33 Sticks的封面图片
33 Sticks

33 Sticks

科技、信息和网络

Calabasas,California 1,256 位关注者

We fundamentally change how people feel about analytics and their futures.

关于我们

Everything we do, we believe in thinking differently about analytics services. The way we think differently is by putting people first, by improving our client’s condition, and by creating amazing work experiences. At 33 Sticks, we aren’t traditional web analysts, we are business advisors that just happen to be really good with data.?

网站
https://www.33sticks.com/about/company-story
所属行业
科技、信息和网络
规模
2-10 人
总部
Calabasas,California
类型
合营企业
创立
2013
领域
startups、web analytics、digital analytics、consumer behavior analytics和customer experience optimization

地点

33 Sticks员工

动态

  • 33 Sticks转发了

    查看jason thompson的档案

    Bridging data and humanity

    The moment people stepped through through the door and into our suite, you could physically see the tension leave their body. The corporate armor fell away. The stress of performance metrics dissolved. What remained was something beautifully, refreshingly human. Thank you to all of the wonderful people who made the very meaningful investment to walk away from the #AdobeSummit conference center, if even for a brief moment, and journey to the top of The Venetian to spend some precious time with us. Much Love ?? https://lnkd.in/gG-udpCs

  • 查看33 Sticks的组织主页

    1,256 位关注者

    The Rule Sandwich, Now for the WebSDK: A Smarter Approach to Implementation The WebSDK has changed the way we think about data collection, but one thing remains the same ? clean, well-structured rules make or break an implementation. Enter The Rule Sandwich ??, a framework we’ve used for years to keep implementations scalable, predictable, and resilient. Now, we’re applying it to the WebSDK. If you’re struggling with rule complexity, conflicting conditions, or unpredictable outcomes, this approach will help you bring order to the chaos. Check out the full breakdown here: https://lnkd.in/g8jEsdbG #AdobeWebSDK #DigitalAnalytics #DataStrategy

  • 查看33 Sticks的组织主页

    1,256 位关注者

    At 33 Sticks, we helped a global manufacturing company transform their struggling analytics program through a focused three-pillar approach: 1. Consolidating fragmented leadership 2. Implementing robust governance frameworks 3. Building the right analytics team structure The results were remarkable: ? 62% reduction in redundant data segments ? Report loading times decreased from 10+ minutes to under 60 seconds ? Data collection completeness drastically improved ? Business user confidence in analytics data increased We believe investing in analytics leadership should precede or accompany investments in tools and technologies. Without the right foundation, even the most advanced platforms will fail to deliver their potential value. Read the full case study to discover our implementation roadmap and key lessons: https://lnkd.in/g58VFMw4

  • 33 Sticks转发了

    查看jason thompson的档案

    Bridging data and humanity

    i'm a bit of a coffee fanatic. This morning i'm enjoying a pour over with The Riverside, an Ethiopian blend, from Onyx Coffee Lab. And speaking of coffee, i recently had an experience with a friend who bought a highly automated espresso machine and i found myself using that experience to explore the intersection of analysis, learning, #GenAI, and automation. Give it a read and let me know what you think? The Espresso Paradox: How Automation Might Be Diluting Our Analytical Expertise -> https://lnkd.in/gZi7_4ic

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  • 33 Sticks转发了

    查看jason thompson的档案

    Bridging data and humanity

    i’m seeing a trend in the analytics agency space that i think is worth calling out. More and more agencies are rolling out “free AI tools” as a lead gen play for their traditional analytics services. Let’s break down how this actually plays out: - You’re invited to try a “game-changing AI agent” that promises to analyze all your digital data. ANALYTICS AUTOMAGICAL??. - The idea of a “free analyst” is exciting because who wouldn’t want to save a ton of money on an experienced analyst? Or maybe it’s appealing because your company can’t afford to hire a full-time analyst in the first place. - Enter the real world. The AI-generated “insights” are...ahhhhh... underwhelming. - The tool struggles with data quality, lacks context, or just wasn’t designed well enough to deliver anything useful. - Enter the upsell. To actually get meaningful insights, you’ll need the agency’s traditional analytics services for that. i know, this isn’t a new or innovative tactic, these types of lead magnets have been around forever. But it feels like the AI hype cycle is supercharging the sketchiness of these so-called funnels. Classic bait-and-switch. P.S. The agency partners truly worth working with won’t lure you in with overhyped promises only to upsell you later. And they absolutely won’t rely on smoke and mirrors or let AI do all the heavy lifting while conveniently ignoring its major limitations.

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  • 33 Sticks转发了

    查看jason thompson的档案

    Bridging data and humanity

    20 years ago, Avinash Kaushik said something like, for every $10 you spend on analytics software, you should be spending $90 on smart data people. And yet, here we are. Most companies have flipped that equation (probably even worse now with "AI"). Billion-dollar brands are replacing their data experts with AI agents some kid whipped up using Claude over the weekend. Data vendors announcing their 18th round of funding, which means the market will be attacked by even more sales FUD pushing us to buy even more tools we don't need because...NOW WITH AI?? But i'm not sure more tools, even AI agents promising to magically answer all our questions, are going to fix much. In fact, i'll argue they are going to amplify existing problems that fundamentally are not technology problems: - Low data literacy rates (people don't know how to use the data they have) - Non-existent data governance (20 different definitions for what a purchase is) - Dirty, untrustworthy data (garbage in, garbage out) - Lack of senior executive support (because "data-driven" sounds nice in theory, until it conflicts with egos) Do we really need more tools right now or do we need to actually, perhaps 20 years too late, invest in the people and frameworks that make the data useful in the first place and then align our tools, and the AI that will automatically make everyone in the org a 10x analyst, accordingly?

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