{"id":7463,"date":"2022-11-21T22:10:47","date_gmt":"2022-11-22T06:10:47","guid":{"rendered":"https:\/\/sparktoro.com\/blog\/?p=7463"},"modified":"2022-11-23T09:06:21","modified_gmt":"2022-11-23T17:06:21","slug":"which-3rd-party-traffic-estimate-best-matches-google-analytics","status":"publish","type":"post","link":"https:\/\/sparktoro.com\/blog\/which-3rd-party-traffic-estimate-best-matches-google-analytics\/","title":{"rendered":"Which 3rd-Party Traffic Estimate Best Matches Google Analytics?"},"content":{"rendered":"\n

In July and August of this year, SparkToro solicited (via Twitter, our email newsletter, LinkedIn, Facebook, and Instagram) voluntary sharing of Google Analytics data from the web marketing community. Over 1,000 participants shared their websites’ traffic (through GA’s oAuth function) with us. We then acquired metrics from four providers of traffic estimate data\u2014SEMRush<\/a>, Datos<\/a>, SimilarWeb<\/a>, and Ahrefs<\/a>*\u2014and compared these against Google Analytics’ reported numbers. In addition, we gathered Moz’s Domain Authority<\/a> metric and Google Trends’ branded search volume<\/a> estimate. All of these metrics used the same reporting period: the 12 months from June 2020 – June 2021.<\/p>\n\n\n\n

(*After collecting these metrics and publishing this post, it was pointed out that Ahrefs’ traffic estimate does not attempt to be inclusive of all traffic sources<\/a>, only organic search visits, which we did not attempt to capture or measure accuracy on. Apologies!)<\/p>\n\n\n\n

Our goal was twofold:<\/p>\n\n\n\n

  1. To uncover a reliable, third party provider whose traffic numbers had high correlation and low variance with GA numbers so we could (hopefully) but that data and put it into SparkToro’s audience research product<\/a>.<\/li>
  2. To share our findings about the relative accuracy of each metric with the marketing community so other marketers and analysts could make better use of this information in their own competitive analyses.<\/li><\/ol>\n\n\n\n

    First, the bad news\u2014today, we don’t believe any of these traffic estimates are accurate enough to include in SparkToro. I’m hopeful that changes in the future, and that maybe this project nudges providers to improve.<\/p>\n\n\n\n

    Second\u2014the good news. After extensive data-gathering, wrangling, MySQL-and-Excel wrestling, we’ve got a lot of interesting numbers to share with y’all. Let’s get into it.<\/p>\n\n\n\n

    Which 3rd-Party Estimate is Most Often Within +\/- 30% of Google Analytics’ Users?<\/h2>\n\n\n\n

    Choosing a singular way to splice and present this data is folly, which is why you’ll find a half dozen methodologies presented in this post. But, if I were to pick my favorite, and the one I’ll be referring to most in the years ahead (until someone does a larger scale study), it would be this chart:<\/p>\n\n\n

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    \"\"<\/a><\/figure>\n<\/div>\n\n\n

    Open a new window with the full-sized image<\/a><\/p>\n\n\n\n

    To get results that are the most generous to the 3rd-party traffic estimate providers, we did four things:<\/p>\n\n\n\n

    1. We chose to use Google Analytics’ Users metric<\/a>, which purports to measure what the product once called “unique visitors.” We found that this metric lines up best with the 3rd-party providers, even those whose estimates are for total visits\/all sessions.<\/li>
    2. We used the following metrics from each provider (which correlated best with any of the GA metrics we tried): SEMRush’s “visits,” pulled from their tool’s web interface with the following settings: (root domain), (all devices), (worldwide), (deviation range: OFF); Ahrefs’ “traffic performance” (including all subdomains), (monthly volume), (avg. organic traffic); Datos’ monthly sessions (all traffic, sent to use by the Datos team); SimilarWeb’s “total visits” (worldwide; sent to us by the SimilarWeb team); Google Trends’ “Average Interest Over Time” collected from the web interface; and Moz’s “Domain Authority” and “Linking Root Domains” count, (pulled from their API). <\/li>
    3. 1,053 websites shared traffic with us, but we cleaned this dataset significantly before running our analyses. We excluded sites for which the 3rd parties had no data, and any that appeared to have gaps or errors in their GA-reported traffic (e.g. a missing or partial month, perhaps from removing the GA collector from their website or a subset of pages).<\/li>
    4. We also removed a few dozen outliers from the remainder, which brought correlations and ranges of accuracy into better alignment. For the data and graphs below, 641 websites were used, giving us 7,692 unique data points to compare (12 months X 641 sites).<\/li><\/ol>\n\n\n\n

      In the chart above, each of the four data providers is represented alongside the % of times their traffic metric was within 30% of Google Analytics’ reported “Users” for the month. Importantly, we’ve also segmented the websites by how much traffic they receive, using six total buckets:<\/p>\n\n\n\n