<p>Hi everyone! I am Rakesh from the Mercari’s Seller Engagement team. Recently I had the opportunity to mentor an Intern at Mercari. His name is <a href="https://github.com/Priyansh-Kedia">Priyansh</a> and this article summarizes part of his work on using Firebase for client side machine learning models.</p>
<h2>Introduction</h2>
<p>The availability of huge data, advances in powerful processing and storage, and the increasing demand for automation and data-driven decision making has led to the widespread popularity of machine learning today.</p>
<p>Many companies want to use machine learning but it can be very expensive, the cost associated with developing, training and deploying can be significant, as well as the cost of using specialized hardware like GPUs, cloud computing resources, storage and networking.</p>
<p>Traditionally machine learning models are deployed on the server side but the popularity to enable client side machine learning is also increasing. There are many advantages to using AI on the edge.</p>
<ul>
<li>No internet connection required.</li>
<li>Do not need to send data to the server.</li>
<li>Real time inference, with extremely low latency.</li>
<li>Little to no server cost.<br />
<img src="https://storage.googleapis.com/prd-engineering-asset/2023/04/144f3720-image3-1024x348.png" alt="" /></li>
</ul>
<h2>Why we wanted to use ML on client side</h2>
<p>We at mercari are always trying to increase the value in our marketplace by building many innovative features. Barcode listing is one such powerful feature which makes sellers list easily by scanning the barcode of certain categories of items.</p>
<p><img src="https://storage.googleapis.com/prd-engineering-asset/2023/04/c31b2d22-image2-1024x889.png" alt="" /></p>
<p>Sometimes users, especially new users are not fully aware of all our features and barcode listing is such a feature. To address this and to improve the customer experience, we wanted to use Edge AI. So, we built a new feature called listing dispatcher, which uses an ML model on client side to predict if the category of item being listed by the seller supports barcode listing or not using the item’s first photo.</p>
<p><img src="https://storage.googleapis.com/prd-engineering-asset/2023/04/634182fc-image1.gif" alt="" /></p>
<h2>How to use ML on client devices?</h2>
<p>We can use one of the popular open source mobile libraries called <a href="https://www.tensorflow.org/lite">TensorFlow Lite</a> (in short also called TFLite) for client side machine learning inferences. It has many key features like</p>
<ul>
<li>Optimized for on-device machine learning.</li>
<li>Supports multiple platforms.</li>
<li>Support across many languages like Java, Swift, etc.</li>
<li>High performance.</li>
<li>Support many common machine learning tasks like image classification, object detection, etc.</li>
</ul>
<p><img src="https://storage.googleapis.com/prd-engineering-asset/2023/04/268480bd-image4.png" alt="" /></p>
<p>We can simply embed the TensorFlow Lite model to an android or iOS app while packaging it to apk or ipa. This is one of the easiest ways to use ML on the client side but it has certain limitations. </p>
<ul>
<li>ML models are usually huge and packaging it with the app will increase the app size and can lead to drop in installations on iOS app store or Android play store.</li>
<li>An additional overhead to optimize the ML model for compression, for which we may need to compromise with model accuracy.</li>
<li>It can also affect the development flow of the client side.</li>
</ul>
<p><img src="https://storage.googleapis.com/prd-engineering-asset/2023/04/a60a0475-image5.png" alt="" /></p>
<p><center><br />
Embedding the tflite model on Android by adding it to app assets<br />
</center></p>
<p>Is there a better way?&#8230;</p>
<h2>Firebase Machine Learning Custom Model</h2>
<p>It is one of the services of <a href="https://firebase.google.com/docs/ml/use-custom-models">Firebase</a> which enables you to deploy your own custom ML model on edge devices. We don&#8217;t need to package the app with ML model, but instead the client will download the ML model after installation in background on demand from remote only once and reuse it for subsequent inferences. </p>
<p><img src="https://storage.googleapis.com/prd-engineering-asset/2023/04/be38b0cc-image7-972x1024.png" alt="" /></p>
<p>There are many advantages to using this method</p>
<ul>
<li>ML models are hosted on Firebase.</li>
<li>Helps in version control of ML models and ML model management.</li>
<li>Decoupling of client side development flow and ML development flow.</li>
<li>Ensuring that the app always uses the latest version of ML model.</li>
<li>Can configure conditions on when to download the latest model.</li>
<li><a href="https://firebase.google.com/docs/ml/android/ab-test-models">A/B test two versions of a model</a></li>
</ul>
<p><img src="https://storage.googleapis.com/prd-engineering-asset/2023/04/66d12291-image6-1024x631.png" alt="" /></p>
<p><center><br />
Firebase console to use custom machine learning<br />
</center></p>
<p>Even though using Firebase dynamic model loading has many advantages, We need to be mindful of the following</p>
<ul>
<li>Error handling &#8211; Fallback to default model or disable ML feature, if a new version of ML model has errors.</li>
<li>When should we download the model? For example download only when the user is connected to a wifi etc.</li>
</ul>
<h2>Conclusion</h2>
<p>ML is very useful in a wide range of use cases like object detection, barcode scanning etc. There are various challenges to using ML on the client side but Firebase Custom Model has made it so easy with APIs to fetch ML models from remote, model management, faster experimentation and ability for developers to customize configurations.</p>


