Goal of task

• Analyse the following numbers in detail:
  • Referring sites
  • Landing page visits
  • Which other pages are being visited mostly inside the site?
  • Clicks to Gumroad plugin
    • Number of effective sales once Gumroad plugin has been clicked
• Come to conclusions
  • What is the effective conversion rate?
    • Take landing page visits vs. sales
  • Compare German and English site results

Implementation

Analysis of numbers

Referring sites

We identify the following categories of referring sites:

• Search engines (2’966 – 69%)
  • The bulk of it coming from Google (2’907)
• Google Ads (set up by myself) (~700 – 16%)
  • doubleclick.net (573)
  • adsensecustomsearchads.com (67)
  • googleads.g.doubleclick.net (23)
  • tpc.googlesyndication.com (26)
  • These one brought traffic (maybe through Google Ads)
    • livestrong.com (39)
    • muyfitness.com (4)
• Topic-specific sites (Judo websites) (199 – 5%)
  • jcbn.ch (69)
  • judotalk.com (59)
  • cafe.naver – Korean site (38)
  • agallina.blogspot.com (17)
  • nipponbasel.ch (16)
• Social media (162 – 4%)
  • Facebook (86)
  • Twitter (62)
  • Reddit (14)
• Others (~250 – 6%)
  • WordPress Android app (112)
  • Seo promoters (~100)
    • Seo-2-0.com (65)
    • Anti-crisis-seo.com (15)
    • etc.

Conclusions

• We have identified 4 categories of referring sites (search engines, Google Ads, topic-specific sites and Social media)
• The big weakness of these statistics is that they don’t tell us if there is a difference in conversion rate depending on where a user comes from.
  • From experience we noticed that sales happened when topic-specific sites wrote an article about us
  • But getting topic-specific sites to write about the KKB was way more work-intensive than putting up a Google Ad
  • In order to find out the conversion rate of traffic sources we need to find a way to connect traffic sources with their conversion rates. This will require another tool beside Jetpack.

Website usage by pages

Breakdown of English pages

Breakdown of German pages

User engagement

By user engagement we measure how many other pages a user looks at after visiting the landing page.

We’re noticing a significant difference in user engagement between the visitors per language:

• English speaking visitors: 4.5% user engagement
• German speaking visitors: 26.5% user engagement

Clicks leaving the page (actions)

The following user actions (clicks) are worth mentioning here:

• Gum.co (sales button): This links to the Gumroad page, needed to purchase the product. We will analyse this one in detail below.
• Addtoany (social media button): Users can use these buttons to share our website on social media. It’s an interesting feature and seems to have been used a few times.
• Amazon: We also published our product on Amazon (see The Kumikata Bible on Amazon). Very few people klicked on it. The goal was to give the product extra credibility.

Gumroad plugin (sales) and convesion rate

The following two links are the purchase buttons for the English and German versions of the product:

• English (gumroad.com/l/kInE) -> 42 clicks
• German (gumroad.com/l/wIOH) -> 24 clicks

The previous article Analyzing sales numbers of KKB provided us with the following sales numbers:

Calculation of conversion rates:

We’re noticing that the German site has a higher user engagement and a higher sales conversion rate.

We can only speculate why that is. It might be related to the fact that the author is better known in the German speaking realm and that the referring sites brought users who know the author as well.

These questions can only be answered if we can connect referring sites with sales.

Results / Learnings

• We have identified four categories of referring sites:
  • Search engines, mainly Google (69%)
  • Google Ads (set up by myself) (16%)
  • Topic-specific sites (Judo websites) (5%)
  • Social media (4%)
• At the moment we can’t tell which referring site drive sales conversion
  • There is a noticeable difference in sales conversion between English and German speaking visitors. This might indicate a difference in sales conversion depending on the referring sites.
• It is important to find a way to create a link between referring sites and sales conversions

Next steps

• Find a way to connect referring sites to sales conversions