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Session 7 - Experiments & Trends - Coggle Diagram
Session 7 - Experiments & Trends
Design, conduct & analyze experiments
AB testing
(split testing) = randomized controlled experiment that compares 2 or more versions of a variable to determine which one performs better
creating 2 versions of a website or app, each with a slight diff
(ex: one version might have a diff. headline, call to action, or button color; the performance of each version is measured)
can be measured in a number of ways, such as click-through rates, conversion rates, or revenue
Example
on e-commerce industry
Company
: Zappos;
Test
: Headline of a product listing;
Variant A
: "Save 20% on this amazing dress!";
Variant B
: "Limited-time offer: Get this dress for only $49.99!"
Results
:
Variant B
outperformed
Variant A
by a significant margin, with a 16% increase in
click-through rate
(CTR) and a 22% increase in
conversion rate
=> the more specific & actionable headline
(i.e., "Limited-time offer: Get this dress for only $49.99!")
was more effective at persuading users to click on the product listing & make a purchase
Case study
Integrated exercise data driven decision making
Current trends & challenges within digital analytics
Current Trends
Hyper-Personalization
: Use of real-time cust. data & analytics to create personalized experiences for cust. => increasing availability of cust. data & the ability to use ML to analyze this data
Integration of Many Diff Data Sources
: DA teams asked to integrate data from a wide variety of sources
(websites, mobile apps, social media, and CRM systems)
=> can be a challenge, as diff data sources may have diff formats and structures.
Application-Based Landscape
: DA landscape is becoming more app-based, with teams using a variety of tools & technologies to collect/analyze/present data => can make it difficult to track & measure the effectiveness of DA initiatives
Challenges
Ethics & Legal Aspects
:
Ex
: Personal data; Data breaches & Privacy concerns
New Technologies
:
Ex
: Deepfakes; Metaverse; Generative AI; Blockchain; & Virtual and Augmented Reality = technologies that have the potential to revolutionize the way we collect, analyze, and present data
Customer Retention Management
(Netflix case)
"Customer retention rates are decr. & you want to set up a campaign to boost customer retention"
-> Identifying & implementing strategies to retain existing customers & prevent churn
Artificial intelligence
(AI),
Big Data
, and
Machine Learning
(ML) can play a significant role in understanding cust. behavior, predicting churn risk, & developing personalized retention campaigns
Data Collection & Preparation; Customer Segmentation & Profiling; Churn Prediction Modeling; Personalized Retention Campaigns; Continuous Monitoring & Improvement
Personalize the user experience; Identify content trends; Resolve customer issues