Model management for client side ML powered by Firebase

Blog

<h2>■Overview</h2>
<p>We are pleased to announce that the &quot;<a href="https://ceur-ws.org/Vol-3318/short20.pdf">Want robust explanations? Get smoother predictions first.</a>&quot; by Engineer Deddy Jobson of the Mercaril AI team has been accepted for the <a href="https://project.inria.fr/aimlai/">AIMLAI workshop</a> of the international conference &quot;<a href="https://www.cikm2022.org/">CIKM2022</a>&quot;.<br />
CIKM (The Conference on Information and Knowledge Management) is one of the most prestigious international conferences in the field of data mining and information retrieval, and is held annually by researchers from around the world.</p>
<p>The accepted papers were presented at the AIMLAI workshop of &quot;CIKM 2022&quot;  held at Atlanta, Georgia, USA in October 2022.</p>
<h2>■Key points of presentation</h2>
<ul>
<li>We improve the robustness of model-agnostic ML interpretability methods like LIME and SHAP by smoothening the predictions of the model before passing it through the explainer. </li>
<li>We find an improvement in the robustness of the explanations as measured by a variant of the Lipshitz score we introduce in our paper. </li>
</ul>
<h2>■Background</h2>
<ul>
<li>Model-agnostic interpretability methods like LIME and SHAP help explain the predictions made by complex ML algorithms increasing their trustworthiness. </li>
<li>However, they suffer from a lack of robustness where small perturbations to the input can greatly change the explanations provided by these methods even when the prediction of the output does not change. </li>
<li>This undermines trust in the explanations and therefore hinders adoption of the ML model. </li>
</ul>
<h2>■Summary of paper</h2>
<ul>
<li>We propose a simple approach to improve the robustness of the explanations. </li>
<li>For each data point, we add gaussian noise to the input, pass it through the model, and get the output. We do this multiple times and take the average for each data point. </li>
<li>The resulting predictions are smoother making the explanations provided by LIME and SHAP more robust and consequently trustworthier.</li>
</ul>
<h2>■About MDS Team</h2>
<p>The Marketing Data Science Team makes use of statistics, machine learning, and mathematical optimization to improve the efficiency of marketing campaigns. We actively learn and adopt the state of the art methods used in the literature to solve technical problems and publish papers along the way. </p>


Mercari AI’s research “Want robust explanations? Get smoother predictions first.” accepted to CIKM 2022’s workshop

<h2>■Overview</h2>
<p>We are pleased to announce that the &quot;Estimating the impact of coupon non-usage&quot; by Engineer Deddy Jobson of the Mercaril AI team has been accepted for the <a href="https://upliftworkshop.ipipan.waw.pl/">Uplift Modeling Tutorial &amp; Workshop</a> collocated with the international conference &quot;<a href="https://2022.ecmlpkdd.org/">ECML PKDD 2022</a>&quot;.<br />
ECML PKDD (“European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases”) is one of the top conferences in the field of data mining and machine learning, and is held annually by researchers from around the world.</p>
<p>The accepted papers were presented at the workshop of &quot;ECML PKDD 2022&quot; held at Grenoble, France in September 2022.</p>
<h2>■Key points of presentation</h2>
<p>We look into the phenomenon of users not redeeming discount coupons.<br />
We use causal analysis to quantify the negative business impact of the lack of usability of marketing coupons. </p>
<h2>■Background / 研究の背景</h2>
<p>Mercari runs many marketing campaigns in the form of coupons which can be redeemed by users when making purchases.<br />
While the marketing campaigns are very successful, we observe that a substantial number of users who have access to the coupon fail to make use of it.<br />
While we can measure the coupon redemption rate and find the number of users who bought items without coupons, we can’t directly estimate the counterfactual number of users who didn’t buy a coupon but would have been able to use the coupon they were given. </p>
<h2>■Summary of paper / 研究概要</h2>
<p>We created a causal diagram and used a bayesian model to estimate the business impact of the lack of usage of the coupon.<br />
We show through our experiments that campaigns can greatly benefit from an increase in usability of the coupons we distribute. </p>
<h2>■About MDS Team</h2>
<p>The Marketing Data Science Team makes use of statistics, machine learning, and mathematical optimization to improve the efficiency of marketing campaigns. We actively learn and adopt the state of the art methods used in the literature to solve technical problems and publish papers along the way.</p>


