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function _createMdxContent(props) {
  const _components = Object.assign({
    p: "p",
    ul: "ul",
    li: "li",
    strong: "strong",
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    span: "span"
  }, _provideComponents(), props.components);
  return _jsxs(_Fragment, {
    children: [_jsx(_components.p, {
      children: "In our four-part series, 'Under the Hood: Koala's Data Platform,' we're sharing our insights on the key ingredients necessary for a world-class first-party intent solution and strategies to help sales and marketing teams get the most out of their first-party website traffic."
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsx(_components.li, {
        children: _jsx(_components.strong, {
          children: "Part 1: Website deanonymization: the definitive technical guide;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-2-collecting-and-modeling-first-party-intent-data-for-b2b",
          children: "Part 2: Collecting and modeling first-party intent data for B2B;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-3-finding-signals-from-the-noise",
          children: "Part 3: Finding Signal(s) from the noise;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-4-incorporating-intent-into-your-rev-ops-architecture",
          children: "Part 4: Incorporating intent into your Rev Ops architecture."
        })
      }), "\n"]
    }), "\n", _jsx("hr", {}), "\n", _jsx(_components.img, {
      src: "/blog/2024-03-22/part-01/cover.png",
      alt: "Website deanonymization: the definitive technical guide"
    }), "\n", _jsx(_components.p, {
      children: "Most B2B websites get tens-to-hundreds of thousands of monthly visitors. A simple count of the visitors that you'd see in an analytics tool may be interesting for marketing teams to understand how traffic is growing or what sources it's coming from, but in order for this traffic to be actionable for a sales team, you'll want to deanonymize the traffic."
    }), "\n", _jsx(_components.p, {
      children: "In this post, we'll walk you through the technical details of website deanonymization – how it works, Privacy 101, best practices for sales vs. marketing use cases, layering in first-party identity data, and resolving identity across your different data sources."
    }), "\n", _jsxs(_components.h3, {
      id: "how-it-works",
      children: ["How it works", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#how-it-works",
        children: _jsx(_components.span, {
          className: "icon icon-link"
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      })]
    }), "\n", _jsx(_components.p, {
      children: "When someone first shows up on a website, the only data you have is an IP address. If you're following privacy laws, the best thing you can do with an IP address is to look up the company it’s associated with. There are many tools that do this, but we chose to partner with Clearbit Reveal, which we found to be the best-in-class IP tool. All IP-to-company tools leverage probabilistic matching, since IPs are constantly changing, as are the computers connected to each network."
    }), "\n", _jsx(_components.p, {
      children: "While each IP-to-company data set is a bit different, these datasets are typically built by joining together from Internet Service Providers (eg., which companies have which IP ranges), data from browser extensions (eg., an Adblocker tool), data from their own ecosystem (eg., Clearbit has a free Chrome Extension), and other datasets. Critically, this data is intentionally anonymized so that no Personally Identifiable Information (PII) can be found."
    }), "\n", _jsx(_components.p, {
      children: "Match rates vary, but typically about 65% of your traffic can be deanonymized in this way."
    }), "\n", _jsxs(_components.h3, {
      id: "privacy-101",
      children: ["Privacy 101", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#privacy-101",
        children: _jsx(_components.span, {
          className: "icon icon-link"
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      })]
    }), "\n", _jsx(_components.p, {
      children: "There are new tools coming into the market that offer a \"magic database\" of visitor-level identity without them giving you an email address. They \"achieve GDPR compliance\" by ignoring all EU traffic (ie., it would be illegal to do this in the EU, so they scope it to the US). While these tools claim to be legal in the US, we don't believe this is a privacy-friendly practice that will sustain in the long-term, as this is against the spirit of both GDPR and CCPA. This technique also relies on the shaky technical foundation of 3rd party cookie pooling, which browsers are making every effort to make go away."
    }), "\n", _jsx(_components.p, {
      children: _jsx(_components.strong, {
        children: "Finally, the most critical point for you: these tools use a \"give to get\" data model — as you install their tracking, you are also consenting to give your customers' PII back to their data graph. If you do want to use one of these tools, you should update your privacy policy to disclose that you'll be giving your customers' end-user data to one of these tools. These tools typically have recommended language to add to the privacy policy, so you'll want to do that before installing the tool. You may be obligated to notify all customers of your updated privacy policy as well."
      })
    }), "\n", _jsxs(_components.h3, {
      id: "best-practices-for-sales-vs-marketing-use-cases",
      children: ["Best practices for Sales vs. Marketing use cases", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#best-practices-for-sales-vs-marketing-use-cases",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "Because all IP-to-company data sets use a probabilistic model, there's an inherent tradeoff between optimizing for match rate vs. match accuracy. We've noticed that marketing teams and sales teams have pretty different viewpoints about whether or not a \"false positive\" match is okay:"
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsx(_components.li, {
        children: "Marketing teams tend to be more willing to accept false positives because they can still drive blended advertising performance even if the audience isn't perfect"
      }), "\n", _jsx(_components.li, {
        children: "Sales teams tend to distrust datasets with false positives because time spent chasing leads that aren't actually real quickly destroys trust in the data."
      }), "\n"]
    }), "\n", _jsx(_components.p, {
      children: "If you look at ABM tools, they tend to be procured by Marketing teams (and configured to match as broadly as possible) and then pushed on the Sales teams, but Sales teams don't really trust the data."
    }), "\n", _jsx(_components.p, {
      children: "While there's no one-size-fits-all answer, you likely will want to tune your willingness for false positives (Clearbit Reveal gives a range of Low, Medium, High and Very High confidence out of their ML model). At Koala, we primarily use Koala for a sales use-case and thus our tolerance for false positives is very low, so we only accept matches with a \"Very High\" confidence. (We don't want our team alerted on things that could be false positives and are happy to trade off some potential matches for that.)"
    }), "\n", _jsxs(_components.h3, {
      id: "first-party-identity-graph",
      children: ["First-party identity graph", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#first-party-identity-graph",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsxs(_components.p, {
      children: ["There is a data source that is 100% privacy-friendly, much more accurate than an IP-to-account match, gives you person-level identity, and every company has access to: ", _jsx(_components.strong, {
        children: "the first-party id graph that your users give you."
      })]
    }), "\n", _jsx(_components.p, {
      children: "There are three primary ways that someone can identify themselves on your website – all of these are opportunities to \"upgrade\" your anonymous profile to a known user."
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsxs(_components.li, {
        children: [_jsx(_components.strong, {
          children: "Form-fills."
        }), " When someone fills out one of your forms, Koala can automatically detect that and use that to upgrade the profile."]
      }), "\n", _jsxs(_components.li, {
        children: [_jsx(_components.strong, {
          children: "Application sign-up / sign-in."
        }), " When someone logs into your application, they are giving you their email (note: this may not actually happen via a form-fill if they are using SSO/Google OAuth)."]
      }), "\n", _jsxs(_components.li, {
        children: [_jsx(_components.strong, {
          children: "Email click-through."
