Rodolphe Dougoud

Real Time Report is coming on #PiwikPro ! I have the privilege to get an early access to this very promising feature. I'm testing it right now and you will find below an example I built quickly. The real time reports are completely customizable: - A nice library of chart types - All the dimensions we need for live metrics drill down - All visitors and hits metrics Exhaustive to allow to be specific! It is what we need, be specific. I'm convinced that Piwik Pro users will be happy soon to discover this new dashboard section that can help on some use cases. Moreover, I had the chance to share some examples & thoughts on this topic in an article wrote by Aleksandra Szczepańska-Dziubek from Piwik PRO , with the participation of Juliana Jackson : "Real-time reporting: The complete guide" You will find the link to this article in the comment. PS: No insomniac American people reading my blog right now 😛

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As we wrap up the year, I want to give a shoutout to some awesome people I’ve learned a lot from on LinkedIn this year. 📊 Marc Di Gaspero and Adrien Vermeirsch – If you care about online sample and data quality, their research-on-research is an absolute must-read. There are very few companies doing *real* research in this space, so well done, you! 👏 🌟 Edward Zavertyaev – Dropping tips on data visualization that make complicated stuff look effortless (and beautiful). His new video posts are 🔥. 🔬 Joel Anderson – Approaching synthetic data scientifically to get the industry one step closer to use cases that truly add value. 🤖 Josh Seltzer – Patiently explaining AI without any marketing BS and answering all our questions. 📰 Kelvin Claveria – If you’re not subscribing to Kelvin's newsletter #FreshInsightsFriday, you’re missing out. 🎙️ Karen Lynch and Lenny Murphy – #TheExchange is all you need to stay on top of the latest #mrx news. 💬 Ray Poynter – For the best discussions about AI and synthetic data. 🎤 Special mention to Catherine “Mickey” Hill – My amazing co-speaker at Greenbook #IIEXNorthAmerica 2025, who convinced me on a whim to team up and do this together! 🤗 Thanks for making LinkedIn a better place! 🙌   Who’s on your “must-follow” list this year? 

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🔥Hot Stat Alert!🔥 Our monthly What Worries the World survey explores what the public thinks are the most important social and political issues, drawing on more than ten years of data to place the latest scores in context. In this edition #inflation remains the number one concern for the 27th consecutive month according to ou What Worries the World survey. However, worry over rising prices is at its lowest level since April 2022, now at a third (33%) on average across 29 countries . See the full results : https://lnkd.in/e2T8Kb-R

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I recently started to follow Andy Kriebel, Stephanie French and Aurélien Vautier on LinkedIn, all of whom produce high-quality educational content on data visualization. Could you recommend anyone else I should follow in this field? #DataViz #Visualization #Data #DashboardDesign

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One big misconception about media mix modeling is that it's a big data problem. It's not. MMM is actually a small data problem – and that's what makes it fascinating. Here's what you actually need for a solid MMM: • 2-3 years of historical data • Daily spend data across channels • Aggregate KPIs to model against That's it. We're talking about 1200 rows of data with ~30 columns. It fits in an Excel sheet. The real challenge isn't handling massive amounts of data – it's extracting meaningful insights from a relatively small dataset. That's why MMM requires sophisticated statistical approaches. That being said, the data do need to be both granular and accurate. For example, sometimes brands haven’t done a good job of tracking their historical marketing activity, or the data is trapped with an agency. They have the spend numbers ("we spent $20M on TV last year!") but can't tell you when that money was spent. If their agency has the data and won't share it... that's a problem. One of my most common recommendations is to get your marketing data into a data warehouse NOW, whether you're planning to do MMM or not. This isn't just about preparing for MMM – it's about having the foundation for: • Better reporting • Spend visualization • Revenue correlation insights When you're ready for MMM, you'll thank yourself for having clean, organized data ready to go. Thoughts? What other data challenges have you encountered when setting up MMM?

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AI overviews and AI search bots will change the game long term. Adrien Menard shared some advises on how to get ready now for 2025 shift and how Botify can support you ! 1️⃣ Ensure your site can be found by all search engine bots, including AI platforms, 2️⃣ Build up and maintain the SEO fundamentals that power organic rankings on Google and Bing, as AI platforms currently use those rankings for live results & links, 3️⃣ Satisfy your customer’s intent at every stage of the journey with quality content that can be found by AI and traditional search engines, 4️⃣ Re-examine how you measure visibility and organic revenue as AI impacts metrics like CTR and impressions. We are ready to support you ! Hit me up and we can chat about what is next ! ⚡️Download the report here: https://bit.ly/3D8uFr9

