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Is Making Fake 24/h Trading Volumes The Best Marketing Strategy for Crypto Exchanges?

11 minutes

By Hacken

The whole crypto community recently saw a tremendous success of some newcomers on the crypto exchange market. BitForex, FCoin, and CoinEx managed to break into the CoinMarketCap list of TOP-10 exchanges by daily trade volume. Despite their young age, they have almost overtaken renowned crypto exchange titans such as Binance, OKex and Huobi. Further, they have already surpassed KuCoin, Kraken and Upbit. The relevant questions are “why?” and “how?”.

The issue of fake volumes on crypto exchanges is like a UFO: some people claim to witness it but there is no evidence and metrics to prove its existence. Finally, thanks to Crypto Exchange Ranks, it seems that we eventually get the scent.

Prequel: Some weeks ago, Hacken’s CEO, Dmitriy Budorin, received an attractive offer to list HKN on one of the top 10 crypto exchanges, which was unknown to us. The nasty trick was the price. Usually, it costs around $1,000,000 — $2,000,000 to get Binance or its peers. BitForex wanted a humble price of 30 BTCs.

Dmitriy decided to start an investigation. He engaged the Hacken Marketing and the CER Analytical teams to analyze the fast growth of BitForex’s daily trading volume (DTV) to determine its nature, source, and causes.

In our research, we compare the social media and community activity of Kraken, KuCoin, and Hacken to those of BitForex’s. In terms of trading volume per unique user, we compare BitForex’s results to the ones of TOP crypto exchanges including Binance, OKex, HuobiPro, HitBTC, Bittrex, Poloniex, and KuCoin. We analyzed the correspondence between the volume of trades and the number of users.

Consequently, the findings indicate that driving up fake 24h trading volumes is an extremely effective marketing technique for a modern crypto exchange.

Analysis from the Open Sources

Trading Volume

We had to start from the basics: identify the trading volume of BitForex. Coin Market Cap (CMC) and Crypto Compare Data (BitForex hasn’t been listed on CryptoCompare despite its fast growth) are the first sources that come to mind. To receive more objective data, we decided to compare Kraken and KuCoin to BitForex. During the last two months, DTV of BitForex has increased from several million to $208M. Consequently, the exchange went from TOP70 to TOP7 on the CMC rankings.
In turn, Kraken’s DTV is $75M, and KuCoin’s is $27M.

Market Share and Website Traffic

Further, we used Similar Web. Using the paid version of the platform, we analyzed the amount of monthly traffic, unique visitors (UV), and unique users (UU) on BitForex website.

The inaccuracy of estimations was within 2 weeks but we can still draw conclusions about the approximate number of users: we considered unique visits and the percentage of refusals (Bounce Rate) and determined the number of unique users who have spent more than 3 seconds on the website this month.

The formula is UU = UV * (1 – Bounce Rate)

In June, the number of BitForex’s Unique Visitors was 68K with the bounce rate of 57,5%. In turn, Kucoin’s UV was 1.93M with a bounce rate of 54,1%. Kraken’s number of UV was 1.02M with a 36,7% bounce rate. (fig. 5)

Let’s compare BitForex with the KuCoin and Kraken exchanges.

Number of Unique Users
From the numbers received, we see that the number of UU of BitForex is 29K. In turn, Kucoin has 889K unique users. Kraken has 666K unique users. KuCoin’s number of UU is 30 times higher than that of BitForex, Kraken’s number of UU is higher by 23 times.  

Number of Unique Visitors vs Trading Volume
Despite the fact that Kraken and Kucoin have many more visitors than BitForex, the trade volume per day on BitForex is several times higher. Specifically, BitForex’s (198K monthly visits) trading volume is 2.36 times higher than that of Kraken (20.4M monthly visits) and 7.7 times higher than that of Kucoin (31M monthly visits).

BitForex traffic analysis by channels

  • 47% are Direct Visits to the site (this channel most likely displays the visits of users of the exchange since they are unlikely to use referral links).
  • 29% is Referral Traffic that comes from CoinMarketCap (about 90%). Obviously, as the exchange is at the top.
  • 16% Organic Search. The mix of users and new visitors.
  • 3% Social Traffic. The exchange has a small community.
  • 3% Paid Advertising + 0.28% display ads from Google. The exchange does not spend money on paid promotion and does not lead traffic through PPC (Pay Per Click).
  • 2% E-mail Distribution. The income of traffic from the email base of the crypto exchange isn’t large. Considering the fact that usually, an email base of a company contains its most active users, we can presume that the email base of BitForex even isn’t so big as its number of UU (29K).