        }), " When someone clicks on the links in your emails, you can insert an identifier in the URL (eg., getkoala.com/pricing?ko_e=", _jsx(_components.a, {
          href: "mailto:tido@getkoala.com",
          children: "tido@getkoala.com"
        }), ") and use that to identify the user. Funny bug: there are clients such as Microsoft Email Advanced Threat Protection that will obfuscate the email parameter in your link, so we've needed to find a way to detect when that's happening and ignore these emails."]
      }), "\n"]
    }), "\n", _jsxs(_components.h3, {
      id: "resolving-identity",
      children: ["Resolving identity", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#resolving-identity",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "If you have a single profile (cookie ID), you may receive several different pieces of identity information from different sources (your IP-to-account service, your form-fills, an application login, etc.). For this, you'll need an identity resolution algorithm. This may sound like a tiny edge case, but it actually comes up all the time! Here are a few examples:"
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsx(_components.li, {
        children: "If I have an anonymous IP match and a known work email, I want the work email"
      }), "\n", _jsx(_components.li, {
        children: "If someone submits both a work and a personal email into different forms, I want the work email"
      }), "\n", _jsx(_components.li, {
        children: "If I have a form-fill and a separate email that the user logged in with, I want the user login email (these tend to be more verified)"
      }), "\n"]
    }), "\n", _jsx(_components.p, {
      children: "We find that sweating these details around identity is one of Koala's most noticeable differences. Note the importance of the data model here — without modeling it correctly as profiles in advance, you aren't able to really solve this identity resolution problem fully. Legacy ABMs have known about this problem for years but due to the limitation in the core data model haven't been able to adapt."
    }), "\n", _jsxs(_components.p, {
      children: ["This is the most surprising thing I've learned since day 1 of Koala: ", _jsx(_components.strong, {
        children: "for a PLG company, 75-80% of active traffic on the marketing website on any given day is prospect-level identified traffic (ie., they've done some kind of identification in the past)."
      }), " If non-PLG, but we still see identity graphs get up to 30-40% once they are fully hydrated. We constantly hear the objection \"ah, I won't install this in my app because those people are already customers and thus not interesting\", but not taking full advantage of your first-party identity graph is a mistake (even if you're not PLG, this compounds over time and will be critically helpful for cross-sell, upsell, and managing POCs)."]
    }), "\n", _jsx(_components.img, {
      src: "/blog/2024-03-22/part-01/01.png",
      alt: "Resolving identity"
    }), "\n", _jsxs(_components.h3, {
      id: "conclusion",
      children: ["Conclusion", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#conclusion",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "Now that you have a good feel for how deanonymization works, you may want to learn more about how to collect and model this data for B2B use cases, how to find signal(s) from the noise, and how to incorporate this intent data into the rest of your rev ops infrastructure."
    }), "\n", _jsx("hr", {}), "\n", _jsx(_components.p, {
      children: "In this 4-part series, we’re exploring the key ingredients necessary for a world-class first-party intent solution and strategies to help sales and marketing teams get the most out of their first-party website traffic. Go further and explore the next articles in our series here:"
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsx(_components.li, {
        children: _jsx(_components.strong, {
          children: "Part 1: Website deanonymization: the definitive technical guide;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-2-collecting-and-modeling-first-party-intent-data-for-b2b",
          children: "Part 2: Collecting and modeling first-party intent data for B2B;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-3-finding-signals-from-the-noise",
          children: "Part 3: Finding Signal(s) from the noise;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-4-incorporating-intent-into-your-rev-ops-architecture",
          children: "Part 4: Incorporating intent into your Rev Ops architecture."
        })
      }), "\n"]
    })]
  });
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Part 1: Website deanonymization: the definitive technical guide

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function _createMdxContent(props) {
  const _components = Object.assign({
    p: "p",
    ul: "ul",
    li: "li",
    a: "a",
    strong: "strong",
    img: "img",
    h3: "h3",
    span: "span",
    em: "em"
  }, _provideComponents(), props.components);
  return _jsxs(_Fragment, {
    children: [_jsx(_components.p, {
      children: "In our four-part series, 'Under the Hood: Koala's Data Platform,' we're sharing our insights on the key ingredients necessary for a world-class first-party intent solution and strategies to help sales and marketing teams get the most out of their first-party website traffic."
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-1-website-deanonymization-the-definitive-technical-guide",
          children: "Part 1: Website deanonymization: the definitive technical guide;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.strong, {
          children: "Part 2: Collecting and modeling first-party intent data for B2B;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-3-finding-signals-from-the-noise",
          children: "Part 3: Finding Signal(s) from the noise;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-4-incorporating-intent-into-your-rev-ops-architecture",
          children: "Part 4: Incorporating intent into your Rev Ops architecture."
        })
      }), "\n"]
    }), "\n", _jsx("hr", {}), "\n", _jsx(_components.img, {
      src: "/blog/2024-03-22/part-02/cover.png",
      alt: "Collecting and modeling first-party intent data for B2B"
    }), "\n", _jsx(_components.p, {
      children: "In this post, we'll walk you through the technical details of collecting first-party intent data (including a few advanced techniques you may want to consider), the right data model for B2B, and how to join data across your GTM stack."
    }), "\n", _jsxs(_components.h3, {
      id: "collecting-data",
      children: ["Collecting data", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#collecting-data",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "If you want to track the entire lifecycle of your prospect from the moment they first find out about the product through conversion, you'll need a first-party cookie identifier to identify each logical visitor. In order to do this, you'll need a pixel (or \"Javascript SDK\") installed on all of the places you want to track first-party intent."
    }), "\n", _jsx(_components.p, {
      children: "You'll want to ensure that this code is very small – the bigger the code, the more it will impact performance of your website. We've worked hard over the years to slim this code down as much as possible so that we meet the bar for the most performance-obsessed websites in the world. At the time of writing this, Koala's pixel size is ~30kb."
    }), "\n", _jsx(_components.p, {
      children: "Here is some data you'll likely want to collect:"
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsxs(_components.li, {
        children: [_jsx(_components.strong, {
          children: "Auto-capture all page views."
        }), " It's tedious to go through all of your pages, so you'll likely want to auto-capture all of these. This is fairly straightforward for a traditional website, but note that this requires a little extra work to do if you're using a single-page app architecture."]
      }), "\n", _jsxs(_components.li, {
        children: [_jsx(_components.strong, {
          children: "Auto-capture all form submissions."
        }), " You'll likely want to capture identity data from form submissions (eg., demo requests). If you do this, be sure to automatically omit any sensitive data (eg., password, secret API keys, and credit cards)."]
      }), "\n", _jsxs(_components.li, {
        children: [_jsx(_components.strong, {
          children: "Auto-capture active session time."
