Lauren Owen Data Analysis

Digital Analytics / UX Research / Product Optimization

Linkedin Resume

“Lauren Owen's actions, creations, and suggestions have resulted in considerable growth for Metta. She had a deep understanding of the people using our product, and her reports and designs were clear and actionable.”

Vince Favilla
Founder
Metta

Baynote/Kibo

Optimized websites and emails for several Fortune 500 clients and over 100 small to mid-sized businesses.
Helped customize A.I. algorithms for merchandizing.
This helped up conversion rate by 23% on average.
Daily reviewed customers websites and emails to provide optimizations and health-checks.
Monitored web analytics and ran A/B tests.
Customers completed more sales, remained more loyal, ease of use was fine-tuned, and merchandising surprised and delighted customers.

Web Analysis work was performed on a daily basis at Baynote.

"Lauren works tirelessly to manually tweak recommendation quality over all our customer sites and provided valuable feedback to the engineering team in terms of tuning our algorithms. She shows an exceptional understanding of the mechanics and trade offs involved in our often highly mathematical models of user behavior."

Team Member
Baynote/Kibo
(Full recommendation viewable on Linkedin)

Data Analysis for Covid-19

Google had very jagged data for Covid-19 deaths:

I created a graph of a rolling 7-day average. This gave us a view of if cases were really going up, on average. They were. This helped me predict what the virus numbers might be doing the following week.

About a week later, Google started doing the same rolling 7 day average.

Metta

As Metta's UX and Marketing Strategist, I explored the motivations for one segment of users: Our target persona wished to have a partner who appreciated her as uniquely wonderful.

My ads typically:

Increased clicks by 32% on the site, indicating more engagement for initial users than in the past.
Increased registered users by 6%.
Increased percent of profiles with pictures by 19%.

Other Accomplishments:

Analytics: A/B tested placement of “See Your Matches” button.
Conducted User Experience and User Interface Research studies.
Asked deep dive questions to get to the heart of user needs and motivations.
Observed participants' use of app, and asked questions to find out where they would go for what action, and when they were stuck, frustrated, or delighted.
Discovered another barrier to entry was speed of the site, helped us reinvest in site speed.
Created detailed reports and recommendations based on synthesized data, which increased growth.

Expedia
(Speculative Redesign through UC San Diego Program)

In my redesign:

Map shows distance from attractions, increasing motivation to purchase.
1-5 stars provides visual reassurance which eases the sale along, and reduces anxiety.
Other options are offered on the right side which was a proven strategy tested at other companies.
And many more proven strategies.

Appetitely Meal Kit Delivery Service: Recapturing Users

Problem: Appetitely (a small start-up fictional company modeled after popular meal-kit-delivery companies such as HungryRoot) has users unsubscribing from their paid service everyday.
Research Question: Why do people unsubscribe from Appetitely? What are the top specific things, by rank, that cause this among users canceling the service?
Method: As each customer cancels, they will see this survey prompt immediately. Survey will be given to every user that cancels over a 14 day period.

Example call to action:

Survey Design:

A larger text box for open-ended question answers encourages more detailed response.
Answers are NOT forced/required so that we can receive more data.
Likert Scales (degrees of satisfaction) are balanced, and questions are designed for clarity and non-bias.

The Results:

Reasons Customers Canceled:

Above, you can easily see that the price of the food is a major contributor to why people canceled.
One solution: Canceling customers could be offered a discounted rate.
Please check out the survey below to see how the questions are worded for clarity and non-bias:

View Survey

Wikipedia

This project was conducted through Cooper's UX Certification Program in partnership with Wikipedia, and leveraging a group of recruited target market individuals.

Goal: Wikipedia desired more volunteer hours, and wanted to re-design their mobile app.
My Method:

Interviewed power users.
Crunched data to create a character/persona that we would be designing for.
Created design/strategy to help users get what they strongly desired: being seen as experts by their community.
Collaborated with a multi-functional team on interviews, data-synthesis, and design.

My design/strategy:

Users could earn badges which designated users' level of expertise.
Badges could be posted on LinkedIn, Facebook, and more.
Wikipedia would have a monthly featured "expert" user.

Presentation:

Presented findings live with slide deck to Wikipedia Stakeholders, using multimedia presentation with wireframes and illustrative visuals.

More Visualization Projects in Tableau
(Shaded for a typical user and the top 8 types of color blindness)

In this visualization, it becomes clear that there's some connection between "wealth" (darker shades) and higher average "happiness scores" (5 to 7.5). (Note: Correlation does not equal causation.)

https://public.tableau.com/shared/RYW7R9PKK

Correlation of money and happiness:

https://public.tableau.com/views/moneyandhappiness_16451282234900/Money

**The graph merely shows that money is often found where happiness is. The correlation coefficient is 0.78 (1 being the highest). It's highly correlated.