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#139 Causal Inference with Carlos Avello - Marketing Science Lead

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コンテンツは Felipe Flores によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Felipe Flores またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作権で保護された作品をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

We have Carlos Avello, Marketing Science Lead at Amazon (Director of Marketing Analytics at eBay at the time of recording) joining us for an insightful chat on Causal Inference and how it can help businesses understand causes and impacts for better decision making.

Even before ecommerce started, Carlos was already working within an extremely data-driven business model. He started his journey in direct response marketing, tracking data from coupons and later analyzing it.

In his own words, he explains causal inference is based on evidence you collect of the impact a certain marketing effort had on your overall sales. Experimentation is the core of causal inference, and specifically A/B tests are used very often.

Quotes:

  • "Causal inference is based on evidence you collect of the impact a certain marketing effort had on your overall sales."
  • "Experimentation is at the core of it. You cannot get into causal inference if you do not compare two different populations with different treatments."
  • "Marketing is the product of a partnership between the advertiser and the advertising platform. Two different companies and there are things that we are ready to share and some others that are tricky to share, like customer data. If you want to setup a test where you are suppressing treatment to some people and allowing others to be exposed to the treatment and then compare the final results on sales you need to find a solution were you are saving enough information so the advertiser knows who has been exposed and who has not and later they can compare the sales."

Thanks to our sponsor:

Talent Insights

Read the full episode summary here: Ep #139

Enjoy the show!

--- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message
  continue reading

268 つのエピソード

Artwork
iconシェア
 
Manage episode 282705806 series 2310475
コンテンツは Felipe Flores によって提供されます。エピソード、グラフィック、ポッドキャストの説明を含むすべてのポッドキャスト コンテンツは、Felipe Flores またはそのポッドキャスト プラットフォーム パートナーによって直接アップロードされ、提供されます。誰かがあなたの著作権で保護された作品をあなたの許可なく使用していると思われる場合は、ここで概説されているプロセスに従うことができますhttps://ja.player.fm/legal

We have Carlos Avello, Marketing Science Lead at Amazon (Director of Marketing Analytics at eBay at the time of recording) joining us for an insightful chat on Causal Inference and how it can help businesses understand causes and impacts for better decision making.

Even before ecommerce started, Carlos was already working within an extremely data-driven business model. He started his journey in direct response marketing, tracking data from coupons and later analyzing it.

In his own words, he explains causal inference is based on evidence you collect of the impact a certain marketing effort had on your overall sales. Experimentation is the core of causal inference, and specifically A/B tests are used very often.

Quotes:

  • "Causal inference is based on evidence you collect of the impact a certain marketing effort had on your overall sales."
  • "Experimentation is at the core of it. You cannot get into causal inference if you do not compare two different populations with different treatments."
  • "Marketing is the product of a partnership between the advertiser and the advertising platform. Two different companies and there are things that we are ready to share and some others that are tricky to share, like customer data. If you want to setup a test where you are suppressing treatment to some people and allowing others to be exposed to the treatment and then compare the final results on sales you need to find a solution were you are saving enough information so the advertiser knows who has been exposed and who has not and later they can compare the sales."

Thanks to our sponsor:

Talent Insights

Read the full episode summary here: Ep #139

Enjoy the show!

--- Send in a voice message: https://podcasters.spotify.com/pod/show/datafuturology/message
  continue reading

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