        }), " This one is a bit more advanced, but one of the most important signals for intent scoring is how much active session time has been spent by your prospect. A session that lasted 2 minutes is very different from one that lasted twenty minutes, even if they had the same number of pageviews. Capturing active session time is challenging – you only want to accrue session time when the user is engaged with the page, and importantly you can't have it accrue when the site is open in a background tab. We've fixed countless bugs around time-zone changes, looping videos causing session time to stay \"active\" when it shouldn't, and the other kinds of weirdness you find when you're accruing session time across millions of pageviews a day. Here's one example of a homepage that was challenging to get session time right for, since the looping video was causing us to think the tab was always active:"]
      }), "\n"]
    }), "\n", _jsx(_components.img, {
      src: "/blog/2024-03-22/part-02/01.gif",
      alt: "Collecting data"
    }), "\n", _jsxs(_components.h3, {
      id: "product-analytics-data",
      children: ["Product analytics data", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#product-analytics-data",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "Koala allows optional collection of product analytics data. At a minimum, this should be leveraged for resolving identity of anyone signing up / logging into the platform (see Part 1), but can also be incredibly useful data for sellers to have at their fingertips to understand which end-users are already using the app. This is especially useful in a PLG context so you can get an understanding of who your champions might be, or a POC context where you can see how the POC is trending in terms of usage."
    }), "\n", _jsx(_components.p, {
      children: "You'll want to consider if any in-product activity is helpful for your business, and if so consider adding it into your intent dataset. Reps will appreciate having all of this engagement and product usage data available on a single pane of glass."
    }), "\n", _jsxs(_components.h3, {
      id: "b2b-data-model",
      children: ["B2B data model", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#b2b-data-model",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "In the first days of Koala, the most critical question we had to answer was: how should we model our data? We were fairly familiar with a standard \"product analytics\" data model from our time at Segment, which has best-in-class data collection and identity resolution capabilities. As we prototyped in the first few months of Koala, we realized that there was one key thing missing from the classic product analytics data model – a first-class \"Account\" object."
    }), "\n", _jsx(_components.p, {
      children: "In our initial prototype a month into the company, we realized we didn't have proper support for Accounts, so we paused and took another month to fix that. At the time (pre-product and pre-PMF!), this felt like an eternity, but because data models are often one-way doors, it felt necessary. We landed with the following data model:"
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsxs(_components.li, {
        children: ["Accounts - accounts are uniquely identified by their ", _jsx(_components.strong, {
          children: "domain"
        }), ". This makes sense because every company in the world has a unique domain (their website), and is also similar to how most people reason about their CRMs. As your Rev Ops team will be the first to tell you, there is still further complexity to handle here, since many companies have different domains (we are only two years old and already have ", _jsx(_components.em, {
          children: "koala.live"
        }), " and ", _jsx(_components.em, {
          children: "getkoala.com"
        }), "). Koala handles this by checking a list of domain aliases when joining different data sources together."]
      }), "\n", _jsxs(_components.li, {
        children: ["Visitors - visitors are uniquely identified by their ", _jsx(_components.strong, {
          children: "email"
        }), ". We chose email rather than some kind of user ID because email is used across all tools. If we haven't yet seen an email for a visitor, we give them a unique random identifier that is stored in a first-party cookie. We'll continue to accrue activity on their profile with that identifier until we see an email associated with it. More on how identity resolution works below."]
      }), "\n"]
    }), "\n", _jsxs(_components.h3, {
      id: "joining-data-across-tools",
      children: ["Joining data across tools", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#joining-data-across-tools",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "Once we had a solid data model, we wanted to ensure that we could join the data across all of your tools. For this, we chose to store our data in Elasticearch, so that we could slice and dice this data. Here's an example of how you can slice-and dice data across multiple data sources (intent data, firmographic enrichment, CRM, and product billing)."
    }), "\n", _jsx(_components.p, {
      children: "Show me all companies who match the following criteria:"
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsx(_components.li, {
        children: "with Surging or Heating intent [intent];"
      }), "\n", _jsx(_components.li, {
        children: "with an ICP Fit score of 'A' [firmographic enrichment];"
      }), "\n", _jsx(_components.li, {
        children: "who don't have an active Opportunity [CRM];"
      }), "\n", _jsx(_components.li, {
        children: "who are on our free plan [product billing]."
      }), "\n"]
    }), "\n", _jsx(_components.img, {
      src: "/blog/2024-03-22/part-02/02.png",
      alt: "Resolving identity"
    }), "\n", _jsxs(_components.h3, {
      id: "unlocking-reps",
      children: ["Unlocking reps", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#unlocking-reps",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "One of our larger customers joked that prior to Koala, they had spent an insane amount of data science resources building propensity-to-buy models and then promptly… locking it up in a data warehouses where no sales rep could actually benefit from it. And in fact: the strategy of many Sales tech tools has been to deliver the simplest/narrowest possible sliver of data \"X passed a threshold and became an MQL.\" In talking to hundreds of sales teams, we've seen that having only a thin window into the data leaves them frustrated, unable to act, and generally distrusting the tool."
    }), "\n", _jsx(_components.p, {
      children: "While it is true that you probably only want to interrupt their daily workflow with a high-intent alert, once you surface something to them, they are going to want to do deep research and need a full analytics tool to support that. As such, we've landed on a companion research app that shows you the complete picture of a prospect:"
    }), "\n", _jsx(_components.img, {
      src: "/blog/2024-03-22/part-02/03.png",
      alt: "Resolving identity"
    }), "\n", _jsx(_components.p, {
      children: "And the complete picture of an account:"
    }), "\n", _jsx(_components.img, {
      src: "/blog/2024-03-22/part-02/04.png",
      alt: "Resolving identity"
    }), "\n", _jsx(_components.p, {
      children: "We've found that reps love understanding (1) a complete picture of everyone involved, (2) having as much identity to work with as possible, (3) any relevant UTM data about the initial source of the traffic, and (4) a summary of the most important recent things."
    }), "\n", _jsxs(_components.h3, {
      id: "conclusion",
      children: ["Conclusion", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#conclusion",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "Once you have this down, you'll be able to create dynamic audiences of both accounts and visitors. These audiences are an important primitive in your rev ops stack, as they can be used for marketing use cases (ad retargeting, marketing automation, marketing analytics, etc.) and sales use cases (seeing who is active on your target account list, territory carving, a daily or weekly lead list to work out of, etc.). We'll dive more into what you can do in Part 4."
    }), "\n", _jsx("hr", {}), "\n", _jsx(_components.p, {
      children: "In this 4-part series, we’re exploring the key ingredients necessary for a world-class first-party intent solution and strategies to help sales and marketing teams get the most out of their first-party website traffic. Go further and explore the next articles in our series here:"
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-1-website-deanonymization-the-definitive-technical-guide",
          children: "Part 1: Website deanonymization: the definitive technical guide;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.strong, {
          children: "Part 2: Collecting and modeling first-party intent data for B2B;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-3-finding-signals-from-the-noise",
          children: "Part 3: Finding Signal(s) from the noise;"
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        children: _jsx(_components.a, {
          href: "/blog/part-4-incorporating-intent-into-your-rev-ops-architecture",
          children: "Part 4: Incorporating intent into your Rev Ops architecture."
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Part 2: Collecting and modeling first-party intent data for B2B

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    children: [_jsx(_components.p, {
      children: "In our four-part series, 'Under the Hood: Koala's Data Platform,' we're sharing our insights on the key ingredients necessary for a world-class first-party intent solution and strategies to help sales and marketing teams get the most out of their first-party website traffic."
