The role of web analytics is to support business goals and provide sound rationale for key business decisions. In order to discover insights and develop plans for action from the data it needs to be approached with a holistic point of view. The abandonment rates of digital shopping carts should be viewed in conjunction with other metrics and in the context of the customer journey. Web analytics tends to focus on individual sessions however it is very important to have a customer centric approach; in this way web analytics can transform the way that business approach decision making.

1. RETAIL WEB ANALYTICS –
DEVELOPING ACTIONS FROM
THE DATA
Alejandra Leon

2. ©InterIKEASystemsB.V.2015
To create
a better
everyday
life for the
many
people.

3. Facts and figures from BY2014

4. Facts and figures from BY2014

5. Web Analytics at IKEA
WA is the process of
understanding our online
presence in order to:
Provide the right foundation
to make decisions
optimize our site/apps
optimize our marketing
efforts
…or both…to increase
conversion

6. Web Analytics insight potential
6
• What is the ROI of
our marketing
efforts?
• Where do visitors
come from?
• Are we placing the
right ads for the
right targets?
Offline
Difficult
follow up
Online
Great
insights!

7. The paradigm of web analytics 1.0
Clickstream
Insights

8. Banners
SEO
Media
Press
SEM
Calendar
Apps
Mailings
PPC
Metrics
Other
Sites
IKEA.com
Costs
Audiences
Stores
IKEA
Sales
Products
Pages
Carts
Paradox of data

9. HOME DEP PIP CART CHEKOUT
PAGE
Gallery clicks
Hover Action
Click on slide
Products displayed in page
Product colours
Price filter – amount range
Scrolls
Hover Action
Related products
Hover for prod info
Click on read more anchor
Delete from cart Click on slide
Name
Login attempts
Hover Action
ORDER
PAGE PAGE PAGE PAGEPAGE
Not all user interactions with the site
need to be measured
PRODUCT REF
CTR ADDED TO
CART
UNITS
CHECK OUT FLOW
START
ORDERCAMPAIGN NAME
VIEWED!

10. How we deal with data overload at
IKEA
10
• NOT everything is
measured
• ONLY needful data
should be collected
• Data MUST be
subordinated to
business requirements
and not vice versa

11. The paradigm of web analytics 2.0
Clickstream
Multiple outcomes
analysis
Experimentation and
testing
Voice of
Customer
Competitive
intelligence
Insights
The what
The how much
The why
The what else
The Gold

12. The process of web analytics
Set
Goals
Measure
ReportAnalyze
Optimize
Key Performance
Indicators (KPI)
Tracking
The right metrics
to the right people
Draw conclusions
and test new ideas
Improve the
site or drive
more traffic
Re-evaluate
the goals

13. How can Web Analytics help you
accomplish business goals?
13
Define your
goal
How to reach
goal
Action
Evaluation
Improvement
Promote new Wireless Charging
Furniture
Ads, Press releases, Site Visibility,
Special Offers
Creatives, Agencies, MKT, eCommerce
Visits, Traffic Sources, Campaigns, Product
Views, Orders, Sales, Shares (KPI´s)
Best performing products, channels with
higher ROI, best creatives, targeted audiences
Practical example

14. Clickstream
iOS AND WEB
MICROSITES CATALOGUE /
DIGITAL
PUBLICATIONS
IKEA APP
DESKTOP
IKEA.com
MOBILE
AND
1 Global 1 x Country
iOS

15. Experimentation and testing
DESKTOP
IKEA.com
MOBILE

16. IKEA bookbook

17. Voice of the customer
If you marry the what with the why,
you’ll have a lifetime of happiness
…better understand how
the IKEA catalogue is used
…to assess the importance
of structure and
storytelling
…to fuel creative
brainstorms of paths to
follow in the future

18. Competitive intelligence

19. Insights
“Segmentation
is the key to
finding
insights. You
segment, or
you die”
Avinash
Kaushik

20. Orders
Checkout
Cart Addition
Product View
Cart Open
Cart Views Cart Removal
Basic retail events
Sales
Units
Shipping costs
Insights

21. Insights
Product number:
20206806
Product View
Cart Addition
Stock Check Manual
Product Price
Sales
Orders
Units
Shipping Costs

22. Questions you can answer through
product reports
Ranks:
Most / less (viewed, added to
cart, sold…) products (names,
id, articles, HFB…)
Cross-sale:
Which products (names, id,
articles, HFB…) where sold
together with (specific product,
name, id..)
X Specific user type
X Specific device Type
X Specific delivery method
(purchases)
X Specific ZIP
X Specific order value
X Specific Shipping Cost
X Specific user types
USE SEGMENTATION AND…

23. Big data
A lot of our
growth will come
from doing what
we already know
in a much better
way.