Marketing Technology Trends

Cold Calling Is Dying. Here’s What’s Replacing It. The numbers don’t lie: • Cold call success rates have dropped to 2.3% in 2025, down from 4.8% last year (Cognism). • 72% of sales calls never reach a person, and it takes 8+ dials to connect with just one prospect. • Only 28% of reps still view cold calling as effective. Meanwhile, high-performing teams are doing something different. Research-Driven, Insight-Led Outreach Wins: • Reps who thoroughly research their prospects are 3x more likely to succeed (Clevenio). • Prospect-specific research can lift conversions by ~30%. • Insight-led outreach builds trust before a call is ever placed. Email and Social Are Outpacing Phone-First Approaches: • Personalized cold emails outperform generic ones by 32%; average reply rates are 8–9%. • 78% of social sellers outsell peers, and social-enabled teams hit quota 66% more often. Takeaway: 1. The call is no longer the first touchpoint. It’s the third or maybe the fourth; it’s only viable once you have demonstrable engagement via other channels. 2. Buyers start with research—so should you. Start with research. Deliver value. Leverage email and social. Then—and only then—call with context. You’re no longer the teacher like when you were knocking on doors. 3. This is how modern sales works. And this is how trust is built at scale. Welcome to the future, my friends. 🙌🏾 #NervousSystemsStrategist #SalesLeadership #ModernSelling #ColdCalling #SalesDevelopment #InsightSelling #SalesStrategy #SalesEnablement

The full State of Martech 2026 report is here. 🙌 And it’s free. Ungated. Attached directly to this post as a PDF. No landing page. No form to fill out. No “leave a comment and we’ll DM you” hoop. Frans Riemersma and I have spent the past 6 months analyzing where martech is really headed — across the 15,505 products in the 2026 Marketing Technology Landscape, our survey of 200+ marketing leaders, and a deep dive into 70 AI use cases reshaping marketing. A few of the big stories inside: 1️⃣ The martech landscape is effectively flat for the first time in 15 years — but underneath that headline, the market is anything but static. 1,488 products were added. 1,367 were removed. We reveal which categories are growing, shrinking, or churning out the old with the new. 2️⃣ It took the martech landscape 15 years to reach 15,000 products. The MCP server ecosystem hit nearly twice that in 18 months. Anthropic, OpenAI, Google, and Microsoft all converged on the same open protocol — companies that agree on roughly nothing else. We unpack what this means for the integration physics that have shaped martech for two decades, and where agentic gravity is pulling the stack next. 3️⃣ The 20-year debate between “consolidate on a suite” and “best-of-breed” has a 2026 answer: neither. The stack is stratifying. AI-native tools are winning the creation layer. Incumbent SaaS still owns much of the orchestration layer, where workflows and data already live. And homegrown AI is showing up where proprietary data, business logic, brand experience, or customer context really matter. Across our AI use case data, leading teams aren’t choosing between AI in existing SaaS, new AI-native tools, or homegrown solutions. They’re often using all three — sometimes for the same use case. 4️⃣ AI doesn’t make martech complexity disappear. It exposes it. When a human is in the loop, they can quietly compensate for bad handoffs, messy data, disconnected systems, outdated product information, and fuzzy governance. When an AI agent is orchestrating a customer interaction, answering a buyer’s question, recommending a product, or triggering a workflow, your internal architecture leaks directly into the customer experience. We dig into what context engineering means for marketing ops in this new reality — and why the inner game now shapes the outer game more than ever. Huge thanks to GrowthLoop, Hightouch, Knak, MoEngage, Pegasystems, Progress Software, and SAS for sponsoring this research and making it possible for us to share it freely with the community. 🙏 Download the PDF below. Read it. Share it. Challenge it. Give us your hot takes. The chrysalis stage of martech is messy — but something beautiful is taking shape. 🦋 #marketing #martech #AI #MartechDay

