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Z K Z Risk of being hit by Google update has doubled in months June Gill

Originally from the Netherlands but now li ing in London H e holds a BSc nd MSc ith distinction Gillis has e xperience as freelance digital marketer and was manager of an onlin e marketing programme at Unibet N ow he is responsible for all data ga

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Z K Z Risk of being hit by Google update has doubled in months June Gill






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90 Digital i Gaming Reports: SEh Risk in iGaming Risk of being hit by a Google update has doubled in 17 months June 2013 - Gillis van den Broeke About the A uthor Gillis van den Broeke is the Director of Data & Operations at 90 Digital . Originally from the Netherlands, but now li v ing in London . H e holds a BSc . a nd MSc. w ith distinction. Gillis has e xperience as freelance digital marketer and was manager of an onlin e marketing programme at Unibet. N ow he is responsible for all data gathering, data mining, data analysis, and operation management activities at 90 D igital . Email the author for questions and feedback: G illis.vandenbroeke@90digital. com Visit linked in profile: uk.linkedin.com/in/gillisvandenbroeke/ A bout 90 Digital 90 Digital , the agency driven by d ata . They leverage data and ana lytics for measurably better outcomes for all parties. Based in London and specialising in the iGaming industry , they provide a dvanced search planning and strategy , complex content campaigns and effective social marketing. For more information, and a fresh copy of this report visit : 90digital .com Executive Summary Google is tightening its fight against ‘webspam’ in their rankings. In the iGaming sector, many companies that depend on Google traffic for their revenue are exposed to risk of losing rankings in the near future. To understand how much risk sites are exposed to, w e gathered data on 4378 domains ranking in the top 20 search results in Google.co.uk for 210 keywords during 1 7 month , representing 767,190 monthly search volume. 67% o f these sites were affiliates, 8 % operators, and 25% other sites. 54% were considered small, and 46% large. The major findings were that:  Risk of losing 6 or more ranking positions , but remain ing in the top 20, doubled over the last 17 months, from 4% to 8%.  Risk of disappear ing from the top 20 remained stable during 2012, but rose in the first 5 months of 2013 f rom 17% to 26%. Note that this percentage includes sites that dropped for reasons othe r than ‘spammy links’. The risk is higher if you are a casino or bingo site. If you are an affiliate you are also exposed to more risk , although operators risk exposure is catching up quickly with affiliates. Being bigger is still good, but it matters less than it used to. The data suggests that Google is getting serious about fighting webspam . T he risk of losing rankings is increasing i f your backlink profile contains a lot of ‘spam’. The recommended strategy is high quality content and outreach to get ‘go od’ links that google will not devalue. Is this is an expensive exercise, deliberate planning and structured execution is recommended. , Introduction Your r isk in doing SEO (search engine optimisation) is increasing. By how much? This report gives insig ht. We answer the following questions : What do the top 20 search results across iGaming look like? What is the chance you get hit by an update ? How did penguin 2.0 affect search results across iGaming ? To help you understand what the risk is SEO in and h ow it is changing, we analysed almost 69,593 data points covering 17 months of rankings for 210 iGaming keywords on Google.co.uk. SEO in iGaming is risky because the potential rewards are high. This means that companies push the limits of Google, and Goo gle pushes back by devaluing links it considers spam . Understanding risk is important, because the reve nue streams of many companies depend on o rganic s earch tr affic. They would be severely if they did not receive search traffic. Thus, quantifying the risk is essential. If you push too far, your domain will be toasted. In part 1 of the report we analyse top 20 results of the top iGaming keywords. You will learn which sectors are most expensive, the ratio of affiliates to operators , and the distribution of small and large sites. Part 2 will enable you to understand the trends in the risk of getting hit by an update and disappearing from the top search results. We show you evidence that risk has nearly doubled over t he last 17 months , and that it is accelerat ing. C asino and bingo sites are most exposed. Long term trends will be made visible and data for poker, bingo, casino and sports - betting, operators and affiliates , large and small sites will be analysed. PART 1 : I ndustry Landscape Part 1 look s at the ‘search engine landscape’ in iGaming. This part gives you valuable insights