What everyone in the fashion industry should know about SEO in New Zealand? Big data analysis

SEO strategy – a key principle

The key principle of an SEO strategy is to increase the relevancy between your brand website and searches being made across the web, allowing the content to be more easily found over time. As a result, your website is placed higher up in the funnel of the customer’s research process and purchase path. In order to show the most relevant results for a specific query, Google uses over 200 ranking signals. Many of them have more than 50 variations within a single factor to determine the order of search results.

The main idea of the analysis

To understand which factors influence organic ranking and build more effective SEO strategies Whites Agency and Pure SEO analysed search results of a specific vertical in New Zealand. The analysis was based on Big Data and a reverse engineering approach.

In the context of SEO, a reverse engineering approach aims to analyze the search results and distinguish features of the winners, which translates into building precise SEO strategies.

The study involved the analysis of a large number of ranking signals, based on users’ search behavior. In general, it is noteworthy how ranking can differ between desktop and mobile, especially for off-page factors.

• Firstly, we analysed technical SEO factors and it was carried out through Lighthouse. The main factors that have been taken into account are the Speed Index, Time to First Contentful and First Meaningful Paint, Estimated Input Latency, Time to First Byte, Network requests, and DOM size.
• The second part of the study analyses off-page factors, such as the number of inbound links and referring domains, citation flow, and trust flow. (To access this part of the analysis, please fill the form on the bottom of the article.)
• Finally, the third stage involved analysis of on-page factors such as URLs, metadata, HTML headings, images, internal linking, content, and structured data.

Main conclusions

Looking at the completed analysis, there are some results that were expected. For example, meta titles that have exact query word matches – especially weighted towards the beginning – rank higher in SERPs. Similar results occur when <h1> headings contain exact matches, and they are less than 20 characters long.

Executive Summary – chosen results:

• URL Character length vs. Length of query – It seems that Google is favoring shorter URLs, ranking them in response to long and specific queries. This might be related to the site structure, and how link equity is passed from the homepage to pages that are deeper down in the site architecture. Further research (subsequent graphs) shows that this relationship is more complicated. For short phrases (1-2 words) there is a preference for short addresses. But for longer phrases the preference is the opposite – that is, for better positions urls are longer -deeper. It is also indicated by further url depth analysis.
• DOM Size – Google suggests avoiding a large DOM size, however, the results show that landing pages with a high number of elements within their DOM usually rank higher.
• A number of Internal Outlinks on a page – Most of the landing pages analysed have a high number of internal outlinks, and while this is normal for an e-commerce site, it is surprising that a higher number of internal outlinks corresponds to a higher ranking. Internal linking is a major ranking factor, however, excessive on-page linking would dilute link equity, reducing how much is passed effectively to subcategories and product pages. Outlinks: in the case of mobile results in TOP 3, the number of links coming from the output pages is lower. The algorithm prefers the speed of obtaining a direct result over a wide selection (possibility of further navigation).
• Social Signals – Having outlinks to social media channels, and generally being active on social media instills trust in users. However, the presence of social outlinks doesn’t seem to affect rankings for both mobile and – especially- desktop.
• Pinterest appears to have a strong position in SERPs on a desktop in New Zealand’s clothing market. Its visibility on mobile is much lower notwithstanding the ranking position.
• Presence of H2 headings – It’s interesting to note that the landing pages in top positions have a low number of H2 headings – usually two, or sometimes only one. TOP 3 results for a large number of queries have keyword-optimised H2 tags, which indicates that there is a clear improvement opportunity within H2 headings for websites to benefit from.
• In the case of long-tail queries above 2 words – the mobile results are matched more precisely than desktops. Both the depth of the category and the query-title match are the evidence of this. In the case of short queries (strong, single and double word), desktops’ results are more matched.
• Long texts, especially for desktops – a clear preference for longer texts in high positions. However, for mobile results, the median is 200 words lower.
• Images – we may see, that there are more images on the TOP in positions in SERPs.
• There are significant differences between Australia and New Zealand in terms of domains that rank best.
• Lighthouse time factors – purely technical indicators (ttfb, first tpu idle, network requests, bootup time) have lower dependencies than those “felt” by the user – especially First Meaningful Paint.

New Zealand’s clothing market

Due to its geographical isolation and relatively low population – about 5 million inhabitants in a country as large as the United Kingdom – e-commerce in New Zealand is quite different from other OECD countries. Big marketplaces and e-commerce sites such as Amazon, eBay or Zalando, to name a few, are not yet in the market. However, the retail clothing vertical is very competitive due to the presence of both local and Australian retailers.