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-1-website-deanonymization-the-definitive-technical-guide",
          children: "Part 1: Website deanonymization: the definitive technical guide;"
        })
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        children: _jsx(_components.a, {
          href: "/blog/part-2-collecting-and-modeling-first-party-intent-data-for-b2b",
          children: "Part 2: Collecting and modeling first-party intent data for B2B;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.strong, {
          children: "Part 3: Finding Signal(s) from the noise;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-4-incorporating-intent-into-your-rev-ops-architecture",
          children: "Part 4: Incorporating intent into your Rev Ops architecture."
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    }), "\n", _jsx("hr", {}), "\n", _jsx(_components.img, {
      src: "/blog/2024-03-22/part-03/cover.png",
      alt: "Finding Signal(s) from the noise"
    }), "\n", _jsx(_components.p, {
      children: "I was speaking recently with a CRO at a $5B B2B SaaS company this past week, and he gave me feedback on my pitch: \"Lead scoring? Don't call it that. I've literally never met a lead score I liked.\" He suggested I call it a sales prioritization engine. I confess that this blog post used to be called \"Lead Scoring\"… but I had to change the name after his take (it strongly resonated)."
    }), "\n", _jsx(_components.p, {
      children: "Whatever we want to call it, of the 50M events per month we get from our larger customers, we certainly can't send that many alerts to their team of 100 reps, so we must find a way to extract signal from the noise."
    }), "\n", _jsxs(_components.h3, {
      id: "filter-out-bad-leads",
      children: ["Filter out bad leads", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#filter-out-bad-leads",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "There is one aspect of traditional lead scoring that is helpful — doing scoring against an ICP, so you can see if the company is even relevant for a sales process. This helps you ignore companies that will never buy — either they are too small, aren't in the right industry, don't have enough web traffic, are in the wrong geography, etc."
    }), "\n", _jsx(_components.p, {
      children: "Rather than rely on a set of hard rules (\"must be 50-500 employees, must be in US, must be in SaaS industry\"), we can learn from your Closed Won population to understand what traits they have in common. By weighting each trait independently and combining the weights, we're able to come up with a composite score that gives us an estimate of how close each account is to ICP."
    }), "\n", _jsxs(_components.p, {
      children: ["Koala assigns an ICP Fit based on the firmographic characteristics in the account. You can combine ", _jsx(_components.a, {
        href: "http://getkoala.com/companies",
        children: "Koala account data"
      }), ", Clearbit data, and your CRM data to build a multi-dimensional Fit score. This model is built by performing a frequency test between the converted population (typically customers won by a sales-led motion) against the population (typically everyone on your website). We bucket the scores into four grades, A for the top 25% of traffic, B for the next 25%, C for the next 25%, and D for the bottom 25%. We encourage interpreting these scores as follows:"]
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsxs(_components.li, {
        children: [_jsx(_components.strong, {
          children: "A: Bullseye ICP"
        }), "\n", _jsxs(_components.ul, {
          children: ["\n", _jsx(_components.li, {
            children: "Converts: 3.0-3.3x higher than avg"
          }), "\n"]
        }), "\n"]
      }), "\n", _jsxs(_components.li, {
        children: [_jsx(_components.strong, {
          children: "B: Fringe ICP"
        }), "\n", _jsxs(_components.ul, {
          children: ["\n", _jsx(_components.li, {
            children: "Converts: 0.5-0.9x lower than avg"
          }), "\n"]
        }), "\n"]
      }), "\n", _jsxs(_components.li, {
        children: [_jsx(_components.strong, {
          children: "C: Ignore for Outbound; Qualify carefully for Inbound"
        }), "\n", _jsxs(_components.ul, {
          children: ["\n", _jsx(_components.li, {
            children: "Converts: 5x worse than avg"
          }), "\n"]
        }), "\n"]
      }), "\n", _jsxs(_components.li, {
        children: [_jsx(_components.strong, {
          children: "D: Ignore for Outbound; Qualify very carefully for Inbound"
        }), "\n", _jsxs(_components.ul, {
          children: ["\n", _jsx(_components.li, {
            children: "Converts: 10x worse than avg"
          }), "\n"]
        }), "\n"]
      }), "\n"]
    }), "\n", _jsx(_components.p, {
      children: "Here's what that distribution looks like for a customer who has >500 conversions. Their ICP blends Employees, Industry, Estimated ARR, Traffic Rank, and number of tools in Tech Stack (typically a company that has already bought a lot of tools tends to lean a little more toward \"buy\" on the \"build vs buy\" spectrum)."
    }), "\n", _jsx(_components.img, {
      src: "/blog/2024-03-22/part-03/01.png",
      alt: "Filter out bad leads"
    }), "\n", _jsx(_components.p, {
      children: "Generally our models tell us that it's worth focusing on 25% of the accounts, paying some attention to the next 25% of accounts (think: marketing automation instead of human touch), and completely ignoring 50% of the accounts on your site."
    }), "\n", _jsx(_components.p, {
      children: "These ICP scores don't tell you anything about the company's timing of when they are in-market, so we need to lean on intent to do that."
    }), "\n", _jsxs(_components.h3, {
      id: "figure-out-which-intent-converts",
      children: ["Figure out which intent converts", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#figure-out-which-intent-converts",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "Sales needs a reason to reach out. In order to alert the sales team, we need to find that reason. For this, Koala allows you to set up Intent Signals. You can set these up manually (eg., \"Pricing Page Visit\", \"Technical Docs View\", \"Sign-up\", \"Completed Onboarding\") or you can let Koala's intent scoring model suggest Intent Signals to you. Koala's intent scoring model analyzes all intent actions (form fills, content consumption, and product events) and scores each one based on how often each action leads to a conversion."
    }), "\n", _jsx(_components.p, {
      children: "It's easier to show than explain, so here's a report of what marketing content consumption leads up to the main activation moment we care about in the product (\"Intent Signal Created\")."
    }), "\n", _jsx(_components.p, {
      children: "Most customers use \"Opportunities entering SQO stage within 28d\" as their conversion event. One surprising thing most customers find is that they have particular content that convert to SQO almost as well if not better than inbound demo requests – it's just hard to know what content to go after without this analysis!"
    }), "\n", _jsx(_components.img, {
      src: "/blog/2024-03-22/part-03/02.png",
      alt: "Figure out which intent converts"
    }), "\n", _jsxs(_components.h3, {
      id: "real-time-constantly-decaying-intent-scoring",
      children: ["Real-time, constantly-decaying intent scoring", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#real-time-constantly-decaying-intent-scoring",
        children: _jsx(_components.span, {
          className: "icon icon-link"
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    }), "\n", _jsx(_components.p, {
      children: "Finally, this intent is rolled-up into a score 0-100. The score takes into account active session time, pageviews, number of users, how many intent signals recently happened, etc. Here's a screenshot of what this looks like in the app )each bar represents 1 day):"
    }), "\n", _jsx(_components.img, {
      src: "/blog/2024-03-22/part-03/03.png",
      alt: "Real-time, constantly-decaying intent scoring"
    }), "\n", _jsxs(_components.h3, {
      id: "rep-feedback-loops",
      children: ["Rep feedback loops", _jsx(_components.a, {
        "aria-hidden": true,
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        className: "heading-anchor",
        href: "#rep-feedback-loops",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "We have never deployed a perfect set of Koala rules on the first try. Be sure to build into your process an iterative feedback loop. Here's a few questions you can try:"
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsx(_components.li, {
        children: "Show reps a bunch of A / B accounts - ask reps if any of them seem graded too high?"
