This document provides a roadmap for personalization in ecommerce. It discusses challenges like lack of data integration and unclear ROI. It then outlines a progression of personalization strategies starting with basic web analytics like customer demographics and site behavior. Next steps involve enhancing data with surveys and purchase history to define customer segments. Integrating online and offline customer data through activities like click-and-collect and push notifications is also recommended. A case study demonstrates how connecting online browsing data to member IDs improved targeted event marketing. The overall goal is to develop a unified customer view across channels.

1. Knowing Me,
Knowing You
Creating a simple roadmap for
ecommerce personalisation

2. What’s
holding us
back?
Lack of ownership
Rare to find a centralised
personalisation structure with
spokes across the teams it
touches.
Data blindness
So much data, so little time. Data
is often in silos and disconnected
e.g. website vs. stores.
No clear ROI model
It’s shiny and new and people
struggle to know how to model the
financial benefits, so investment
goes to ‘known’ channels.

3. Creating a roadmap
✗
✓

4. Start simple – web analytics
data
What do we know?
What can we do with it?
New vs. return
customer.
Tailor brand value messages.
Geography.
Tailor USP bar to be country
specific.
Gender / Age.
Showcase relevant products.
Traffic source.
Replicate campaign creative /
tailor landing pages.
Device.
Promote relevant mobile
content.

5. Personalisation starts with the
basic things

6. A helping hand…

7. Progression: data enhancement

Integrate VoC data points - match customer feedback with online behaviour >
most common is survey.



Simple – create segments and compare behaviour.
Advanced – statistical regression (e.g. cluster analysis) to increase sophistication of
segments*.
Define buying cycles at category/product level – retargeting tailored to individual
buying journey.


Promote helpful content to customers when they’re indicating interest in a
product/service e.g. customer views sofas on 2 visits in X days but doesn’t buy –
email sofas buying guide.
When they come back, use content zone to surface this guide with strong CTA.

eCRM to build the customer profile.

Customer browsing/order data influencing on-site marketing – don’t blanket bomb
real-estate messages.
* Interesting IBM research paper: How to get more value from your survey data

8. Power of surveys + analytics
The ultimate aim is to identify customer segments for targeting

9. Encourage customers to share
data

10. Use what you already know:
site search
I previously bought 3x polo
shirts online for delivery to the
same store (Oxford Street):
• Jack & Jones
• Criminal
• Duck and Cover
What else could be done
with my data?

11. Are retailers missing a trick?
Order by 7pm tonight and collect from our Oxford Street store tomorrow after 12pm
If the customers uses click & collect,
promote store delivery
New from your favourite brands:
Use purchase history to surface
relevant brands/products

12. The end game: tying up loose
ends
Wow, you
remembered me!

13. And keep your house tidy

Basket.

Wishlist.

Giftlist.

Recently viewed.

My favourites.

My sizes.

My brands.
to name but a few….

14. Multi-channel: why not this?
Push notification within store geo-fence:
“Hi James, welcome back. New Ted Baker range now available
with £10 off when you spend over £100. Enjoy!”
Engagement from CSA based on recent history:
“Hi James, I see you recently went to the Paul Smith clinic at
Oxford Street. Did you have a good time? Would you like us to
alert you of any future events in that store?”
Engagement from Personal Shopper”
“Hi James. I know you usually buy from brands like Paul Smith
and Ted Baker but can I suggest something a bit different this
time, we’ve got an amazing new range from Ralph Lauren.”

15. Case study: member
organisation

The challenge:
 Online and offline data not connected for the utopian ‘single customer view’.

The brief:
 Identify a low cost way of increasing the connectivity to enable cross channel data to be
used to improve targeted marketing.

The approach:
 Focus on 1 offline activity to connect dots with online behaviour – events.
 Tag all browsing activity to member ID captured via analytics.
 Use analytics browsing data to identify user journeys relevant to events.
 Export data and run queries to identify event prospects based on closely defined criteria:
 Purchase activity.
 On-site content activity.
 In-app content activity.
 Match against demographics of core event audience.
 Targeted email campaign with personalised message in ‘My Account’.

16. The results

Increase in open rate of event email campaigns.

Increased response rate from members.

Increased number of attendees.
Long term goal:

Increase overall member satisfaction for this segment – measured via biannual satisfaction survey.

17. Thank you!
@jamesgurd