Mercari AI’s research “Estimating the impact of coupon non-usage” accepted to ECML/PKDD 2022’s workshop

<h2>Overview</h2>
<p>We are pleased to announce that &quot;Counterfactual Learning with General Data-generating Policies,&quot; a paper co-authored by Team AI engineer Akihiro Shimizu and Yusuke Narita, founder of Hanjuku Kaso, Inc. and assistant professor at Yale University, has been accepted to the plenary session of AAAI 2023, an international conference on artificial intelligence.</p>
<p>AAAI (Association for the Advancement of Artificial Intelligence) is one of the most prestigious conferences in the field of artificial intelligence. This is the 37th year of the conference, and 1,721 papers have been accepted out of 8,777 submissions (acceptance rate of 19.6%).</p>
<p>The following schedule of presentation times is planned</p>
<p><strong><br />
Sunday, February 12, 2023, 2:00 PM - 3:15 PM (GMT-5)<br />
</strong></p>
<h2>Key points of presentation</h2>
<p><a href="https://arxiv.org/abs/2212.01925">Counterfactual Learning with General Data-generating Policies</a></p>
<ul>
<li>Proposed a method for Off-policy Evaluation that can be applied to a wide class of existing measures, including non-probabilistic measures.</li>
<li>Developed a method for improving algorithms using data naturally generated by the algorithm.</li>
<li>Analyzed coupon distribution policies in Mercari using the proposed method. The effectiveness of coupon measures can now be measured by using data from Mercari.</li>
</ul>
<h2>Background</h2>
<p>An important part of algorithmic decision making is to predict the performance of new decision-making algorithms (also called measures) that have not yet been used. However, A/B testing, in which old algorithms are randomly assigned to users and compared with new algorithms, can be time-consuming to develop and can lead to dissatisfaction due to unfairness to some users who could not use the new functionality.</p>
<p>Therefore, it is important to solve this problem by using &quot;Off-policy Evaluation,&quot; which attempts to estimate the performance of a new algorithm using only the data naturally generated by the old algorithm. However, it is known that current Off-policy Evaluation techniques are difficult to use for non-probabilistic measures such as coupon distribution algorithms.</p>
<h2>Summary of paper</h2>
<p>Therefore, we have proposed a new method of ex-post evaluation that can be applied to a wide range of existing measures, including non-probabilistic measures such as coupon distribution algorithms.</p>
<p>The proposed method is developed based on the observation that when an algorithm makes a decision, the data generated from it will almost always include natural experiments. For example, in the case of supervised learning used in coupon distribution algorithms, etc., decisions are often made based on whether or not some predicted variable exceeds a certain criterion value, such as whether or not to distribute coupons, etc. However, even though the situation before and after the criterion value is almost identical In our proposed method, the decision-making process is based on such a coincidence. Our proposed method considers these differences in decision making caused by chance factors as local natural experiments, and uses them to compare the performance of new measures.</p>
<p>By using data from Mercari, we have verified that our proposed method can solve the problems of real data and A/B testing in a real-world problem. Using the proposed method, it is possible to estimate the performance of a new coupon and its business impact through ex-post evaluation rather than A/B testing.</p>
<h2>About MDS Team</h2>
<p>The MDS (Marketing Data Science) team uses statistics, machine learning, and mathematical optimization to improve the efficiency of marketing campaigns.</p>
<h2>About Hanjuku Kaso, Inc.</h2>
<p>A startup that combines data, algorithms, mathematics, and thought to design the future of business, policy, and society.<br />
The company's strengths are &quot;market design,&quot; &quot;counterfactual machine learning,&quot; and &quot;causal inference,&quot; and it conducts joint projects and research with numerous companies as well as its own basic research and software development.<br />
The collaboration involves Yusuke Narita, the company's founder and an assistant professor at Yale University; Kyohei Okumura, a PhD student in the Department of Economics at Northwestern University; and Kohei Yata, an assistant professor at the University of Wisconsin-Madison.</p>


Mercari AI’s collaborative research paper “Counterfactual Learning with General Data-generating Policies” accepted to AAAI 2023

