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หากมีสิ่งหนึ่งที่เจ้าของเว็บไซต์ทุกคนต้องการมาตั้งแต่ยุคเริ่มต้นของอินเทอร์เน็ต นั่นก็คือทราฟฟิกที่มากขึ้น
ยิ่งมีผู้เข้าชมมากขึ้น ก็ยิ่งมีผู้คนมาซื้อสินค้า จองบริการ อ่านคอนเทนต์ของคุณ และสร้างรายได้มากขึ้น มีกลยุทธ์ที่ใช้ได้จริงมากมายในการดึงดูดผู้คนให้เข้ามายังเว็บไซต์ของคุณมากขึ้น แต่คุณควรโฟกัสที่กลยุทธ์ไหนนั้นจะเปลี่ยนไปตามสถานการณ์ของคุณ
เจ้าของเว็บไซต์บางรายอาจต้องทุ่มกับ SEO ให้มากขึ้น ขณะที่บางรายอาจต้องมุ่งเน้นไปที่ทราฟฟิกแบบเจาะจงกลุ่มเป้าหมายที่การโฆษณาแบบชำระเงินสามารถนำมาได้ และอีกหลายรายก็อาจต้องการให้ความสำคัญกับการทำให้ทราฟฟิกที่มีอยู่แล้วกลับมาอีกครั้ง คำตอบที่ “ใช่” ขึ้นอยู่กับปัญหาที่คุณกำลังเผชิญอยู่โดยตรง
ต่อไปนี้คือ 9 สถานการณ์ที่พบบ่อยที่สุดซึ่งอาจกำลังจำกัดทราฟฟิกเว็บไซต์ของคุณ และวิธีแก้ไข:
ยิ่งมีผู้เข้าชมมากขึ้น ก็ยิ่งมีผู้คนมาซื้อสินค้า จองบริการ อ่านคอนเทนต์ของคุณ และสร้างรายได้มากขึ้น มีกลยุทธ์ที่ใช้ได้จริงมากมายในการดึงดูดผู้คนให้เข้ามายังเว็บไซต์ของคุณมากขึ้น แต่คุณควรโฟกัสที่กลยุทธ์ไหนนั้นจะเปลี่ยนไปตามสถานการณ์ของคุณ
เจ้าของเว็บไซต์บางรายอาจต้องทุ่มกับ SEO ให้มากขึ้น ขณะที่บางรายอาจต้องมุ่งเน้นไปที่ทราฟฟิกแบบเจาะจงกลุ่มเป้าหมายที่การโฆษณาแบบชำระเงินสามารถนำมาได้ และอีกหลายรายก็อาจต้องการให้ความสำคัญกับการทำให้ทราฟฟิกที่มีอยู่แล้วกลับมาอีกครั้ง คำตอบที่ “ใช่” ขึ้นอยู่กับปัญหาที่คุณกำลังเผชิญอยู่โดยตรง
ต่อไปนี้คือ 9 สถานการณ์ที่พบบ่อยที่สุดซึ่งอาจกำลังจำกัดทราฟฟิกเว็บไซต์ของคุณ และวิธีแก้ไข:
1. เว็บไซต์ยังใหม่และมีทราฟฟิกน้อยหรือแทบไม่มีเลย
เว็บไซต์ใหม่สามารถได้รับทราฟฟิกจำนวนมากอย่างรวดเร็วผ่านการโฆษณาแบบชำระเงิน แต่การเริ่มต้นสร้างแหล่งที่มาของผู้เข้าชมที่ยั่งยืนมากกว่าก็มีความสำคัญเช่นกัน
การระบุปัญหา
นี่เป็นปัญหาที่สังเกตเห็นได้ง่ายที่สุด—เว็บไซต์ใหม่มักจะไม่ได้รับทราฟฟิกมากนักเลย จนกว่าคุณจะเริ่มทำการตลาดและบอกให้ผู้คนรู้ว่าเว็บไซต์นี้มีอยู่จริง
- Click-through rate (CTR): the percentage of visitors who click your call to action. For ads, click-through rate means clicks divided by impressions, so it helps to state which of the two you are testing.