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ILLUMINATION - COME GET SOME: I've used this outline from Speero now to benchmark several large companies (running up to 8K experiments a year) and the metric data and verbatim feedback has been absolutely vital in identifying problems and knowing where to invest. Having hundreds of other companies in the dataset means a useful set of comparison metrics. The useful part of this survey, aside from being able to compare yourself to others, is that it also covers the dimensionality of responses; not just an "average" set of metrics, this digs deeper into what the differences are between companies starting out - and those that have truly transformational practice. Learn what the differences are. Exploring the connections between what you do, what stage of growth your experimentation practice is at and what signals show progression - makes this one of the most important reads of the year. Thanks for producing this and sharing it with others, Ben Labay and team :-) #experimentation #abtesting #programme #metrics #benchmarking #CRO #culture #KPIs #OKRs #scaling #growth

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Market research is evolving. The conversations paint a clear picture...traditional web monitoring solutions are falling short. High volumes of irrelevant data. Manual data verification slowing things down. Missing real-time sentiment shifts. Fragmented insights from scattered sources. These challenges are costing researchers time, accuracy, and opportunities. But there's another way. Imagine a world where AI filters out the noise, leaving you with only relevant data. Where data verification is automated, freeing you to focus on analysis. Where sentiment shifts are tracked instantly, informing your strategy as it happens. Where fragmented data sources come together, giving you a complete picture. This isn't just imagination. It's happening now. AI-powered open-source intelligence is transforming market research. It's not about doing something different. It's about doing something that matters to researchers. Faster insights. Cleaner data. Real-time intelligence. The future of B2B market research is here.

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Round up time! Four recent articles about data quality and my spicy take 🌶️ Marc Di Gaspero and Adrien Vermeirsch from Potloc have done extensive research on data quality, and now Marc is sharing some key insights! Out of over 26,000 entrants, the average was 22 daily survey attempts (!), with similar results across five major online panels and five sample exchanges/DIY solutions.  The best part: High-frequency survey takers is an easy problem to solve - why don’t we? Because less traffic = less revenue. 🤷 In the article “$urprise! Re$pondent$ are not altruistic,” Vignesh Krishnan at Rep Data discusses the significant drop in incentives over the past 10 years (his rough estimate is 71% 😮). While increasing incentives could potentially attract more fraud, he argues that as more good respondents participate, the proportion of bad respondents would decrease.  Fair enough—so why don’t we increase incentives? Because it will increase the CPI and not the profit margin. 🤔 Chris Atkins at Yonder Data Solutions writes about how “the research industry is focusing too much on managing the symptoms of poor data quality rather than addressing its root causes.” I agree 100%. Our response to the data quality crisis is to build bigger armies. We need fraud detection software and QC checks, but that’s not fixing the root cause of the problem.  😐 Terence R. McCarron at OpinionRoute writes about how ‘conversion rate’ is what dictates the CPI, not the old ‘Incidence Rate.’ This matters because quality removals lower conversion rates, which is generally bad for business if you’re a sample provider. He says, “quality is not a priority that’s assured through the economic model.” Amen to that. 🙌 Links in the comments 👇 Your thoughts? 💬 #rmx #dataquality #surveys 

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Want to do spatial sentiment analysis, your solution might be here. Mohammad Arashi, Leila Marvian and colleagues, share an R-package with various functionalities to do so. Read all about it in their Blog post hosted in our ever growing series focusing on integrating geospatial and statistical data:

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If you use the right predictive model, you can have an outsized influence on the "movable middle." Here's how our approach is moving the needle for our clients: 1. Model Selection: We're flexible. Whether it's a regression model or a sophisticated machine learning algorithm, we choose what works best for the specific challenge at hand. 2.Customer Ranking: Our models rank customers from most to least likely to exhibit a desired behavior. 3. Decile Division: We split these rankings into 10 equal groups, or deciles. From there, we can lump customers into one of three categories: -Top Deciles: They'll likely engage without extra incentives. -The Middle: This is often where the greatest opportunity lies. -Bottom Deciles: Unlikely to engage regardless of efforts. We focus on that potentially movable middle group. By testing different offers, messages, and channel mixes, we can influence behavior where it matters most. By understanding where customers fall in these deciles, we can optimize marketing efforts and allocate resources for maximum impact. #DataDrivenMarketing #CustomerInsights

