In Microsoft Ads, what is the precise effect of setting a 'Target CPA' bidding strategy without adequately analyzing historical conversion data?

Setting a 'Target CPA' (Cost Per Acquisition) bidding strategy in Microsoft Ads without adequately analyzing historical conversion data can lead to inefficient spending and unpredictable campaign performance. The Target CPA strategy uses machine learning to automatically set bids to get as many conversions as possible at or below the target cost you set. However, this strategy relies on having sufficient historical conversion data to accurately predict optimal bids. Without this data, the system has no reliable basis for making bidding decisions. This can result in several negative outcomes. First, the campaign may spend its budget inefficiently, bidding too high for clicks that are unlikely to convert, or too low and missing out on valuable conversion opportunities. Second, the system may struggle to generate any conversions at all if the target CPA is unrealistic based on the actual cost of acquiring customers. Finally, campaign performance can fluctuate wildly as the system attempts to learn and adjust, leading to unstable results. A proper analysis of historical data helps determine a realistic and achievable target CPA, giving the bidding strategy a solid foundation for success. For example, if your product sells for $100 and your profit margin is $20, setting a Target CPA of $5 might be unrealistic and lead to few or no conversions.