Dimensions

A good business-related prop bet question (or question for those “predictions markets”): What would you set the over/under at for the number of live discount codes for a DTC brand? Or maybe a better one: What percentage of brands would be able to answer the question of “how many discount codes do you have live” without looking?

From my experience talking with brands, discounts specifically, but offers more generally, end up unwieldy (even if they can be effective at capturing margin you’d otherwise leave on the table). I’ve been asking the question recently, “What’s your offers strategy?”. The most common response, “there is none”. No single person owns them, and every department/team ends up with their own approach. 

All that to say, I doubt many brands would be able to accurately answer the question posed above, much less answer the intent behind each. 

A part of the problem is that there are so many different types of offers floating around. You have a welcome offer, abandoned cart offers, Labor Day promotions, end of season clearance, bundle discounts, a free shipping threshold, a BXGY, affiliate codes, … You get the picture! How can we possibly make sense of all of this and know if all the offers collectively ladder up cohesively?

I don’t have a fully fleshed out version of a strategy, but wanted to progress our collective thinking with a framework.

If you break down an offer, there are three dimensions to it:

1. Who: The customer (or customer segment) being targeted.
   Does everyone get this? Do you need to know someone? Previously purchased? Or maybe never purchased?

2. What: The offer being made
   Is 10% off enough? Maybe 20%? A free gift? Spend X Get Y? $10 off your first order.

3. When: The timeframe in which the offer can be redeemed by the customer
   Is this always live on our site? Or just during holidays? Or maybe it’s triggered based on a user action like abandoning cart!

A framework using these dimensions, the ‘3D-Offer’(?)¹ basically forces you to spell out each of these components, which, on its own, creates structure. 

The advantage here, though, is much more than just being able to answer a question about what’s live.

You can start to see how these offers might overlap with each other or end up creating conflict. And while it might be more bottoms up than most people would like for creating a strategy, it starts to show what strategy you’ve organically adopted. Optimizing your offers gets a lot easier from here. 

Are we aggressive enough in our acquisition offers, given our growth targets? Do we need better offers (or different offers) for our existing customer file to improve repurchase rates? 

You can answer these questions by experimenting on these offers to prove incremental profit (Intelligems supports this).

And, if you’re segmenting offers by customer segment, you can then cut that data to see if there are overlapping traits between the segment you targeted and another customer segment to test scaling the offer to additional “who” segments.

The wins here are both ops related and growth related. If you build a 3D-Offer framework (maybe I really should trademark this?), I think you end up with a clear picture on what you’re offering to which customers and when. That gets you a handle on execution and, more importantly, which parts are working.

1  Maybe I should trademark this?