Omnichannel is the order of the day. And as the consumer journey becomes more complex, marketers are analyzing changing customer behavior and using technology to adapt to it. The consumer has become agnostic regarding the acquisition channel and the only thing he is looking for is to have a unique shopping experience that allows him to satisfy his needs, regardless of whether he does it through a physical store or an e-commerce .
As companies adjust to this inescapable digital transformation, the first party data, (first party data / proprietary data), takes on more and more weight to solve the problems that arise. Reaching the right audience is and will be one of the main objectives of marketing, and it is precisely the analysis of data through machine learning that allows access to the most valuable information today: what they like, are interested in and /o worries consumers.
The complexity of data processing
Omnichannel is beginning to be the natural way in which the consumer operates, however, for companies it can be a problem when processing data. The importance of first party data is undeniable, but when this data originates from different channels, its processing becomes complicated. This is where Google Cloud appears as the perfect tool for consolidating this data, and therefore, for extracting valuable information.
When they go hand in hand, data and technology can help companies face this challenge, facilitating the processing of first party data and thus being able to extract more useful insights. With this alliance, companies can better understand their audience and improve the performance of their marketing actions. Thanks to the application of Google Cloud technology, Carrefour has managed to actively listen to consumers by generating a client-centric data infrastructure.
On the other hand, there are cases where ensuring excellent customer service both offline and online can be complex. Omnichannel exposes a complex panorama for brands and makes evident the greater demand on the part of the consumer. Once again, knowing the audience is the key. In the case of Carrefour, all this information was centralized thanks to the combination of Google Cloud technologies with Google Marketing Platform. All this has allowed them to generate 360º knowledge of customers, which is the basis for generating personalized user experiences.
The importance of generating a unique shopping experience
In addition to this complexity when it comes to sharing data, it is time to leave demographic segmentation behind and start creating clusters based on needs.
Once you have this deep knowledge of your customer base, thanks to proprietary data combined with Google Marketing Platform technology, you can create personalized shopping experiences that allow you to create high customer engagement, as well as increase customer satisfaction. profitability of marketing actions.
At the same time, by using Machine Learning algorithms to group customers based on similar patterns, Carrefour, together with its partner Merkle , managed to generate personalized messages and offers.
Technology as a tool to deal with a limited sector
On other occasions, technology is the key to overcoming challenges that arise from the very nature of the sector. This is what happened to Luckia , a Spanish company dedicated to online gaming. This sector is characterized by being very competitive and operating under exhaustive regulation, in addition to having users with little loyalty and a high probability of changing brands. Added to this are the limitations of Google's policy regarding audiences and remarketing strategies for gambling customers.
As a result, Luckia was facing rising costs - both media and recruitment - and needed an alternative to achieve efficiency. To achieve this, Performics, a media agency of Publicis Groupe, approached Luckia with a strategy based on creating weighted conversions in Search Ads 360 for search-qualifying leads, in order to first collect enough volume to use machine learning. and then implement more efficient supply strategies. This approach helped reduce acquisition costs, also forcing Google's algorithm to naturally exclude users and customers, which had a very positive impact on results.
Performics' approach paid off and Luckia's trust is rewarded as she achieves 185% FTD (First Time Deposit) increases. This improvement enables the implementation of a more effective CPA bidding strategy, reducing CPA by 19.5% and increasing CR by 5%.
The importance of correct bid optimization
But not only that, data has great value in bidding strategies. In the case of Carrefour, its traditional optimization did not take into account the CLTV (Customer Lifetime Value) of the customers captured. Analyzing the CLTV allows not only to improve marketing decision-making but also to direct campaigns towards attracting high-value users.
Using an ad-hoc machine learning model, Carrefour has been able to identify long-term, high-value online consumer behaviors using that signal as a source of optimization for Search Ads 360's automated bidding tools .
The digital age has become more important than ever and the only way to face it is by generating centralized knowledge of customers to offer personalized experiences that translate into greater satisfaction and an improvement in the profitability of marketing actions.