Furthermore, in the last couple of years, big clothing brands such as H&M, Zara, and UK-based retailers Next and Asos have entered the market, forcing existing players to review their digital strategy efforts. The very recent launch of the first real New Zealand-based online marketplace – The Market – will change the game even further.

Report Details

The scraping was performed on 16/06/2019. Both desktop and mobile Google search results were scraped down to 50th position using universal Google search endpoint: https://www.google.com/search with New Zealand geolocation flag gl=nz and English language interface hl=en, ln=lang_en. We analyzed 10099 keyphrases, ranging from 1 to 6 words, from the clothing category and downloaded over 134 Gb of raw data. Only static HTML files were analyzed (no javascript rendering). Some of the sites like Amazon, Youtube, Facebook, Adidas, Kohl’s, Asos and others blocked our scraper and we didn’t get any data for them.

The occurrence of particular websites in TOP 3 – desktop

The occurrence of particular websites in TOP 3 – mobile

Differences in the number of domains Australia vs. New Zealand – desktop

Lighthouse factors

This stage of the study analyses technical SEO factors and it was carried out through Lighthouse. The main factors that have been taken into account are the Speed Index, Time to First Contentful and First Meaningful Paint, Estimated Input Latency, Time to First Byte, Network requests, and DOM size. Below we present some of them:

DOM size

Avoid an excessive DOM size. Browser engineers recommend pages contain fewer than ~1,500 DOM elements. The optimal spot is a tree depth <32 elements and fewer than 60 children/parent element. A large DOM can increase memory usage, cause longer [style calculations], and produce costly [layout reflows].

First Meaningful Paint

First Meaningful Paint measures when the primary content of a page is visible.

Performance score

On-site factors

The average distance of exact matches from the beginning of the title

In case an exact match appeared in the title, we wanted to check where it was located. The options were as follows: at the beginning, in the middle or at the end of a given title. For that reason, we introduce the notion of distance as the number of words from the beginning of the title that precedes the query. For example for query “tight dress” and title “red tight dress” the distance will be equal to 1 since there is one word (“red”) that precedes the query phrase. The histogram below shows data for websites that had exact matches, so the volume is below 20%. Websites were grouped by their organic position in threes (1-3, 4-6, etc.) and distance was split into 5 class of depth 0, 1, 2, 3, 4 and 5+. Everything is presented in a stacked bar fashion showing the percent of sites with particular distance value.

Exact matches in H2

A small number of H2s also means that we might not be able to get exact keywords for different queries. The number of pages containing exact queries in their H2 content is rather low: only up to 7% in TOP 3 results. We should remember that a page is usually ranked on more than one phrase and placing all the phrases in one H2 is simply impossible. However, there is a positive correlation between the value and the position (The graph illustrating the number of headings in each domain appears to be flat. Hence we may summarize the slope for the length of H2 as follows: adding longer H2 with additional descriptions can help in ranking for additional phrases).

Number of images

The number of images factor indicates how many img html tag are present in the body of the html file. Websites were grouped by their organic position in threes (1-3, 4-6, etc.) and aggregated values of the ranking signal were calculated. We chose the median as the main metric and 40-60 percentiles as indicators of distribution spread. In the figure, we show aggregated metrics for desktop and mobile search results. The bold blue line is the median and faded blue shadow comprises outcomes between 40 and 60 percentiles. We clearly see that the number of images drops with a higher position and mobiles tend to have fewer images than desktops.

Key findings & summary

1. SSL – doesn’t affect your position in SERPs (over 3% of pages from TOP3 have no encryption protocol implemented. The graph of the presence of SSL on particular positions indicates that in places 4-6 encryption occurs even more often than in TOP3).
2. Title – only 18% (for mobile) and 19% (for desktop) of the titles in TOP3 contained an exact query. There is a significant difference between mobile and desktop as we have less accurate matches on mobile phones. Further analysis indicates a stronger fit on short phrases (1-2 words). Still, about 40% of short phrases do not have an exact match in the title, which indicates a deeper understanding of the language by the algorithm.
3. H2 – focusing on longer H2 may give you an opportunity for optimization. An opportunity is also that on average there are few H2 headers – entering them or increasing their number may be a quick win for the website.
4. Images – we may see, that there are more images on the TOP in positions in SERPs.

What about other ranking factors? Do you want to see more findings, such as:

• Majestic off-site factors
• URL
• Internal outlinks
• Presence of unordered lists
• HTML
• ….and more!

Fill the form below and enjoy the full data-driven report that we prepared with our partner PURE SEO.