Today, we’ll address another common question I hear from clients: How do you define the trade-in value of a device?
First, let’s set the stage:
- You are in charge of a (phone) trade-in program
- One of the main OEMs is launching its new product a month from now
- Leadership asked you to create a promotion for the occasion, with a 2 months shelf life
- The sales team wants the trade-in value to be as high as possible, but the CFO imposed a maximum investment of $100/product
- You expect Competitors (other trade-in providers) to come to market with their own aggressive offers
- Due to your TI model specificities, you expect the first phones to be available to resell a full month after launch. This accounts for the time to receive the devices, triage, put them up for sale and receive the purchase order
- The last products returned will be resold another 3 months later. Yes, the program only lasts 2 months but it takes 3 months to get the last products back in the market. More on this in the “Geek’s note” below
Next, a few more concepts and critical assumptions from above:
- Different trade-in business models will have different timelines. The scenario here is fairly standard but not universal
- Advanced solutions offer a mid-program repricing opportunity. This is also easier for ecommerce models since you won’t have physical assets (e.g., pricing sheets) or agent training to worry about. In this article we’ll assume this is not possible: easier to manage for the sales and retail teams, harder to price for you. Life is not fair
- We’ll also assume you have access to sound pricing curves. Without them, the whole exercise would be like throwing darts. If needed, this data can easily be purchased today. It would not be perfect (cf. grading standards discussion in this article) but better than nothing. The example today is meant to be slightly simplified but still realistic overall => we have the price curves!
The stage is now set.
How do you now estimate the trade-in values?
This process is significantly more complicated than ones detailed in prior articles (e.g., outsourcing decision; asset monetization). There are a few entry points possible. You will need to iterate – thank you Excel! Worse, you will also need to make many assumptions. Believe me, the former consultant in me hates to assume. Here though… yeah, you pretty much have to cross fingers very hard that your assumptions aren’t wildly off the mark.
Ok, enough with the wait. Let’s dive in! Here is the process I recommend you follow:
- Take existing prices for the main used products in the market
- Calculate the expected prices in a few weeks. In today’s example, you’d estimate prices 2.5 months from now
- Increase these values by $100 across the board (the max investment allowed by our CFO)
- Estimate the share that each type of phone represents in the market today. This will be your starting point to calculate what phones you will receive
- Review the program objectives. Are you trying to attract owners of recent flagship phones? older devices? Perhaps iOS or Samsung users specifically? From a strategic standpoint, it probably makes sense to confirm the target is large enough by the way. Focusing on owners of white LG G3 probably wouldn’t make much sense right now for example
- Increase the trade-in value for these targets by $50 and reduce the price of the non-target ones by an equal amount
- Guesstimate how many more of these phones you will receive as a result as a % of total. What about the expected grade you will receive?
- Recalculate the total program expected cost per phone. This should now be higher than $100
- Reduce trade-in values here, adjust expected volumes there… Yes, this is more art than science
- Recalculate the total expected cost again. You should be getting close to $100
- Review the TI values for the higher % phones. Does the amount sound appealing to consumers? What do you expect other TI programs to offer instead?
- If you are an OEM, are your flagship values in the same range as your competitors’? Any other competitive positioning consideration you need to accomodate for? Adjust the values and % as needed
- Recalculate the expected investment. Are we close now? If not, iterate again
- Now account for consumer fraud and other losses. 5% each might be a good place to start if this is one of your first times.
- Build some margin of safety. Newer devices tend to come back in better condition than older ones. If you target recent products, this will help. Most other surprises will be negative though
I could go on. At some point, your model will be “good enough”. As the old saying goes, better be roughly right than precisely wrong. Settle on reasonable values, run the program, learn and improve.
One more thing.
There will be a time to make aggressive assumptions in your modeling. Right now, your organization might still be a little worried. The first few times especially, you may want to err on the side of caution. You’d rather show the CFO a lower-than-expected financial incentive than the alternative. It might even be the difference between running another program next time and seeing the program discontinued… or worse!
Sounds scary? My team and I ran dozens of these scenarios over the years. I suspect we also made every costly mistake you can dream of. A $5 mistake times 100k devices becomes a loss of $0.5M.
Don’t have time and money to waste? Need some handholding? Feel free to reach out and see if I can help you navigate these key decisions.