The Journey to Machine-Learned Re-ranking

Interview

<p>Tomohiro Furusawa (<a href="https://twitter.com/rilmayer_jp">@furufuru</a>), a member of Mercari’s AI Team, has held a variety of positions in his three years at Mercari, including PM, engineer, and scrum master. Despite shifting among these roles, he has always been involved in improving the discovery experience at Mercari. We sat down with him to talk about how Mercari’s Recommendation Team has developed since its establishment, and some of the things he wants to work on in the future.</p>
<h2>Profile</h2>
<p>Tomohiro Furusawa (<a href="https://twitter.com/rilmayer_jp">@furufuru</a>) | Product Manager<br />
Tomohiro discovered the joys of data science and software engineering, and before he knew it, he made the leap to the world of IT. After finishing graduate school, he used his background in library and information science to develop products for information retrieval and recommendations. He joined Mercari Inc. in 2019 to work on search evaluations as a member of the Data Science Team. He then established the Recommendation Team and is currently handling product improvement as a Product Manager.</p>
<h2>“Since my days as a student, I’ve always been figuring out how to organize information and make it easier to use.”</h2>
<p><strong> ──Can you tell us a little about what you’re working on right now? </strong></p>
<p>Right now, I’m on a team focused on improving the core Mercari experience, which is the buying and selling experience. My own particular focus on that team is improving the buying experience.</p>
<p>When you think about customers purchasing items, and how exactly they find an item they want, the most common way is that they search for it. Searching is a process where a customer comes up with an idea of something they want, so they look for the item on Mercari, find it, and then they buy it.</p>
<p>Beyond searches, though, we’re also working to improve the “discovery experience.” This involves finding new purchasing needs or finding items based on purchasing needs without making any special effort to find it—in other words, without needing to search. You might open Mercari, and items you’re interested in or thinking about buying are right there. Or, while you’re searching for one item, you find another item you’re interested in, and you buy that one. That sort of thing is how we define the “discovery experience.”</p>
<p>Recommendations are a powerful tool in improving the discovery experience.<br />
Mercari currently gives customers recommendations by controlling three units. One is “content,” which is about introducing items of interest. Then there are “topics,” AKA search criteria, which are item groupings that can be interpreted as such. Finally, there are “items,” which are the items customers actually purchase.</p>
<p><img src="https://storage.googleapis.com/prd-ai-asset/2022/12/219d7b80-image_1-1024x683.jpg" alt="" /></p>
<p><center></p>
<p>Tomohiro Furusawa (@furufuru)<br />
Furusawa has consistently been involved in recommendations since joining Mercari in 2019</p>
<p></center></p>
<p>When I was in college, I worked on library and information science. Through academics I studied how to collect and store large amounts of knowledge and information and how to make it easily accessible to a wide range of people. Looking back at my career since my days as a student, I feel like I’ve always been involved in figuring out how to organize large amounts of information and make it easier to use.</p>
<p>When I was a student, I thought about a career as a librarian, but when I started interning as an engineer and data scientist, I found myself really interested in manufacturing and data usage. So, after I graduated, I decided to become a data scientist at Recruit. Recruit, which is where I worked before Mercari, has a lot of contact points with customers through all sorts of life events, so it was interesting just to look at the data. That said, I was also almost painfully aware that data is meaningless just by itself, and that its real significance lies in how it’s used. That’s when I started digging deeper into the technology of searches and recommendations.</p>
<p><strong> What’s the difference between a “search” and a “recommendation?”</strong></p>
<p>When we want a certain type of information, we use keywords and other things to ask questions through some sort of app, right? Then we look at all the results and get the information we want. That series of actions is what we call a “search,” and a search system is the mechanism that makes that experience possible.<br />
However, if we utilize customer data we already have, we can provide information from the service side without customers needing to enter any kind of keywords or other information. A system that can provide information without a customer having to actively ask questions is a recommendation system. A “recommendation” is different from a “search” in that it refers not to an experience, but rather to a means used by the service provider.<br />
Building good search and recommendation systems makes the service easier to use, and I think it ultimately bring us closer to the world I want to create, which is a world where people from all walks of life can access knowledge and information with ease.</p>
<h2>Improving the recommendation system while shifting from position to position</h2>
<p><strong>──Why were you interested in a job at Mercari?</strong></p>
<p>It started when a former colleague of mine was working on improving the search experience at Mercari and invited me to join. Mercari’s user base was growing fast, and the company had a lot of momentum, so I thought changing jobs would give me the chance to dig deeper into search technologies that use a wide range of data.</p>
