際際滷shows by User: MariiaBocheva / http://www.slideshare.net/images/logo.gif 際際滷shows by User: MariiaBocheva / Mon, 11 Mar 2019 08:19:03 GMT 際際滷Share feed for 際際滷shows by User: MariiaBocheva A/B testing, optimization and results analysis by Mariia Bocheva, ATD'18 /slideshow/ab-testing-optimization-and-results-analysis-by-mariia-bocheva-atd18/135597428 sharedabtestingoptimizationandresultsanalysisbymariiabochevaatd18-190311081903
While working with data we usually face several problems: we don't have enough data, we have too much data, we don't know what to do with this data. In this session, I'll show how to make sure you can rely on your data and share my favorite ideas on how you can use Google Analytics and other for A/B testing, optimization and analysis. Youll gain a better understanding on what to look at to answer your UX questions, how to run a test properly and evaluate the its results. ]]>

While working with data we usually face several problems: we don't have enough data, we have too much data, we don't know what to do with this data. In this session, I'll show how to make sure you can rely on your data and share my favorite ideas on how you can use Google Analytics and other for A/B testing, optimization and analysis. Youll gain a better understanding on what to look at to answer your UX questions, how to run a test properly and evaluate the its results. ]]>
Mon, 11 Mar 2019 08:19:03 GMT /slideshow/ab-testing-optimization-and-results-analysis-by-mariia-bocheva-atd18/135597428 MariiaBocheva@slideshare.net(MariiaBocheva) A/B testing, optimization and results analysis by Mariia Bocheva, ATD'18 MariiaBocheva While working with data we usually face several problems: we don't have enough data, we have too much data, we don't know what to do with this data. In this session, I'll show how to make sure you can rely on your data and share my favorite ideas on how you can use Google Analytics and other for A/B testing, optimization and analysis. Youll gain a better understanding on what to look at to answer your UX questions, how to run a test properly and evaluate the its results. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sharedabtestingoptimizationandresultsanalysisbymariiabochevaatd18-190311081903-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> While working with data we usually face several problems: we don&#39;t have enough data, we have too much data, we don&#39;t know what to do with this data. In this session, I&#39;ll show how to make sure you can rely on your data and share my favorite ideas on how you can use Google Analytics and other for A/B testing, optimization and analysis. Youll gain a better understanding on what to look at to answer your UX questions, how to run a test properly and evaluate the its results.
A/B testing, optimization and results analysis by Mariia Bocheva, ATD'18 from Mariia Bocheva
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Research online purchase offline what share of your customers buy in store after visiting your website, Mariia Bocheva, Performance2020'18 /slideshow/research-online-purchase-offline-what-share-of-your-customers-buy-in-store-after-visiting-your-website-mariia-bocheva-performance202018/135596570 sharedresearchonlinepurchaseofflinewhatshareofyourcustomersbuyin-storeaftervisitingyourwebsiteperfor-190311081503
Did you know that ROPO-effect on average is about 40%, but it greatly varies from industry to industry and also depends on brand awareness? In these slides, you'll find a clear, affordable and step-by-step implementation plan on how to do it and share several success stories from the biggest retailers. ]]>

Did you know that ROPO-effect on average is about 40%, but it greatly varies from industry to industry and also depends on brand awareness? In these slides, you'll find a clear, affordable and step-by-step implementation plan on how to do it and share several success stories from the biggest retailers. ]]>
Mon, 11 Mar 2019 08:15:02 GMT /slideshow/research-online-purchase-offline-what-share-of-your-customers-buy-in-store-after-visiting-your-website-mariia-bocheva-performance202018/135596570 MariiaBocheva@slideshare.net(MariiaBocheva) Research online purchase offline what share of your customers buy in store after visiting your website, Mariia Bocheva, Performance2020'18 MariiaBocheva Did you know that ROPO-effect on average is about 40%, but it greatly varies from industry to industry and also depends on brand awareness? In these slides, you'll find a clear, affordable and step-by-step implementation plan on how to do it and share several success stories from the biggest retailers. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sharedresearchonlinepurchaseofflinewhatshareofyourcustomersbuyin-storeaftervisitingyourwebsiteperfor-190311081503-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Did you know that ROPO-effect on average is about 40%, but it greatly varies from industry to industry and also depends on brand awareness? In these slides, you&#39;ll find a clear, affordable and step-by-step implementation plan on how to do it and share several success stories from the biggest retailers.
Research online purchase offline what share of your customers buy in store after visiting your website, Mariia Bocheva, Performance2020'18 from Mariia Bocheva
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Attribution modeling 101, Mariia Bocheva /slideshow/attribution-modeling-101-mariia-bocheva/135595728 owoxbiwebinarattributionmodeling101-190311081116
Half the money I spend on advertising is wasted; the trouble is, I dont know which half, said John Wanamaker, an American merchant, over 50 years ago. Nothings changed much since then, as we still need to know which of our marketing channels work and which ones dont. This is exactly why you need an attribution model!]]>

Half the money I spend on advertising is wasted; the trouble is, I dont know which half, said John Wanamaker, an American merchant, over 50 years ago. Nothings changed much since then, as we still need to know which of our marketing channels work and which ones dont. This is exactly why you need an attribution model!]]>
Mon, 11 Mar 2019 08:11:16 GMT /slideshow/attribution-modeling-101-mariia-bocheva/135595728 MariiaBocheva@slideshare.net(MariiaBocheva) Attribution modeling 101, Mariia Bocheva MariiaBocheva Half the money I spend on advertising is wasted; the trouble is, I dont know which half, said John Wanamaker, an American merchant, over 50 years ago. Nothings changed much since then, as we still need to know which of our marketing channels work and which ones dont. This is exactly why you need an attribution model! <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/owoxbiwebinarattributionmodeling101-190311081116-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Half the money I spend on advertising is wasted; the trouble is, I dont know which half, said John Wanamaker, an American merchant, over 50 years ago. Nothings changed much since then, as we still need to know which of our marketing channels work and which ones dont. This is exactly why you need an attribution model!