      }), "\n", _jsx(_components.li, {
        children: "Show reps a bunch of C / D accounts - ask reps if any of them seem graded too low?"
      }), "\n", _jsx(_components.li, {
        children: "Ask reps to scroll through their Slack feeds and ask for any intent signals that they didn't want to see and why."
      }), "\n", _jsx(_components.li, {
        children: "Ask reps to think of moments in the marketing journey where they'd love to be notified"
      }), "\n", _jsx(_components.li, {
        children: "Ask reps to think of moments in the product experience where they'd love to be notified"
      }), "\n"]
    }), "\n", _jsx(_components.p, {
      children: "You'll need to strike a balance between what the raw data is telling you and what your reps are telling you. Ignore the reps - and no one uses the tool. Ignore the data - and the tool won't work well. In our experience, we've found that reps usually are pointing out real problems, but sometimes they can't figure out the root cause of the problem. We find that taking the feedback, looking for patterns, and then responding with broad model adjustments based on a body of feedback is often the right way to iterate."
    }), "\n", _jsxs(_components.h3, {
      id: "conclusion",
      children: ["Conclusion", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#conclusion",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "Sales is only effective when you figure out how to add value to your prospect. Taking the time to understand where your prospect is in their journey and genuinely adding value will always yield the best results. And Sales leaders who really understand the world from a prospect's lens are usually able to systematize signals and train Sales how to be genuinely helpful when each signal fires."
    }), "\n", _jsx("hr", {}), "\n", _jsx(_components.p, {
      children: "In this 4-part series, we’re exploring the key ingredients necessary for a world-class first-party intent solution and strategies to help sales and marketing teams get the most out of their first-party website traffic. Go further and explore the next articles in our series here:"
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-1-website-deanonymization-the-definitive-technical-guide",
          children: "Part 1: Website deanonymization: the definitive technical guide;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-2-collecting-and-modeling-first-party-intent-data-for-b2b",
          children: "Part 2: Collecting and modeling first-party intent data for B2B;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.strong, {
          children: "Part 3: Finding Signal(s) from the noise;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-4-incorporating-intent-into-your-rev-ops-architecture",
          children: "Part 4: Incorporating intent into your Rev Ops architecture."
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    children: _jsx(_createMdxContent, props)
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Part 3: Finding Signal(s) from the noise

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    children: [_jsx(_components.p, {
      children: "In our four-part series, 'Under the Hood: Koala's Data Platform,' we're sharing our insights on the key ingredients necessary for a world-class first-party intent solution and strategies to help sales and marketing teams get the most out of their first-party website traffic."
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-1-website-deanonymization-the-definitive-technical-guide",
          children: "Part 1: Website deanonymization: the definitive technical guide;"
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        children: _jsx(_components.a, {
          href: "/blog/part-2-collecting-and-modeling-first-party-intent-data-for-b2b",
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        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-3-finding-signals-from-the-noise",
          children: "Part 3: Finding Signal(s) from the noise;"
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        children: _jsx(_components.strong, {
          children: "Part 4: Incorporating intent into your Rev Ops architecture."
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      }), "\n"]
    }), "\n", _jsx("hr", {}), "\n", _jsx(_components.img, {
      src: "/blog/2024-03-22/part-04/cover.png",
      alt: "Incorporating intent into your Rev Ops architecture"
    }), "\n", _jsx(_components.p, {
      children: "Once you have clarity on the signals you care about (eg., \"Viewed Pricing Page for 30+ Seconds\") and the filters (eg., \"'A' Accounts Only\"), then you'll want to ensure that the Signals make it to the right person in real-time. There are a lot of different ways to slice this within tools (CRM, SEP, Slack), so we'll share the architecture more and more companies are adopting."
    }), "\n", _jsxs(_components.h3, {
      id: "routing-strategy",
      children: ["Routing Strategy", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#routing-strategy",
        children: _jsx(_components.span, {
          className: "icon icon-link"
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      })]
    }), "\n", _jsx(_components.p, {
      children: "Think of your Account ICP Fit score as a proxy to answer the question: \"how much could this account potentially pay us?\" And recall from Part 3 that typically the top 25% of traffic ('A' accounts) is a bullseye ICP, next 25% of traffic ('B' accounts) as fringe ICP, and everything else typically can be ignored."
    }), "\n", _jsx(_components.p, {
      children: "As such, for a mature organization with a BDR motion, typically we recommend that all 'A' accounts be reviewed by a person and actioned within 4 hours. For 'B' accounts, we recommend that at least some kind of marketing nurture goes out (can also leverage a BDR motion if there is capacity on the team). For 'C' and 'D' accounts, they are viewable in Koala but we don't recommend doing an outbound motion — and if they come inbound to treat with appropriate skepticism (ie., qualify it a lot before advancing the deal!)."
    }), "\n", _jsxs(_components.h3, {
      id: "sending-signals",
      children: ["Sending Signals", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#sending-signals",
        children: _jsx(_components.span, {
          className: "icon icon-link"
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    }), "\n", _jsx(_components.p, {
      children: "Once you piece together the signals and the routing, it tends to look something like this. Our customers tend to start simple and choose to just send leads to Slack, but some have gotten more advanced over tiem and are doing MQL routing to AEs/BDRs."
    }), "\n", _jsx(_components.img, {
      src: "/blog/2024-03-22/part-04/01.png",
      alt: "Sending Signals"
    }), "\n", _jsxs(_components.h3, {
      id: "modeling-intent-in-your-crm",
      children: ["Modeling Intent in your CRM", _jsx(_components.a, {
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        className: "heading-anchor",
        href: "#modeling-intent-in-your-crm",
        children: _jsx(_components.span, {
          className: "icon icon-link"
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    }), "\n", _jsx(_components.p, {
      children: "Here are some other common ways we see to model intent in your CRM."
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Importing High-IntentAccounts into CRM."
      }), " You're likely to have a lot of accounts on your website that aren't in your CRM. Most customers auto-import any accounts showing high intent that are missing from their CRM. Before you do this, you'll want to ensure that you have good domain hygiene on your accounts, so that you don't import duplicate accounts."]
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Importing High-Intent Contacts into CRM."
      }), " You may want to route unowned contacts who do high-intent activities to Sales. Typically, this is done by enrolling the high-intent contact into a Campaign when the action happens."]
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Creating custom records for key intent signals (Salesforce) or writing them on the Activity Timeline (HubSpot)."
      }), " This can be a nice way to show key intent signals in the UI of the CRM, so that key intent context exists right where people are doing work."]
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Enriching Accounts with fit scores."
      }), " If you don't already have some kind of account score in your CRM, you may want to consider writing Koala's scores across your CRM."]
