# Interest Scores & Classification ### About this export | Field | Value | | --- | --- | | **content_type** | lesson | | **platform** | contentstack-academy | | **source_url** | https://www.contentstack.com/academy/courses/data-insights-data-ingestion-profile-construction/data-insights-course-3--interest-scores-classification | | **course_slug** | data-insights-data-ingestion-profile-construction | | **lesson_slug** | data-insights-course-3--interest-scores-classification | | **markdown_file_url** | /academy/md/courses/data-insights-data-ingestion-profile-construction/data-insights-course-3--interest-scores-classification.md | | **generated_at** | 2026-04-28T06:55:44.158Z | > Part of **[Data Ingestion & Profile Construction](https://www.contentstack.com/academy/courses/data-insights-data-ingestion-profile-construction)** on Contentstack Academy. **Academy MD v3** — structured for retrieval; no quiz or assessment keys. #### Video details #### At a glance - **Title:** 20-data-insights-interest-scores-classification - **Duration:** 5m 40s - **Media link:** https://cdn.jwplayer.com/previews/iRhAPCRQ - **Publish date (unix):** 1752879751 #### Streaming renditions - application/vnd.apple.mpegurl - audio/mp4 · AAC Audio · 113718 kbps - video/mp4 · 180p · 180p · 138386 kbps - video/mp4 · 270p · 270p · 155796 kbps - video/mp4 · 360p · 360p · 172959 kbps - video/mp4 · 406p · 406p · 183666 kbps - video/mp4 · 540p · 540p · 221294 kbps - video/mp4 · 720p · 720p · 279070 kbps #### Timed text tracks (delivery) - **thumbnails:** `https://cdn.jwplayer.com/strips/iRhAPCRQ-120.vtt` #### Transcript But we covered our behavioral scores as well, right? The momentum and propensity, those are really, really important behavioral statistics, behavioral scores that help fuel and empower these models. The other one that is maybe the most important thing, certainly one of the most important things in the kind of Linux coming together with content stack story is our interest scores. So you saw them, I think, in the very first conversation where we go back to Petsy, turn on our trustee Chrome extension. So at the bottom of the Chrome extension, you'll see a set of interest scores. These, for most customers, just come out of the box. So we'll talk about how they actually work, what we're doing to classify the content associated, but ultimately what it allows you to do as a customer is as I browse, you'll see in real time and it might be kind of like subtle because I've used this profile a lot, but you'll see my scores change with every single piece of content that I interact with. So as I go here and I look at pet carriers, obviously, and this is like a sandbox demo account, but you'll see those scores, like again, it's a little bit kind of like hard to see in the bigger scale, but they're being recalculated every single time that an event comes into the pipeline, which is super, super useful in one, helping you understand anonymous users, which is most users in the case of marketing. That's where a lot of other CDPs fail and fall short as they don't talk about the importance of anonymous and how that particular product helps with the anonymous use cases. It's always around. We help you get your data together and build known profiles and it will, at the end of the day, it's pretty easy to mark two people that, you know, if you have their email address and everything about them, it's much more difficult when you only have the information that they give you in sort of like different browser sessions, essentially. So kind of to just start at how all of this works, these values on the profile are called interest scores. So if I go to the raw details for this particular user and find. So the thing that's actually happening is for every single one of the topics, which we'll talk about topics here in a second, there is a score of how much or how little I am interested. This works the exact same way that all of our other scores do. So not only do you understand what I'm interested in, you can target the people that have an above average interest in a specific topic on your website. So that granularity is super important. But what you see in the bar is essentially this data getting digitized. Today how this works is every time that a user visits your site that's been tagged, we get a URL. Our system actually goes out, scrapes that URL and runs it through a series of different systems to do NLP, image analysis, some of those kind of things to ultimately uncover the topics that are associated with a particular document. So for instance, if I close this, give us some more screen real estate. If I go into our Petsy sandbox account real quick and just go to like documents with images, what it's actually doing automatically. So this is with no configuration from the user other than saying, you're okay to classify this domain. We're going to go out, we're going to look at the content, we're going to pull that content in, we're going to understand what topics it's about, what images are on it, all of this information. So that every time that a user visits that particular URL, we know what the content is about. And then we can start to build scores for how much the topics on that particular document align with all of the other interests that we've seen from that particular user. So it's essentially, we