CRAP Talks – What We’ve Learned So Far

Opened my eyes a little bit to data manipulation

Learning how product teams are organised in companies

A strong strategy on scaling a product team

How to do geo magic with Alteryx

Structure / motivations and goals of product teams

Some​​ great tips on how data can be visualized through different lenses and ow to scale a team

I'm not a data analyst but John's talk was incredibly engaging so just fun to learn about something that I don't touch on in my day job.

Alteryx and data visualisation are awesome

Repotting plants can be tricky 😉 I especially loved Alice's talk about product and product team development. And I'm totally buying Alteryx...if I can ever afford it 😉

"We need to define what 'better' means"

Use of spatial analytics, product team building

About spatial data

To think about our data in new ways. To try more brutal A/B tests. To always re-evaluate team structure regularly.

Lots of context about the growth patterns of product teams.

Spatial​​ analysis and how to "re-pot" a product team in an S(perhaps M)E

Justifications for not throwing an AB test at every argument

More about AB testing

that I might have been on the religion side of data, that defining processes and sticking to them is critical

How to distinguish religion from science 😉

Vision and strategy of a high efficient test culture; debate on bias of data

How other businesses implement CRO

That all data is biased. To think outside the box. The power of resource and budget.

About how Hotels.com Work with CRO, and about data science and how important it is to remember about sociological science too

Analysing test program metadata is a good idea I've thought about before but was inspired to implement after Arina's talk. Also great to see people questioning the validity of traditional analytical methods/processes (Jonny/Shaun)

How other companies do things and challenges to conventional theories

Test with integrity

All about slam poetry.

I learned that hotels.com apply a “learn rate” to measure that they have found learning and an “inconclusive rate” to measure that they are learning from the projects they choose

To make use of the abundant amount of human psychology research out there. Instead of spending time AB testing everything when the research had already been done. (From Shaun’s talk!)

How large client side teams work, their processes and vision.

That we focus on the wrong things during ab testing and we should have a more robust strategy when it comes to how we test, for how long and data analysis

To be suspicious of experimentation evangelicals

CRAPsters are good-natured

Importance of Automation of results

To question what data is

I liked the learnings from the stock marketing for measuring potential

The good questions Joanna asks her colleagues.

Finally! A proper ai definition. Seems as if the tech bubble is just about buzzwords.

Understanding how to be​​ honest about the value that your work actually may bring before anything gets built.

product management cycle

About customer features at Just Eat, and the end of the world

Daniels talk was very knowledge and inspiring in terms of industry trends and where AI is headed. The​​ stuff around kpi contribution and how JustEat use machine learning was insightful.

That the AI super brain will destroy the world

Metrics that Simon evaluates, Interleaved a/b testing in recommendations, Difference between AI and automation, etc.

ICE framework, What AI really is (and isn’t)

Ai definition and problems. Other talks elaborate on things I heard about but it was good nevertheless

Lots about how other companies approach product and analytics

Everything seemed really applicable, but probably the 3 key questions in the first talk

The end of the world is nigh!