Martin Stein

Attention consumer marketers and consultants - watch this helpful guide on how to go from question to insights in a few quick steps using Prodege, LLC ‘s Pollfish Inc platform. Combining an easy to use platform with best quality audience and instant insights is our answer to the biggest pain points in #marketresearch : speed, Quality and cost #mrx And we are not done yet. Our relentless pursuit of perfection continues - stay tuned for our upcoming new product announcements

2

1 Comment

There is a powerful and transformative change that is happening right now in the world of business insights creation and activation that can deliver AI readiness that is accurate, secure, fast, and scalable. I’d recommend starting with this video if you’re on point to make AI real for your organization.

10

At the Databricks Data+AI Summit 2024, I was captivated by the dialogue surrounding AI. What struck me most was the clear divide between Generative AI's perceived potential and the practical challenges of its implementation. Despite its immense promise, Generative AI still has a long way to go. 𝗞𝗲𝘆 𝗣𝗼𝗶𝗻𝘁𝘀: Generative AI models often don't meet the precise needs of industries like finance and healthcare. Approx. 85-90% of AI projects fail to reach production due to misalignment and poor infrastructure. Regulatory compliance is critical, especially in highly regulated sectors. Slick demos can be misleading; practical implementation needs a deep understanding of business needs. Incremental progress and continuous data quality evaluation are essential.

22

Does Snowflake's MODERN MARKETING DATA STACK 2025 report illustrate and potentially entrench a global enterprise software innovation gap? Let's look at the numbers. 📊 Out of 89 standout "Marketing and Advertising-Specific Tools and Platforms" and "Data Tools and Platforms" featured in the report, only 9 are based outside the US of A 🏈⚾🌭🥧. That's a staggering 90% of innovation driven by U.S.-based companies. 🤔 Here's the breakdown: Non-US Leaders Adverity -> Austria Dataiku -> France Funnel -> Sweden Piano -> Netherlands Snowplow -> UK Non-US Ones to Watch Matillion -> UK Omnata -> UK StackAdapt -> Canada Supermetrics -> Finland This raises important questions: - Why is enterprise software innovation so concentrated in the US (at least relative to the Snowflake ecosystem)? - Is it venture capital, is it structural, a deeper talent pool or something else? - What will it take for other markets to produce enterprise software powerhouses that capture positions that are 2x or 3x the size of non-US orgs in lists like in this Snowflake report? With the rise of AI capabilities, data privacy challenges, and global digital transformation requirements, it's imperative for more regions to step up and diversify the innovation landscape, compete more effectively and win. Otherwise, the future of global data systems will be [remain] overwhelmingly shaped by U.S. interests and priorities. Your thoughts? Download the Snowflake report here: https://lnkd.in/eaq4pP_M #EnterpriseInnovation #GlobalTech #DataTools #MarketingTech #InnovationMatters

7

Malcolm M. Bonner Right on! I think it's misguided to believe that everything worth knowing and expressing can be captured solely from the data. The root of the issue is the purpose of the analysis. If an analyst is using the model to simply summarize the relationships expressed in the data, then they're best to only use the data. But who want's that from a marketing mix model? If you think so, then just question why it's important to iterate on the specification of the model so much. The unspoken truth about marketing mix is that typical analysts iterate to no end to ensure they get "the right" answer. The problems with marketing mix models come from the data, not the methods. The problems are collinearity, low variance and sparsity. They are the direct result of management actions to drive the business. The data cannot be relied as the exclusive source of information to inform such important matters as marketing effectiveness. In my view, marketing mix models are a decision-making solution and so they need to be working with the full knowledge estate. The knowledge estate of the marketers as decision makers. This includes but is not limited to all the sources of information listed by Malcolm M. Bonner. For a more in depth discussion on this topic, please see my post on https://lnkd.in/eMvrTFkU

4

1 Comment

New on the CDPI blog: Amperity details how to use #GenerativeAI to improve personalization in marketing. https://lnkd.in/e4JSZjgS #AmperityCDP #Databricks #DatabricksLakehouse #LakehouseCDP

8

📣 Exciting news — starting July 1, GrowthLoop’s AI-powered audience segmentation and discovery tool will be available as a Native App in the Snowflake #AIDataCloud. ❄ As one of the first composable CDPs on Snowflake to launch a Cortex AI solution, GrowthLoop is unlocking new ways for marketers to quickly segment and activate their customer data while leveraging generative AI natively, alongside their Enterprise Snowflake Data. That means smarter AI suggestions, faster campaign launches, more rapid experimentation, and personalized cross-channel content delivery. As a part of the launch, GrowthLoop also becomes the first CDP that qualifying customers can fund fully with Snowflake Credits through Snowflake's Marketplace Capacity Drawdown (MCD) Program Read more about the integration: https://lnkd.in/e6s2m7UT #marketing #martech #DataWarehouse #snowflake Luke Ambrosetti Sridhar Ramaswamy Erin Foxworthy David Wells Bill Stratton Ross Armstrong Patrick Crosby Matt Warneke