<p>After joining Mercari, I started by evaluating the existing search system. All services—not just search functions—of course need to be built first, but they also then need to be improved through evaluation cycles.</p>
<p>With our service, a search is considered to have gone well when a series of experiences is born—for example, when you enter a keyword for something you want, you find the perfect item, and then make your purchase. On the flip side, a search doesn’t go well when, say, a customer is looking for an item but can’t find it using keywords, or a lot of items with little relevance show up in the search results.</p>
<p>Another issue unique to Mercari is that the search results sometimes show only items in good condition that can be purchased right away, which means the customer can’t compare and consider other items as well. The Search Team was using trial and error, repeatedly defining and evaluating what makes a search a success or a failure, to figure how to improve the search process.</p>
<p><img src="https://storage.googleapis.com/prd-ai-asset/2022/12/c184be5d-image_2-1024x683.jpg" alt="" /></p>
<p>We established Mercari’s Recommendation Team after that. I’d actually been quietly working on a prototype of the recommendation system before the team was formed, but the company also decided to focus on the recommendation system, so I ended up supporting the establishment of the team as a PM. After a while, Aki Saarinen (@akis) joined the team as PM and I shifted back to being an engineer.</p>
<p>Moving through the development process as an engineer, I found that a good development system hadn’t been created at the team level, so I acted as scrum master to fix the group’s development system while handling my engineer duties as well. After a while, things came together as an organization and the Recommendation Team started producing results, but there was also a shortage of PMs at the time, so I went back to being a PM again. So, although I’ve always been involved in the recommendation system, my job title has gone from PM, to engineer, to scrum master, and then back again.</p>
<h2>Thoughts on getting recommendation features to contribute to GMV</h2>
<p><strong>──Have you hit any walls in your career so far?</strong></p>
<p>Actually, it took some time before the Recommendation Team was able to successfully contribute to sales. Before the current system, items on the home screen were recommended to customers based on their past activity, but appearance wise, it was just a timeline listing one item after another. From there, we increased the types of components in the recommendation system to display recommended keywords and categories based on a history of customers’ recent activity. That was when we first saw a big boost in sales. Instead of just recommending items, we also display why an item is recommended and define functions for components that allow for deeper and easier exploration, and I think that has made things easier to understand for our customers.</p>
<p><img src="https://storage.googleapis.com/prd-ai-asset/2022/12/5fb7f5d4-image_3-1024x679.png" alt="" /></p>
<p><center><br />
メルカリのホーム画面の変遷<br />
</center></p>
<p><strong>Related Article：<a href="https://mercan.mercari.com/articles/27899/">No Two Home Screens are Alike – The Reasons Behind Mercari’s “Uniquely Difficult” Personalization | mercan ＊This article is only available in Japanese</a> </strong></p>
<p>The home screen went through big changes in terms of UI appearance during this period, but we also revamped background systems at the same time. To handle the labor-intensive process of developing home screen functions in a short period of time, we reworked most of the content displayed by the client into a form we can manipulate on the back end. We had always thought it would be great if the system could be modularized so that we could just change the back end to switch between home screens, but it took until around 2020 before we could actually develop and deploy it.</p>
<p><strong>──Are there any other things you want to try or challenge yourself with at Mercari in the future?</strong></p>
<p>The recommendation system is still using only relatively simple logic, so I’d like to try applying a broader range of technologies to solve advanced problems. We still need to address a lot of things, like developing data utilization infrastructure for the recommendation system. If you’re someone who loves approaching things from a variety of logical perspectives, definitely get in touch with us!</p>
<p>Mercari is a high-traffic service used by a huge number of customers, so someone with experience in operating a large-scale service will probably have an easier time grasping the essential points intuitively. That said, even if there are some things they don’t know, we love working with people who enjoy meeting challenges head-on!</p>
<p><img src="https://storage.googleapis.com/prd-ai-asset/2022/12/3e6f9193-image_4-1024x683.jpg" alt="" /></p>
<p><a href="https://ai.mercari.com/en/articles/ai/furufuru_interview_2/">In the second part of the interview</a>, Tomohiro talks about the things he focused on while working at Mercari, and Mercari’s unique organizational culture, as someone with experience in a range of positions, including PM, engineer, and scrum master.</p>
<ul>
<li>Photo : Shun Nakayama (<a href="https://shunnakayama.myportfolio.com/">Web</a>/<a href="https://twitter.com/shunnakayama_">Twitter</a>)</li>
<li>Text : Ayumi Iga (<a href="https://twitter.com/aymmm_i">Twitter</a>)</li>
</ul>