    }), "\n", _jsxs(_components.h3, {
      id: "conclusion",
      children: ["Conclusion", _jsx(_components.a, {
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        children: _jsx(_components.span, {
          className: "icon icon-link"
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      })]
    }), "\n", _jsx(_components.p, {
      children: "The trickiest thing with intent is that it is fleeting. If you catch it in the moment, it's wildly powerful — you are catching someone with a problem in their moment of research. However, it requires operational excellence to make sure that this intent is always delivered on time, that reps know what to do with it, and that action happens. Without speedy action, the whole system is for naught."
    }), "\n", _jsx(_components.p, {
      children: "We recommend starting simple on your intent journey – just start sending some of these intent moments to Slack! From there, if the channels get noisy, you can break them up into different channels for different people. And once you're starting to hit a breaking point with that, you can start to automate more of the workflow. Be sure to keep testing and iterating along the way — learning how to respond correctly to different kinds of intent is a hard strategic puzzle to figure out, but can generate a ton of high-quality of pipeline once you crack the nut!"
    }), "\n", _jsx("hr", {}), "\n", _jsx(_components.p, {
      children: "In this 4-part series, we’re exploring the key ingredients necessary for a world-class first-party intent solution and strategies to help sales and marketing teams get the most out of their first-party website traffic. Go further and explore the next articles in our series here:"
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-1-website-deanonymization-the-definitive-technical-guide",
          children: "Part 1: Website deanonymization: the definitive technical guide;"
        })
      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
          href: "/blog/part-2-collecting-and-modeling-first-party-intent-data-for-b2b",
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      }), "\n", _jsx(_components.li, {
        children: _jsx(_components.a, {
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Part 4: Incorporating intent into your Rev Ops architecture

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    em: "em"
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  return _jsxs(_Fragment, {
    children: [_jsx(_components.img, {
      src: "/blog/2024-02-06/koala-2-0.jpg",
      alt: "Koala 2.0"
    }), "\n", _jsx(_components.p, {
      children: "We're releasing Koala 2.0: warm outbound that actually works."
    }), "\n", _jsx(_components.p, {
      children: "We've seen a lot of people talking about automating their outbound motion."
    }), "\n", _jsx(_components.p, {
      children: "There's even a so-called \"warm outbound\" movement that simply spams anyone visiting their site. This strategy completely misses the mark."
    }), "\n", _jsx(_components.p, {
      children: "Instead: When you run warm outbound based on real first-party intent signals, you can get incredible results. We've been doing this for months with our pilot customers and have seen it:"
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsx(_components.li, {
        children: "75% open rate"
      }), "\n", _jsx(_components.li, {
        children: "15% reply rate."
      }), "\n", _jsx(_components.li, {
        children: "10% book a meeting"
      }), "\n"]
    }), "\n", _jsx(_components.p, {
      children: "One of our pilot customers already sourced & closed a two-year, $250k+ TCV deal in their first month."
    }), "\n", _jsx(_components.p, {
      children: _jsx(_components.strong, {
        children: "How do I get there?"
      })
    }), "\n", _jsxs(_components.h2, {
      id: "prospect-level-identity",
      children: ["Prospect-level identity", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#prospect-level-identity",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsxs(_components.p, {
      children: ["Legacy ABM tools aren't able to capture identity across your marketing & product stack. Others are relying on 3rd party cookies which are getting phased out completely by browsers. That's why we're announcing our Identity Layer today. Koala captures identity from first-party data – data that ", _jsx(_components.em, {
        children: "you own"
      }), " – email clicks, product sign up, demo requests, and more. The average PLG customer sees up to 75% of their traffic deanonymized with Koala. But you don't have to be PLG – our average non-PLG customer sees ~30-40% of their traffic deanonymized as well. Koala has been built with first-party identity in mind from the start."]
    }), "\n", _jsxs(_components.h2, {
      id: "picking-the-right-intent-signals",
      children: ["Picking the right intent signals", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#picking-the-right-intent-signals",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "Most customers don't know which intent signals to try. Sure, there's obvious stuff like the pricing page and demo contact abandoners (and you will get lift from these!), but finding the intent that statistically leads to sales opportunities is going to lead to more pipeline and happier prospects (and a lower spam rate!)."
    }), "\n", _jsxs(_components.h2, {
      id: "adding-value",
      children: ["Adding value", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#adding-value",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "Once you have an intent signal that converts well, you have to add value by showing you can be genuinely helpful. Simply sending an email that says \"Let's book a meeting\"... probably isn't going to book a meeting. The best campaigns are much more subtle."
    }), "\n", _jsxs(_components.ul, {
      children: ["\n", _jsx(_components.li, {
        children: "Find someone who is stuck at the tech docs and have a Sales Engineer reach out automatically."
      }), "\n", _jsx(_components.li, {
        children: "Find someone who is immersed with your site's product tour and strike up a conversation about the features they are touring."
      }), "\n", _jsx(_components.li, {
        children: "Find someone who just hit an error in your app or reading about your rate limits and offer to unstick them."
      }), "\n", _jsx(_components.li, {
        children: "Find someone who is using one of your more advanced features and offer to share best practices."
      }), "\n"]
    }), "\n", _jsxs(_components.h2, {
      id: "the-best-part",
      children: ["The best part?", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#the-best-part",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "You can build your growth engine, without talking to our Sales team:"
    }), "\n", _jsx("a", {
      href: "https://app.getkoala.com/signup?utm_source=blog&utm_campaign=koala_2_0",
      children: _jsx(_components.p, {
        children: "Sign Up"
      })
    }), "\n", _jsx(_components.p, {
      children: "We're also happy to help:"
    }), "\n", _jsx("a", {
      href: "https://getkoala.com/contact?utm_source=blog&utm_campaign=koala_2_0",
      children: _jsx(_components.p, {
        children: "Book a Demo"
      })
    })]
  });
}
function MDXContent(props = {}) {
  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);
  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {
    children: _jsx(_createMdxContent, props)
  })) : _createMdxContent(props);
}
return {
  default: MDXContent
};


Koala 2.0

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const {Fragment: _Fragment, jsx: _jsx, jsxs: _jsxs} = arguments[0];
const {useMDXComponents: _provideComponents} = arguments[0];
function _createMdxContent(props) {
  const _components = Object.assign({
    p: "p",
    strong: "strong",
    em: "em",
    h2: "h2",
    a: "a",
    span: "span",
    img: "img"
  }, _provideComponents(), props.components);
  return _jsxs(_Fragment, {
    children: [_jsx(_components.p, {
      children: "Today marks a new chapter of Koala. We launched Koala 1.0 just about a year ago with the vision of building the best-in-class tool for sellers to act on high-intent leads in their patch. As Koala has grown from dozens to hundreds to now thousands of active sellers, we’ve seen one pretty unexpected but common trend: marketing teams and founders get FOMO about what's happening in Koala and want to see for themselves. Here's how the conversation typically goes:"
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Marketer:"
      }), " woah this is AWESOME data, so so so helpful to see such detail!", _jsx("br", {}), "\n", _jsx(_components.em, {
        children: "... 10 minutes later ..."
      }), _jsx("br", {}), "\n", _jsx(_components.strong, {
        children: "Marketer:"
      }), " I want to use this in all my reporting. Where's my top-level dashboard?"]