Attribution modeling 101, Mariia Bocheva from Mariia Bocheva
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Golden punchcard. Mariia Bocheva. Superweek 2018. /slideshow/golden-punchcard-mariia-bocheva-superweek-2018/135595302 goldenpunchcard-190311080923
Some tricks for Google Analytics that can simplify your life. ]]>

Some tricks for Google Analytics that can simplify your life. ]]>
Mon, 11 Mar 2019 08:09:23 GMT /slideshow/golden-punchcard-mariia-bocheva-superweek-2018/135595302 MariiaBocheva@slideshare.net(MariiaBocheva) Golden punchcard. Mariia Bocheva. Superweek 2018. MariiaBocheva Some tricks for Google Analytics that can simplify your life. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/goldenpunchcard-190311080923-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Some tricks for Google Analytics that can simplify your life.
Golden punchcard. Mariia Bocheva. Superweek 2018. from Mariia Bocheva
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Data Lakes in Real Life: Analyzing Analysts to Improve Process Efficiency, Superweek 2019 /slideshow/data-lakes-in-real-life-analyzing-analysts-to-improve-process-efficiency-superweek-2019/135515516 datalakesinreallifesuperweek2019-190310154603
Growing a team requires a lot of time, effort, and proper management tools. The pain points are common; inefficient distribution of tasks, no time to teach and coach new employees, seasoned analysts didnt have enough time for R&D and skills improvement, no idea how much time a given employee spent at which tasks.. the list goes on. Check out how to improve task estimation, how to ensure that painfully-learned lessons are shared with everybody and how to balance project priorities.]]>

Growing a team requires a lot of time, effort, and proper management tools. The pain points are common; inefficient distribution of tasks, no time to teach and coach new employees, seasoned analysts didnt have enough time for R&D and skills improvement, no idea how much time a given employee spent at which tasks.. the list goes on. Check out how to improve task estimation, how to ensure that painfully-learned lessons are shared with everybody and how to balance project priorities.]]>