- Bounce rate: the percentage of sessions that end without any engagement. Classic
analytics counted every single-page visit as a bounce. Google Analytics 4 counts a session as a bounce when it lasts 10 seconds or less, has no key event and includes only one page view. - Conversion rate: the percentage of visitors who take the action you want them to take.
- Scroll depth: how far down the page people scroll while reading your content.
- Abandonment rate: the percentage of people who start your desired action (say, filling in a form) and fail to complete it.
- Average order value (AOV): total revenue divided by the number of orders.
- Revenue per visitor: total revenue divided by the number of visitors, including those who bought nothing.
แม้ว่า SEO มักถูกยกให้เป็นแหล่งที่มาของทราฟฟิกที่ดีที่สุดสำหรับเว็บไซต์ โดยที่ Google เพียงรายเดียวสร้างการเข้าชมเกือบ 63% ของทั้งหมด แต่ก็ไม่ใช่กระบวนการที่รวดเร็ว และในฐานะเจ้าของเว็บไซต์มือใหม่ คุณย่อมต้องการผลลัพธ์ที่วัดผลได้ตอนนี้
ต่างจากการเติบโตที่ช้าแต่สม่ำเสมอจากการทำบล็อก โฆษณาแบบชำระเงินสามารถนำคลื่นผู้เข้าชมมาได้แทบจะทันที หากมีงบประมาณและการกำหนดเป้าหมายที่เหมาะสม
คุณสามารถลงโฆษณาบนโซเชียลมีเดีย ใน หรือในรูปแบบแบนเนอร์โฆษณาดั้งเดิมที่เราทุกคนนึกถึงเมื่อได้ยินคำว่า “โฆษณาออนไลน์” สิ่งเหล่านี้สามารถออกแบบและกำหนดเป้าหมายได้อย่างแม่นยำอย่างน่าทึ่งด้วยเกณฑ์หลากหลายรูปแบบ เปลี่ยบแทบทุกคนให้กลายเป็นผู้เยี่ยมชมเว็บไซต์ที่มีแนวโน้มได้
แต่ละแบบมีการใช้งานที่เหมาะสมในแบบของตัวเอง:
ต่างจากการเติบโตที่ช้าแต่สม่ำเสมอจากการทำบล็อก โฆษณาแบบชำระเงินสามารถนำคลื่นผู้เข้าชมมาได้แทบจะทันที หากมีงบประมาณและการกำหนดเป้าหมายที่เหมาะสม
คุณสามารถลงโฆษณาบนโซเชียลมีเดีย ใน หรือในรูปแบบแบนเนอร์โฆษณาดั้งเดิมที่เราทุกคนนึกถึงเมื่อได้ยินคำว่า “โฆษณาออนไลน์” สิ่งเหล่านี้สามารถออกแบบและกำหนดเป้าหมายได้อย่างแม่นยำอย่างน่าทึ่งด้วยเกณฑ์หลากหลายรูปแบบ เปลี่ยบแทบทุกคนให้กลายเป็นผู้เยี่ยมชมเว็บไซต์ที่มีแนวโน้มได้
แต่ละแบบมีการใช้งานที่เหมาะสมในแบบของตัวเอง:
วิธีแก้ไข
เว็บไซต์ใหม่เป็นช่วงเวลาที่น่าตื่นเต้นสำหรับนักการตลาด เพราะมาพร้อมกับศักยภาพมากมายและโอกาสในการกำหนดทิศทางได้อย่างแท้จริง คำตอบที่ตรงไปตรงมา (แม้จะเรียบง่าย) สำหรับปัญหานี้ก็คือ ณ จุดนี้คุณสามารถลองทำอะไรก็ได้ และเริ่มสร้างยอดเข้าชมเพิ่มขึ้นอีกเล็กน้อย อย่างไรก็ตาม มีกลยุทธ์อยู่สองสามอย่างที่เหมาะสมที่สุดในช่วงเริ่มต้นนี้
คุณสามารถรับมือกับปัญหานี้ได้สองวิธี: ทางออกระยะยาว หรือวิธีแก้ที่รวดเร็วกว่า
คุณสามารถรับมือกับปัญหานี้ได้สองวิธี: ทางออกระยะยาว หรือวิธีแก้ที่รวดเร็วกว่า
The great thing about A/B tests is that they can be used to test almost any element of your landing pages. You might want to focus on the effects of something technical like loading times, or on something as small as how visible your call-to-action button is. The colour itself matters less than contrast. A button that blends into the page is easy to miss whatever its colour, so a test of contrast or size usually tells you more than a test of orange against blue.
