Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Lessons Learnt From Sending Over 3.5 Billion Emails


Published on

Solo session with Kathryn Wright

Published in: Business
  • Be the first to comment

  • Be the first to like this

Lessons Learnt From Sending Over 3.5 Billion Emails

  1. 1. Lessons learnt from sending over 3.5 billion emails ASE 2019 @kathrynwright81
  2. 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. 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. 4. Which channels do you expect to have the most success with? Email is still out in front! Great retention channel.
  5. 5. Treat each of your customers like the individuals they are Not as a cohort Or a segment …Or a demographic
  6. 6. If not, looking at just my age, home ownership status, marital status this is what I get advertised 🙄
  7. 7. Basics Deliverability Open Rate Click Through Rate Purchase Rate Purchase Amount Repeat Purchase List size - Engaged! Attrition Rate Re-engagement
  8. 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. 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. 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. 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. 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