Sun, 10 Mar 2019 15:46:03 GMT /slideshow/data-lakes-in-real-life-analyzing-analysts-to-improve-process-efficiency-superweek-2019/135515516 MariiaBocheva@slideshare.net(MariiaBocheva) Data Lakes in Real Life: Analyzing Analysts to Improve Process Efficiency, Superweek 2019 MariiaBocheva Growing a team requires a lot of time, effort, and proper management tools. The pain points are common; inefficient distribution of tasks, no time to teach and coach new employees, seasoned analysts didnt have enough time for R&D and skills improvement, no idea how much time a given employee spent at which tasks.. the list goes on. Check out how to improve task estimation, how to ensure that painfully-learned lessons are shared with everybody and how to balance project priorities. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/datalakesinreallifesuperweek2019-190310154603-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Growing a team requires a lot of time, effort, and proper management tools. The pain points are common; inefficient distribution of tasks, no time to teach and coach new employees, seasoned analysts didnt have enough time for R&amp;D and skills improvement, no idea how much time a given employee spent at which tasks.. the list goes on. Check out how to improve task estimation, how to ensure that painfully-learned lessons are shared with everybody and how to balance project priorities.
Data Lakes in Real Life: Analyzing Analysts to Improve Process Efficiency, Superweek 2019 from Mariia Bocheva
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舒从 仗仂亠从亳仂于舒 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 /slideshow/ss-86590478/86590478 softline-owox28-180123164925
75% 仗仂仍亰仂于舒亠仍亠亶 亳 仂于舒 于 亳仆亠仆亠亠, 舒 仗仂从仗舒ム 于 仂仍舒亶仆-仄舒亞舒亰亳仆舒. 56% 仗仂从仗仂从 于 仄舒亞舒亰亳仆舒 仂于亠舒ム 仗仂仍亠 亳亰亠仆亳 仂于舒仂于 于 亳仆亠仆亠亠. 亅亳 亳 从舒仆仂亠亳于亠亠 仍ミ英 舒仆舒仍亳亳从仂于 亳 仄舒从亠仂仍仂亞仂于 亞仂于仂, 仂 亳仆亠仆亠-仄舒亞舒亰亳仆舒仄 亳 仂亰仆亳仆仄 亠礆 亢亳亰仆亠仆仆仂 仆亠仂弍仂亟亳仄仂 亳仗仂仍亰仂于舒 从于仂亰仆 舒仆舒仍亳亳从, 仂弍 仗舒于亳仍仆仂 仂亠仆亳于舒 亠从亳于仆仂 亠从仍舒仄. 亠仄仂 仆舒 仂, 仄仆仂亞亳亠 从仂仄仗舒仆亳亳 亟仂 亳 仗仂 仆亠 仆舒仂亳仍亳 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳, 仂亳弍仂仆仂 仗仂仍舒亞舒, 仂 仂 仍仂亢仆仂, 亟仂仂亞仂 亳 仆亠弍亠亰仂仗舒仆仂 亟仍 亳 亟舒仆仆. Softline 亳 OWOX BI 仄 舒亰于亠亳于舒ム 于亠 舒亳 亳 仗亠亟弍亠亢亟亠仆亳 仗仂 仗仂于仂亟 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 亳 舒从舒亰于舒ム, 从舒从 仗仂于亳 亠从亳于仆仂 亠从仍舒仄仆 从舒仄仗舒仆亳亶 于 亳仆亠仆亠亠, 亳仗仂仍亰 亟舒仆仆亠 仂 仗仂亟舒亢舒 亳亰 于仆亠仆仆亳 IT-亳亠仄. 