity-Driven: Pose questions or offer intriguing hints. Example: "Unveiling Insights: [X] Revealed." Urgency and Scarcity: Emphasize time-sensitive offers. Example: "Limited Time: Exclusive Access Ends Tonight." Social Proof: Leverage testimonials and trends. Example: "Join Thousands: The Solution They're Choosing." Personalized Relevance: Tailor messages to individual recipients. Example: "John, Your Personalized Offer Awaits." Concise Clarity: Maintain subject lines under 50 characters for optimal mobile performance. Example: "VIP Access: Unlock Now." The Role of AI in Email Optimization: Predictive Analytics: AI analyzes extensive data to forecast subject line performance. Real-Time A/B Testing: Automated A/B testing refines messaging dynamically. Dynamic Personalization: Content adapts based on user behavior and demographics. Preo Communications specializes in data-driven email marketing, leveraging AI-powered insights to enhance engagement and drive results. We focus on aligning with audience behavior and industry trends to achieve optimal campaign performance. How are you optimizing your email subject lines for maximum impact? Share your strategies below. #EmailMarketing #MarketingStrategy #CustomerEngagement #AIinMarketing #PreoCommunications #DigitalMarketing #DataAnalytics

You can be the most technical data person in the world. But it doesn’t matter if you aren’t a good communicator. Of course, that takes years of practice. It might be harder to learn than the technical skills. But here's a few hacks you can use to 80:20 your way to effective communication: 1) Put important dashboards on a repeating cadence. The best way to stay both visible and valuable to stakeholders is to give them what they need before they need it. 2) Speak data, not English. A lot of times (it depends on context), stakeholders only want the raw facts—and not the opinions that come with them. 3) The best communicators talk in stories. Storytelling in data is as important a skill as any other. It’s the best way to paint a picture around your findings, and help drive home your conclusions. This might seem contrary to point (2), but a fact-focused story can achieve both. 4) Run training sessions. As a rule of thumb, someone who hasn't been trained on your BI tool won't use it. Aim to run training sessions quarterly, and update users on how to use your stack whenever you make important data assets. 5) Make Loom your best friend. Every data question you get is a key opportunity to educate an end-user (or multiple of them) on your data system. Each time you send an answer, record and share a Loom detailing how the user can find this for themselves. It's something they can go back to, and it's something that might be helpful for multiple people in the future. 6) Share the data on how people are using the data stack. How well you can do this depends on what tools are in your stack. I’ll shamelessly plug our product here—with Zenlytic, Admins can see what their end-users are asking for (and what they wish they could ask for). Sharing success with the team makes them more likely to adopt your tools. 7) Start a plot-of-the-week channel. This is another one that’s great for both visibility and culture-building, not to mention helping familiarize your team with your data function. Share interesting findings, key insights, or anything else you think others within your org will find valuable.

The longer I work in Data and AI, the more I realize that communication is the key. It's easy to think that data science, machine learning, or artificial intelligence is all about programming and complex math. While technically true, this is just half the story. 𝐁𝐮𝐭 𝐡𝐞𝐫𝐞'𝐬 𝐭𝐡𝐞 𝐫𝐞𝐚𝐥 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧: Is the value in the technical complexity or in how you communicate it? One phrase I remember from my communication coach now is, "You can always communicate everything." It's a simple phrase, but you can get something from others by communicating it right. How true the word above is reflected in my experience. When working for a client or employed, I was expected to solve problems with my technical expertise. In the early days, I will discuss the solution and result using many technical terms. You know what happens? A mess. Business people mostly do not understand how our technical things work. Many don't even want to know as long as we are solving their problems. Everything is all about the business, after all. When I became a founder, my train of thought also changes: 𝐃𝐨𝐞𝐬 𝐭𝐡𝐞 𝐭𝐞𝐜𝐡 𝐬𝐞𝐥𝐥 𝐢𝐭𝐬𝐞𝐥𝐟 𝐨𝐫 𝐢𝐬 𝐢𝐭 𝐚𝐥𝐥 𝐚𝐛𝐨𝐮𝐭 𝐭𝐡𝐞 𝐬𝐭𝐨𝐫𝐲? Well, I will answer that it's how you package the tech in a nice story. You can even see it yourself: the most engaging technical content has a story behind it. So, communication is important even if you work in technical fields. Here are some tips you can use to improve communication as data people: ✅𝐊𝐧𝐨𝐰 𝐘𝐨𝐮𝐫 𝐀𝐮𝐝𝐢𝐞𝐧𝐜𝐞: Tailor your message to what matters—code for peers, impact for leaders. ✅𝐓𝐞𝐥𝐥 𝐚 𝐃𝐚𝐭𝐚 𝐒𝐭𝐨𝐫𝐲: Structure insights as Problem → Insight → Impact for clarity. ✅𝐒𝐢𝐦𝐩𝐥𝐢𝐟𝐲 𝐒𝐦𝐚𝐫𝐭𝐥𝐲: Use analogies to relate complex ideas without losing depth. ✅𝐕𝐢𝐬𝐮𝐚𝐥𝐬 𝐖𝐢𝐧: A good chart speaks louder than a thousand data points. ✅𝐄𝐥𝐞𝐯𝐚𝐭𝐨𝐫 𝐏𝐢𝐭𝐜𝐡 𝐑𝐞𝐚𝐝𝐲: Explain your project in 30 seconds—what, why, so what. ✅𝐀𝐬𝐤 𝐟𝐨𝐫 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤: You're on point if non-technical folks get it. ✅𝐂𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: Own your insights—clarity with confidence earns trust. Do you have any experience and tips you want to share? Discuss it below!👇 Want to learn more and get daily data science tips in your email inbox? Subscribe to my Newsletter>>> https://lnkd.in/g639tmpD ——————— You don't want to miss #python data tips + #datascience and #machinelearning knowledge + #AI. Follow Cornellius Y. and press the bell 🔔 to learn together. ———————

How your data team is perceived is how they’re treated. If the business sees you as helpful but replaceable, you’ll stay stuck in the support lane. If they see you as strategic thought partners, you’ll be brought in early and trusted often. The difference, often, is in how you communicate. Here are 3 communication shifts to try out: 1. Stop speaking in outputs. Speak in outcomes. ❌ “We built a dashboard for marketing.” ✅ “We helped clarify which campaign was driving highest LTV.” 2. Pre-frame requests with strategic language. When someone asks for a report or data pull, try: “Can you share what decision this is tied to?” 3. Communicate like a thought partner, not a task manager. ❌ “We’ll have this by Friday.” ✅ “Based on what you’re solving for, we’re prioritizing accuracy over speed. We’ll share a draft Friday, then fine-tune together.” This signals ownership, alignment, and shared responsibility, not just task delivery. Thankfully, none of this requires new tooling. #dataleadership #softskills #datateams #dataculture #analytics #thoughtpartnership