There’s not much point sending direct mail without direct mail targeting. Doh.
Let’s look at this through the imaginary unit of currency – Mailbucks ($) – with an imaginary marketing department tasked with selling premium memberships for a general wholesale warehouse – MegaBoxCo.
Our four heroes need to figure out the best way to target the potential list they have access to – 60,000 new startups per month.
MegaBoxCo are au fait with their numbers and at the simplest level they work to a three year customer lifetime (profit) value of 1500$ (500$ per year, three year lifespan).
Marketer #1, Ashley A: 60,000 startups! Let’s hit everyone!
Ashley thinks the way to go is to target the entire mailing list every month and spend 60,000 stamps, estimating a response rate of 2.5% to net 1500 new clients.
Ashley’s campaign costs 60,000$ but only actually converts at 0.5%, pulling in a healthy, if disappointing 300 new clients theoretically ostensibly worth 450,000$ and an individual customer acquisition cost of 200$.
The problem is, 50% of the new members aren’t worth anything much – they’re just normal shoppers keen on a bargain and have heard MegaBoxCo is brilliant for televisions.
Ashley’s targeting strategy was basically no targeting. Just get it out there and get as many new customers on the books as possible. He scores:
| Send to 60K list | 60,000$ cost |
| Respondents | 300 |
| Response Rate | 0.5% |
| Junk Respondents | 150 |
| Actual Segment LTV after removing junk | 225,000$ |
| Profit | 165,000$ |
Pretty good, but could be better.
Marketer #2, Blaine B: “Wait a minute, why are we targeting holding co’s, dormant co’s and weird stuff like zoological gardens?”
Blaine spends an afternoon identifying the very rough edges of the SIC codes in the list and removing them. These people were never going to respond no matter what the offer, so in the first segmentation and targeting mistake Ashley had basically spent money to put mail directly in the recycling bin. Targeting is about sending to the people most likely to respond profitably to your offer.
Blaine’s segmentation efforts shave 10,000 pointless prospects off the target list leaving 50,000. The same number of people overall respond, but now the percentage response rate goes up, acquisition cost goes down and lifetime profit contribution is higher.
We still have the 50% non contributing customers, and things are slightly better:
| Send to 50K list – removed bad 10K | 50,000$ cost |
| Respondents | 300 |
| Response Rate | 0.6% |
| Junk Respondents | 150 |
| Actual Segment LTV after removing junk | 225,000$ |
| Profit | 175,000$ |
Marketer #3, Chadwick C: “Why don’t we just target the prospects that make us the most money? I mean, IT consultants are great, but they don’t have stores to stock?”
Chad takes a day building a target audience to mail directly based on existing profitable clients and more or less slashes the list in half. The number of actual respondents also halves of course because now we’re not sending to those who would join but contribute nothing. Just the good ones.
Now things get exponentially better.
| Send to 30K list | 30,000$ cost |
| Respondents | 150 |
| Response Rate | 0.5% |
| Junk Respondents | 0 |
| Actual Segment LTV after removing junk | 225,000$ |
| Profit | 195,000$ |
Marketer #4, Daphne D: “Um, why are you guys sending to Scotland when our furthest store from London is in Manchester?”
Daphne realises that the list is still nationwide and there’s a quick win to stop targeting anyone too far north. On average she knows her brand-loyal customers will drive up to 15 miles to shop, any further than that and they’ll lose to their competition.
But as she delves into the data more, she realises that geographic targeting can go deeper than north or south and she reduces the list to a 20 mile radius (safe side) around each of her 27 outlets throughout the British Isles.
Daphne’s efforts shave a further 1500 off the list but also loses 5 conversions from particularly enthusiastic buyers in more remote areas. With such high LTV, this targeting test is deemed a failure.
| Send to 28.5K list | 28,500$ cost |
| Respondents | 145 |
| Response Rate | 0.5% |
| Junk Respondents | 0 |
| Actual Segment LTV after removing junk | 217,500$ |
| Profit | 189,000$ |
#5, Daphne Again: “Oh wait a minute, we know that 20% of our target market is tradespeople…”
… and 15 of their stores are right next to SuperToolBox Outlets. She speculates that a separate campaign to tradespeople – plumbers, electricians and so on – would yield higher conversion rates. Bribing them with a free 10$ lunch voucher (cost of 2$ when redeemed) she doubles the response from tradespeople, adding an additional 30 respondents! It costs her an additional 120$ because she has to fund 60 free lunches, but adds another 45,000$ ish onto the bottom line.
| Send to 30K list | 28,500$ cost |
| Respondents | 150+30=180 |
| Response Rate | 0.6% |
| Junk Respondents | 0 |
| Actual Segment LTV after removing junk | 270,000$ |
| Profit | 239,880$ |
Targeting Is Boss
We could continue… we’ve already moved the needle from 175K to 240K (almost +40% profit) by just doing the obvious with a couple of little experiments.
From here we might look at:
- Director profiles (turns out that 99% of new customers for MegaBox are founding their first company – if they’re directors already they’re often members).
- We could squeeze out more conversion from our lists in the same way we had specific campaigns for specific trades.
- We could try some targeting based on timing – some sectors buy a few weeks after founding, some need to stock up right away.
- Proximity to the stores, geographic messaging, based on local events or nearby alternatives. Even proximity to competitor’s stores.
- We might go for some good old fashioned keyword filtering to block out obvious unwanted guests.
- Director ages – MegaBox shoppers are 30-65, we might shave off some untargeted recipients by blocking anyone outside that range.
Before anything else, direct mail targeting should be considered, evaluated and tested.
With The Mailing Co you have the ability to target down to an individual level.