The Past and Future of Mercari’s Discovery Experience, from the PM who Launched the Recommendation Team

<p>Tomohiro Furusawa (<a href="https://twitter.com/rilmayer_jp">@furufuru</a>), a member of Mercari’s AI Team, has always been involved in improving the discovery experience in his three years at Mercari.  <a href="https://ai.mercari.com/en/articles/ai/furufuru_interview_2/">In the first part of the interview</a>, Tomohiro talks about how Mercari’s Recommendation Team has developed since its establishment, and some of the things he wants to work on in the future.</p>
<p>In the second part, he talks about the things he’s focused on while working at Mercari, and Mercari’s unique organizational culture, as someone with experience in a range of positions, including PM, engineer, and scrum master.</p>
<h2>Profile</h2>
<p>Tomohiro Furusawa (<a href="https://twitter.com/rilmayer_jp">@furufuru</a>) | Product Manager<br />
Tomohiro discovered the joys of data science and software engineering, and before he knew it, he made the leap to the world of IT. After finishing graduate school, used his background in library and information science to develop products for information retrieval and recommendations. He joined Mercari Inc. in 2019 to work on search evaluations as a member of the Data Science Team. He then established the Recommendation Team and is currently handling product improvement as a Product Manager.</p>
<h2>Why experience as both a PM and an engineer makes the development process smoother</h2>
<p><strong> ──You have held a variety of positions in your three years at Mercari, including PM, engineer, and scrum master. Have you ever felt like you’re able to add value at Mercari precisely because you have experience as both a PM and an engineer?</strong></p>
<p>Yes. One way is that I can approach technical debt in the same way I approach functional improvements. When you build a product, you might create technical debt that doesn’t directly affect customer value but does affect the development experience, and thus the speed of improvement over the long term, and that’s something I now pay attention to. My engineering background also made it easy for me to figure out things like how much time it would take to develop a certain function, and which elements I should focus on. I feel like I’m able to communicate with engineers quickly and smoothly.</p>
<p>Another way is that I can look at things from the perspective of a PM even when I’m working as an engineer. When we launched the Recommendation Team, the internal organization and development environment weren’t as mature as they are today. So, when it came time to launch the new recommendation function, a lot of changes needed to be made in the system, and I was able to communicate with everyone with a good idea of which members we’d need to push forward with the development.</p>
<p>To give a little more detail, at that time, we had to make all our changes while striking a balance between the client code, the back-end microservice code, and the code for multiple peripheral microservices. That required communicating with the engineers on each team, and talking to the designers and the CS team as well. I remember communicating with a bunch of different people throughout the company just to put out one function.</p>
<p>Those sorts of efforts made it possible to move forward with the development of the recommendation functions.</p>
<p><img src="https://storage.googleapis.com/prd-ai-asset/2022/12/a67047d0-image_3-1024x683.jpg" alt="" /></p>
<h2>As a Tech PM, the ability to work with data is essential</h2>
<p><strong> ──What are some of the skills you need to be a Tech PM at Mercari?</strong></p>
<p>The first thing I think a Tech PM at Mercari needs is the ability to work with data. You need to be able to analyze data to drive your decisions—to look at the data in front of you and interpret what’s happening, define “success” and “failure” based on customer behavior as seen in the data, and if something is failing, determine the cause and plan your next move.</p>
<p>When we put a feature in place, we decide in advance how we will measure success before we release it. The easiest way to measure success is to see if the feature increased sales. That said, a single function rarely causes sales to explode, so we often follow our customers’ behavior to see if there has been any change from before a function was deployed. Sometimes the numbers change as expected, and sometimes they stay the same. Sometimes they get worse! Sometimes figures we were monitoring which led to sales improved, but sales themselves actually dropped. A situation like that means the hypothesis was wrong in the first place and needs to be reworked from scratch. This is a job where you’re constantly looking at data, making decisions, and repeating the process, so I think data analysis skills are essential.</p>