In marketing, choosing the right campaign strategy — such as whether to reach customers through SMS or email — is critical. These decisions shape how effectively brands connect with their audiences. In a recent tech blog, Klaviyo’s data science team shared how they used uplift modeling and counterfactual learning to help marketers deliver more personalized campaigns at scale. The team began with a simple but powerful insight. Instead of defining audience segments first and then randomizing within each group to test different strategies, it’s mathematically equivalent to randomizing treatments first and segmenting afterward. In practice, this means you can run a single randomized experiment — for example, comparing SMS versus email — across the entire audience, and later analyze how different subgroups responded to each treatment. Building on this foundation, the team applied uplift modeling to estimate how each recipient would respond under different treatments. The result is a system that predicts which customers are more likely to engage via SMS versus email — and automatically personalizes campaign delivery accordingly. The team ultimately turned this approach into a product feature, empowering marketers to design smarter, data-driven strategies with minimal manual testing. It’s a great example of how causal inference and machine learning can go beyond analysis — directly shaping how real-world marketing decisions are made. #DataScience #MachineLearning #UpliftModeling #CounterfactualLearning #Personalization #Marketing – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gBgBiTJj

For a long time, MarTech kept solving the same problem by adding more tools. More dashboards, more features, more “connect everything to everything”. Somewhere along the way, stacks became heavier not smarter. Most brands don’t actually have a loyalty or retention problem. They have an attention problem. Customers don’t leave because your product suddenly became bad. They leave because your brand slowly fades out of their daily life. No friction, no drama...Just… absence. What’s coming next in MarTech isn’t another wave of shiny features. It’s a shift in how systems are built. Less campaign thinking, more "always-on" systems that react to people, not calendars. Less rigid segmentation, more fluid understanding of where someone is, right now. AI is going to accelerate this, but not in the way most decks describe it. It won’t magically make marketing better. But it will make bad marketing very obvious. If your strategy is noise, AI gives you more noise. If your strategy is relevance, AI finally makes that scalable. Loyalty is also about to be redefined. Not as points or tiers, but as a byproduct of being useful, showing up at the right time and not wasting people’s attention. The best brands in the next few years won’t feel like they’re “doing marketing”. They’ll feel present, familiar. Almost natural in a customer’s life.

Here's a costly mistake I see founders make repeatedly: They think product validation means they understand their customers. Let me explain the difference, because it's existential: Product validation tells you: - If people can use your product - Whether features work as intended - If users are generally satisfied But deep customer discovery reveals: - The real triggers that drive purchase decisions - Hidden anxieties that slow adoption - True competitive alternatives (hint: often not who you think) When founders skip proper customer discovery: → Marketing messages don't resonate → Customer acquisition costs go up → Conversion rates are bad → Retention suffers Good customer discovery gets to the heart of: 1. Customer Understanding → The "why" behind customer decisions → Deep motivations and pain points → Real decision-making journeys 2. Go-to-Market Reality → What actually drives purchasing decisions → Which channels reach customers when they're ready to buy → How long your sales cycle really is Which, in turn, helps you decide: → Where to allocate your limited resources first → How to sequence your marketing investments → How to brand your product so it’s as sticky as possible Companies that get this right see the difference quickly: - Marketing messages that instantly resonate with prospects - Lower customer acquisition costs across channels - Faster sales cycles with higher conversion rates - Resources focused on what actually drives growth Remember: Good product feedback alone isn't enough. You need to understand the deep "why" behind customer decisions before you scale. ♻️ Found this helpful? Repost to share with your network. ⚡️ Want more content like this? Hit follow Maya Moufarek.

Artificial intelligence offers transformative potential for businesses, particularly in predictive sales analysis and marketing strategy optimization. By collecting and analyzing comprehensive data from various sources, such as sales histories, customer interactions, and online behavior, companies can utilize AI's predictive power to stay ahead in the market. AI's predictive modeling, leveraging machine learning and deep learning, uncovers patterns in vast datasets—helping businesses tailor their strategies effectively. Inventory management also benefits from AI by predicting product demand, optimizing stock levels, and reducing surplus risk. AI enhances personalized marketing strategies by tailoring messages and campaigns based on individual customer preferences and behaviors, boosting engagement and brand loyalty. Dynamic pricing supported by AI enables real-time price adjustments based on buying patterns and market conditions, ensuring competitiveness and profitability. Customer sentiment analysis through AI provides insights from reviews and social media, allowing businesses to refine their products and marketing efforts. Lastly, integrating AI solutions requires robust infrastructure, ensuring companies can scale and adapt their technological capabilities to evolving market needs without disrupting operations. This adaptability fosters continuous innovation and efficiency. #ai #sales #marketing