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𝗝𝗼𝗶𝗻 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗚𝗼-𝗧𝗼-𝗠𝗮𝗿𝗸𝗲𝘁! 🌟 Attention B2B Founders, CMOs, CROs, and GTM Teams! 🌟 We are entering the age of Agents. If you are knowledgeable about this topic or looking to start new projects, you might find it challenging to create Agents and adapt to the latest technology. Transforming your strategies and operations successfully will not be easy. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗧𝗲𝗮𝗺𝘀 𝗳𝗼𝗿 𝗚𝗧𝗠 𝗧𝗲𝗮𝗺𝘀  At Empler AI, we are excited to develop our upcoming AI Team feature. We will empower you to have 24/7 AI Teams composed of multiple Agents. You will assign missions to your new teammates (AI Teams) to help you achieve your goals. This process will not require months, developing investment, coding, or high-level technical knowledge. You can enter a different future in just a few days or hours! We have already started receiving amazing requests. If you would like to be notified at our launch, share your pain points and goals with us before launch, take advantage of the %𝟱𝟬 𝗱𝗶𝘀𝗰𝗼𝘂𝗻𝘁𝗲𝗱 pre-sale opportunity, or see what we are up to, please fill out our contact form! Let’s embrace the future together! 🚀 https://lnkd.in/dnw-jkPM #AI #SAAS #B2B #Enterprise #Startups #Business #Growth #Innovation #Technology #EmplerAI #FutureOfWork #Agents #AITeams #GenerativeAI

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Internal notes from a colleague after their meeting with a data leader about their data marketplace : "He transparently said they were prioritizing 1st party data for the marketplace to prove out and get buy in from the business, and this engagement won't be until Q1 of next year" We often hear this : 'First we need to get our internal data sorted and then we think about external data'. Is this the right way to think about it? This is a false economy for a few reasons. 1. External vs internal : It's different people and resources. Vendors can help. Procurement can help. Internal data depends on very different people. They're different problem sets. 2. It's not about the data, it's about the use case. Whether it's a new data platform, a catalogue, a marketplace, a gateway, a lineage system, I think I can categorically say the right thing to do is focus on the high impact business use cases (that often depend on a mix of internal and external) to drive adoption and impact, e.g. the big rocks like automating workflows (KYC, credit memos, underwriting) 3. External can actually be easier, faster, cheaper, and for some use cases richer than internal. Why wait until a huge data transformation is complete in order to deliver high impact data? What do you think?

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Those interested in interoperability, including laboratory interoperability should comment through August 23rd on the expanded Data Usability Implementation Guide. The guide focuses on health data exchange across EHR, HIE, Public Health, and laboratories, leveraging published standards to lift everyone up together and help retain vital information in exchanges for downstream users.

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Complexity tackled with simplicity = Product + Design + Engineering WIN See how Mixpanel is committed to the concept of "lego for analytics" to add powerful reporting but with simplicity. Preview this concept in our recent shipped stuff. Happy testing! - 1. More powerful funnels: https://lnkd.in/gGUq2bZK 2. Revenue-weighted funnels: https://lnkd.in/g7JBer3q 3. Mix and match metrics in a chart: https://lnkd.in/gmXxgSj4 4. Standardized metric and behavior definitions: https://lnkd.in/g9Z7yucD 5. Behavioral segmentation to find "aha" moments: https://lnkd.in/gr-eFh7M

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Excellent study from Damian Rollison and the SOCi, Inc. team which seems to confirm my longtime hypothesis around citations (https://lnkd.in/gxZQwdx3): the only citations that are valuable are the ones that rank in Google for the keywords you want to be known for. https://lnkd.in/g49DMSeN

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Over the past few months we've analyzed hundreds of proposals for MVPs from agencies. Here's where the most common scope/cost expansion comes from: 1. Multiple user roles 2. Automating low-volume processes Will quickly expand on both... 1. Multiple user roles This is especially common for marketplaces or consumer apps. If the goal is to validate the demand, often the leaner solution is to build for the hard/important side and find a way to hack together the value for everyone else. With each new role you need a new set of permissions/user management and you need to map that across every part of your product. The reason multiple roles get baked into proposals is because that's not obvious to newer/non-technical people. 2. Automating low-volume processes. Analogous to the trap of "don't build a network assuming the network already exists" I see a ton of proposals that are doing something fancy to handle a workflow (billing, fulfillment, matching, etc) that is easy to do manually at low volumes. The point of an MVP is to test whether the thing is valuable, and only if it is build out the expensive stuff that will allow you to scale. There may be exceptions to this (although I haven't seen them) but if your MVP is handling 100% of the work, there's fat to cut. People look at great product teams and assume they win because they are faster at shipping, but the reality is that they are just ruthless about cutting scope especially in the early days. You don't have to be an amazing engineer to cut scope by half and go twice as fast. We're learning a ton as we continue to analyze proposals and work with founders. If you want us to take a look at a proposal you have from an agency and tell you if we think there's fat to trim — reach out. Or we can build one for you from the ground up.

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