hdtSSP May Raise Media¡¯s Earnings by 30-50% on Ad Network ¡ªAn Interview With Colin Han, CPO of hdtMEDIA
2014-07-28
hdtMEDIA recently rolled out a platform designed for high-quality media¡ªhdtSSP which renders richer customer resources for brand advertising. Mr. Colin Han, founder and CPO of hdtMEDIA, talks about hdtSSP in an exclusive interview with Madisonboom. He notes that hdtSSP may offer media with a 30-50% increase of profits on Ad Networks.
 
Madisonboom: How does SSP fare in China currently?
 
Colin Han: In my opinion, the development of hdtSSP is in line with that of SSP in China. Back in 2011 when we were in the US, it dawned upon us that RTB would be the future trend of digital advertising. After we returned to China, we tried to start an Ad Exchange platform. The time was not yet ripe, however, for few knew about RTB, and the market was not yet educated. Then giants like Google entered this area in China, the market got further educated, and RTB market gradually matured. But due to the absence of a sophisticated DSP platform to realize real time bidding, SSP remained nothing but a castle in the air in the last couple of years. Last year, Ad Exchange propelled the whole industry, and DSP platform grew increasingly mature. Finally the first half of this year saw the real takeoff of SSP.
 
Madisonboom: Would you please introduce the newly-launched hdtSSP to us? What¡¯s new about it?
 
Colin Han: hdtSSP, affiliated with hdtMEDIA Group, is an ad service platform for media. It can manage revenue channels, increase their earnings and enrich customer resources for brand advertising. Stock transaction and management can be carried out through programmatic buying or the traditional way of Ad Network.
 
hdtSSP has two merits: firstly, media can manage stock more efficiently on SSP platform and earn more through programmatic buying. Secondly, with automatic optimization algorithm, hdtSSP optimized the supply of Ad Network and Ad Exchange: real-time resource distribution and matching brings higher successful utilization rate of resource flows.
 
Madisonboom: hdtMEDIA has focused on advertising platforms for digital media for years. What are your advantages in developing SSP?
 
Colin Han: Our strength mainly lies in rich media resources from years of accumulation and a high-end DPS platform.
 
Our SSP platform is connected to all high-quality media resources ready for advertising, such as ifeng.com, rayli.com.cn, etc. Meanwhile we try to improve ad places in rich media so that advertisers find them better choices for brand advertising. Rich media is one of our prime focuses. We have rich experience, sophisticated technological support and a competent team. Generally speaking, hdtSSP is a high-end platform of media flow made for brand advertising.
 
Besides, hdtSSP also links with DPS platforms of many 4A adverting companies. It recently connected with PPB of WPP¡¯s Xaxis. Advertising giants like P&G all have their own global DSP systems, and we will establish connections with them as soon as possible.
 
Madisonboom: What good can hdtSSP do to DSP?
 
Colin Han: DSP is one of the upstream links of SSP. hdtSSP can provide DSP with high-quality media flows and customized private market, which facilitates popular ad procurement by brand advertisers. We can also offer media flow in rich media to them.
 
Functions related to audience targeting, such as Cookie Mapping, are also provided. They support connection with a third-party DMP and can provide media data to DSP. Such technologies underlie a feasible solution to DSP audience buying.
 
Madisonboom: Currently, both RTB and programmatic buying are centered upon ¡°audience management¡±. As an expert in the field, what is the biggest challenge facing SSP? What are the characteristics of ¡°audience management¡± of hdtSSP?
 
Colin Han: Data is now the biggest challenge. Third-party DMP platform has not been developed due to market immaturity and problem about integrity. Advertisers therefore have no choice but to rely on data provided by media aside from first-hand data like membership information and website data. For them, media data is of great value, so they are willing to pay more in RTB.
 
Suppose there is a travel service website like Ctrip and one of its users searched travel tips and flight information about Maldives. During RTB bidding, airlines with flights to Maldives can make higher biddings based on such data, because customer demands render more earnings for media.
 
Besides, hdtSSP applies cutting-edge technologies and big data technologies like cloud computing on ¡°audience management¡±, which technically bolster complicated audience analysis of over 100 million people every day.
 
Madisonboom: How does hdtSSP sell more resources and gain more profits for media owners?
 
Colin Han: As a platform for media optimization, SSP mainly help media achieve programmatic buying. hdtSSP accommodates standard RTB real-time bidding and PPB(Programmatic Premium Buying). Users can either make transactions through open market or carry out transactions based on self-made rules through private market. In the latter manner, they don¡¯t need to open their flows to platforms like Google and Taobao.com where they compete with long-tail and low-priced media. Through private market built upon hdtSSP, media can offer DSP to high-quality advertisers so as to realize more value of their flow.
 
Currently our SSP platform is still connecting with media partners, and we have launched some tentative cooperation and ads with media that cooperated with Ad Network before. During the connection tests, we found that media revenue has grown by 30-50%, and that selling price of ad place is correlated to intensity of bidding competition. The fiercer the competition is, the higher the revenue rises.
 
In terms of optimization of traditional platform of Ad Network, demands vary with different Ad Networks. For example, some of them show more interest in flows in Beijing, Shanghai and Guangzhou. We can figure out closing rates with different audience by conducting algorithm on past ad closing rates, and then flows of audience with higher closing rates are provided to media correspondently. For instance, an Ad Network registers enormous demand for flows in Guangdong, and then we prioritize media flows in Guangdong in supply. Besides, the whole algorithm is open to dynamic adjustments based on ad closing rates. In this way, media resources sell better and revenues are optimized.