亰仆舒亠亠: -舒从 仂亠仆亳 亠从亳亠 于仂亰仄仂亢仆仂亳 舒仆舒仍亳亳从亳 于 从仂仄仗舒仆亳亳 亳 仂仗亠亟亠仍亳 亰仂仆 束仗仂于亳舒仆亳晛. -舒从 于磦亳 亠弍仂于舒仆亳 从 亳亠仄亠 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 亳 舒亰舒弍仂舒 亠仍亠于 仄仂亟亠仍. -舒从 仗仂舒仗仆仂 于仆亠亟亳 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 舒亠仂仄 亠从舒 亟仍 弍亳亰仆亠舒. -舒从 仗仂仄仂 仗仂亟从仂于 OWOX BI 亳 Google 仂弍亠亟亳仆亳 于 Google BigQuery 于亠 亟舒仆仆亠, 仆亠仂弍仂亟亳仄亠 亟仍 从于仂亰仆仂亶 舒仆舒仍亳亳从亳: 亟亠亶于亳 仗仂仍亰仂于舒亠仍亠亶 仆舒 舒亶亠 亳 于 仄仂弍亳仍仆 仗亳仍仂亢亠仆亳, 舒仂亟 仆舒 亠从仍舒仄, 亟仂仂亟, 于仗仂仍仆亠仆仆亠 亰舒从舒亰, 亰于仂仆从亳 亳 email-舒仍从亳. -仂亳亳 仗亠舒 仆舒亳 从仍亳亠仆仂于: 从舒从 仂仆亳 仆舒仂亳仍亳 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 亳 亳仗仂仍亰仂于舒仍亳 仗仂仍亠仆仆亠 亟舒仆仆亠 亟仍 亟仂亳亢亠仆亳 于仂亳 弍亳亰仆亠-亠仍亠亶. 亟亠 仗仂仍亠亰仆仂: Ecommerce 亳 retail 仗仂亠从舒仄, 舒仆舒仍亳亳从舒仄 亳 仄舒从亠仂仍仂亞舒仄.]]>

75% 仗仂仍亰仂于舒亠仍亠亶 亳 仂于舒 于 亳仆亠仆亠亠, 舒 仗仂从仗舒ム 于 仂仍舒亶仆-仄舒亞舒亰亳仆舒. 56% 仗仂从仗仂从 于 仄舒亞舒亰亳仆舒 仂于亠舒ム 仗仂仍亠 亳亰亠仆亳 仂于舒仂于 于 亳仆亠仆亠亠. 亅亳 亳 从舒仆仂亠亳于亠亠 仍ミ英 舒仆舒仍亳亳从仂于 亳 仄舒从亠仂仍仂亞仂于 亞仂于仂, 仂 亳仆亠仆亠-仄舒亞舒亰亳仆舒仄 亳 仂亰仆亳仆仄 亠礆 亢亳亰仆亠仆仆仂 仆亠仂弍仂亟亳仄仂 亳仗仂仍亰仂于舒 从于仂亰仆 舒仆舒仍亳亳从, 仂弍 仗舒于亳仍仆仂 仂亠仆亳于舒 亠从亳于仆仂 亠从仍舒仄. 亠仄仂 仆舒 仂, 仄仆仂亞亳亠 从仂仄仗舒仆亳亳 亟仂 亳 仗仂 仆亠 仆舒仂亳仍亳 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳, 仂亳弍仂仆仂 仗仂仍舒亞舒, 仂 仂 仍仂亢仆仂, 亟仂仂亞仂 亳 仆亠弍亠亰仂仗舒仆仂 亟仍 亳 亟舒仆仆. Softline 亳 OWOX BI 仄 舒亰于亠亳于舒ム 于亠 舒亳 亳 仗亠亟弍亠亢亟亠仆亳 仗仂 仗仂于仂亟 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 亳 舒从舒亰于舒ム, 从舒从 仗仂于亳 亠从亳于仆仂 亠从仍舒仄仆 从舒仄仗舒仆亳亶 于 亳仆亠仆亠亠, 亳仗仂仍亰 亟舒仆仆亠 仂 仗仂亟舒亢舒 亳亰 于仆亠仆仆亳 IT-亳亠仄. 亰仆舒亠亠: -舒从 仂亠仆亳 亠从亳亠 于仂亰仄仂亢仆仂亳 舒仆舒仍亳亳从亳 于 从仂仄仗舒仆亳亳 亳 仂仗亠亟亠仍亳 亰仂仆 束仗仂于亳舒仆亳晛. -舒从 于磦亳 亠弍仂于舒仆亳 从 亳亠仄亠 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 亳 舒亰舒弍仂舒 亠仍亠于 仄仂亟亠仍. -舒从 仗仂舒仗仆仂 于仆亠亟亳 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 舒亠仂仄 亠从舒 亟仍 弍亳亰仆亠舒. -舒从 仗仂仄仂 仗仂亟从仂于 OWOX BI 亳 Google 仂弍亠亟亳仆亳 于 Google BigQuery 于亠 亟舒仆仆亠, 仆亠仂弍仂亟亳仄亠 亟仍 从于仂亰仆仂亶 舒仆舒仍亳亳从亳: 亟亠亶于亳 仗仂仍亰仂于舒亠仍亠亶 仆舒 舒亶亠 亳 于 仄仂弍亳仍仆 仗亳仍仂亢亠仆亳, 舒仂亟 仆舒 亠从仍舒仄, 亟仂仂亟, 于仗仂仍仆亠仆仆亠 亰舒从舒亰, 亰于仂仆从亳 亳 email-舒仍从亳. -仂亳亳 仗亠舒 仆舒亳 从仍亳亠仆仂于: 从舒从 仂仆亳 仆舒仂亳仍亳 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 亳 亳仗仂仍亰仂于舒仍亳 仗仂仍亠仆仆亠 亟舒仆仆亠 亟仍 亟仂亳亢亠仆亳 于仂亳 弍亳亰仆亠-亠仍亠亶. 亟亠 仗仂仍亠亰仆仂: Ecommerce 亳 retail 仗仂亠从舒仄, 舒仆舒仍亳亳从舒仄 亳 仄舒从亠仂仍仂亞舒仄.]]>
Tue, 23 Jan 2018 16:49:25 GMT /slideshow/ss-86590478/86590478 MariiaBocheva@slideshare.net(MariiaBocheva) 舒从 仗仂亠从亳仂于舒 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 MariiaBocheva 75% 仗仂仍亰仂于舒亠仍亠亶 亳 仂于舒 于 亳仆亠仆亠亠, 舒 仗仂从仗舒ム 于 仂仍舒亶仆-仄舒亞舒亰亳仆舒. 56% 仗仂从仗仂从 于 仄舒亞舒亰亳仆舒 仂于亠舒ム 仗仂仍亠 亳亰亠仆亳 仂于舒仂于 于 亳仆亠仆亠亠. 亅亳 亳 从舒仆仂亠亳于亠亠 仍ミ英 舒仆舒仍亳亳从仂于 亳 仄舒从亠仂仍仂亞仂于 亞仂于仂, 仂 亳仆亠仆亠-仄舒亞舒亰亳仆舒仄 亳 仂亰仆亳仆仄 亠礆 亢亳亰仆亠仆仆仂 仆亠仂弍仂亟亳仄仂 亳仗仂仍亰仂于舒 从于仂亰仆 舒仆舒仍亳亳从, 仂弍 仗舒于亳仍仆仂 仂亠仆亳于舒 亠从亳于仆仂 亠从仍舒仄. 