51

6 Comments

Every new lead for Speero that is interested in our testing program work, I ask for analytics access and we do this type of analysis. "Where is the money?" ROI-type analysis. With this we can get a feel for potential relative velocity and impact for not only AB testing, but ALL work done on this journey. We often add other KPIs, but always start with this revenue-based perspective. Each metric can have it's own table. Eventually this table gets automated, and linked into the testing infrastructure so pre-registration of test ideas include this prioritization as well. If you have a testing program, do you have this? If you don't have this analysis per device type, for your major channels, you should. Ping me and we can do this for you.

80

20 Comments

lockrKeys 🔐 It’s just a matter of clicking a button, Stephanie Mazzamaro said. “That’s it.” In today’s shifting landscape, publishers must leverage first-party audience data while maintaining flexibility and control. The pressure to iterate quickly often leads to rushed implementations, wasting resources and risking data integrity. But simply testing everything without being able to measure or adjust is a costly mistake. lockr’s Audience Integration Manager (AIM) helps publishers avoid this by allowing seamless integration and removal of providers without the need for complicated code changes. This flexibility is essential, as first-party data becomes increasingly valuable with the disappearance of third-party cookies. AIM ensures publishers control how data is used, shared, and protected. As the digital ecosystem evolves, AIM allows publishers to differentiate themselves with precision, not volume. By maintaining control and portability, publishers can effectively manage their most valuable asset — their audience — and drive future growth. Allison Schiff dives deep on all things lockr, AIM, lockrKeys, and the expansion of our offering in today's AdExchanger piece. Check out the link in the comments. Appreciate the support by Mathieu Roche at ID5.io, Travis Clinger at LiveRamp, and Gabe Richman The Trade Desk.

69

5 Comments

One of my favorite independent AI benchmarking services has come out with an extremely useful guide to choosing an AI chatbot. It can be hard to keep up with all of the AI tools and models. Artificial Analysis is becoming an essential tool to help, and they've just released their chatbot comparison here: https://lnkd.in/g2DJVvBa This is really comprehensive, and can also help you zero in on the best tool for your most frequent use cases. Worth checking out.

16

💡#AI readiness tip: Address the skills gap fast & early. Learn why — and dive into more recommendations & insights for optimizing your architecture — in this report from BARC: https://okt.to/HCdpmu

58

Cube now has a fully supported integration with Klipfolio PowerMetrics - a cool metrics platform that teams up with your current BI and analytics tools to make life easier for non-techy business folks. 📊 👨‍💻 Take a look at our latest blog post: https://lnkd.in/gfT_fiwW #BI #Analytics #Tools

2

Everyone strives to be more efficient, especially when managing technology that is a cost center. The more advanced the tech, the more variables come into play. So, what often distinguishes good practitioners from great ones is their knowledge of a vendor pricing structure, so they can make informed recommendations or decisions. I enjoyed this simple example about storage in BigQuery. https://lnkd.in/eXfM8EnS

17

5 Comments

The Value of Monitoring Data Quality … an ounce of prevention is worth a pound of remediation! In a world of data-driven decision-making where data is closely associated with profit, ensuring data quality is paramount. Yet, many organizations find themselves in a reactive mode, spending valuable resources fixing data issues after they've already caused disruptions. This approach is not only costly but also inefficient. 🔍 The Real Cost of Reactive Data Quality Management: Time: Teams spend countless hours identifying and correcting errors. Money: Financial resources are diverted to fix issues instead of being used for innovation. Trust: Confidence in data-driven insights can be eroded, impacting decision-making. 🚧 Why Prevention is Better: Proactive Monitoring: Implementing a robust data quality monitoring framework helps detect issues before they escalate. Consistency: Ensures that data remains reliable and accurate across all processes. Efficiency: Reduces the need for extensive manual interventions, freeing up resources for strategic initiatives. 🔧 A Preventative Approach to Data Quality: Automated Checks: Utilize automated tools and processes to continuously monitor data quality. Early Detection: Identify and address potential issues early, minimizing their impact. Data Governance: Establish clear policies and standards for data management. By shifting focus from fixing issues to preventing them, organizations can save significant time and money, while also building stronger trust in their data. Start monitoring your #Snowflake data quality today with Dynamic Data Quality: https://lnkd.in/gYPJ64qm #DataQuality #Data #DataDriven #Innovation

4

Wondering why your GA4 data in the reporting UI doesn't always match up with what you're seeing in BigQuery? Take a look at our new blog post to find out what can cause data mismatches, and how you can minimize them. https://lnkd.in/g8R4cZJF

16