The important thing is to only test a single variable with each iteration of the site. This means that you can draw solid conclusions about its impact without muddying the waters with questions over which change caused which result.
The important thing is to only test a single variable with each iteration of the site. This means that you can draw solid conclusions about its impact without muddying the waters with questions over which change caused which result.
2. เว็บไซต์ได้รับทราฟฟิกที่ตรงกลุ่มอยู่แล้ว แต่ต้องเข้าถึงผู้คนให้มากขึ้น
การวิเคราะห์คือกุญแจสำคัญในการทำความเข้าใจว่าอะไรเป็นตัวสร้างทราฟฟิกให้กับเว็บไซต์ของคุณ ใช้สิ่งเหล่านี้เพื่อทำซ้ำแนวทางที่ประสบความสำเร็จมากที่สุดของคุณและลดการคาดเดาออกไป คุณยังสามารถใช้โซเชียลมีเดียและการตลาดแบบพันธมิตรเพื่อขยายการเข้าถึงไปยังกลุ่มเป้าหมายที่เกี่ยวข้องได้อีกด้วย
- Time: organic traffic growth tends to be a slow, steady process. Paid traffic can start gathering information almost instantly, allowing a much quicker answer to your hypothesis.
- Control: paid campaigns let you decide how many visitors arrive each day, when the test starts and stops, and how much it costs. You can also keep the targeting identical for the whole test, so the only thing that changes between version A and version B is the page itself. The 50/50 split between versions is something you set up on your own site, as Step 5 explains, because the ad platform alone cannot guarantee it.
- Segmentation: paid advertising lets you choose who sees your ads by location, device and interests. The visitors in your test then resemble the market you want to win, so the result is more relevant to the customers you care about. The result applies to that audience only. A page that wins with mobile visitors in one country may not win with desktop searchers somewhere else.
In short, using paid ads to generate the traffic for your split tests means that you get more relevant results, quicker.
3. ทราฟฟิกเว็บไซต์ลดลง
การเปลี่ยนแปลงของการไหลเวียนทราฟฟิกอาจเกิดขึ้นได้จากหลายสาเหตุ ดังนั้นจึงสำคัญที่จะต้องทำให้ส่วนผสมทางการตลาดของคุณมีความหลากหลายมากที่สุดเท่าที่จะเป็นไปได้ หากต้องการผลลัพธ์ที่รวดเร็ว ให้พิจารณาทำรีทาร์เก็ตติ้งกับผู้เข้าชมเดิมผ่านโฆษณาหรืออีเมล
การระบุปัญหา
การลดลงของทราฟฟิกเว็บไซต์จะแสดงให้เห็นบนแดชบอร์ดการวิเคราะห์ของคุณ รายงานที่คุณพบที่นั่นควรช่วยให้คุณทราบได้อย่างชัดเจนว่าโฟลว์ใดมีการเปลี่ยนแปลง และจากตรงนี้ คุณสามารถเริ่มปะติดปะต่อได้ว่าทำไมสิ่งเหล่านั้นจึงเปลี่ยนไป
วิธีแก้ไข
บางครั้ง สิ่งที่เคยได้ผลอาจหยุดได้ผล และทราฟฟิกเว็บไซต์ของคุณก็จะลดลง อาจมีนับไม่ถ้วนเหตุผลอยู่เบื้องหลัง; โดยสาเหตุที่พบบ่อยที่สุดคือ:
Step 3: Isolate Your Variable
The variable is the change that we’re testing. To ensure that the data we’re collecting is clear and valid, it’s important to only change one element for each test (you can always run another test later to check other theories you may have).