    }), "\n", _jsx(_components.p, {
      children: "In this post, we'll cover five questions that we had about our business and how we used Koala Dashboards to answer those questions in seconds, without any SQL."
    }), "\n", _jsxs(_components.h2, {
      id: "question-1---are-we-consistently-growing-daus",
      children: ["Question #1 - Are we consistently growing DAUs?", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#question-1---are-we-consistently-growing-daus",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Background:"
      }), " Our current GTM goals are to continue to grow at 20% m/m for the next six months. Our first and primary goal when making any change is to ensure that we are providing daily active value to our customers, and that they choose to come back daily. I posed this question to the team when we first launched 12 months ago, and they built a v0 of Dashboards about a year ago that I've used daily to ensure that we continue to provide value to our users daily."]
    }), "\n", _jsx(_components.p, {
      children: "Here's what it looks like:"
    }), "\n", _jsx(_components.img, {
      src: "/blog/2023-11-07/use-case-1.png",
      alt: "Use Case 1"
    }), "\n", _jsx(_components.p, {
      children: "Exciting to see DAUs growing at the rate we want - now also ensuring that they are doing high value actions we've defined internally!"
    }), "\n", _jsxs(_components.h2, {
      id: "question-2---are-we-growing-awareness-fast-enough",
      children: ["Question #2 - Are we growing awareness fast enough?", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#question-2---are-we-growing-awareness-fast-enough",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Background:"
      }), " We've been very dialed into the bottom of the funnel, as you can see in question #1, but we recently started wondering if we are feeding the top of our funnel with enough accounts."]
    }), "\n", _jsx(_components.p, {
      children: "Here's how:"
    }), "\n", _jsx(_components.img, {
      src: "/blog/2023-11-07/use-case-2.png",
      alt: "Use Case 2"
    }), "\n", _jsx(_components.p, {
      children: "By the way, when I first asked this question ~4 weeks ago, we weren't growing fast enough here (10% m/m), but once we realized a gap, we kicked things into gear!"
    }), "\n", _jsxs(_components.h2, {
      id: "question-3---twitter-or-linkedin",
      children: ["Question #3 - Twitter or LinkedIn?", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#question-3---twitter-or-linkedin",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Background:"
      }), " Social is hard (especially for me 😅) – but what makes it easier is knowing the ROI is worth it. Personally, I’d like to spend my time doubling down on ", _jsx(_components.em, {
        children: "one"
      }), " fruitful platform instead of spreading myself thin across multiple. I’ve been posting about Koala on both Linkedin and Twitter, and I wanted to understand on a recent campaign which channel was more worth it."]
    }), "\n", _jsx(_components.p, {
      children: "Here's how:"
    }), "\n", _jsx(_components.img, {
      src: "/blog/2023-11-07/use-case-3a.png",
      alt: "Use Case 3A"
    }), "\n", "\n", "\n", _jsx(_components.img, {
      src: "/blog/2023-11-07/use-case-3b.png",
      alt: "Use Case 3B"
    }), "\n", _jsx(_components.p, {
      children: "Answer: LinkedIn! Matches my intuition given where our ICP lives, but good to know that LinkedIn performs approximately 4x better than X across both top of funnel and bottom of funnel metrics."
    }), "\n", _jsxs(_components.h2, {
      id: "question-4---who-to-outbound-for-user-research",
      children: ["Question #4 - Who to outbound for user research?", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#question-4---who-to-outbound-for-user-research",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Background:"
      }), " We discovered a few months back that our customers have surpassed us and are now better Koala users than we are (‼️). We look to our best users to learn from them and inspire the next few quarters of product roadmap. I like to interview our best customers."]
    }), "\n", _jsx(_components.p, {
      children: "Here's how:"
    }), "\n", _jsx(_components.img, {
      src: "/blog/2023-11-07/use-case-4.png",
      alt: "Use Case 4"
    }), "\n", _jsx(_components.p, {
      children: "Turns out that there's someone who has spent 17 hours in the app over the last quarter! He and I now have a meeting next week — excited to share what I learn!"
    }), "\n", _jsxs(_components.h2, {
      id: "question-5---is-our-companies-bet-paying-off",
      children: ["Question #5 - Is our /companies bet paying off?", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#question-5---is-our-companies-bet-paying-off",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Background:"
      }), " We recently opened up a ", _jsx("a", {
        target: "_blank",
        href: "/companies",
        children: "free company database"
      }), " that allows people to find firmographic information about companies and also download CSVs of up to 10k companies. Our strategy around this was to provide value to the world in the hopes that we could help people with a slice of our data, totally for free. Our question: how is this new page performing?"]
    }), "\n", _jsx(_components.img, {
      src: "/blog/2023-11-07/use-case-5.png",
      alt: "Use Case 5"
    }), "\n", _jsx(_components.p, {
      children: "When we first launched /companies, we used a version of the internal dashboard to catch a few people attempting to scrape the site. We have since added multi-tier bot detection, which ensures we're filtering out bots and serving real people. From there, it took a while before SEO started to kick in, but we now are driving more traffic to this page than our homepage. Love to see people taking advantage of our database!"
    }), "\n", _jsxs(_components.h2, {
      id: "give-it-a-spin",
      children: ["Give it a spin!", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#give-it-a-spin",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsxs(_components.p, {
      children: ["If you're a Koala customer, you can give it a spin today. This week only, we're offering a free 90 day trial: ", _jsx(_components.a, {
        href: "https://app.getkoala.com/goto/dashboards",
        children: "https://app.getkoala.com/goto/dashboards"
      })]
    }), "\n", _jsxs(_components.p, {
      children: ["Feedback, questions, comments, or want to chat? Reach out to ", _jsx(_components.a, {
        href: "mailto:tido@getkoala.com",
        children: "tido@getkoala.com"
      })]
    })]
  });
}
function MDXContent(props = {}) {
  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);
  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {
    children: _jsx(_createMdxContent, props)
  })) : _createMdxContent(props);
}
return {
  default: MDXContent
};


Hot off the Press: Koala for Marketers

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function _createMdxContent(props) {
  const _components = Object.assign({
    p: "p",
    strong: "strong",
    h2: "h2",
    a: "a",
    span: "span",
    img: "img"
  }, _provideComponents(), props.components);
  return _jsxs(_Fragment, {
    children: [_jsx(_components.p, {
      children: "We've learned that many of the best B2B companies — especially those with a PLG motion — have woefully inefficient sales conversion of enterprise accounts because they fail to recognize and act on enterprise buying signals."
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Somewhat unintuitively, the strongest indicator for an enterprise sales qualified opp isn't product usage data, but rather when someone in your enterprise ICP does research on your website, help center, and technical docs."
      }), " Most enterprise buyers aren't early adopters sending production data without an Enterprise MSA, so you typically won't find them using the product but rather doing research on your enterprise offering."]
    }), "\n", _jsx(_components.p, {
      children: "Koala Content Reports are different from what you'll see in any other marketing attribution tool, so we wanted to dive deep into the origin story, show an example content report, and teach you how you can apply learnings to drive more pipeline and drive top-level company strategy."