in differences between keywords, sectors, types of sites, and size of sites. THE KEYWORDS For our research, w e analysed the top 20 positions in Google for 210 keywo rds. We used historic monthly ranking data from SEMrush .com going back to January 2012. The Keywords are the top iGaming keywords in the UK, divided into the sectors bingo, casino, poker and sports - betting . Branded keyword s are excluded from the dataset , b ecause they would skew results in favour of big brands . In total, these 210 keywords generate more than t hree quarters of a million search queries every month ( 767 , 190 to be exact). Figure 1 shows that the sectors casino and bingo have bigger search volumes than poker and betting. The average c ost per c lick (CPC) of these keywords is 18. AdWords CPC prices are an indication of the commercial value of traffic from a keyword . This is true r egardless whether traffic is paid or organic. Figure 2 shows that the average CPC of casino keywords is outrageous , hitting almost 35. The rest hovers around 10. Combining C PC and volume, we can calculate how much it would theoretically cost to buy the organic traffic on AdWords . The total figure is 23,308,401 . More than 23 million Pounds Sterling e very month. More than half comes from Casino. As can you can see in the table below NR OF KEYWORDS TOTAL TRAFFIC AVERAGE COST PER CLICK VALUE OF TRAFFIC BINGO 30 219 , 800 13.76 4,476,047 CASINO 60 247 , 000 34.93 1 4,978,809 POKER 60 145 , 750 9.83 2,066,491 SPORTS - BETTING 60 154 , 640 10.79 1,787,055 GRAND TOTAL 210 767 , 190 17.84 23,308,402 Figure 2 : Average CPC values (UK) by sector 219800 247000 145750 154640 BINGO CASINO POKER SPORTS - BETTING Monthly Volume (UK) Figure 1 Monthly Search Volume (UK) by sector 13.76 34.93 9.83 10.79 BINGO CASINO POKER SPORTS - BETTING Average CPC (UK) THE WEBSITES By sector During the 17 month s of data analysed , more than ten thousand (10,683 to be exact) different URLS ranked i n the top 20 positions. These URLS belonged t o 4378 different domains. This r eport compares data on the level of domains because this is the relevant level of detail for most businesses. The ‘ d omains’ in figure s 1 and 2 show there are more different domains and URLS in casino . As this chart shows the domains we see over time, it suggests that domains rank for shorter amounts of time on casino keywords, before they are replaced by others. It seems that competitio n is higher. This makes perfect sense when learned that the Cost per click of casino keywords is 3 times higher than others. You may notice that the Domains and URLS do not add up. The reason is that some domains and URLS will rank for keywords from multip le categories. The difference is small though, about 8%, and 12% for URLS . This shows how most websites focus on one sector within the iGaming industry. The ‘ n ormalised’ metric shows how many domains and urls there would be if there were 60 keywords for Bingo, instead of 30, assuming linear extrapolation. NR OF KEYWORDS DOMAINS DOMAINS (NORMALIS ED) URL S URLS (NORMALIS ED) BINGO 30 537 1074 1282 2564 CASINO 60 1859 1859 3396 3396 POKER 60 1242 1242 2811 2811 SPORTS - BETTING 60 1126 1126 3237 3237 GRAND TOTAL 210 4378 5003 10683 12209 Figure 3 : Number of domains per sector (normalised) 1074 1859 1242 1126 BINGO CASINO POKER SPORTS - BETTING Number of domains (normalised) 2564 3396 2811 3237 BINGO CASINO POKER SPORTS - BETTING Number of URLs (normalised) Figure 4: Average CPC values (UK) by sector (normalised) By Category Not all sites are the same. In the iGaming search ecosystem, the two relevant types of sites are the op erators and affiliates. Operators provide payment facility and provide access to the games. Although they normally do not host or own the games, customers recognize the operator brands as the entity they interact with. Affiliates make their money deliverin g new customers to the operators. For many a ffiliates, G oogle traffic is their major source of income. Figure 5 shows there are roughly 8 affiliates for every operator in the top 20 search results . Sites in the ‘o ther ’ category are for example Wikipedia, a nd Facebook . They also take a significant part of the search results . DOMAINS URLS AFFILIATE 2890 5789 OPERATOR 366 1800 OTHER 1056 2975 GRAND TOTAL 4312 10564 affiliate 67% operator 8% other 25% Domains Figure 5: domains by category By Size In Search e ngine optimization, there is a difference between small and large sites . Large sites rank more easily on a large number of phrases due to their authority. To determine size, Majestic SEh.com’s Trust F low metric is used. Trust Flow measures how many backlinks from trustworthy sites a domain has . This metric is found to correlate well with total organic traffic. Using Trust Flow, our definition of size not only include s traffic volume and incoming links, but also a measure real world trustworthiness. The cut off point for small sites is a Domain Trust flow of 20. Higher