The variable could be almost any element of your landing page, but commonly tested ones include things like:
The variable could be almost any element of your landing page, but commonly tested ones include things like:
- Hero image: the top banner on your page, the first thing people see when they visit
- ปัญหาการติดตามข้อมูล: บางครั้งการอัปเดตเว็บไซต์ของคุณอาจทำให้เกิดปัญหากับซอฟต์แวร์วิเคราะห์ข้อมูลของคุณ ลองโหลดหน้าเว็บของคุณด้วยตัวเองและดูว่าการเข้าชมของคุณถูกบันทึกไว้หรือไม่
- คู่แข่งแซงอันดับคุณ: SEO ไม่เคยหยุดนิ่ง และหากคุณประสบความสำเร็จ มีแนวโน้มว่าคู่แข่งของคุณจะสังเกตเห็นและเริ่มเลียนแบบกลยุทธ์ของคุณแล้ว
- Position of CTA: where the call to action sits on the page
- CTA wording: how the CTA is presented to visitors
- Form size: the number of fields someone has to fill in
- Copy: the text that populates your page
- Loading time: does quicker loading result in more conversions
Of course, you could run an A/B test on any element of your page, though the ones listed above are likely to have the biggest impacts and be the easiest to draw clear conclusions from.
Step 4: Prepare Your Pages
The final step before we can start generating traffic and running the test is to prepare your pages. For most landing-page tests, two versions work best. Each extra version splits your traffic further, so a test with three versions needs at least 50% more visitors in total than a test with two. Comparing several versions at once also raises the chance of a false winner.
You can still run an A/B/n test with three or four versions if your traffic allows it. Either way, each version should differ from the control in one element only. If you want to test several elements together, that is a different method called multivariatetesting.
Your existing page is the “A” part and acts as a control, setting the baseline. The version with the single change is the “B” element and allows you to test your hypothesis.
You can still run an A/B/n test with three or four versions if your traffic allows it. Either way, each version should differ from the control in one element only. If you want to test several elements together, that is a different method called multivariatetesting.
Your existing page is the “A” part and acts as a control, setting the baseline. The version with the single change is the “B” element and allows you to test your hypothesis.
Step 5: Set Up Your Paid Traffic
Most paid ad platforms have built-in experiment features, but they are built to compare ads and campaigns, not landing pages. In Google Ads, for example, a campaign experiment splits your budget between an original campaign and a trial campaign. That is useful for testing bids or ad copy, but it does not guarantee that your two landing pages receive equal or comparable visitors.
The reason is that ad platforms optimize delivery. When two ads point to two different pages, the platform’s algorithm decides who sees each ad, and it shows each one to the people it predicts will respond. Researchers call this divergentdelivery. A study in the Journal of Marketing found that it can distort the size of an ad test result and even reverse its direction.
The safer setup is to send all paid visitors to one campaign URL and split them on your own site. A testing script or a server-side rule assigns each visitor at random to version A or version B, 50/50. The same rule stores the assignment in a first-party cookie, so a returning visitor always sees the same version. Without this, one person can see both versions, and their behaviour then counts toward both groups and blurs the difference you are trying to measure.
If your setup forces you to use two separate campaigns, give them identical targeting, the same start time and the same daily cap. Before you read any results, compare the device and location mix of the two groups. If one group has far more mobile visitors than the other, the comparison is not fair.
