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Wenn es eine Sache gibt, die sich jeder Website-Betreiber seit den Anfängen des Internets gewünscht hat, dann ist es mehr Traffic.
Mehr Besuche bedeuten mehr Menschen, die einkaufen, Dienstleistungen buchen, Ihre Inhalte lesen und Umsatz generieren. Es gibt Dutzende bewährte Strategien, um mehr Besucher auf Ihre Website zu bringen, aber worauf Sie sich konzentrieren sollten, hängt von Ihrer jeweiligen Situation ab.
Einige Website-Betreiber sollten vielleicht verstärkt auf SEO setzen, während andere sich auf den zielgerichteten Traffic konzentrieren müssen, den bezahlte Werbung bringen kann, und wieder andere darauf, dass die Besucher, die sie bereits haben, immer wieder zurückkehren. Die „richtige“ Antwort hängt davon ab, mit welchem Problem Sie genau konfrontiert sind.
Hier sind 9 der häufigsten Situationen, die Ihren Web-Traffic einschränken könnten, und wie Sie sie beheben können:
Mehr Besuche bedeuten mehr Menschen, die einkaufen, Dienstleistungen buchen, Ihre Inhalte lesen und Umsatz generieren. Es gibt Dutzende bewährte Strategien, um mehr Besucher auf Ihre Website zu bringen, aber worauf Sie sich konzentrieren sollten, hängt von Ihrer jeweiligen Situation ab.
Einige Website-Betreiber sollten vielleicht verstärkt auf SEO setzen, während andere sich auf den zielgerichteten Traffic konzentrieren müssen, den bezahlte Werbung bringen kann, und wieder andere darauf, dass die Besucher, die sie bereits haben, immer wieder zurückkehren. Die „richtige“ Antwort hängt davon ab, mit welchem Problem Sie genau konfrontiert sind.
Hier sind 9 der häufigsten Situationen, die Ihren Web-Traffic einschränken könnten, und wie Sie sie beheben können:
1. Die Website ist neu und erhält wenig bis keinen Traffic
Eine neue Website kann durch bezahlte Werbung schnell einen Zustrom an Traffic erhalten, aber es ist ebenso wichtig, mit einer nachhaltigeren Quelle für Besuche zu beginnen.
Das Problem erkennen
Dies ist das am einfachsten zu erkennende Problem – eine neue Website wird wahrscheinlich kaum Traffic erhalten, bis Sie mit dem Marketing beginnen und die Leute wissen lassen, dass sie existiert.
- Suchanzeigen: am besten geeignet, um dringenden Bedarf gezielt anzusprechen und für Dienstleistungen, nach denen Menschen wahrscheinlich nicht spontan suchen.
- Display-Anzeigen und Videos: allgemeiner. Ideal, um ein bestimmtes Angebot bekannt zu machen, Interessen gezielt anzusprechen und die Markenbekanntheit zu steigern.
- 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.
Während SEO oft als die beste Traffic-Quelle für Websites angesehen wird und allein Google fast 63 % aller Besuche generiert, ist es kein schneller Prozess, und als neuer Website-Betreiber möchten Sie messbare Ergebnisse jetzt.
. Im Gegensatz zum langsamen, stetigen Wachstum durch Blogging können bezahlte Anzeigen mit dem richtigen Budget und der passenden Zielgruppenausrichtung nahezu sofort eine Welle von Besuchen bringen.
Sie können Anzeigen in sozialen Medien, in oder als die klassischen Banneranzeigen schalten, an die wir alle denken, wenn wir den Ausdruck „Online-Werbung“ hören. Diese lassen sich anhand unterschiedlichster Kriterien äußerst präzise gestalten und ausrichten und machen so fast jeden zu einem potenziellen Website-Besucher.
Jede hat ihren eigenen idealen Einsatzbereich:
. Im Gegensatz zum langsamen, stetigen Wachstum durch Blogging können bezahlte Anzeigen mit dem richtigen Budget und der passenden Zielgruppenausrichtung nahezu sofort eine Welle von Besuchen bringen.