than 20 is considered large . T rust flow is distribution according to a power law, this means that the top 25% of sites will have a trust flow higher than 2 0 . As shown in figure 6 , in the top 20 iGaming search engine results, there are similar amounts of large and small sites. DOMAINS URLS LARGE SITES 1982 7118 SMALL SITES 2330 3446 GRAND TOTAL 4312 10564 Large sites 46% Small sites 54% Domains Figure 6 : domains by Size By Category & Size Finally we will drill down to the combinations of size and category. We differentiate between large affiliates, small affiliates, large operators and small operators. This chart show n leaves out the ‘other’ sites which are not so relevant in the commercial iGaming ecosystem. Figure 7 shows h ow few operators there actually are compared to affiliates. And also how many big affiliates there are. DOMAINS OPERATOR AFFILIATE GRAND TOTAL SMALL 250 971 1221 LARGE 116 1919 2035 GRAND TOTAL 366 2890 3256 large affiliate 30% large operator 8% small operator 3% small affiliate 59% Domains Figure 7: domains by category and size PART 2: Understanding Risk In part 1 we looked at the landscape of important iGaming keywords, and highlighted the difference between the sectors bingo, casino, poker, and sports - betting. We also looked at the differences between operators and affiliates, and compared small to large sites. Part 2 gives insight into the risk o f being hit by an update that causes you to drop significantly. B ecause the commercial value of ranking for some keywords is so high , e very company ‘playing’ in this space is exposed to the risk of losing rankings due to unnatural link pro files. Understanding this risk is a requirement for managing it . Risk of being hit We use two metric s to assess the levels of risk. The first is ‘risk of being hit’ by an update penalty but staying in the top 20. The second is risk of disappearing from the top 20 . Figure one shows how the risk of being hit by an update has almost doubled since January 2012. The red line (actual observations) is spikey, showing that actual amount of sit es being hit fluctuates. The amount of sites during the last two months is at an all - time high. This shows how Google is tightening its algorithm to fight webspam. The black dotted trendline (linear regression) shows that chances of being hit went from 4% to almost 8% in 17 months . Its R squared value is over 0.5. This means that the trend fits the data reasonably well. We can therefore assum e that this trend will continue. Your risk of penalty increases roughly 3%* every month . * ( (8 % - 4%) / 17 months) / 8% =3.125%. The latest value is slightly below 8%, but this is a rough estimation only . R = 0.5136 0.0% 1.0% 2.0% 3.0% 4.0% 5.0% 6.0% 7.0% 8.0% 9.0% 10.0% Risk of being hit Risk of being hit As proxy for getting a being hit by an algo update or penalty , we measure the number of URLs that were 14 or higher previous month that drop 6 or more posi tions, but remain in the top 20. Such a big drop is not a normal event. Probably, you received a penal ty, o r an algorithm update devalued your links. The root caus e is the same. Your rankings were kept buoyant by spammy links. And Google corrected that error. T Figure 8: Risk of being hit over time Risk of disappearing Figure 10 shows that the r isk of disappearing (Blue line) for the top 20 ha s fluctuated between 15% and 20% during 2012, but in 2013 it has seen a stronger upswing. The dotted trendline (linear regression) shows a very slow increase over time. However, the R squared value of 0.23 suggests not a great fit with the data. This means that it appears hard to predict the future risk of disappearing from the past 17 months. However, when we zoom in and focus on only on 2013, the picture changes as you can see in figure 9. The linear regression line has a R squared of 0.97. This mean s that the data is almost a perfect ly straight line. It seems that Google has increase d the number of URLs that are dropped from the top 20 every month. The number of URLS that disappear grows by a staggering 12% a month. This data, in combination with Goo gle’s commitment to fighting webspam suggests that the growth will continue. However, at some point it will have to slow down, otherwise rankings would totally change every month. Risk of Disappearing This metric is the % of sites that in the top last month, and dis a p pe a r e d from the top 20 this month. URLS disappear for multiple reasons, including being replaced by another of the same domain. However, a significant amount is likely due to penalties. R = 0.9709 0% 5% 10% 15% 20% 25% 30% 1/1/2013 2/1/2013 3/1/2013 4/1/2013 5/1/2013 Risk of dissaparing since January 2013 R = 0.232 0% 5% 10% 15% 20% 25% 30% Risk of disappearing Figure 9: Risk of disappearing since January 2013 Figure 10: Risk of disappearing R isk of Penalty by Sector Which sectors are the most risky to operate? Figure 11 shows that casino is the most risky sector, closely followed by