The reason is that ad platforms optimize delivery. When two ads point to two different pages, the platform’s algorithm decides who sees each ad, and it shows each one to the people it predicts will respond. Researchers call this divergentdelivery. A study in the Journal of Marketing found that it can distort the size of an ad test result and even reverse its direction.
The safer setup is to send all paid visitors to one campaign URL and split them on your own site. A testing script or a server-side rule assigns each visitor at random to version A or version B, 50/50. The same rule stores the assignment in a first-party cookie, so a returning visitor always sees the same version. Without this, one person can see both versions, and their behaviour then counts toward both groups and blurs the difference you are trying to measure.
If your setup forces you to use two separate campaigns, give them identical targeting, the same start time and the same daily cap. Before you read any results, compare the device and location mix of the two groups. If one group has far more mobile visitors than the other, the comparison is not fair.
How Much Traffic Should I Buy?
There is no single number of visits that makes a test accurate, because the number depends on four inputs. The first is your baseline conversion rate, which is how often the control page converts today. The second is the smallest lift you want to be able to detect. The third and fourth are the significance level and the statistical power, which most teams set at 95% confidence and 80% power.
Here is how the numbers work out for a page that converts at 4%. To detect a 20% lift (from 4.0% to 4.8%), you need about 10,300 visitors per version. To detect a 10% lift (from 4.0% to 4.4%), you need about 39,500 per version, almost four times as many. A page that converts at 2% needs about 80,700 visitors per version to detect a 10% lift, while a page at 10% needs about 14,800.
Duration does not change these numbers. A test that reaches 10,300 visitors per version in 14 days is as reliable as one that takes 30 days to reach the same total, as long as both cover full weeks. You can turn the sample size into a budget before you start: visitors per version × 2 × cost per visit. At an example cost of $0.50 per visit, the 20% lift test above costs about $10,300.
If that budget is too high, you have two honest options. You can test a bolder change, because larger lifts need far fewer visitors. Or you can choose a primary metric with a higher base rate, such as engagement rate: at around 50%, 1,000 visitors per version can detect a difference of about 6 percentage points. The method behind these numbers is called samplesizedetermination, and free online calculators do the arithmetic for you.
Here is how the numbers work out for a page that converts at 4%. To detect a 20% lift (from 4.0% to 4.8%), you need about 10,300 visitors per version. To detect a 10% lift (from 4.0% to 4.4%), you need about 39,500 per version, almost four times as many. A page that converts at 2% needs about 80,700 visitors per version to detect a 10% lift, while a page at 10% needs about 14,800.
Duration does not change these numbers. A test that reaches 10,300 visitors per version in 14 days is as reliable as one that takes 30 days to reach the same total, as long as both cover full weeks. You can turn the sample size into a budget before you start: visitors per version × 2 × cost per visit. At an example cost of $0.50 per visit, the 20% lift test above costs about $10,300.
If that budget is too high, you have two honest options. You can test a bolder change, because larger lifts need far fewer visitors. Or you can choose a primary metric with a higher base rate, such as engagement rate: at around 50%, 1,000 visitors per version can detect a difference of about 6 percentage points. The method behind these numbers is called samplesizedetermination, and free online calculators do the arithmetic for you.
Step 6: Run the test
Before the first visitor arrives, you fix the sample size from the previous step and the duration needed to reach it. We recommend whole weeks (7, 14, 21 or 28 days), so every weekday appears equally in both versions. Then you wait for the test to finish before you decide anything.
The reason is that random noise is largest early in a test. If you check the results every day and stop the first time one version looks significant, your real false-positive rate rises far above the 5% you planned. Statisticians call this optionalstopping, and marketers often call it peeking.
