Post by account_disabled on Jan 21, 2024 21:52:25 GMT -8
A summary of auto strategies The era of manual bid management is long gone, and most advertisers now use automated bidding strategies. Autostrategies work according to the following principle: the specialist configures the campaign, choosing one of the auto strategies that meets his goals: target price per conversion, target return on investment in advertising, etc.; after launch, the campaign learns for some time, collects data and many signals that will help optimize advertising; after learning, the system itself makes decisions about display settings and bidding. Simply put, auto strategies raise the price for everything that works and lower the bids for ineffective ads.
The benefits of this method are obvious: saving C Level Executive List time; reducing the influence of the human factor - there is no risk that the PPC specialist will not pay attention to something or forget to adjust the bids; budget allocation based on clear data, not intuition or false conclusions; Google's algorithms can predict the likelihood of a targeted action and adjust ads accordingly because they have data on all users. But there is also the other side of the coin: how you teach campaigns - that's how they will work. And if something is not taken into account, then you can be left without showing ads at all or get completely non-targeted leads. This is exactly the situation that can happen if you do not take into account the conversion to a call.
Why train campaigns with call data at all I will explain with an example. Ringostat is a B2B platform designed for marketers, PPC specialists, web analysts, etc. That is, for "digital" people who, it would seem, prefer to do everything online. If we at Ringostat were to proceed from this, we would only track conversions that occur on the site. For example, filling out an online application for a demo or contacting the chat. The statistics that Google would receive would be 30% incorrect. After all, we have on average just so many calls from customers. The system would consider that certain campaigns are not working and lower bids on them. And as a matter of fact, they could massively bring calls - that is, the most warm and interested leads.
The benefits of this method are obvious: saving C Level Executive List time; reducing the influence of the human factor - there is no risk that the PPC specialist will not pay attention to something or forget to adjust the bids; budget allocation based on clear data, not intuition or false conclusions; Google's algorithms can predict the likelihood of a targeted action and adjust ads accordingly because they have data on all users. But there is also the other side of the coin: how you teach campaigns - that's how they will work. And if something is not taken into account, then you can be left without showing ads at all or get completely non-targeted leads. This is exactly the situation that can happen if you do not take into account the conversion to a call.
Why train campaigns with call data at all I will explain with an example. Ringostat is a B2B platform designed for marketers, PPC specialists, web analysts, etc. That is, for "digital" people who, it would seem, prefer to do everything online. If we at Ringostat were to proceed from this, we would only track conversions that occur on the site. For example, filling out an online application for a demo or contacting the chat. The statistics that Google would receive would be 30% incorrect. After all, we have on average just so many calls from customers. The system would consider that certain campaigns are not working and lower bids on them. And as a matter of fact, they could massively bring calls - that is, the most warm and interested leads.