Click To Conversion Time (Part I): What This Metric Can Tell You About Your Clientele

An often-overlooked metric is the time taken from the first click on an ad to the final conversion of the keyword. That is, the total conversion time for a keyword in a given campaign. While this metric may seem relatively unimportant to ROI (for instance), it can help you understand your clients and also help you better manage your campaigns. In this article, I’ll demonstrate a few ways in which conversion time can be very useful.

Consider an apparel retailer which sells several lines of clothing including menswear, womenswear, kids clothing, wedding and bridal wear, footwear etc. As a retailer, you would like to know if customers who buy more expensive items take more time on average to make their purchase. It seems like a reasonable hypothesis, but what does the data say?

Aparallel Retailer

While the connection is not exactly linear (the linear regression only explains about 38% of the variance), there is a connection. Further, it appears that three campaigns Women 2, Women 1 and Nightwear take a disproportionately long time to convert compared to the order size. Apart from the (ahem!) obvious conclusion that women take a longer time to buy than men, what are the implications of this finding? Here are some helpful tips to keep in mind.

Evaluate your keyword performance. Don’t treat all keywords the same across your campaigns. Keywords in a campaign where the average order size is larger than average, should be evaluated after longer periods of time. A wedding campaign conversion takes 5-6 times longer to convert than an underwear keyword. A smart way to approach this is to evaluate the performance of closely clustered campaigns together but independently of other clusters. Wedding keywords would be evaluated separately but underwear, kids etc. should be evaluated together.

Use the appropriate retargeting duration. If you are running a search and display retargeting campaign, the above chart gives you a hint of the retargeting duration and frequency for every consumer type. A consumer typing a wedding related keyword should at least be retargeted for six days while a consumer looking for footwear should be retargeted for a shorter duration such as 2-3 days.

Know your comparison shoppers. Buyers with a lower click to conversion time are likely to spend less time comparison shopping than a longer conversion time buyer. Hence, wedding dress buyers are more likely to comparison shop than footwear buyers. Your ad copies should address these diverse populations.

Carefully evaluate the attribution window of individual campaigns. Make sure you understand the right window to attribute a conversion to a click. If you have a short attribution window for a wedding dress campaign, your campaign will appear to be underperforming. However, too long an attribution window and you would likely be giving too much credit. Surely, one cannot give credit for a purchase that followed an ad seen 2 months ago!

The last point regarding revenue attribution is a complex and important topic. However, conversion times can help you estimate the correct attribution window. My next column will address this important topic. Stay tuned.

Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.

Related Topics: Channel: Analytics | Search & Analytics


About The Author: is Director, Business Analytics at Adobe. He leads a global team that manages the performance of over $2 BN dollars of ad spend on search, social and display media at Adobe.

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  • Bradd Libby

    “Consider an apparel retailer which sells several lines of clothing including menswear, womenswear, kids clothing, wedding and bridal wear, footwear etc.”

    Judging from the title of your chart, I assume that these clothing lines do not intersect. :)

  • George Michie

    Bradd, you are funny!

    Siddharth, always nice to see folks pay attention to the data. Well done.

  • sidshah

    Bradd: Perhaps in the future we can do a special post on cross-dressing campaigns. Unfortunately, the volume on those is low . I wonder why :)

    George: Thanks! I think you will find the upcoming post interesting too.

  • GE.GAO

    Hey Sid,

    This is really an interesting post and I happen to be testing around this concept around the past month.

    The punchline is: the conversion duration is actually an issue of revenue distribution. in other words, how do we setup cookie life to relate a sales to an certain ad?

    Here is the problem I had during my test.
    The conversion duration is product based. We know an expensive product could take longer to convert, which can probably be measured and thus given a longer cookie life. However, the ads pointing to this conversion can vary.

    Using your example, we can setup a longer cookie life for the wedding dress campaign for sure. But the ads in this campaign may end up with an underwear conversion. On the other hand, ads on a different/broader keyword(especially brand term) may produce a wedding dress conversion. In either situation, longer cookie life in wedding dress campaign does not help in accurate revenue distribution.

    I can think of two ways to solve this problem:
    1, Accumulate enough data to discount the wedding dress campaign revenue to adjust the cookie life.
    2, Associate actual conversion revenue to the exact ads, meaning dynamic/multiple cookie life, which is technically sophisticated .

    While this is a potential can-do, whether it is worthwhile or not may vary by cases.

  • sidshah

    Hi Ge,
    Thanks for your comment. My suggestion is you can view the click to conversion time on a campaign by campaign basis and decide what should be the cookie life. As part of the test set a really long cookie life like 30 days and record all conversions. You can then assume the time taken to record 95% of conversions as the cookie life for the campaign. Whether your ads contains broad keywords or not is not so important as making sure you are attributing the revenue to the ad correctly on an overall basis.My next post will cover this in more detail.

  • GE.GAO

    Hmmm, failed to make myself clear.

    When I mentioned broader keywords, I meant keywords not in this Wedding Dress Campaign.

    Say the site is called, brand keywords like “Edress” in Brand Campaign and general keywords like “dress” in General Campaign may generate a good number of wedding dress conversions. To attribute the revenue to Wedding Dress Campaign correctly, we need to filter out such revenue from actual wedding dress conversions. When Wedding Dress Campaign keywords end up with casual dress sales, which may generate less revenue, we may also need to filter out/adjust such revenues. The quest for accurate revenue distribution may be costly…

    Looking forward to read your next post on this issue. :)


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