A fixed plan cannot remove chance, but it keeps the risk of a false winner at the level you chose. It also protects your data from short-lived distortions such as:
The reason is that random noise is largest early in a test. If you check the results every day and stop the first time one version looks significant, your real false-positive rate rises far above the 5% you planned. Statisticians call this optionalstopping, and marketers often call it peeking.
A fixed plan cannot remove chance, but it keeps the risk of a false winner at the level you chose. It also protects your data from short-lived distortions such as:
- Time-of-day and day-of-week patterns
- A few heavy users who visit many times
- One-off events, such as a competitor’s sale or a news story
Selecting a “winner” before the test has run its full course simply because it seems to be doing better in the early stages risks implementing a falsepositive and damaging your results long-term.
What represents a “fair” test will vary from case to case, but the general rule is that you need to run it for long enough and with enough visitors for your results to reach statistical significance. The sample size you calculated before launch tells you what “long enough” and “enough visitors” mean for your test.
What represents a “fair” test will vary from case to case, but the general rule is that you need to run it for long enough and with enough visitors for your results to reach statistical significance. The sample size you calculated before launch tells you what “long enough” and “enough visitors” mean for your test.
Step 7: Evaluate Your Results and Implement the Winner
Once your test has run its full length, you can evaluate the results. This means two things: first, checking to see if your hypothesis was correct and your primary metric was improved by changing your chosen variable, and secondly, looking at your other metrics to check for unintended consequences.
If your primary metric has improved by a statistically significant margin and your guardrail metrics have not worsened, your test has been successful, and you can replace the original page with the new version. If the impact was negative, keep the control and scrap the new iteration. If the difference is not significant either way, the test is inconclusive. The change made no measurable difference at this sample size, and keeping the control is usually the cheaper choice.
If you find that your primary metric has improved but your other important metrics have also been affected, you’ll have to make a judgment call about which version of your landing page is best to use. Weigh up the impacts carefully and consider further testing to refine your page further.
If your primary metric has improved by a statistically significant margin and your guardrail metrics have not worsened, your test has been successful, and you can replace the original page with the new version. If the impact was negative, keep the control and scrap the new iteration. If the difference is not significant either way, the test is inconclusive. The change made no measurable difference at this sample size, and keeping the control is usually the cheaper choice.
If you find that your primary metric has improved but your other important metrics have also been affected, you’ll have to make a judgment call about which version of your landing page is best to use. Weigh up the impacts carefully and consider further testing to refine your page further.
4. ทราฟฟิกกำลังเพิ่มขึ้น แต่เว็บไซต์ดึงดูดผู้เข้าชมผิดกลุ่ม
ทราฟฟิกไม่ได้มีคุณค่าเท่ากันทั้งหมด ลองใช้เวลาไปกับการทำ Local SEO และโฆษณาแบบกำหนดเป้าหมาย โดยพิจารณาจากกลุ่มผู้เข้าชมที่มีความเกี่ยวข้องกับธุรกิจของคุณมากที่สุด
Statistical Significance
หากระบบวิเคราะห์ของคุณแสดงให้เห็นว่าจำนวนผู้เข้าชมเพิ่มขึ้น แต่คุณไม่เห็นว่ายอดขายและการสมัครเพิ่มขึ้นตามไปด้วย เว็บไซต์ของคุณอาจกำลังดึงดูดคนผิดกลุ่มอยู่ เมื่อคุณสังเกตเห็นปัญหานี้แล้ว คุณก็สามารถเริ่มย้อนวิเคราะห์ความพยายามทางการตลาดของคุณ และกำหนดเป้าหมายให้แม่นยำยิ่งขึ้นไปยังทราฟฟิกที่เกี่ยวข้องได้
Sample Size and Duration
While these should have been set right at the start of the testing process, it’s worth checking them again when evaluating your results. There are tools out there that can help you determine what sample sizes and timescales you should be aiming for to reach statistical significance in your results.