go out, we scrape the content, we turn it into topics. And then every time that the user then sees that content, we understand what it's about, we can associate that with their scores and we can update them in real time. So we're actually going through what we call classifying the documents. So if I go into, for instance, this like Elegant Paws Cat Carrier, again, make-believe content. And this is the thing that I wanted to touch on. One of the things that is really cool, and my biased opinion about Linux is that the same exact identity resolution model, the way that we handle profiles and that graph and all of that kind of stuff, works in the exact same way for content. So we essentially out of the box build a user table, and that's how we're able to associate all the different profiles with mark together. We also have a content table. So for every document, we go out, we analyze that document, we collect the information about that document, and we essentially build document profiles inside of Linux. So on the screen, they're not as pretty, because we haven't spent as much time sort of like showcasing this particular thing. But you can see that like for a piece of content, this URL that we just clicked on, you understand the hashed value, which is how we associate it with the user, the number of users that have seen it, the different information, the entire body of that document, the topics, the header image, the primary image, we can pull in meta tags, whether it's fail, like all of this meta information around not just the content, but this sort of document that ultimately users interact with. Thank you. Thank you. #### Subtitles (WebVTT) ```webvtt WEBVTT 1 00:00:00.000 --> 00:00:18.400 But we covered our behavioral scores as well, right? 2 00:00:18.400 --> 00:00:22.560 The momentum and propensity, those are really, really important behavioral statistics, behavioral 3 00:00:22.560 --> 00:00:25.840 scores that help fuel and empower these models. 4 00:00:25.840 --> 00:00:29.800 The other one that is maybe the most important thing, certainly one of the most important 5 00:00:29.800 --> 00:00:35.720 things in the kind of Linux coming together with content stack story is our interest scores. 6 00:00:35.720 --> 00:00:45.440 So you saw them, I think, in the very first conversation where we go back to Petsy, turn 7 00:00:45.440 --> 00:00:53.920 on our trustee Chrome extension. 8 00:00:53.920 --> 00:00:59.560 So at the bottom of the Chrome extension, you'll see a set of interest scores. 9 00:00:59.560 --> 00:01:02.320 These, for most customers, just come out of the box. 10 00:01:02.320 --> 00:01:05.920 So we'll talk about how they actually work, what we're doing to classify the content associated, 11 00:01:05.920 --> 00:01:11.320 but ultimately what it allows you to do as a customer is as I browse, you'll see in real 12 00:01:11.320 --> 00:01:14.400 time and it might be kind of like subtle because I've used this profile a lot, but you'll see 13 00:01:14.400 --> 00:01:18.760 my scores change with every single piece of content that I interact with. 14 00:01:18.760 --> 00:01:23.920 So as I go here and I look at pet carriers, obviously, and this is like a sandbox demo 15 00:01:23.920 --> 00:01:27.440 account, but you'll see those scores, like again, it's a little bit kind of like hard 16 00:01:27.440 --> 00:01:32.320 to see in the bigger scale, but they're being recalculated every single time that an event 17 00:01:32.320 --> 00:01:38.640 comes into the pipeline, which is super, super useful in one, helping you understand anonymous 18 00:01:38.640 --> 00:01:42.000 users, which is most users in the case of marketing. 19 00:01:42.000 --> 00:01:45.880 That's where a lot of other CDPs fail and fall short as they don't talk about the importance 20 00:01:45.880 --> 00:01:50.760 of anonymous and how that particular product helps with the anonymous use cases. 21 00:01:50.760 --> 00:01:51.760 It's always around. 22 00:01:51.760 --> 00:01:55.280 We help you get your data together and build known profiles and it will, at the end of 23 00:01:55.280 --> 00:01:58.920 the day, it's pretty easy to mark two people that, you know, if you have their email address 24 00:01:58.920 --> 00:02:03.760 and everything about them, it's much more difficult when you only have the information 25 00:02:03.760 --> 00:02:10.400 that they give you in sort of like different browser sessions, essentially. 26 00:02:10.400 --> 00:02:17.280 So kind of to just start at how all of this works, these values on the profile are called 27 00:02:17.280 --> 00:02:18.960 interest scores. 28 00:02:18.960 --> 00:02:26.480 So if I go to the raw details for this particular user and find. 29 00:02:26.480 --> 00:02:30.520 So the thing that's actually happening is for every single one of the topics, which 30 00:02:30.520 --> 00:02:35.240 we'll talk about topics here in a second, there is a score of how much or how little 31 00:02:35.240 --> 00:02:36.480 I am interested. 32 00:02:36.480 --> 00:02:39.160 This works the exact same way that all of our other scores do. 33 00:02:39.160 --> 00:02:42.560 So not only do you understand what I'm interested in, you can target the people that have an 34 00:02:42.560 --> 00:02:45.960 above average interest in a specific topic on your website. 35 00:02:45.960 --> 00:02:48.000 So that granularity is super important. 