<p>I also think it’s essential for any PM, not just Tech PMs, to be able to keep ROI in mind constantly. Being able to think intuitively and build strategies on how to implement effective measures at a lower cost is an important skill to have.</p>
<p><img src="https://storage.googleapis.com/prd-ai-asset/2022/12/35fc0787-image_2-1024x683.jpg" alt="" /></p>
<h2>“Mercari engineers aren’t just people who build whatever a PM comes up with” was a shock to me.</h2>
<p><strong> ──Tell us about any particular skills you gained or growth you’ve experienced in the Mercari organization.</strong></p>
<p>Maybe the ability to communicate things clearly. Mercari is a diverse organization and there are members from a wide variety of backgrounds. When I first started, there were a lot of times where I thought I was speaking clearly, but the other members and I weren’t understanding each other. When I would tell my team “this is what we’re going to do,” I would sometimes get comments like “I really don’t see the value in working on this feature.” When I looked back on why they weren’t seeing the value in what I was saying, I realized I’d been trying to communicate it too carefully, which had the opposite effect of making it harder to understand. I thought about, first, what was most important to get across and, second, where the value of what we were doing lies, from the standpoint of conveying information to members from different backgrounds in an easy-to-understand manner. Then I worked hard to strip down the amount of information and extract just the most important points. I found that as a PM, focusing on choosing information to keep or discard based on the backgrounds of the other members made it easier for us to work together.</p>
<p>Also, in a pyramid-shaped organization, information flows from the top down, right? Mercari also has a bottom-up flow, where information moves horizontally, and members communicate with each other to get approval from upper levels. The unique communication style Mercari uses to create this bottom-up flow is something else I’ve picked up.</p>
<p><strong> ──Is there anything else you can tell us about the unique culture at Mercari?</strong></p>
<p>One of the things that struck me when I joined Mercari was that there are so many ideas coming from engineers. It really hit me how Mercari engineers aren’t just people who build whatever things a PM comes up with. Their stance tends to be “we’re not going to build something if we don’t want to or if we don’t see its significance,” so I always try to make sure I can explain the meaning behind what I’m creating.</p>
<p>On the other hand, from an engineers’ perspective, it’s not considered acceptable to just say “OK!” when a PM asks you to create something. The entire team must be convinced, in terms of whether there are any systemic problems, what the cost of implementation will be, and what the feature’s significance is.</p>
<p><img src="https://storage.googleapis.com/prd-ai-asset/2022/12/a67047d0-image_3-1024x683.jpg" alt="" /></p>
<p>So, as a PM, I have to communicate the value and context of the feature to the engineers and get them on board, even if the company has already decided it’s a feature that has to be made. Nothing good comes from one-sided communication where I just say, “Build this!”</p>
<p>Since I have experience as both an engineer and a PM, I understand how both sides feel. As a PM, I learned the necessity of having engineers work after accurately communicating background information and meaning to them, and as an engineer, I felt like I needed to be proactive about giving my opinions to PMs.</p>
<h2>The Mercari discovery experience “still has a lot of room for improvement”</h2>
<p><strong> ──Have you achieved what you set out to do at Mercari when you first came on board? </strong></p>
<p>I’ve consistently been working on improving the search and discovery experience ever since I joined Mercari. Although I think the customer experience has actually gotten better, it still has a lot of room for improvement. Mercari doesn’t have any boundaries that say you can only go so far as an engineer, so you’ll be encouraged to take on challenges, and in my experience, I was able to actually put the Recommendation Team on track as a PM. The will of individual workers is something the company values, as long as it matches the direction of the company.</p>
<p>Looking back now, I was lucky to have the chance to work as a PM despite joining Mercari as a data scientist, so as long as it fits the direction I want to follow in the future, I’d like to keep challenging myself in that role.</p>
<p><img src="https://storage.googleapis.com/prd-ai-asset/2022/12/2298cb41-image_4-1024x683.jpg" alt="" /></p>
<ul>
<li>Photo : Shun Nakayama (<a href="https://shunnakayama.myportfolio.com/">Web</a>/<a href="https://twitter.com/shunnakayama_">Twitter</a>)</li>
<li>Text : Ayumi Iga (<a href="https://twitter.com/aymmm_i">Twitter</a>)</li>
</ul>


