Launching an a/b testing & optimization tool brings with it a great deal of temptation. It is very appealing to just want to jump in and start deploying tests to see what it can do.
What are some of the traps when launching an a/b testing tool and optimization program? What are some of the common mistakes when getting started with optimization? What should you avoid?
On this week’s episode of the 33 Tangents Podcast, Jason and Jim are joined by Jason Boal to continue the conversation around testing, optimization, & personalization programs. This week they discuss the common pitfalls organizations run into when integrating these tools and building these programs.
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#302 - What Is Preventing Digital Analytics From Becoming Indispensable?
#301 - What Does It Mean To Be Boring Yet Profitable?
#300 - Developing A Testing Roadmap
#299 - The Core Analysis Your Analytics Practice Must Be Doing
#298 - Tag Management Methodologies
#297 - The Draw Of Over-Engineering Analytics Data Collection Solutions
#296 - The Architecture Pitfalls of Single Vendor Solutions
#295 - The Risks, Costs, And Potential Benefits Of Vendor Lock-In
#294 - Customer Experience Breakdowns Caused by Siloed Data
#293 - What Can Be Done To Prevent An Analytics Practice From Developing Conflicts Of Interest?
#292 - How Can Conflicts Of Interest Happen With An Analytics Practice?
#291 - Running An Analytics Practice So That It is Viewed as More Than Just A Cost Center
#290 - From The Archives: How Can A Low Level Of Data Literacy Impact An Organization?
#289 - From The Archives: What Will Make Someone Successful in Digital Analytics?
#288 - Building An Analytics Practice Like It Is Its Own Business
#287 - From The Archives: What’s The Deal With Server-Side Tracking?
#286 - From The Archives: Buying and Selling Services: Do RFPs Actually Provide Value?
#285 - From The Archives: Why Is Unlimited PTO So Polarizing?
#284 - From the Archives: Employee Autonomy and PTO
#283 - Talking About The Value of Data, or Rather, The Cost Of Bad Data With Stewart Alaniz
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