eBay joined NMPi's 2016 Digital Seminar on "Transforming Data-Driven Marketing." Everything happens for a reason; but on some occasions that reason is clearer than others. Phuong Nguyen explores how data can reveal the logic behind apparently serendipitous events, drawing on insights from eBay’s 19 million UK monthly users to explain seemingly unexplainable trends in consumer behaviour.

1. The Science of Making
Better Predictions
Phuong Nguyen
Director of Advertising

2. eBay’s credentials
• One of the world’s biggest repositories of data
• One of the UK’s biggest media publishers:
19 million
unique users per
month.
6 billion
ad impressions
per month.
41%
reach of UK adult
population.
Source: eBay internal data 2015.

3. 20 minutes on ebay.co.uk
By the time I finish, the following will have been bought on eBay:
12pushchairs
100pairs of jeans
10TVs
28iPads/tablets
20cars
Source: eBay internal data 2015.

4. Data drives insights on eBay
Our data isn’t limited only to purchases.
Shopper
insights
This forms an enviable list
of observed not inferred
shopping behaviours few
if any can match.
Buy Bid
Search View

8. 3 things we couldn’t
have predicted

9. 3 things we couldn’t
have predicted

10. 3 things we couldn’t
have predicted

11. How can we
deliver better
predictions?

12. 1. Validate efficacy
of data
Inferred vs observed

13. 2. Understand the
impact of data decay

14. Understand the impact of data decay
Example: Length of shopping windows
50,000,000 transactions on eBay UK every month
DaysMonths versus
Source: eBay internal data 2015.

15. Consideration
window varies
markedly
between
products and
categories
eBay Advertising Christmas Tracker is based on search data from ebay.co.uk in 2014.
Peak windows of influence correspond to protracted peaks for searches within each category.
Home Furniture
& DIY
Peak window of influence:
2nd November - 6th December
CATEGORIES
THE COUNTDOWNTO CHRISTMAS
September October November December
September October November December
Christmas Day
Cameras &
Photography
Peak window of influence:
2nd November - 13th December
Clothes, Shoes
& Accessories
Peak window of influence:
12th October- 6th December
Computers,Tablets
& Networking
Peak window of influence:
2nd November - 6th December
DVDs, Films
& TV
Peak window of influence:
16th November - 24th December
Mobile Phones
& Communication
Peak window of influence:
21st September - 15th November
Sound
& Vision
Peak window of influence:
9th November - 6th December
Toys &
Games
Peak window of influence:
5th October - 6th December
Video Games
& Consoles
Peak window of influence:
16th November - 13th December

16. 3. Use quality data to
explore ‘hypothesis’
driven trends

17. Use quality data to explore ‘hypothesis’
driven trends
Example: SanDisk
SanDisk wanted to sell more memory products but avoid
wastage. eBay specifically targeted only those shoppers who:
• Were currently in-market for memory products
• Had recently bought a host device
Results
• Sales tripled
• Net promoter scores increased across all metrics studied
• Broadcast level of audience reached (2.2m unique individuals) but all qualified leads

18. 4. Dig deep into
the data to reveal
‘serendipitous’ trends

19. Insight driven advertising solutions
Example: The Co-operative Electrical
Shoppers of bakeware are incredibly diverse.
The Co-operative Electrical wanted to target them
with a campaign for kitchen appliances.
Bespoke home bakers segment targeted to coincide
with the final of cult TV show The Great British Bake Off.

20. 5. Productise your
predictions

21. Predictive targeting case study:
New Mums
High Street branch of chemists
Campaign
performance
(CTR)
Baby category targeting: 0.05%
Baby keyword targeting: 0.07%
New Mums targeting: 0.11%

22. Summary
1. Validate efficacy of data (Inferred vs observed)
2. Understand the impact of data decay
3. Use quality data to explore ‘hypothesis’ driven trends
4. Dig deep into the data to reveal ‘serendipitous’ trends
5. Productise your predictions

23. Let’s hope predictive analytics can help us
all make the right choice in the future

24. Thank you for your time
@phuongster