36 00:02:48.000 --> 00:02:52.400 But what you see in the bar is essentially this data getting digitized. 37 00:02:52.400 --> 00:02:58.200 Today how this works is every time that a user visits your site that's been tagged, 38 00:02:58.200 --> 00:02:59.680 we get a URL. 39 00:02:59.680 --> 00:03:04.860 Our system actually goes out, scrapes that URL and runs it through a series of different 40 00:03:04.860 --> 00:03:11.760 systems to do NLP, image analysis, some of those kind of things to ultimately uncover 41 00:03:11.760 --> 00:03:15.300 the topics that are associated with a particular document. 42 00:03:15.300 --> 00:03:22.540 So for instance, if I close this, give us some more screen real estate. 43 00:03:22.540 --> 00:03:28.500 If I go into our Petsy sandbox account real quick and just go to like documents with images, 44 00:03:28.500 --> 00:03:30.700 what it's actually doing automatically. 45 00:03:30.700 --> 00:03:34.900 So this is with no configuration from the user other than saying, you're okay to classify 46 00:03:34.900 --> 00:03:36.480 this domain. 47 00:03:36.480 --> 00:03:39.540 We're going to go out, we're going to look at the content, we're going to pull that content 48 00:03:39.540 --> 00:03:43.780 in, we're going to understand what topics it's about, what images are on it, all of 49 00:03:43.780 --> 00:03:45.860 this information. 50 00:03:45.860 --> 00:03:51.100 So that every time that a user visits that particular URL, we know what the content is 51 00:03:51.100 --> 00:03:52.100 about. 52 00:03:52.100 --> 00:03:57.620 And then we can start to build scores for how much the topics on that particular document 53 00:03:57.620 --> 00:04:01.140 align with all of the other interests that we've seen from that particular user. 54 00:04:01.140 --> 00:04:05.780 So it's essentially, we go out, we scrape the content, we turn it into topics. 55 00:04:05.780 --> 00:04:09.940 And then every time that the user then sees that content, we understand what it's about, 56 00:04:09.940 --> 00:04:13.060 we can associate that with their scores and we can update them in real time. 57 00:04:13.060 --> 00:04:15.980 So we're actually going through what we call classifying the documents. 58 00:04:15.980 --> 00:04:22.100 So if I go into, for instance, this like Elegant Paws Cat Carrier, again, make-believe content. 59 00:04:22.100 --> 00:04:25.100 And this is the thing that I wanted to touch on. 60 00:04:25.100 --> 00:04:29.100 One of the things that is really cool, and my biased opinion about Linux is that the 61 00:04:29.100 --> 00:04:34.540 same exact identity resolution model, the way that we handle profiles and that graph 62 00:04:34.540 --> 00:04:38.820 and all of that kind of stuff, works in the exact same way for content. 63 00:04:38.820 --> 00:04:42.540 So we essentially out of the box build a user table, and that's how we're able to associate 64 00:04:42.540 --> 00:04:44.940 all the different profiles with mark together. 65 00:04:44.940 --> 00:04:46.660 We also have a content table. 66 00:04:46.660 --> 00:04:51.980 So for every document, we go out, we analyze that document, we collect the information 67 00:04:51.980 --> 00:04:57.760 about that document, and we essentially build document profiles inside of Linux. 68 00:04:57.760 --> 00:05:01.340 So on the screen, they're not as pretty, because we haven't spent as much time sort of like 69 00:05:01.340 --> 00:05:04.040 showcasing this particular thing. 70 00:05:04.040 --> 00:05:09.940 But you can see that like for a piece of content, this URL that we just clicked on, you understand 71 00:05:09.940 --> 00:05:14.420 the hashed value, which is how we associate it with the user, the number of users that 72 00:05:14.420 --> 00:05:20.220 have seen it, the different information, the entire body of that document, the topics, 73 00:05:20.220 --> 00:05:24.580 the header image, the primary image, we can pull in meta tags, whether it's fail, like 74 00:05:24.580 --> 00:05:30.260 all of this meta information around not just the content, but this sort of document that 75 00:05:30.260 --> 00:05:31.820 ultimately users interact with. 76 00:05:39.940 --> 00:05:40.940 Thank you. 77 00:05:40.940 --> 00:05:41.940 Thank you. ``` ```transcript [00:00] But we covered our behavioral scores as well, right? [00:18] The momentum and propensity, those are really, really important behavioral statistics, behavioral [00:22] scores that help fuel and empower these models. [00:25] The other one that is maybe the most important thing, certainly one of the most important [00:29] things in the kind of Linux coming together with content stack story is our interest scores. [00:35] So you saw them, I think, in the very first conversation where we go back to Petsy, turn [00:45] on our trustee Chrome extension. [00:53] So at the bottom of the Chrome extension, you'll see a set of interest scores. [00:59] These, for most customers, just come out of the box. [01:02] So we'll talk about how they actually work, what we're doing to classify the content associated, [01:05] but ultimately what it allows you to do as a customer is as I browse, you'll see in real [01:11] time and it might be kind of like subtle because I've used this profile a lot, but you'll see [01:14] my scores change with every single piece of content