    }), "\n", _jsxs(_components.h2, {
      id: "the-origin-story",
      children: ["The Origin Story", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#the-origin-story",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "In our early days, during our quest to find product-market fit, we were interviewing as many fast-growing B2B PLG companies that would talk to us. We were trying to learn about the ways in which these companies were leveraging product usage data to drive upsell, cross-sell, and expansion. But what we found really surprised us."
    }), "\n", _jsx(_components.p, {
      children: "Much to our surprise, the most advanced PLG companies were not building models based on product data. Yes, somewhere in the company there was some BI dashboard that reported on all of the over-quota users and these companies had set up an inside sales team focused on getting those accounts right-sized, but that wasn't where they were really generating the majority of their pipeline."
    }), "\n", _jsx(_components.p, {
      children: "So what were the most advanced companies doing? They were working with their data science teams to build sophisticated MQL definitions. Specifically, they were trying to understand every single action that a prospect could take across the entire buying journey — including content consumption, form fills, product lifecycle events — and they were trying to understand which of these actions was most correlated with a sales qualified opp being created."
    }), "\n", _jsx(_components.p, {
      children: "What several of these companies had found is that high product usage wasn't actually the biggest predictor of true enterprise sales qualified opps. Enterprises buy software very differently from the initial \"PLG\" cohort of early adopters. Enterprises are much more risk-averse — for instance, a true enterprise probably isn't going to send real data into the system without a legal agreement in place. What they found is that enterprises were actively engaged and \"in\" the store, but instead of driving product usage, they were consuming a lot of very specific marketing content. They were trying to understand how the enterprise offering worked — checking out the security posture, whether or not existing enterprise systems could integrate with the product, how various advanced features worked, etc."
    }), "\n", _jsx(_components.p, {
      children: "If you zoom out slightly, this makes perfect sense: (most) people don't read technical docs for fun — people read this content because they have a problem they are trying to solve and they are a bit stuck, so they need help. And it turns out: if you try to start a conversation with someone who is stuck right at the moment they need help, they are about 50-100x more likely to start a sales conversation than they would be on the average Tuesday morning."
    }), "\n", _jsxs(_components.h2, {
      id: "an-example-content-report",
      children: ["An example Content Report", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#an-example-content-report",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsx(_components.p, {
      children: "Koala Content Reports replicate the same process that these advanced data teams were doing in-house. Before we get into the nitty gritty of the data science, here's a screenshot of a Koala customer's actual Content Report (we've anonymized the URLs to use Koala pages, but the data is from a real customer and representative of most Content Reports):"
    }), "\n", _jsx(_components.img, {
      src: "/blog/2023-10-31/content-reports-example.png",
      alt: "Content Reports Example"
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Conversion event."
      }), " In the report you see, the conversion event we chose was a Stage 1 -> Stage 2 transition. Like many companies, this customer is very focused on using demand gen to drive Stage 2 opps, which is when the Sales team formally accepts the opportunity and begins to work it."]
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Conversion window."
      }), " In the report you see, the conversion window is 28 days. Because content consumption often happens for a week leading up to a demo being booked and then another 1-2 weeks can go by before the opp is qualified and formally accepted by Sales, this customer felt like a 28 day conversion window was most relevant."]
    }), "\n", _jsx(_components.p, {
      children: "As you might expect, the best-converting action for this company is when a demo form fill happens. 13% of the time this demo form-fill turns into a Stage 2 Opp within 28 days. However, there's a lot of other actions that customers can take that are almost as predictive of a Stage 2 Opp within 28 days. What's even more surprising about this is that the demo form fill is a highly systemized process — this particular company has an SLA to get a meeting booked within 30 minutes. But these other actions weren't systematized at all when we first built the content report you're seeing, and yet they convert almost as well."
    }), "\n", _jsxs(_components.h2, {
      id: "how-to-use-content-reports",
      children: ["How to use Content Reports", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#how-to-use-content-reports",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Triggering sales plays."
      }), " The most obvious thing that you'd want to do with a Content Report is to use your highest-converting content as a trigger to start a sales play — chances are you have content that converts as well as an inbound demo request! For anything you'd like the Sales team to act on, you can send an Intent Signal through Koala or even set up an automation (PS - Koala can do that too!)."]
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Understanding different parts of the journey."
      }), " One of our customers wanted to understand the different parts of their buying journey, so they made three different content reports to understand what actions led to self-service trials being started, what actions led to SQOs being created, and what actions led to deals going to Closed Won. Interestingly, they found pretty different patterns — their help center articles and guides were very influential in driving self-service trials, their enterprise feature docs were influential in driving net new opps, and their legal documents played a surprisingly important role in driving forward late-stage pipeline."]
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Informing pricing & packaging."
      }), " A few of our customers have been surprised at the topics that were leading to SQOs. For instance, one customer realized that some of the features they had made available on the self-service plan were shooting off the charts as features that predicted a sales conversation. After layering in some qualitative research, they realized that despite the feature being available on self-service, it was often too complicated to actually use without support, so it was leading to Sales conversations. They are now shipping an update to pricing and packaging that removes this feature from the self-service plan (and instead are adding some other features that weren't actually leading driving sales conversations)."]
    }), "\n", _jsxs(_components.p, {
      children: [_jsx(_components.strong, {
        children: "Understanding which content is performing best."
      }), " You can slice and dice your Content Reports in lots of ways — for instance, if you want to see which blog post or case study converts the best, you can easily filter. We've seen customers use this to figure out which content is working so they can make more content like that, but also so that they can leverage their best content in sales plays."]
    }), "\n", _jsxs(_components.h2, {
      id: "further-reading",
      children: ["Further reading", _jsx(_components.a, {
        "aria-hidden": true,
        tabIndex: "-1",
        className: "heading-anchor",
        href: "#further-reading",
        children: _jsx(_components.span, {
          className: "icon icon-link"
        })
      })]
    }), "\n", _jsxs(_components.p, {
      children: ["We obsessed over hundreds of Content Reports for leading hypergrowth SaaS companies to understand what type of content leads to a higher win rate... we compiled it into our own report. Check it out: ", _jsx(_components.a, {
        href: "https://getkoala.com/content-report-download",
        children: "https://getkoala.com/content-report-download"
      }), "."]
    }), "\n", _jsx(_components.p, {
      children: "We're looking forward to hearing your feedback as we prepare for our biggest announcement yet, coming soon!"
    }), "\n", _jsxs(_components.p, {
      children: ["Interested in speaking with sales? Contact ", _jsx(_components.a, {
        href: "mailto:support@getkoala.com",
        children: "support@getkoala.com"
      })]
    }), "\n", _jsxs(_components.p, {
      children: ["Feedback, questions, comments, or want to chat? Reach out to ", _jsx(_components.a, {
        href: "mailto:tido@getkoala.com",
        children: "tido@getkoala.com"
      })]
    })]
  });
}
function MDXContent(props = {}) {
  const {wrapper: MDXLayout} = Object.assign({}, _provideComponents(), props.components);
  return MDXLayout ? _jsx(MDXLayout, Object.assign({}, props, {
    children: _jsx(_createMdxContent, props)
  })) : _createMdxContent(props);
}
return {
  default: MDXContent
};


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