The optimism of #Purpose & science of #design in forging #digital customer experiences! Opinions my own!

Some interesting factoids how detailed marketing execution swings the response pendulum in our favour:  

• In recent MarketingSherpa research,for example, emails sent around 9 a.m. achieved click-through rates 15% higher than those sent at 4 p.m.
• A study by eROI found Wednesday was the most successful day to send emails, while late afternoon was the optimal time. 
• And lately there’s been a lot of buzz about Sundays being a great day for emails because many people want to clear their inboxes before starting the week.

One size doesn’t fit all.

 It’s not enough to be targeted sending out communications – testing responses coming back is of equal importance. How else do we learn about and assimilate individual customer preferences? It’s worth our while testing the response to different communications we send at different times and on different days to find out the best days and times for our company and our customers.

Take for example, a campaign targeting salary accounts within the Bank. If we had a monthly mailing cycle, i.e. all communications sent once a month – we would be getting say half of the responders, maybe even less. Why? Some companies may be crediting their employee salaries in the 3^rd week of the month – and some on the 2^nd week etc.

So if we split the single (monthly) mailing cycle into 4, one per week, with roughly a quarter of the volume each, we would presumably be enjoying significantly higher response rates.

4. Let Your Subscribers Tell You

This is part science, part art. When it comes to determining the optimal frequency of contacts by type, why not let our customers tell us themselves. We could employ qualitative methods – don’t shun what’s not quantitative – it gets the job done!

Qualitative methods such as a survey at sign up, a facility for them to indicate their preferences (opt in/out) and an option to limit the number of contacts they receive instead of opting out altogether.

Inactivity

If we define what inactivity really means, we would have groups of customers without favorable response to our contacts, in a given time frame. So an inactive customer, for example, could be some who hasn’t responded to our contacts for more than X = 6 months, and for the times that he/she did respond, the responses were negative.

Preferences

Our customers may explicitly (in a survey) specify their preferred communication channels, but the onus is on us to really understand which channels they transact with us in. There’s a difference. While customers may wish to consume information (e.g. product brochures, research advice etc) via a certain channel, they may opt to transact (close the deal, promise to purchase etc) using another.

No hardcore analytics required: start by tabulating our available outbound/inbound channels and overlay an interaction map using customer contact and response histories. So if we have X channels, the number (Y) of total transactions received for Customer (Z) would be bucketed by type.