Basically, the longer your test runs for and the more people it includes, the more sure you can be of the long-term sustainability of any improvements that you find. There are, of course, limits to this: no marketing team has an unlimited timescale or budget for buying traffic, so it’s important that realistic numbers are set and checked.
While you look at your sample sizes, it is worth checking that each version received the share of visitors you planned. If you set a 50/50 split and see 5,400 visitors on one version and 4,600 on the other, something is wrong with the setup, not with your luck. This problem is called a sampleratiomismatch, and a simple chi-squared test confirms it.
A sample ratio mismatch usually comes from the setup, not from the traffic source. Typical causes include a redirect that loses some visitors, a tracking tag that fails to fire on one version, or bot filtering that removes more visits from one group. If the check fails, the results cannot be trusted, and the fix is to find the cause and rerun the test.
Basically, the longer your test runs for and the more people it includes, the more sure you can be of the long-term sustainability of any improvements that you find. There are, of course, limits to this: no marketing team has an unlimited timescale or budget for buying traffic, so it’s important that realistic numbers are set and checked.
While you look at your sample sizes, it is worth checking that each version received the share of visitors you planned. If you set a 50/50 split and see 5,400 visitors on one version and 4,600 on the other, something is wrong with the setup, not with your luck. This problem is called a sampleratiomismatch, and a simple chi-squared test confirms it.
A sample ratio mismatch usually comes from the setup, not from the traffic source. Typical causes include a redirect that loses some visitors, a tracking tag that fails to fire on one version, or bot filtering that removes more visits from one group. If the check fails, the results cannot be trusted, and the fix is to find the cause and rerun the test.
Assessing External Factors
Unfortunately, the internet is not a sterile test environment and external factors will probably creep into your results. While using paid traffic can help to minimize this potential statistical pollution, it can’t totally eliminate it.
These factors could include all kinds of things, from seasonal changes in behaviour (people being more open to e-commerce ads around the holidays) to other marketing campaigns that you may be running. Technical issues, such as one page loading slightly slower than the other, may also have a surprisingly large impact on your results. If no one waits long enough to see your test variable, you can’t really test it.
These factors don’t invalidate your results, but they should be carefully considered before you start applying them.
These factors could include all kinds of things, from seasonal changes in behaviour (people being more open to e-commerce ads around the holidays) to other marketing campaigns that you may be running. Technical issues, such as one page loading slightly slower than the other, may also have a surprisingly large impact on your results. If no one waits long enough to see your test variable, you can’t really test it.
These factors don’t invalidate your results, but they should be carefully considered before you start applying them.
Audience Segmentation
While you can pull headline figures from your A/B tests such as “5% more sign-ups”, you can also look at how different groups of visitors responded. This is especially useful with paid traffic, because you chose the audience and your analytics records each visitor’s device, location and traffic source.
The tempting approach is to slice the results by every dimension available, such as device, browser, country, age and gender. The problem is that every slice is another chance for luck to produce a false winner. With 10 segments tested at the 5% threshold, the chance that at least one shows a “significant” difference by luck alone is about 40%, a pitfall known as the multiple comparisons problem. Each slice also holds only part of your sample, so its numbers are less reliable.
It also helps to build segments from your own analytics rather than from ad-network profiles. Profiles built from third-party cookies are incomplete, because several major browsers block those cookies by default and consent banners reduce the visits you can measure.
The practical approach is to name one or two segments before the test starts, and device type is the most useful choice for most landing pages. For example, if you planned a mobile versus desktop comparison and desktop visitors convert far more often on version B, you have a solid finding to act on. Any pattern you spot in a segment you did not plan is a hypothesis for your next test, not a result.
The tempting approach is to slice the results by every dimension available, such as device, browser, country, age and gender. The problem is that every slice is another chance for luck to produce a false winner. With 10 segments tested at the 5% threshold, the chance that at least one shows a “significant” difference by luck alone is about 40%, a pitfall known as the multiple comparisons problem. Each slice also holds only part of your sample, so its numbers are less reliable.