Discoveries through a career as both PM and engineer at Mercari

<h2>Overview</h2>
<p>The 1st Workshop on End–to-End Customer Journey Optimization was co-located with the KDD 2022 conference. The theme of the workshop was to discuss ways to improve the user experience across the entire customer journey. </p>
<h2>Key points of presentation</h2>
<p><a href="https://arxiv.org/abs/2207.14798">Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions</a></p>
<p>We summarize our major contributions as follows: </p>
<ul>
<li>We propose a framework of promotion decision making by targeting customer direct and enduring effect. Our framework consists of direct and enduring response predictions, incentive allocation decision making and business impact evaluation. </li>
<li>We propose a customer direct and enduring effect (CDEE) model, which provides predictions of the direct purchase propensity and the enduring purchase amount of each customer. </li>
<li>We compare the CDEE model against benchmarks on two promotional campaigns in our company and achieve a significantly better performance.</li>
</ul>
<h2>Background</h2>
<p>In the e-commerce marketplace, marketers run various types of promotions as a part of the customer relationship management (CRM) strategy in order to maintain customer relationships and guide consumers towards desired actions. In Mercari, multiple treatment promotions are also a common marketing strategy. To better cater to each customer’s needs, personalized promotion decision making is necessary and has garnered the attention of the research community. Some research optimizes promotion decision making by applying purchase prediction. While the modeling and optimization on the direct purchase probability brings profits to the business, there is still room for improvement. </p>
<p>First, different customers contribute differently to the revenue even when they complete a purchase. For promotions which aim to maximize revenue, we should consider not only the purchase probability but also the purchase amount in the decision making process.</p>
<p>Second, promotions can induce purchases but these often only turn out to be one-shot deals. On top of that, promotions might cause purchase acceleration or stockpiling. Therefore a post promotion dip in purchases is widely observed in marketing. To create promotions that aim to build long-term customer engagement and boost customer loyalty, the post-promotion period effect should be taken into account.</p>
<h2>Summary of paper</h2>
<p>To target the efficient customers from the perspective of revenue during and post promotion, we propose a framework of promotion decision making by modeling customer direct and enduring response. The direct response corresponds to the customer’s re- sponse during the promotion period, while the enduring response corresponds to the customer’s response during and after the promotional period. Our framework consists of both direct and enduring response predictions, incentive allocation decision making and business impact evaluation. </p>
<p>We propose a customer direct and enduring effect (CDEE) model which provides predictions of the direct purchase propensity and the enduring purchase amount of each customer. </p>
<p>We formalize the incentive allocation problem. Our optimization goal is to pick the best incentive types for customers maximizing the total enduring purchase amount while keeping the cost under budget. </p>
<p>To estimate the effect of decision making, we apply an unbiased evaluation approach on the business metrics with randomized control trial (RCT) data.We compare our method with benchmarks using two promotions in Mercari and achieve significantly better results on both model evaluation and business metrics.</p>
<h2>About MDS Team</h2>
<p>In the Marketing Data Science (MDS) team, we use statistics, machine learning, and mathematical optimization to improve the efficiency of marketing campaigns. </p>


Mercari AI’s research paper —”Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions”—accepted to KDD2022 1st Workshop

Blogs by mercari AI team, delivering information about AI technology and organization

Mercari AI’s research “Want robust explanations? Get smoother predictions first.” accepted to CIKM 2022’s workshop

Mercari AI’s research “Estimating the impact of coupon non-usage” accepted to ECML/PKDD 2022’s workshop

Mercari AI’s collaborative research paper “Counterfactual Learning with General Data-generating Policies” accepted to AAAI 2023

The Past and Future of Mercari’s Discovery Experience, from the PM who Launched the Recommendation Team

Discoveries through a career as both PM and engineer at Mercari

Mercari AI’s research “Want robust explanations? Get smoother predictions first.” accepted to CIKM 2022’s workshop

Mercari AI’s research “Estimating the impact of coupon non-usage” accepted to ECML/PKDD 2022’s workshop

Mercari AI’s collaborative research paper “Counterfactual Learning with General Data-generating Policies” accepted to AAAI 2023

The Past and Future of Mercari’s Discovery Experience, from the PM who Launched the Recommendation Team

Discoveries through a career as both PM and engineer at Mercari

Mercari AI’s research “Want robust explanations? Get smoother predictions first.” accepted to CIKM 2022’s workshop

Mercari AI’s research “Estimating the impact of coupon non-usage” accepted to ECML/PKDD 2022’s workshop

Mercari AI’s collaborative research paper “Counterfactual Learning with General Data-generating Policies” accepted to AAAI 2023

The Past and Future of Mercari’s Discovery Experience, from the PM who Launched the Recommendation Team

Discoveries through a career as both PM and engineer at Mercari

Latest Posts

Model management for client side ML powered by Firebase

Mercari AI’s research “Want robust explanations? Get smoother predictions first.” accepted to CIKM 2022’s workshop

Mercari AI’s research “Estimating the impact of coupon non-usage” accepted to ECML/PKDD 2022’s workshop

Mercari AI’s collaborative research paper “Counterfactual Learning with General Data-generating Policies” accepted to AAAI 2023

The Journey to Machine-Learned Re-ranking

The Past and Future of Mercari’s Discovery Experience, from the PM who Launched the Recommendation Team

Discoveries through a career as both PM and engineer at Mercari

Mercari AI’s research paper —”Personalized Promotion Decision Making Based on Direct and Enduring Effect Predictions”—accepted to KDD2022 1st Workshop