亠仄仂 仆舒 仂, 仄仆仂亞亳亠 从仂仄仗舒仆亳亳 亟仂 亳 仗仂 仆亠 仆舒仂亳仍亳 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳, 仂亳弍仂仆仂 仗仂仍舒亞舒, 仂 仂 仍仂亢仆仂, 亟仂仂亞仂 亳 仆亠弍亠亰仂仗舒仆仂 亟仍 亳 亟舒仆仆. Softline 亳 OWOX BI 仄 舒亰于亠亳于舒ム 于亠 舒亳 亳 仗亠亟弍亠亢亟亠仆亳 仗仂 仗仂于仂亟 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 亳 舒从舒亰于舒ム, 从舒从 仗仂于亳 亠从亳于仆仂 亠从仍舒仄仆 从舒仄仗舒仆亳亶 于 亳仆亠仆亠亠, 亳仗仂仍亰 亟舒仆仆亠 仂 仗仂亟舒亢舒 亳亰 于仆亠仆仆亳 IT-亳亠仄. 亰仆舒亠亠: -舒从 仂亠仆亳 亠从亳亠 于仂亰仄仂亢仆仂亳 舒仆舒仍亳亳从亳 于 从仂仄仗舒仆亳亳 亳 仂仗亠亟亠仍亳 亰仂仆 束仗仂于亳舒仆亳晛. -舒从 于磦亳 亠弍仂于舒仆亳 从 亳亠仄亠 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 亳 舒亰舒弍仂舒 亠仍亠于 仄仂亟亠仍. -舒从 仗仂舒仗仆仂 于仆亠亟亳 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 舒亠仂仄 亠从舒 亟仍 弍亳亰仆亠舒. -舒从 仗仂仄仂 仗仂亟从仂于 OWOX BI 亳 Google 仂弍亠亟亳仆亳 于 Google BigQuery 于亠 亟舒仆仆亠, 仆亠仂弍仂亟亳仄亠 亟仍 从于仂亰仆仂亶 舒仆舒仍亳亳从亳: 亟亠亶于亳 仗仂仍亰仂于舒亠仍亠亶 仆舒 舒亶亠 亳 于 仄仂弍亳仍仆 仗亳仍仂亢亠仆亳, 舒仂亟 仆舒 亠从仍舒仄, 亟仂仂亟, 于仗仂仍仆亠仆仆亠 亰舒从舒亰, 亰于仂仆从亳 亳 email-舒仍从亳. -仂亳亳 仗亠舒 仆舒亳 从仍亳亠仆仂于: 从舒从 仂仆亳 仆舒仂亳仍亳 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 亳 亳仗仂仍亰仂于舒仍亳 仗仂仍亠仆仆亠 亟舒仆仆亠 亟仍 亟仂亳亢亠仆亳 于仂亳 弍亳亰仆亠-亠仍亠亶. 亟亠 仗仂仍亠亰仆仂: Ecommerce 亳 retail 仗仂亠从舒仄, 舒仆舒仍亳亳从舒仄 亳 仄舒从亠仂仍仂亞舒仄. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/softline-owox28-180123164925-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 75% 仗仂仍亰仂于舒亠仍亠亶 亳 仂于舒 于 亳仆亠仆亠亠, 舒 仗仂从仗舒ム 于 仂仍舒亶仆-仄舒亞舒亰亳仆舒. 56% 仗仂从仗仂从 于 仄舒亞舒亰亳仆舒 仂于亠舒ム 仗仂仍亠 亳亰亠仆亳 仂于舒仂于 于 亳仆亠仆亠亠. 亅亳 亳 从舒仆仂亠亳于亠亠 仍ミ英 舒仆舒仍亳亳从仂于 亳 仄舒从亠仂仍仂亞仂于 亞仂于仂, 仂 亳仆亠仆亠-仄舒亞舒亰亳仆舒仄 亳 仂亰仆亳仆仄 亠礆 亢亳亰仆亠仆仆仂 仆亠仂弍仂亟亳仄仂 亳仗仂仍亰仂于舒 从于仂亰仆 舒仆舒仍亳亳从, 仂弍 仗舒于亳仍仆仂 仂亠仆亳于舒 亠从亳于仆仂 亠从仍舒仄. 亠仄仂 仆舒 仂, 仄仆仂亞亳亠 从仂仄仗舒仆亳亳 亟仂 亳 仗仂 仆亠 仆舒仂亳仍亳 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳, 仂亳弍仂仆仂 仗仂仍舒亞舒, 仂 仂 仍仂亢仆仂, 亟仂仂亞仂 亳 仆亠弍亠亰仂仗舒仆仂 亟仍 亳 亟舒仆仆. Softline 亳 OWOX BI 仄 舒亰于亠亳于舒ム 于亠 舒亳 亳 仗亠亟弍亠亢亟亠仆亳 仗仂 仗仂于仂亟 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 亳 舒从舒亰于舒ム, 从舒从 仗仂于亳 亠从亳于仆仂 亠从仍舒仄仆 从舒仄仗舒仆亳亶 于 亳仆亠仆亠亠, 亳仗仂仍亰 亟舒仆仆亠 仂 仗仂亟舒亢舒 亳亰 于仆亠仆仆亳 IT-亳亠仄. 亰仆舒亠亠: -舒从 仂亠仆亳 亠从亳亠 于仂亰仄仂亢仆仂亳 舒仆舒仍亳亳从亳 于 从仂仄仗舒仆亳亳 亳 仂仗亠亟亠仍亳 亰仂仆 束仗仂于亳舒仆亳晛. -舒从 于磦亳 亠弍仂于舒仆亳 从 亳亠仄亠 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 亳 舒亰舒弍仂舒 亠仍亠于 仄仂亟亠仍. -舒从 仗仂舒仗仆仂 于仆亠亟亳 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 舒亠仂仄 亠从舒 亟仍 弍亳亰仆亠舒. -舒从 仗仂仄仂 仗仂亟从仂于 OWOX BI 亳 Google 仂弍亠亟亳仆亳 于 Google BigQuery 于亠 亟舒仆仆亠, 仆亠仂弍仂亟亳仄亠 亟仍 从于仂亰仆仂亶 舒仆舒仍亳亳从亳: 亟亠亶于亳 仗仂仍亰仂于舒亠仍亠亶 仆舒 舒亶亠 亳 于 仄仂弍亳仍仆 仗亳仍仂亢亠仆亳, 舒仂亟 仆舒 亠从仍舒仄, 亟仂仂亟, 于仗仂仍仆亠仆仆亠 亰舒从舒亰, 亰于仂仆从亳 亳 email-舒仍从亳. -仂亳亳 仗亠舒 仆舒亳 从仍亳亠仆仂于: 从舒从 仂仆亳 仆舒仂亳仍亳 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 亳 亳仗仂仍亰仂于舒仍亳 仗仂仍亠仆仆亠 亟舒仆仆亠 亟仍 亟仂亳亢亠仆亳 于仂亳 弍亳亰仆亠-亠仍亠亶. 亟亠 仗仂仍亠亰仆仂: Ecommerce 亳 retail 仗仂亠从舒仄, 舒仆舒仍亳亳从舒仄 亳 仄舒从亠仂仍仂亞舒仄.
