purchase prediction by statistical analysis (統計技術を用いた商品購買予測)
DESCRIPTION
TRANSCRIPT
Takashi Umeda (梅田卓志)
@umekoumeda
Oct. 20th , 2012
Purchase prediction
by statistical analysis
楽天技術研究所 Rakuten Institute of Technology
Value Proposition
Third Reality
Vision Tokyo & NY
& Paris
Strategic R&D organization for Rakuten
Biography
3
• Takashi Umeda
• Twitter : @umekoumeda
Profile
Work
Purchase prediction >>> Users’ Benefits
Prediction of purchase interval
Seasonality forecasting
Preference prediction
Biography
4
• Takashi Umeda
• Twitter : @umekoumeda
Profile
Work
Purchase prediction >>> Users’ Benefits
Prediction of purchase interval
Seasonality forecasting
Preference prediction
Objective
• Predict the users’ purchase interval
• Focus on non-durable goods
5
Example of application
30 days 30 days 30 days
Past Future Past Future
Buy Buy Buy Buy
Example of application
30 days 30 days 30 days
Past Future Past Future
Buy Buy Buy Buy Remind！
We can remind users
just before next purchase
30 days
そろそろ、買い時では？
Users’ benefits
Prevent users from forgetting to purchase
Empty
I forgot to
purchase!
It’s time to
purchase!
Few
NG OK
Notification
How can we predict
purchase interval ?
Data set
Purchase history in rice category
Target users :
Users purchasing over 4 times in one year
Pick Up
Example of users with only fixed intervals
BUY
30 days
BUY BUY
31 days 29 days
BUY
Past Future
BUY
30 days
BUY BUY
31 days 29 days
BUY
Past Future
All purchase intervals are fixed. It’s about 30 days.
We call those users as “Users with only fixed intervals”
Example of users with only fixed intervals
Coverage of users with only fixed intervals
Target
Users with only fixed intervals
(Predictable users)
About 11%
• Target : Users purchasing over 4 times in 1 year
Too low coverage !
Not practical !
Example of users with a few outlier intervals
BUY
31days 60 days 29 days
BUY BUY BUY
30 days
BUY
BUY
31days 60 days 29 days
BUY BUY BUY
30 days
BUY
Most of purchase intervals are fixed.
It’s about 30 days.
Example of users with a few outlier intervals
BUY
31days 60 days 29 days
BUY BUY BUY
30 days
BUY
A few intervals are outlier intervals
Example of users with a few outlier intervals
BUY
31days 60 days 29 days
BUY BUY BUY
30 days
• There are a lots of users with
many fixed and a few outlier intervals
• We call those users as
“Users with a few outlier intervals”
BUY
Example of users with a few outlier intervals
BUY
31days 60 days 29 days
BUY BUY BUY
30 days
BUY
Cause for the outlier interval
Why outlier intervals happened
?
31days 60 days 29 days 30 days
Cause for the outlier interval
5kg 10kg 5kg 5kg 5kg
• If consumer purchased more, interval had been longer
• This type of users account for 22 %
Coverage of users with a few outlier intervals
Predictable users
47%
Users with a few outlier intervals
36%
• Target : Users purchasing over 4 times in 1 year
Target
Users with only fixed intervals
11%
Trends in any other categories
Predictable users exist in not only rice category
but also any other categories.
Ratio of predictable users
33.4 % 55.3 % 46.1 % 43.2 %
Items which we should show
57%
In the reminding system, it’s better to show
the item which has been purchased before.
Users repeatedly purchase
the same item at the same shop
Users repeatedly purchase
different items - Items sold at the same shop (10%)
- Same priced items (14%)
43%
Items which we should show
21%
In the reminding system, it’s better to show
various kinds of items.
Users repeatedly purchase
the same item at the same shop
Users repeatedly purchase
different items - Items sold at the same shop (32%)
- Same priced items (31%)
79%
Summary
There are many predictable users 47% in the rice category
We can remind users at the right moment !
It makes users happy ! Prevent users from forgetting to purchase item
Message
There are many
Fixed interval users
It make users happy !
• In rice category,
those users account for 47%.
• Many categories have same trends
By using detected fixed interval,
We can remind users
just before next purchase
Users can avoid from forgetting to
purchase regular buying items
Purchase Prediction
Users’ Benefits
Message
There are many
Fixed interval users
It make users happy !
• In rice category,
those users account for 47%.
• Many categories have same trends
By using detected fixed interval,
We can remind users
just before next purchase
Users can avoid from forgetting to
purchase regular buying items
Purchase Prediction
Users’ Benefits
If you come up with any idea,
feel free to tweet via twitter
@umekoumeda