Direct Visits, Referral Traffic from CMC, and Organic Search (92% in total) are the main traffic sources for BitForex. Paid and Social traffic, as well as email delivery, are barely utilized (8% in total). Therefore, it’s evident that the company doesn’t spend money on Paid Marketing and organic community building through Social Media Marketing and Email Subscription Base.


The number of subscribers on social networks is an important factor that determines the number of users. This number must be at least twice as large as the number of users.


BitForex — slightly more than 2k

Kraken — 315k

KuCoin — 295k

Hacken — 10k

The largest number of likes and reposts on BitForex’s Twitter is 41 and 23 correspondingly, but that is the case for one post only. The average figures are 4 and 2 correspondingly. KuCoin collects from 100 to 200-300 retweets in one post, and Kraken has similar indexes.

Evidently, the TOP7 Exchange has weaker Twitter activity than the TOP 15 and the TOP 45 exchanges.  


The number of messages in the Bitforex Telegram group is eight times higher than that of Hacken, but the velocity of messages is almost the same (the difference of 26 messages). At the same time, Kucoin TG group has 5.3K more members, but the number of messages per period is 4,5 times higher. The activity of the TG groups makes us think that BitForex uses bots and fake accounts with an aim to increase the total number of members.

We can only assume that BitForex has a maximum of 4-5k of real users, and the rest accounts are fake/bots. The amount of people in the group is not surprising: due to the breakthrough of this exchange in the rating, the group has a constant influx of people. Still, due to long response time and fake accounts, people leave it immediately or stay inactive. In conclusion, Bitforex does not focus on building the community to increase its trading volume organically.  

Other Social Media Channels

No new viral and social BitForex campaigns on YouTube and Facebook.


As we have already discovered through SimilarWeb, the exchange receives the bulk of the new traffic through the referral source — CoinMarketCap; thus, the platform immediately attracts attention. Here’s the explanation: creating and implementing marketing and communication strategies and building a community in an organic way is more expensive than forging trade volumes.

BitForex forges trading volumes to stay on the very top of the rating and draw the attention of crypto investors and new users. However, they have certain issues with processing and converting traffic into users as there is no clear policy regarding interaction with the audience. As a result, the exchange doesn’t have a large number of real supporters and loyal community on social media, Telegram, or email.

Volume per User Comparative Analysis

In order to display the inconsistency of such a “success” strategy, we’ve chosen to compare the Total Trade Volume per User (TTVPU) metric for the currency pair with the highest liquidity – BTC/USDT for the following list of exchanges: Binance, Okex, HuobiPro, HitBTC, Bittrex, Poloniex, KuCoin, and BitForex.

To calculate Total Volume, we took historical trade data for the BTC/USDT pair traded through June 2018. The histogram (Fig. 15) visually displays the distribution of the TTVPU value for each exchange. An approximate number of users was estimated using the monthly unique visitors metric with respect to their average bounce rate. Then, we built a simple ratio by dividing the Total Monthly Volume of trade by the UU metric, which we determined in Analysis from the Open Sources part of this article. We received the following results:

As we can see from the calculations, BitForex has an average trade volume of 12,824 USDT when it comes to BTC/USDT traded through June 2018. BitForex’s TTVPU metric is more than two times higher than that of Huobi, four times higher than the average TTVPU metric for all observed exchanges, and five times higher than the same metric for OKex, which was previously suspected in trade volume manipulations along with Huobi.


Considering our analysis results, it’s evident that BitForex is likely pumping its trade volume using wash trade, the most common means available. While it can be carried out in different ways, wash trade typically uses large transactions/trading orders to reduce the risk of loss. This practice is consistent with the analysis results that we’ve received and served as additional proof of BitForex’s volume manipulation activity.

The Research’s Inferences

As we have proved above, BitForex has no wide, active, and loyal community in social networks that could ensure such a high trading volume. Therefore, it stands to reason that driving up fake 24h trading volumes is a more effective marketing technique than the fair nurturing of the users base through wide media presence and community management. This approach grants a platform a constant influx of high-quality referral traffic from CMC and avoids spending tangible budgets on paid advertising and PR, community building, and maintenance. In return, it enables an exchange to earn money by listing various tokens as a top exchange. 30 BTC for a spot on a TOP10 Exchange is an extremely beneficial deal for TGE startups, isn’t it?

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Table of contents

  • Analysis from the Open Sources
  • Volume per User Comparative Analysis
  • The Research’s Inferences

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