Post on 15-Jul-2015
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IxD ∩ DS
IxDA Helsinki 23.4.2015
Jouni Kallunki, Data Scientist @ReaktorNow @jouni9
What is Data Science?(Almost) Synonyms:
Machine learning, statistical modelling, analytics
A field between coding and statistics
Mathematical and/or statistical modelling of user behaviour and events
How is it related to IxDProvide means to improve services through:
Search, sort, personalisation, prediction
Assist design process through:
Analysis of user behaviour, finding patterns, related things, user paths
Testing
IxD tells how the analysis results and predictive components are built into the actual products and services
Where’s the $$$Two main goals:
Directly increase revenue from each session, better conversion and up-sell
Relevant especially for services used only once
=> Always Be Closing
=> Sell more! Sell expensive!
Make the service better, keep users coming back
Crucial for often used services
=> beat the competition.
Quick moneyAlways be closing
Increase probability of buying
Increase purchase size
Predict what add-ons you can sell or how expensive
Methods
Typically a prediction of how likely user is to buy each product
Finding optimum between likely small purchase and seldom large one
Remove unnecessary elementsShow only the relevant stuff, but what is relevant?
Predict what user is looking for based on:
her usage history, what she has done in this session and context
What should be done with the predictions?
How to balance reduction and choosing from all possibilities?
Search should be extensive, but return only relevant results and sorted meaningfully
Again: what is relevant?
Algorithms to sort found matches
Can also suggest similar items not matching exactly
Make searching easy
Assist browsingLarge catalogs make it impossible to find anything.
Search is good, but maybe don’t know exactly what I’m looking for.
Or maybe detailed description is lengthy.
Find thing through “something like this” browsing.
To help this:
Find similarities between items, automatically either through item’s properties or user behaviour
AutomateDon’t make people do machines work.
Repeatable simple tasks belong to computers.
Such tasks are most often found in professional tools.
Accounting, inventorying, etc.
How much can be automated? Maybe just suggest?
Usage analysisHow services are actually used can help designing it better.
Recognise:
Different types of users
Motives behind shopping baskets
Trends and other variations in time
Items, actions, persons that are related or similar
TestingTest to find out reality:
Even more important than what users do is how changes in service alter their behaviour. There is no other way to find out.
Good test result analysis:
Statistically valid results, acknowledges uncertainty
Includes variation of users
Not only webMost cases are in web applications and services
Mobile apps provide even more information about location & context
Also, assistance is always in users pocket.
IoT is coming…
IoT =? Interplay of things
What can my refrigerator tell to my oven? Or to grocery shop?
How much we can automate? Or suggest?