舒从 仗仂亠从亳仂于舒 亳亠仄 从于仂亰仆仂亶 舒仆舒仍亳亳从亳 from Mariia Bocheva
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168 1 https://cdn.slidesharecdn.com/ss_thumbnails/softline-owox28-180123164925-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0
于仂仄舒亳亰舒亳 仂亠仂于: 从舒从 仂仗亠舒亳于仆仂 仂弍仆仂于仍 亟舒仆仆亠 亳 仂仍亠亢亳于舒 于舒亢仆亠 仗仂从舒亰舒亠仍亳 /slideshow/ss-86590128/86590128 webinarowoxbi-180123164131
亠 弍于舒ム 亠 亳仗仂于: 舒亰仂于亠, 亠亞仍仆亠 亳 亟亳仆舒仄亳亠从亳亠, 从仂亞亟舒 仆亠 仆亢亠仆 仗仂仂礌仆亶 仄仂仆亳仂亳仆亞, 仆仂 于舒亢仆仂 亰仆舒 仂 从亳亳亠从亳 亳亰仄亠仆亠仆亳 仗仂从舒亰舒亠仍亠亶. 丕亰仆舒亶亠 亠仄 亳 亳仗 仂仍亳舒ム 亟亞 仂 亟亞舒, 从舒从 仆舒仂亳 舒于仂仄舒亳亠从仂亠 仂弍仆仂于仍亠仆亳亠 仂亠仂于 于 舒亰仆 亳亠仄舒 亳 从舒从 仗仂仍舒 于亠亟仂仄仍亠仆亳 仗仂 亳亰仄亠仆亠仆亳 于 仂亠舒. 仗仂亞舒仄仄亠: -舒从亳仄亳 弍于舒ム 仂亠, 从仂亞亟舒 亳 亳仗仂仍亰ム 亳 仗仂亠仄 亳 仆亢仆仂 舒于仂仄舒亳亰亳仂于舒. 亳仄亠 仂亠仂于, 从仂仂亠 仄 亳仗仂仍亰亠仄 于仆亳 从仂仄仗舒仆亳亳, 亟仍 仄舒从亠仂仍仂亞仂于 (仗仂 KPI 亠从仍舒仄仆 从舒仄仗舒仆亳亶 亳 舒亳从), 舒仆舒仍亳亳从仂于 (仗仂 KPI 亠从亳于仆仂亳 舒亶舒) 亳 舒亰舒弍仂亳从仂于 (仗仂 JS-仂亳弍从舒仄, 仂亳弍从舒仄 亠于亠舒). -亠 于 Google Analytics: 从舒从 仆舒仂亳 仂仗舒于从 舒仆亟舒仆 亳 从舒仂仄仆 仂亠仂于 仆舒 email 亰舒亟舒仆仆仂亶 仗亠亳仂亟亳仆仂. -亠 于 Google Sheets: 从舒从 舒于仂仄舒亳亠从亳 仂弍仆仂于仍 仂亠 亟舒仆仆仄亳 Google Analytics 亳 仂仗舒于仍 亳 于 email-于亠亟仂仄仍亠仆亳. -亠 于 Google Data Studio: 从舒从 仆舒仂亳 仂亠 仆舒 亟舒仆仆 Google Analytics. -亠 于 Google BigQuery + Google Sheets: 从舒从 仆舒仂亳 舒于仂仄舒亳亠从仂亠 仂弍仆仂于仍亠仆亳亠 仂亠仂于 于 舒弍仍亳舒 舒亳亠仄 亰舒仗仂仂于 从 GBQ 亳 仂仗舒于仍 仂亠 仆舒 email. -亠 于 Google BigQuery + AppScript: 从舒从 仆舒仂亳 舒于仂仄舒亳亠从仂亠 仂弍仆仂于仍亠仆亳亠 仂亠仂于 于亳亰舒仍亳亰舒亳亠亶 于 Google Data Studio. 亟亠 仗仂仍亠亰仆仂 仆舒仍亳亳从舒仄, 仄舒从亠仂仍仂亞舒仄, 亠仆亳亠从亳仄 仗亠亳舒仍亳舒仄 舒 舒从亢亠 于亠仄, 从仂 舒弍仂舒亠 仗仂亟从舒仄亳 Google 亳 仂亳 仂亠. 仂仍亳亠 亟仂仗仂仍仆亳亠仍仆亠 仄舒亠亳舒仍 仗仂 仍从亠 https://www.owox.com/c/1eh]]>

亠 弍于舒ム 亠 亳仗仂于: 舒亰仂于亠, 亠亞仍仆亠 亳 亟亳仆舒仄亳亠从亳亠, 从仂亞亟舒 仆亠 仆亢亠仆 仗仂仂礌仆亶 仄仂仆亳仂亳仆亞, 仆仂 于舒亢仆仂 亰仆舒 仂 从亳亳亠从亳 亳亰仄亠仆亠仆亳 仗仂从舒亰舒亠仍亠亶. 丕亰仆舒亶亠 亠仄 亳 亳仗 仂仍亳舒ム 亟亞 仂 亟亞舒, 从舒从 仆舒仂亳 舒于仂仄舒亳亠从仂亠 仂弍仆仂于仍亠仆亳亠 仂亠仂于 于 舒亰仆 亳亠仄舒 亳 从舒从 仗仂仍舒 于亠亟仂仄仍亠仆亳 仗仂 亳亰仄亠仆亠仆亳 于 仂亠舒. 仗仂亞舒仄仄亠: -舒从亳仄亳 弍于舒ム 仂亠, 从仂亞亟舒 亳 亳仗仂仍亰ム 亳 仗仂亠仄 亳 仆亢仆仂 舒于仂仄舒亳亰亳仂于舒. 亳仄亠 仂亠仂于, 从仂仂亠 仄 亳仗仂仍亰亠仄 于仆亳 从仂仄仗舒仆亳亳, 亟仍 仄舒从亠仂仍仂亞仂于 (仗仂 KPI 亠从仍舒仄仆 从舒仄仗舒仆亳亶 亳 舒亳从), 舒仆舒仍亳亳从仂于 (仗仂 KPI 亠从亳于仆仂亳 舒亶舒) 亳 舒亰舒弍仂亳从仂于 (仗仂 JS-仂亳弍从舒仄, 仂亳弍从舒仄 亠于亠舒). -亠 于 Google Analytics: 从舒从 仆舒仂亳 仂仗舒于从 舒仆亟舒仆 亳 从舒仂仄仆 仂亠仂于 仆舒 email 亰舒亟舒仆仆仂亶 仗亠亳仂亟亳仆仂. -亠 于 Google Sheets: 从舒从 舒于仂仄舒亳亠从亳 仂弍仆仂于仍 仂亠 亟舒仆仆仄亳 Google Analytics 亳 仂仗舒于仍 亳 于 email-于亠亟仂仄仍亠仆亳. -亠 于 Google Data Studio: 从舒从 仆舒仂亳 仂亠 仆舒 亟舒仆仆 Google Analytics. -亠 于 Google BigQuery + Google Sheets: 从舒从 仆舒仂亳 舒于仂仄舒亳亠从仂亠 仂弍仆仂于仍亠仆亳亠 仂亠仂于 于 舒弍仍亳舒 舒亳亠仄 亰舒仗仂仂于 从 GBQ 亳 仂仗舒于仍 仂亠 仆舒 email. -亠 于 Google BigQuery + AppScript: 从舒从 仆舒仂亳 舒于仂仄舒亳亠从仂亠 仂弍仆仂于仍亠仆亳亠 仂亠仂于 于亳亰舒仍亳亰舒亳亠亶 于 Google Data Studio. 亟亠 仗仂仍亠亰仆仂 仆舒仍亳亳从舒仄, 仄舒从亠仂仍仂亞舒仄, 亠仆亳亠从亳仄 仗亠亳舒仍亳舒仄 舒 舒从亢亠 于亠仄, 从仂 舒弍仂舒亠 仗仂亟从舒仄亳 Google 亳 仂亳 仂亠. 仂仍亳亠 亟仂仗仂仍仆亳亠仍仆亠 仄舒亠亳舒仍 仗仂 仍从亠 https://www.owox.com/c/1eh]]>