It also helps to build segments from your own analytics rather than from ad-network profiles. Profiles built from third-party cookies are incomplete, because several major browsers block those cookies by default and consent banners reduce the visits you can measure.
The practical approach is to name one or two segments before the test starts, and device type is the most useful choice for most landing pages. For example, if you planned a mobile versus desktop comparison and desktop visitors convert far more often on version B, you have a solid finding to act on. Any pattern you spot in a segment you did not plan is a hypothesis for your next test, not a result.
Accounting for Outliers
Depending on the metric you have chosen, a few unusual results can distort your data. Revenue is the classic case: a single large purchase on version B can make it look like the winner, even if most of its visitors bought nothing at all.
The simplest fix is capping, also called winsorizing. You choose a cutoff, such as the 99th percentile of order values, and replace every value above it with the cutoff value. For example, if 99% of orders are below $400, a $5,000 order counts as $400. Every visitor stays in the data, including those who spent nothing, so you can still compare average revenue per visitor fairly.
Capping works better than trimming, which deletes the top and bottom values. In revenue data, the bottom values are mostly visitors who spent nothing, so deleting them inflates the average and hides the people who did not buy.
Some analysts use the Mann-WhitneyUtest instead. It pools the results from both pages, ranks them, and checks whether one group tends to produce larger values. It does not need equal group sizes, but it answers a different question from “which page earns more revenue per visitor,” and it struggles when most values are zero. For most landing-page tests, capping and then comparing averages is simpler and closer to the business decision you need to make.
The simplest fix is capping, also called winsorizing. You choose a cutoff, such as the 99th percentile of order values, and replace every value above it with the cutoff value. For example, if 99% of orders are below $400, a $5,000 order counts as $400. Every visitor stays in the data, including those who spent nothing, so you can still compare average revenue per visitor fairly.
Capping works better than trimming, which deletes the top and bottom values. In revenue data, the bottom values are mostly visitors who spent nothing, so deleting them inflates the average and hides the people who did not buy.
Some analysts use the Mann-WhitneyUtest instead. It pools the results from both pages, ranks them, and checks whether one group tends to produce larger values. It does not need equal group sizes, but it answers a different question from “which page earns more revenue per visitor,” and it struggles when most values are zero. For most landing-page tests, capping and then comparing averages is simpler and closer to the business decision you need to make.
Conclusion: Why You Should Use Paid Traffic For A/B Testing Your Landing Pages
It’s an old truism that there’s always room for improvement, and your landing pages are no exception. While you might be getting great results from your existing pages, you could always be getting more.
A/B testing allows you to try out changes and gather real, solid, statistical information on their impacts before you fully commit. Over the course of a few tests, you can dramatically improve your results without risking mistakes that are hard to reverse.
While you could rely on SEO or some other traffic generation method, using paid ads offers quite a few advantages.
A/B testing allows you to try out changes and gather real, solid, statistical information on their impacts before you fully commit. Over the course of a few tests, you can dramatically improve your results without risking mistakes that are hard to reverse.
While you could rely on SEO or some other traffic generation method, using paid ads offers quite a few advantages.
- You can target the people your site is aimed at by location, device and interests from the first day, so the test reflects the audience you want to win.
- You control the volume, timing and targeting of every visit, so both versions are tested on the same audience under the same conditions. A random split on your own site then keeps the comparison fair.
- Your test can begin almost immediately. Once ads are served, traffic starts to flow quickly, so you reach your planned sample size much sooner than you would by waiting for organic traffic.
- Tagging the campaign with UTMparameters lets you analyse only the visitors who came from your test, so organic, direct and returning visitors do not contaminate the results.
- Control over volume, via your ads platform, means that you can stop the test as soon as you reach your desired number of visitors to each page.
When it comes to running accurate, controlled A/B experiments and gaining useful insights into what changes will drive greater conversions, paid traffic is hard to beat.