Bingo, while sports betting and poker are much less risky. When we analyse change over time i n figure 12, a Bingo has a very strong increase last month. This coincides with the latest penguin 2.0 algorithm update. Further, the chart shows that the lines that were close together in the first half of 2012 are drifting apart. Poker and sports betting are not increasing a lot in risk, while casino and bingo do. 6.5% 7.2% 4.9% 4.9% BINGO CASINO POKER SPORTS - BETTING Risk of being hit Figure 11: Risk of being hit by sector Figure 12: Risk of being hit by sector 0% 2% 4% 6% 8% 10% 12% 14% 16% Risk of being hit Bingo Casino Poker Sports- betting Risk of disappearing by Sector Figure 13 shows that Casino is again the most risky, and interestingly, sports betting is second. When we look at figure 14, t he rising trend identified in the previous section seems to hold for all four sectors. None of them escape Google’s intensified pruning of link profiles . In terms of the amount of risk, casino is clearly more risky than the other three, which seem to be quite equal, with sports betting lowering its risk in the recent months. In short, based on this section, and the previous. It seems that casi no is squarely in Google’s scope for penalties. This is not surprising given the value of its traffic, as shown in Part 1 of this report (figure 2). 0% 5% 10% 15% 20% 25% 30% 35% 40% Risk of disappearing Bingo Casino Poker Sports- betting Figure 13: Risk of disappeari ng by sector 23% 27% 21% 24% BINGO CASINO POKER SPORTS - BETTING average risk of disappearing Figure 14: Risk of disappearing by sector over time Risk of SEO by category When comparing affiliate s, operators and ‘other’ sites, figure 15 shows the risk of being hit by an algorithm updated. From the regular spikes, it appears that affiliates have high risk to be hit. Operators have been spared mostly, but that has changed in the last few month. This change is reason for operator s to have a good look at their backlink profile. The risk of disappearing shows an equal upward trend for all three types of sites. However affiliates risk is significantly higher than operators. It is also rising faster. Her e it seems that the affiliates are the one who have to worry. 0% 2% 4% 6% 8% 10% 12% Risk of being hit affiliate operator other 0% 5% 10% 15% 20% 25% 30% 35% Risk of dissapearing affiliate operator other Figure 15: Risk of being hit by category of site over time Figure 16: Risk of disappearing by category of site over time Risk of SEO by size Large sites are defined as having a aajesticSEh.com’s Trust Flow higher than 20, and small sites 20 and lower. F igure 17 shows how historically small sites had higher risk, but the figures for large and small have been converging. Now they are similar. Figure 18 shows how small sites have a much higher risk of their URL’s disappearing from the top results than do large sites. However much of the recent growth in risk is accounted for by the large sits, with the small sites stabilising . 0% 2% 4% 6% 8% 10% 12% 14% Risk of being hit large small 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Risk of dissapearing large small Figure 1 7 : Risk of disappearing by category of site o ver time Figure 1 8 : Risk of disappearing by category of site over time Conclusion This report looked at 2 complementary metrics of risk in iGaming SEO and found significant evidence of increasing risk. Both metrics have been increasing over time. Risk of being hit had a stable growth since 2012 and the risk of disappearing has seen strong growth since the start of 2013. Both metrics are at an all - time high in the last month. Based on these trends, and knowing Google’s commitment to fighting webspam , we can make the prediction that there will be an increasing number of sites that will lose an important portion of its rankings in the near future. These will be the sites whose link profile has a large portion of ‘spam’ links. The risk will be higher if you are a casino or bingo site, and if you are an affiliate, although operators risk exposure is catching up quickly with affiliates. Size still matters, but not as much as it used to. The Recent penguin 2.0 update is reflected i n the data, as the risk values are at their all - time high. However, the more important notion here is that penguin was not a dramatic change from the trend. Instead, it seemed to fall within the expected range of values. This suggests that the named update s serve a Public Relations (PR) function, and their actual efforts to curb webspam happen on a more incremental basis. What can you do to reduce your risk? If you know your link profile is ‘bad’, you can anticipate reduced rankings in the near future. To c orrect for this, you might decide to go heavy on the high end content creation and outreach to close gap with links that are not considered ‘spam’. This is an expensive exercise. Ultimately, c areful planning will help ensure a stable growth and spend effi ciency .