That’s the thing about feedback—you can’t just ask for it once and call it a day. I learned this the hard way. Early on, I’d send out surveys after product launches, thinking I was doing enough. But here’s what happened: responses trickled in, and the insights felt either outdated or too general by the time we acted on them. It hit me: feedback isn’t a one-time event—it’s an ongoing process, and that’s where feedback loops come into play. A feedback loop is a system where you consistently collect, analyze, and act on customer insights. It’s not just about gathering input but creating an ongoing dialogue that shapes your product, service, or messaging architecture in real-time. When done right, feedback loops build emotional resonance with your audience. They show customers you’re not just listening—you’re evolving based on what they need. How can you build effective feedback loops? → Embed feedback opportunities into the customer journey: Don’t wait until the end of a cycle to ask for input. Include feedback points within key moments—like after onboarding, post-purchase, or following customer support interactions. These micro-moments keep the loop alive and relevant. → Leverage multiple channels for input: People share feedback differently. Use a mix of surveys, live chat, community polls, and social media listening to capture diverse perspectives. This enriches your feedback loop with varied insights. → Automate small, actionable nudges: Implement automated follow-ups asking users to rate their experience or suggest improvements. This not only gathers real-time data but also fosters a culture of continuous improvement. But here’s the challenge—feedback loops can easily become overwhelming. When you’re swimming in data, it’s tough to decide what to act on, and there’s always the risk of analysis paralysis. Here’s how you manage it: → Define the building blocks of useful feedback: Prioritize feedback that aligns with your brand’s goals or messaging architecture. Not every suggestion needs action—focus on trends that impact customer experience or growth. → Close the loop publicly: When customers see their input being acted upon, they feel heard. Announce product improvements or service changes driven by customer feedback. It builds trust and strengthens emotional resonance. → Involve your team in the loop: Feedback isn’t just for customer support or marketing—it’s a company-wide asset. Use feedback loops to align cross-functional teams, ensuring insights flow seamlessly between product, marketing, and operations. When feedback becomes a living system, it shifts from being a reactive task to a proactive strategy. It’s not just about gathering opinions—it’s about creating a continuous conversation that shapes your brand in real-time. And as we’ve learned, that’s where real value lies—building something dynamic, adaptive, and truly connected to your audience. #storytelling #marketing #customermarketing

The Power of AI in Marketing 🌟  I’ve been reflecting on a pressing challenge we face: how to effectively engage an increasingly diverse and discerning customer base in a digital landscape flooded with noise. The Problem: Today’s consumers expect personalized experiences. However, with vast amounts of data and ever-changing preferences, it’s becoming increasingly difficult to deliver tailored marketing that resonates. Traditional methods often fall short, leading to missed opportunities and lower engagement. The Solution: Enter AI-powered marketing. Here’s how we can leverage AI and machine learning (ML) tools to transform our approach: 🔍 Personalization at Scale: AI tools like Segment and Dynamic Yield analyze customer data—demographics, purchase history, online behavior—to create hyper-personalized campaigns. Imagine sending the right message to the right person at the right time, leading to significantly boosted engagement and conversions! 📊 Data-Driven Decisions: Predictive analytics platforms like Google Analytics and Tableau enable us to forecast trends and understand customer preferences. With real-time sentiment analysis tools such as MonkeyLearn, we can adjust campaigns instantly based on what resonates most with our audience. ⚙️ Efficiency and Cost Reduction: AI streamlines our processes by automating repetitive tasks. Tools like HubSpot and Mailchimp can handle email marketing and reporting, freeing our teams to focus on creativity and strategy, enhancing productivity and reducing operational costs. 📈 Measurable Results: Robust analytics from platforms like Adobe Analytics and Kissmetrics provide insights into our campaigns’ effectiveness, allowing for continuous improvement. Imagine tweaking a campaign based on live feedback to maximize our ROI! 🚀 Competitive Advantage: Many competitors are already leveraging AI, making it crucial for us to stay ahead. By investing in AI-powered tools like Salesforce Einstein and IBM Watson, we position ourselves as innovators in our industry, enhancing our brand reputation. AI-powered marketing is not just a trend; it’s a strategic necessity. It helps us overcome the challenge of personalization, deepens customer relationships, enhances efficiency, and drives revenue growth. I’m excited about the possibilities and look forward to collaborating with our team on this journey! #AIPower #MarketingInnovation #DataDriven #Personalization #FutureOfMarketing #digitalmarketing