Sie können Anzeigen in sozialen Medien, in oder als die klassischen Banneranzeigen schalten, an die wir alle denken, wenn wir den Ausdruck „Online-Werbung“ hören. Diese lassen sich anhand unterschiedlichster Kriterien äußerst präzise gestalten und ausrichten und machen so fast jeden zu einem potenziellen Website-Besucher.
Jede hat ihren eigenen idealen Einsatzbereich:
So beheben Sie es
Eine neue Website ist eine spannende Zeit für einen Marketer – sie bringt viel Potenzial und die Chance mit sich, die Richtung wirklich festzulegen. Die ehrliche (wenn auch einfache) Antwort auf dieses Problem ist, dass Sie an diesem Punkt wirklich alles Mögliche ausprobieren könnten, um ein paar zusätzliche Besuche zu generieren. Dennoch gibt es ein paar Strategien, die in dieser frühen Phase am sinnvollsten sind.
Sie können dieses Problem auf zwei Arten angehen: mit einer langfristigen Lösung oder einer schnelleren Maßnahme.
Sie können dieses Problem auf zwei Arten angehen: mit einer langfristigen Lösung oder einer schnelleren Maßnahme.
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. Die Website erhält relevanten Traffic, muss aber mehr Menschen erreichen
Analysen sind der Schlüssel, um zu verstehen, was Ihren Traffic erzeugt. Nutzen Sie sie, um Ihre erfolgreichsten Maßnahmen zu reproduzieren und das Rätselraten zu beenden. Sie können auch soziale Medien und Affiliate-Marketing nutzen, um Ihre Reichweite bei relevanten Zielgruppen zu vergrößern.
- 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. Der Website-Traffic ist zurückgegangen
Veränderungen bei den Besucherströmen können aus vielen Gründen auftreten, daher ist es wichtig, sicherzustellen, dass Ihr Marketing-Mix so vielfältig wie möglich ist. Für schnelle Ergebnisse sollten Sie in Betracht ziehen, frühere Besucher über Anzeigen oder E-Mails erneut anzusprechen.
Das Problem erkennen
Ein Rückgang des Website-Traffics wird in Ihrem Analyse-Dashboard sichtbar. Die Berichte, die Sie dort finden, sollten Ihnen genau zeigen, welche Abläufe sich verändert haben, und von hier aus können Sie damit beginnen, sich ein Bild davon zu machen, warum sie sich verändert haben.
So beheben Sie es
Manchmal scheint das, was bisher funktioniert hat, nicht mehr zu greifen, und Ihr Website-Traffic geht zurück. Dafür kann es unzählige Gründe geben; die häufigsten sind:
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:
- Algorithmusänderungen: Suchmaschinen, soziale Medien und AIs aktualisieren sich regelmäßig und ändern dabei, was sie priorisieren. Ihre Inhalte, die einst gut gerankt haben, könnten jetzt in den Listen weiter unten erscheinen.
- Tracking-Probleme: Gelegentlich kann die Aktualisierung Ihrer Website Probleme mit Ihrer Analysesoftware verursachen. Rufen Sie Ihre Seite selbst auf und prüfen Sie, ob Ihr Besuch erfasst wird.
- Wettbewerber überholen Sie im Ranking: SEO steht nicht still, und wenn Sie erfolgreich waren, haben Ihre Wettbewerber das wahrscheinlich bemerkt und begonnen, Ihre Strategie zu kopieren.
- 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. Der Traffic steigt, aber die Website zieht die falschen Besucher an
Nicht jeder Traffic ist gleich wertvoll. Ziehen Sie in Betracht, Zeit in Ihre lokale SEO und gezielte Anzeigen zu investieren – je nachdem, wer Ihren relevantesten Traffic ausmacht.
Statistical Significance
Wenn Ihre Analysen eine steigende Zahl von Besuchern zeigen, Sie aber keinen entsprechenden Anstieg bei Verkäufen und Anmeldungen feststellen, zieht Ihre Website möglicherweise die falschen Personen an. Sobald Sie dieses Problem erkannt haben, können Sie damit beginnen, Ihre Marketingmaßnahmen rückwirkend zu analysieren und sie gezielter auf relevanten Traffic auszurichten.
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.