Omnichannel is the order of the day. And as the consumer journey becomes more complex, marketers are analyzing changing customer behavior and using technology to adapt to it. The consumer has become agnostic regarding the acquisition channel and the only thing he is looking for is to have a unique shopping experience that allows him to satisfy his needs, regardless of whether he does it through a physical store or an e-commerce .
As companies adjust to this inescapable digital transformation, the first party data, (first party data / proprietary data), takes on more and more weight to solve the problems that arise. Reaching the right audience is and will be one of the main objectives of marketing, and it is precisely the analysis of data through machine learning that allows access to the most valuable information today: what they like, are interested in and /o worries consumers.
The complexity of data processing
Omnichannel is beginning to be the natural way in which the consumer operates, however, for companies it can be a problem when processing data. The importance of first party data is undeniable, but when this data originates from different channels, its processing becomes complicated. This is where Google Cloud appears as the perfect tool for consolidating this data, and therefore, for extracting valuable information.
When they go hand in hand, data and technology can help companies face this challenge, facilitating the processing of first party data and thus being able to extract more useful insights. With this alliance, companies can better understand their audience and improve the performance of their marketing actions. Thanks to the application of Google Cloud technology, Carrefour has managed to actively listen to consumers by generating a client-centric data infrastructure.
On the other hand, there are cases where ensuring excellent customer service both offline and online can be complex. Omnichannel exposes a complex panorama for brands and makes evident the greater demand on the part of the consumer. Once again, knowing the audience is the key. In the case of Carrefour, all this information was centralized thanks to the combination of Google Cloud technologies with Google Marketing Platform. All this has allowed them to generate 360º knowledge of customers, which is the basis for generating personalized user experiences.
The importance of generating a unique shopping experience
In addition to this complexity when it comes to sharing data, it is time to leave demographic segmentation behind and start creating clusters based on needs.
Once you have this deep knowledge of your customer base, thanks to proprietary data combined with Google Marketing Platform technology, you can create personalized shopping experiences that allow you to create high customer engagement, as well as increase customer satisfaction. profitability of marketing actions.
At the same time, by using Machine Learning algorithms to group customers based on similar patterns, Carrefour, together with its partner Merkle , managed to generate personalized messages and offers.
Technology as a tool to deal with a limited sector
On other occasions, technology is the key to overcoming challenges that arise from the very nature of the sector. This is what happened to Luckia , a Spanish company dedicated to online gaming. This sector is characterized by being very competitive and operating under exhaustive regulation, in addition to having users with little loyalty and a high probability of changing brands. Added to this are the limitations of Google's policy regarding audiences and remarketing strategies for gambling customers.
As a result, Luckia was facing rising costs - both media and recruitment - and needed an alternative to achieve efficiency. To achieve this, Performics, a media agency of Publicis Groupe, approached Luckia with a strategy based on creating weighted conversions in Search Ads 360 for search-qualifying leads, in order to first collect enough volume to use machine learning. and then implement more efficient supply strategies. This approach helped reduce acquisition costs, also forcing Google's algorithm to naturally exclude users and customers, which had a very positive impact on results.
Performics' approach paid off and Luckia's trust is rewarded as she achieves 185% FTD (First Time Deposit) increases. This improvement enables the implementation of a more effective CPA bidding strategy, reducing CPA by 19.5% and increasing CR by 5%.
The importance of correct bid optimization
But not only that, data has great value in bidding strategies. In the case of Carrefour, its traditional optimization did not take into account the CLTV (Customer Lifetime Value) of the customers captured. Analyzing the CLTV allows not only to improve marketing decision-making but also to direct campaigns towards attracting high-value users.
Using an ad-hoc machine learning model, Carrefour has been able to identify long-term, high-value online consumer behaviors using that signal as a source of optimization for Search Ads 360's automated bidding tools .
The digital age has become more important than ever and the only way to face it is by generating centralized knowledge of customers to offer personalized experiences that translate into greater satisfaction and an improvement in the profitability of marketing actions.