Posit PBC’s Post

{gt} 0.10.0 is now out! 🎉 Richard Iannone has included a ton of great features in this release: • Nanoplots for showing data trends in your table (any Tufte enthusiasts out there?). Hover over them to see the associated data points! • The ability to create new columns and rows is now very possible • Units notation support for expressing and formatting measurement units • And more! We are always listening to what you want, and we have a few ways you can reach us. Found something strange in {gt}? Have a cool idea? File an issue! Want to ask a question or discuss improvements before filing an issue? Try out the Discussions page in the {gt} repository for that. For news on {gt} and other table packages (like Great Tables), follow the engaging @gt_package account on X/Twitter! Read more: https://lnkd.in/gSfNDH62

I am very excited to announce a new update of our CLVTools package (https://www.clvtools.com/) The package contains well-implemented versions of the most common and powerful models for statistics in marketing. In particular, it features our extended ParetoNBD model that allows for state-of-the-art performance in the prediction of customer behavior. In the newest version of the package, this powerful model is implemented in Rcpp, giving it a tremendous speed boost over the previous version. This makes the model applicable to large-scale data, moving it from academia to the real world. #CLVTools #MarketingData #CustomerBehavior #DataAnalysis More infos here: https://lnkd.in/eFdxkrfD

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I asked AI to generate an image of what it feels like to not have control over your customer data. What do you think?? I was ready to see someone crying at their computer and looking at CSAT scores (or something like that 😅 ), but I feel like this is accurate too! In today's data-driven world, having control over YOUR customer data is paramount. Too often we see enterprises at the mercy of third parties and browser restrictions--they are hardcore feeling the stress from the detrimental implications of client-side browser-based user tracking as it pertains to: -Site performance ➡ poor page load speed, hindered SEO, lower conversions -Data privacy risk ➡ compliance infractions due to potential exposure of sensitive information -Measurement efficacy ➡ inaccurate (and inconsistent) analytics leading to wasted ad spend and poor (re)targeting -Platform restrictions (i.e. browser restrictions like ad blockers) ➡ impacting data collection reliability and reducing the amount of data collected And more... 🛑 Not having control over your customer data (how it's collected, ensuring it is compliant, increasing the amount of customer data you're collecting, etc.) is like owning a prized recipe but not having the secret sauce! 🤔 Imagine this: You're the chef, meticulously crafting a unique dish, but someone else decides the ingredients and the cooking time. Frustrating, right? That's what it feels like when you lack control over your customer data. MetaRouter is like the culinary maestro for your data kitchen! 🍲 With MetaRouter, you reclaim the chef's hat and gain full control over your customer data. It's like having a personalized recipe book, where you decide the ingredients, the measurements, and the cooking techniques with full control and transparency over what is delivered. The solution? Remove tags and move server-side with MetaRouter 💪 I love my job because I get to empower marketers to take back control over their customer data and transforming it into a recipe for success! Learn more! https://lnkd.in/eFXTh339 #DataOwnership #ControlYourData #TakeBackControl #TagRemoval #RemoveTags #ServerSideRevolution

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Happy to share that Shubha Shedthikere and Bhavi Chawla's paper titled 'New-User Product Recommendations for Q-Commerce via Hierarchical Cross-Domain Learning' has been accepted the applied DS track at CODS-COMAD'24. Cold start is a very common problem in recommenders. One of the ways to address this is to use cross-domain data-- use interactions from one business line to jump-start recos in another. This typically involves a) learning a cross-domain mapping-function for the customer (or item) between the source & target domains, and then b) using the learned mapper to derive embeddings for new users in the target domain. We show that such approaches are sub-optimal where (target) sales distribution is long-tailed, making the embeddings noisy. This is further aggravated in quick commerce settings where hyperlocal nuances come into play. We propose a cross-domain mapper that leverages the hierarchy inherent in the product taxonomy and learns multiple mapping functions for customers' product-category preferences between the source & target domains. These category-level preferences are then used to personalize product recos in the target domain (via an LTR model). We show that this outperforms embedding-mapping baseline by 30% in nDCG, on our data. #swiggydatascience

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