The second-price auction, however, does uncover each bidder's real value for the item auctioned. Because the winning bidder pays the second highest bid, there's no real advantage in staying conservative. Each participant bids precisely what they value the item to be worth, while the winner still gets a discount. This advantage gave the second-price method huge favorability in paid search and became the natural go-to for RTB.
Secondly, the second-price auction's idea of truthful bidding heavily relies on the bidders' estimations of their private values - the maximum they want to pay for an auctioned item. But what if advertiser's information about their private values (max CPMs) is wrong or not exactly honest? Marketers' calculations of max bids are usually based on their estimation of Customer Acquisition Costs (CAC) which relies on their specific attribution model. Building such a model is a sophisticated task even the most savvy advertisers rack their brains over. Consequently, many advertisers use significantly simplified and inaccurate attribution models such as first- or last-click that result in flawed data.
Finally, the traditional second-price auction theory doesn't allow for auction participants to have different budgets varying in size and scope. Simply put, how are you getting the most out of your auction, if the highest-bidder has an unlimited budget, while the second-highest is fighting to keep the lights on? If a bidder with a large war chest bids unrealistically high to win the auction knowing no one else would come close, that bidder has guaranteed a win at a substantial discount - at your loss.
In the end, advertisers adopt new bidding strategies where bidding your true private value is no longer the most profitable approach. In attempt to buy the sum total of impressions needed at the lowest rates possible through substitution, advertisers programmatically vary bids from impression to impression and auction to auction. When bids unpredictably and rapidly fluctuate, huge gaps between a winning bid and a second bid may occur. Lack of accuracy in and discrepancies between CACs and max CPMs when different attribution models are applied increase gaps between the advertisers' bids even further. As a result, these gaps discount publisher inventory prices, lower their potential revenue, and jeopardize their position in the market.