Tue, 23 Jan 2018 16:41:31 GMT /slideshow/ss-86590128/86590128 MariiaBocheva@slideshare.net(MariiaBocheva) 于仂仄舒亳亰舒亳 仂亠仂于: 从舒从 仂仗亠舒亳于仆仂 仂弍仆仂于仍 亟舒仆仆亠 亳 仂仍亠亢亳于舒 于舒亢仆亠 仗仂从舒亰舒亠仍亳 MariiaBocheva 亠 弍于舒ム 亠 亳仗仂于: 舒亰仂于亠, 亠亞仍仆亠 亳 亟亳仆舒仄亳亠从亳亠, 从仂亞亟舒 仆亠 仆亢亠仆 仗仂仂礌仆亶 仄仂仆亳仂亳仆亞, 仆仂 于舒亢仆仂 亰仆舒 仂 从亳亳亠从亳 亳亰仄亠仆亠仆亳 仗仂从舒亰舒亠仍亠亶. 丕亰仆舒亶亠 亠仄 亳 亳仗 仂仍亳舒ム 亟亞 仂 亟亞舒, 从舒从 仆舒仂亳 舒于仂仄舒亳亠从仂亠 仂弍仆仂于仍亠仆亳亠 仂亠仂于 于 舒亰仆 亳亠仄舒 亳 从舒从 仗仂仍舒 于亠亟仂仄仍亠仆亳 仗仂 亳亰仄亠仆亠仆亳 于 仂亠舒. 仗仂亞舒仄仄亠: -舒从亳仄亳 弍于舒ム 仂亠, 从仂亞亟舒 亳 亳仗仂仍亰ム 亳 仗仂亠仄 亳 仆亢仆仂 舒于仂仄舒亳亰亳仂于舒. 亳仄亠 仂亠仂于, 从仂仂亠 仄 亳仗仂仍亰亠仄 于仆亳 从仂仄仗舒仆亳亳, 亟仍 仄舒从亠仂仍仂亞仂于 (仗仂 KPI 亠从仍舒仄仆 从舒仄仗舒仆亳亶 亳 舒亳从), 舒仆舒仍亳亳从仂于 (仗仂 KPI 亠从亳于仆仂亳 舒亶舒) 亳 舒亰舒弍仂亳从仂于 (仗仂 JS-仂亳弍从舒仄, 仂亳弍从舒仄 亠于亠舒). -亠 于 Google Analytics: 从舒从 仆舒仂亳 仂仗舒于从 舒仆亟舒仆 亳 从舒仂仄仆 仂亠仂于 仆舒 email 亰舒亟舒仆仆仂亶 仗亠亳仂亟亳仆仂. -亠 于 Google Sheets: 从舒从 舒于仂仄舒亳亠从亳 仂弍仆仂于仍 仂亠 亟舒仆仆仄亳 Google Analytics 亳 仂仗舒于仍 亳 于 email-于亠亟仂仄仍亠仆亳. -亠 于 Google Data Studio: 从舒从 仆舒仂亳 仂亠 仆舒 亟舒仆仆 Google Analytics. -亠 于 Google BigQuery + Google Sheets: 从舒从 仆舒仂亳 舒于仂仄舒亳亠从仂亠 仂弍仆仂于仍亠仆亳亠 仂亠仂于 于 舒弍仍亳舒 舒亳亠仄 亰舒仗仂仂于 从 GBQ 亳 仂仗舒于仍 仂亠 仆舒 email. -亠 于 Google BigQuery + AppScript: 从舒从 仆舒仂亳 舒于仂仄舒亳亠从仂亠 仂弍仆仂于仍亠仆亳亠 仂亠仂于 于亳亰舒仍亳亰舒亳亠亶 于 Google Data Studio. 亟亠 仗仂仍亠亰仆仂 仆舒仍亳亳从舒仄, 仄舒从亠仂仍仂亞舒仄, 亠仆亳亠从亳仄 仗亠亳舒仍亳舒仄 舒 舒从亢亠 于亠仄, 从仂 舒弍仂舒亠 仗仂亟从舒仄亳 Google 亳 仂亳 仂亠. 仂仍亳亠 亟仂仗仂仍仆亳亠仍仆亠 仄舒亠亳舒仍 仗仂 仍从亠 https://www.owox.com/c/1eh <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/webinarowoxbi-180123164131-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> 亠 弍于舒ム 亠 亳仗仂于: 舒亰仂于亠, 亠亞仍仆亠 亳 亟亳仆舒仄亳亠从亳亠, 从仂亞亟舒 仆亠 仆亢亠仆 仗仂仂礌仆亶 仄仂仆亳仂亳仆亞, 仆仂 于舒亢仆仂 亰仆舒 仂 从亳亳亠从亳 亳亰仄亠仆亠仆亳 仗仂从舒亰舒亠仍亠亶. 丕亰仆舒亶亠 亠仄 亳 亳仗 仂仍亳舒ム 亟亞 仂 亟亞舒, 从舒从 仆舒仂亳 舒于仂仄舒亳亠从仂亠 仂弍仆仂于仍亠仆亳亠 仂亠仂于 于 舒亰仆 亳亠仄舒 亳 从舒从 仗仂仍舒 于亠亟仂仄仍亠仆亳 仗仂 亳亰仄亠仆亠仆亳 于 仂亠舒. 仗仂亞舒仄仄亠: -舒从亳仄亳 弍于舒ム 仂亠, 从仂亞亟舒 亳 亳仗仂仍亰ム 亳 仗仂亠仄 亳 仆亢仆仂 舒于仂仄舒亳亰亳仂于舒. 