The Forgotten Metric: Direct Traffic Signals Brand Preference

We all want to know which sites, search engines and keywords are sending us traffic. But what about direct type in traffic? When people access your site URL by typing it in from memory, it can be a great indicator of your brand preference, success of your offline and online marketing efforts and customer satisfaction.

If you’re smart and lucky, you named your site your main-keyword-dot-com and you get search traffic from visitors who use their address bars as search engines. For example, a search on “reusable bags” sends you automatically to “” which sells…you got it, reusable bags.

Other types of type-in visitors could be:

  • Loyal, repeat customers

  • Late stage buyers who’ve already visited your site through search, PPC, email or affiliate link but needed time to make the purchase decision or comparison shop
  • First time shoppers pre-sold from a newspaper article, blog post, social media reviews or word-of-mouth

Type in traffic may even be your highest converting traffic. This post will cover how to create a direct traffic report in Google Analytics, and how to compare direct traffic conversion against your site average. Plus, you’ll learn how to exclude IP addresses for non-customer visits.

How to Access Direct Traffic Reports in Google Analytics

We’re interested in looking at trends in direct traffic to see if the strength and awareness of your brand is increasing over time. Here’s how you get the trend data:

1. Log into your Google Analytics account and click on “Traffic Sources” and “Direct Traffic” in the left hand menu.

You’ll want to make sure you change the default date range to your last quarter, or the past year:

After you change the first date box, don’t forget to click in the second date box once to make the “Apply” button live, then click “Apply.”

2. Changing your graph to “Month” will show you an average figure per month, rather than each daily or weekly record. Believe me, this is much easier to work with. Just remember the figures are monthly averages:

Rolling over any point will show you the month average. The above image is Photoshopped to show you the year over year growth in type-ins for Get Elastic. Comparing this July’s traffic over 2007, I see the blog’s direct visits grew 330%, from 2152 per month to 7142.

3. Compare your direct visit conversion rates to your site average. If you click the little arrow beside “Visits,” you can make some pretty useful graphs.

In this case, we’re going to compare the type in segment to overall Goal 1 conversions for the past year. (Stay in “Month” mode)

The graph will even show you the spread between site and segment (not just for type in, you can use this for other segments of traffic like search engine and keyword):

Don’t Forget IP Filters

But there’s likely another type of visitor you’re tracking – yourself! One thing that can really mess up this metric is tracking your own IP address and the IPs of others who frequent your site but are not customers. These visits could represent SEO, PPC, web design and IT consultants, employees’ home computers, the office IP block, SEOs and other web consultants, ecommerce bloggers (wink) and even competitors.

Direct traffic is not the only stat that suffers when you neglect to filter IP addresses. As mentioned in 8 Stupid Things Webmasters Do To Mess Up Their Analytics, it also:

  • Understates your conversion rates. Your direct type-ins could be your highest converting traffic source, but tracking visits from employees, stakeholders and consultants dilutes your real conversion rate for this segment.

  • Overstates average time on page. You think your visitors are reading every jot and tittle of your copy, when it’s really your marketing team.
  • Messes up your Content stats. Your “Top Content,” “Landing Pages” and “Exit Pages” will may be skewed by tracking the wrong visitors.

How to Filter IPs in Google Analytics

Once you’ve gathered all the IP addresses you need to exclude, go into your Analytics Settings, and find the Filter Manager in the bottom right:

Set up IP filter

You can also filter a range of IPs or use an advanced, cookies-based filter in your office which will compensate for dynamic IPs.

Then add each filter, naming each one intuitively so it’s easy to make edits in the future.

If you add a filter after reading this post, keep that in mind when you create reports in the future. Your direct traffic could drop significantly after creating the filter, and you can’t apply the filter today and change yesterday’s data.

Your trends may not show an upward trend, either. They may spike seasonally or after certain promotions you ran, help wanted ads or other “buzz” about your company. It really depends on you and your business.

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10 Responses to “The Forgotten Metric: Direct Traffic Signals Brand Preference”

  1. Hi Linda, a question regarding sources of “direct traffic”:

    Are there other sources of site visits that can show up as “direct traffic” in analytics tools beyond “type-ins”?

    For example, if someone bookmarks a site, and then later (perhaps in a fresh browser session) visits that site via the bookmark, is that “direct traffic” (since there was no referring web page for that visit)?

    In the case of a blog like Get Elastic, if a visit to the site comes from a non-web-based RSS reader, would that show up as “direct traffic” as well?

  2. @Jon,

    Sure, this post was just about Google Analytics because that’s the only tool I’m familiar with right now, but each tool may have different ways of reporting type-ins, and different referrals they consider type-in.

    RSS traffic usually is reported from the reader, if it’s web based like Bloglines or Netvibes. If it’s an email subscriber, it may or may not show up as an email referral – that depends. Since subscribers come from all over in terms of reader referrals, Feedburner is a much better tool to understand your subscriber habits than Google.

    I really should update this post, I did a bit of research on “reasons for direct traffic” and learned some new things as a result of your comment.

    1. Yes, bookmarks count as direct visits
    2. However, if it’s a repeat visit and the first visit came from, for example, a PPC campaign, then the direct hit, bookmark or otherwise, does not overwrite the initial visit until the cookie expires. There may be a hack for this.
    3. If you missed tagging some of your site’s pages, you could be getting direct traffic from your own site
    4. When IE opens in a new window, this may count as direct traffic

    And a bunch of more reasons here:

    So looking back, I think today’s post is good in theory, but without a way to separate wheat from the chaff (wheat would be visits that were intentionally typed in from the browser), in practice it may not be that useful to look at direct traffic and conversion of direct traffic.

    You bring up a good point and I will consider modifying this post accordingly.

  3. Good article and blocking you own IP address traffic from your reports is very important.

  4. Blocking IP could be done more easily and flexible using the segmentation cookie (for example the solution described at:

  5. [...] 31 update:  Linda Bustos wrote a good treatment of the same thing for Google Analytics, here. [...]

  6. Great post Linda, many people overlook the value in building a brand, and the importance it plays in your overall marketing efforts.

  7. Linda — this is a fantastic look at the most overlooked kind of traffic. Seth Godin would be proud of you… this is just another reminder that we needn’t forget offline marketing principles in doing our work online. We’ve put up a recent video which references your post:

  8. Do you have a recommendation for “an advanced, cookies-based filter … which will compensate for dynamic IPs”? Or even a place to start looking, if not a true recommendation? Sounds promising…

  9. Hi Linda
    This post caught my eye again after a project I did focusing on Direct Visitors.

    It resulted in these 2 blog posts which I expect will be helpful to your readers:
    which deals with typed-in, bookmarked and other types of visit initiators

    and “Scenario 1: Improved Attribution” in–of-many-Google-Analytics-User-Defined-Variables-udv-Revisited

    Brian Katz – Analytics – VKI

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