When was the last time you spent dedicated time listening to your customers? What are they saying about your product on platforms like the Google Play Store? For me, these aren’t casual questions—they’re vital for staying connected to what truly matters. I believe that genuine customer listening opens up a goldmine of insights. While metrics and KPIs show the business’s health, the real insights for growth, innovation, and loyalty come from our customers. Here are some key points that we've embraced at CarDekho & tips which can benefit you as a founder- 𝗘𝗻𝗴𝗮𝗴𝗲 𝗥𝗲𝗴𝘂𝗹𝗮𝗿𝗹𝘆: Feedback is not just something to review once in a while. I make it a habit to check in with our users daily—because those insights help refine the product and drive success. 𝗧𝗮𝗸𝗲 𝗚𝗿𝗼𝘂𝗻𝗱 𝗧𝗲𝗮𝗺 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: Meet your ops teams regularly. They're the key customer-facing advocates and know about the challenges and opportunities. I make sure to meet the on-ground team every month and discuss the customer reviews and actions and implement them accordingly. 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝗰𝘆 𝗮𝗻𝗱 𝗧𝗿𝘂𝘀𝘁: Build trust through transparency, especially regarding pricing, policies, and data handling. By being open and honest with customers, CarDekho has created a more trustworthy brand image, which has been key in retaining customers. Our team has been at the forefront of delivering exceptional customer experience which has helped us to achieve a consistent and upward (NPS) across our group companies. 𝗚𝗮𝘁𝗵𝗲𝗿 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 & 𝗔𝗰𝘁 𝗼𝗻 𝗜𝘁: Sometimes a single review or feedback can reveal more than hundreds of metrics. A recent user pointed out the confusion around EMI calculations, which led us to make a small tweak that significantly improved the user experience. If you are a founder or an aspiring entrepreneur remember that a true understanding comes from consistent listening. Making it a habit to connect with customers and hear their experiences firsthand not only strengthens the product but also builds loyalty and trust. In the end, our customers often tell us what we need to know; it’s up to us to listen closely and act thoughtfully. 😀 #CustomerExperience #ProductInnovation #Entrepreneurship #StartupTips #CarDekho

💪 David v Goliath.... ... How to compete using a Smart Data Strategy... The biggest brands in the category can often easily outspend competitors when it comes to investment in data & insight, and this can give them a clear competitive edge. Smaller businesses are unlikely to be able to match their spend, but they can spend *smarter* to compete more effectively. Here’s how: 🚀  1. Start with High-Impact Data ↳ Market Overview Reports: Affordable sources like Mintel or Euromonitor provide a snapshot of market size, trends & competitor positioning. This helps identify category trends & establish the right areas or Shoppers to target without the ongoing cost of continuous data feeds. ↳ Focus on Key Business Questions: Pinpoint where insight will make the biggest impact e.g. - Detailed understanding of Retailer category performance ahead of a range review to help secure new distribution. - Identifying target consumers & optimal outreach strategies to boost penetration. 🔍  2. Leverage Selective EPOS & Loyalty Data ↳ Market-Level EPOS Data: This can be invaluable for insight into category dynamics & benchmarking KPIs vs competitors whilst avoiding high costs of retailer-specific feeds. ↳ Loyalty Card Data: Although this will only cover one retailer (so no total market read) it can give you very granular insights on sales performance as well as WHO is buying your brand. 🎯 3. Focus on Actionable Insights ↳ Prioritize Impactful Data: Concentrate on insights that can directly drive product development, pricing & promotions. Avoid ‘nice-to-have’ data that doesn’t materially impact your business. ↳ Make the most of the data you need DO have: Manage scope to only buy the data you *need* & make sure each source is *fully* mined. Investing time in analysis instead of buying new data can yield deeper understanding & more opportunities to optimise your brand performance. 📈 4. Scale Data Investments with Business Growth ↳ Mix One-Off & Continuous Feeds: Start with one-off data sources, then add targeted continuous data feeds as you scale. Regularly review usage & actionability & stop reports which don't add value. 🧠 5. Outsmart, Don’t Outspend --> Be Agile ↳ Develop a *Learning* culture : Smaller businesses can move around the Build/Measure/Learn loop much faster than bigger brands - Insight is the rocket fuel you need to power this. Key Takeaway: Strategic Data Use Although small & medium sized businesses will inevitably have less data, if they use what they can afford to answer the right questions & act quickly to execute then they can find a competitive edge of their own. What are your thoughts & experiences - let us know in the comments. Want to find out more? This week's #CategoryWins newsletter digs into this subject in much more detail : See link in comments or my bio ♻️ & if you enjoyed this post, please like & share it with your network. #CategoryManagement #FMCG #CPG #DataStrategy #CompeteSmarter