that I interact with. [01:18] So as I go here and I look at pet carriers, obviously, and this is like a sandbox demo [01:23] account, but you'll see those scores, like again, it's a little bit kind of like hard [01:27] to see in the bigger scale, but they're being recalculated every single time that an event [01:32] comes into the pipeline, which is super, super useful in one, helping you understand anonymous [01:38] users, which is most users in the case of marketing. [01:42] That's where a lot of other CDPs fail and fall short as they don't talk about the importance [01:45] of anonymous and how that particular product helps with the anonymous use cases. [01:50] It's always around. [01:51] We help you get your data together and build known profiles and it will, at the end of [01:55] the day, it's pretty easy to mark two people that, you know, if you have their email address [01:58] and everything about them, it's much more difficult when you only have the information [02:03] that they give you in sort of like different browser sessions, essentially. [02:10] So kind of to just start at how all of this works, these values on the profile are called [02:17] interest scores. [02:18] So if I go to the raw details for this particular user and find. [02:26] So the thing that's actually happening is for every single one of the topics, which [02:30] we'll talk about topics here in a second, there is a score of how much or how little [02:35] I am interested. [02:36] This works the exact same way that all of our other scores do. [02:39] So not only do you understand what I'm interested in, you can target the people that have an [02:42] above average interest in a specific topic on your website. [02:45] So that granularity is super important. [02:48] But what you see in the bar is essentially this data getting digitized. [02:52] Today how this works is every time that a user visits your site that's been tagged, [02:58] we get a URL. [02:59] Our system actually goes out, scrapes that URL and runs it through a series of different [03:04] systems to do NLP, image analysis, some of those kind of things to ultimately uncover [03:11] the topics that are associated with a particular document. [03:15] So for instance, if I close this, give us some more screen real estate. [03:22] If I go into our Petsy sandbox account real quick and just go to like documents with images, [03:28] what it's actually doing automatically. [03:30] So this is with no configuration from the user other than saying, you're okay to classify [03:34] this domain. [03:36] We're going to go out, we're going to look at the content, we're going to pull that content [03:39] in, we're going to understand what topics it's about, what images are on it, all of [03:43] this information. [03:45] So that every time that a user visits that particular URL, we know what the content is [03:51] about. [03:52] And then we can start to build scores for how much the topics on that particular document [03:57] align with all of the other interests that we've seen from that particular user. [04:01] So it's essentially, we go out, we scrape the content, we turn it into topics. [04:05] And then every time that the user then sees that content, we understand what it's about, [04:09] we can associate that with their scores and we can update them in real time. [04:13] So we're actually going through what we call classifying the documents. [04:15] So if I go into, for instance, this like Elegant Paws Cat Carrier, again, make-believe content. [04:22] And this is the thing that I wanted to touch on. [04:25] One of the things that is really cool, and my biased opinion about Linux is that the ``` #### Key takeaways - Connect **Interest Scores & Classification** back to your stack configuration before moving to the next module. - Capture one concrete artifact (screenshot, Postman call, or code snippet) that proves the step works in your environment. - Re-read the delivery versus management boundary for anything you changed in the entry model. ## Supplement for indexing ### Content summary Interest Scores & Classification. Interest Scores & Classification in Data Ingestion & Profile Construction (data-insights-data-ingestion-profile-construction). ### Retrieval tags - Interest - Scores - Classification - data-insights-data-ingestion-profile-construction - lesson 12 - Interest Scores & Classification - data-insights-data-ingestion-profile-construction lesson ### Indexing notes Index this lesson as a primary chunk tagged with lesson_id "12" and topics: [Interest, Scores, Classification]. Parent course slug: data-insights-data-ingestion-profile-construction. Use asset_references URLs as thumbnail hints in search results when present. Never surface LMS quiz content or assessment answers from this file. ### Asset references | Label | URL | | --- | --- | | Video thumbnail: Interest Scores & Classification | `https://cdn.jwplayer.com/v2/media/iRhAPCRQ/poster.jpg?width=720` | ### External links | Label | URL | | --- | --- | | Contentstack Academy home | `https://www.contentstack.com/academy/` | | Training instance setup | `https://www.contentstack.com/academy/training-instance` | | Academy playground (GitHub) | `https://github.com/contentstack/contentstack-academy-playground` | | Contentstack documentation | `https://www.contentstack.com/docs/` |