亳仄亠 仂亠仂于, 从仂仂亠 仄 亳仗仂仍亰亠仄 于仆亳 从仂仄仗舒仆亳亳, 亟仍 仄舒从亠仂仍仂亞仂于 (仗仂 KPI 亠从仍舒仄仆 从舒仄仗舒仆亳亶 亳 舒亳从), 舒仆舒仍亳亳从仂于 (仗仂 KPI 亠从亳于仆仂亳 舒亶舒) 亳 舒亰舒弍仂亳从仂于 (仗仂 JS-仂亳弍从舒仄, 仂亳弍从舒仄 亠于亠舒). -亠 于 Google Analytics: 从舒从 仆舒仂亳 仂仗舒于从 舒仆亟舒仆 亳 从舒仂仄仆 仂亠仂于 仆舒 email 亰舒亟舒仆仆仂亶 仗亠亳仂亟亳仆仂. -亠 于 Google Sheets: 从舒从 舒于仂仄舒亳亠从亳 仂弍仆仂于仍 仂亠 亟舒仆仆仄亳 Google Analytics 亳 仂仗舒于仍 亳 于 email-于亠亟仂仄仍亠仆亳. -亠 于 Google Data Studio: 从舒从 仆舒仂亳 仂亠 仆舒 亟舒仆仆 Google Analytics. -亠 于 Google BigQuery + Google Sheets: 从舒从 仆舒仂亳 舒于仂仄舒亳亠从仂亠 仂弍仆仂于仍亠仆亳亠 仂亠仂于 于 舒弍仍亳舒 舒亳亠仄 亰舒仗仂仂于 从 GBQ 亳 仂仗舒于仍 仂亠 仆舒 email. -亠 于 Google BigQuery + AppScript: 从舒从 仆舒仂亳 舒于仂仄舒亳亠从仂亠 仂弍仆仂于仍亠仆亳亠 仂亠仂于 于亳亰舒仍亳亰舒亳亠亶 于 Google Data Studio. 亟亠 仗仂仍亠亰仆仂 仆舒仍亳亳从舒仄, 仄舒从亠仂仍仂亞舒仄, 亠仆亳亠从亳仄 仗亠亳舒仍亳舒仄 舒 舒从亢亠 于亠仄, 从仂 舒弍仂舒亠 仗仂亟从舒仄亳 Google 亳 仂亳 仂亠. 仂仍亳亠 亟仂仗仂仍仆亳亠仍仆亠 仄舒亠亳舒仍 仗仂 仍从亠 https://www.owox.com/c/1eh
于仂仄舒亳亰舒亳 仂亠仂于: 从舒从 仂仗亠舒亳于仆仂 仂弍仆仂于仍 亟舒仆仆亠 亳 仂仍亠亢亳于舒 于舒亢仆亠 仗仂从舒亰舒亠仍亳 from Mariia Bocheva
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342 1 https://cdn.slidesharecdn.com/ss_thumbnails/webinarowoxbi-180123164131-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0
https://cdn.slidesharecdn.com/profile-photo-MariiaBocheva-48x48.jpg?cb=1676292971 I am a versatile, highly organized and proactive Business Development Manager with 6+ years of experience including marketing and product management background. Have previous experience in various departments coordination and extensive customer service provision. Superb HR, business development, sales, and operations management skills. A dedicated professional who can prioritize projects, multi-task, leverage limited resources and successfully work with a variety of groups, levels, and personalities. owox.com https://cdn.slidesharecdn.com/ss_thumbnails/sharedabtestingoptimizationandresultsanalysisbymariiabochevaatd18-190311081903-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/ab-testing-optimization-and-results-analysis-by-mariia-bocheva-atd18/135597428 A/B testing, optimizat... https://cdn.slidesharecdn.com/ss_thumbnails/sharedresearchonlinepurchaseofflinewhatshareofyourcustomersbuyin-storeaftervisitingyourwebsiteperfor-190311081503-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/research-online-purchase-offline-what-share-of-your-customers-buy-in-store-after-visiting-your-website-mariia-bocheva-performance202018/135596570 Research online purcha... https://cdn.slidesharecdn.com/ss_thumbnails/owoxbiwebinarattributionmodeling101-190311081116-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/attribution-modeling-101-mariia-bocheva/135595728 Attribution modeling 1...