Solo session with Kathryn Wright

1. Lessons learnt from sending over 3.5 billion emails
ASE 2019
kathryn.wright@discountvouchers.co.uk
@kathrynwright81

2. Discountvouchers’ audience
Love good value
DiscountVouchers (veteran emailer) has been in the business of
driving incremental revenue and pleasing consumers with daily
offers via email since 2006!
Pretty old school TBH
Not the biggest
UK only
Very lean team
Affiliate only
Email only

3. 2018 Results
From Traffic Driven By Us
Clicks
Purchases
£3,600,000 Revenue Generated
2,600,000
122,000
71,000,000 Emails Opened

4. Which channels do you expect to have the most
success with?
Email is still out in front! Great retention channel.

5. Treat each of your customers like the
individuals they are
Not as a cohort
Or a segment
…Or a demographic

6. If not, looking at just my age, home ownership status,
marital status this is what I get advertised
🙄

7. Basics
Deliverability
Open Rate
Click Through Rate
Purchase Rate
Purchase Amount
Repeat Purchase
List size - Engaged!
Attrition Rate
Re-engagement

8. They can really only say for sure what they
DON’T want
What do customers want?
So it’s our job to work it out from their
actions
testing and reading the data

9. What can we infer
from the data we
have about them
What do we know
about them from
their actions?
To be useful to merchants!

10. • Personalization. It enables superior customer retention and stronger
acquisition, and therefore leads to sustainable,profitable growth.
• Using simple field insertion for a customer’s name can increase email open
rates by upwards of 18%
• Amazon’s recommendation engine accounts for 35% of the company’s
revenue, which shows that the right product recommendations are invaluable
for retailers.
• Collaborative filtering - also known as “those who bought this also bought that,”
is similar to making recommendations based on what’s trending.
• Personalise email send time
• Interest-based recommendations
Move Beyond Segmented Demographics
Knowledge is Power

11. Merchant Preference
Location
Timing
Age
Gender
Go at least 5
Deep!
% Saving
These variables
for your most
engaged users
become your
shopping list for
finding new usersTheme
Engagement
Historical
Data

12. Leveraging Wisdom of the Crowd
Personalization Built on Predictions
Single User Profile: Lifetime View of
Rules on Deals Selected by
User Interest Behavioural Predictions
Dynamic Email
Interest based personalisation
Higher open rates
Higher Click through rates
Increased purchases
Better deliverability
Reduced churn
Visibility of what your best customer looks like