Calculating ROI for Product Data Optimization

When company leadership asks about the ROI of an idea or pitch, what they're asking is, "will it be worth it?"

A high return on investment, or ROI, is the holy grail of modern business decisions. The money put into a software, a process or a product always has to be less than the return on that investment, or else it makes no business sense. If investing outpaces revenue, all other business dreams are toast.

Everyone thinks they can predict ROI, but predictions of ROI without a boatload of data and like cases are, at best, a hunch. No one can tell the future. What people can do is speak to past examples that match the ROI they're trying to predict. That's why you can believe what a tried and true software provider predicts as ROI over what an untested software might promise.

ROI is only ever calculated accurately when looking backward. Even if you're only one year into a two-year plan, if you see that ROI is lower than predicted, you can make an informed decision to pull the plug.

Knowing how to calculate ROI is a fundamental business skill. Nothing essential is simple, though, and calculating ROI is not as easy in every case as looking up an investment and then retrieving the revenue from that investment. Unlike stocks where there’s a clear “money in” and “money out” metric, business processes have an overlap between investments, processes, and other efforts.

Why’s it so hard to calculate ROI on product data optimization?

We've written about how to calculate ROI on a product information management software (PIM) because that's an investment brands today think a lot about. Our prediction of how much the ROI of our PIM can be for a brand is based on the ROI we've seen time and again from our clients. So, our best hunch is well-informed.

The ROI on our next-gen PIM is easier to calculate than the ROI on any process inside a business, though, because there's a clear cost for the PIM. How do you measure something like the return on product data optimization? That’s an overall process that involves boots on the ground, so the “investment” part of the equation isn’t as clear.

Product data optimization is the business process of pooling and then synchronizing product data to improve that data’s completeness, accuracy, and appeal to the end consumer. Product data includes everything from titles and product descriptions to the imagery and image descriptions and even the format of product videos.

Is the total cost of product data optimization, then, a number you could find right now if you had to? Probably not.

Here's how to calculate the investment in product data optimization

To get to the final ROI of product data optimization efforts, we need two figures:

Revenue resulting from optimization/cost of product data optimization

Start by defining the total cost of product data optimization. The real cost includes the cost of any software or apps used, plus the time spent by each team member who touches product data, plus the opportunity cost from those team members spending time in spreadsheets instead of working on something else, plus the opportunity cost to sub-standard “optimizations” or slow time to market due to clunky processes.

The cost of product data optimization will be much higher for a brand whose head marketing manager optimizes product data than for a brand whose PIM software allows delegation of data management to other team members.

Here's how you can get a quick-and-dirty number that will at least provide a solid "hunch" of ROI:

1. Start with whatever you spend on software like a PIM (or even your Microsoft or Google suite if you manage product data in spreadsheets). That cost is definitely an investment in product data optimization. Let's call this cost factor “X.”
2. Next, take the average hours spent on product data optimization in one month by all team members who touch it. Multiply each team member's respective hours by the average hourly salary for that person. Let's call this total sum of salary pay factor “Y.”
3. Then take the same number of average monthly hours (the total of all team members) and multiply it by the average salary of the team members you would prefer to delegate product data management to. Let's call this “Z.”
4. Subtract Z from Y. Let's call this factor “M,” because it's a multiplier of lost opportunities where thought leaders should be spending their time elsewhere.
5. Now, plain and simple: take Y and multiply it by your M factor. Add your X cost to that final number. Slap a dollar sign in front of it, and that's your monthly investment in product data optimization!

(Y x M) + X = cost of product data optimization (per month)

Here's how to calculate the return on product data optimization

It seemed impossible to calculate the cost of something as abstract as product data optimization, but you just did it.

It's just as hard to isolate the exact gains of product data optimization, making the "return" in your return on investment another act of mathematical gymnastics.

In fact, the return on your product data optimization is only easy to measure if there's been a decisive moment in time when something about your product data optimization process changed.

These are some of the big changes that can clue you into the value of your product data optimization efforts:

• When a new person took over the optimization
• When you launched to e-commerce
• Or when you began work with a new seller
• When you merged multiple spreadsheets of product data into one
• When you started optimizing product collateral (images and video)
• When you invested in a PIM

If you have any one of these “bookmark” occasions or any similar point when product data management changed in a big way, isolate the increase in revenue after that change was made. If you went from working with six sellers to a dozen, for instance, take the average monthly revenue from the dozen and subtract the old average when you worked with just six. There's a difference you can measure, and it’s a clear value you can assign to your efforts in product data optimization, in this case for multiple sellers.

Predict Your ROI and Then Calculate ROI Post Factum

Use the information you gather through these exercises to make smarter decisions about how much you put into your product data optimization. Are you putting too much time in? Do your optimizations need to be better?

With product data optimization, there’s almost always a way to reduce the cost.

If your investment is too high, the easiest way to reduce it is to move your many spreadsheets and image libraries into one single-source-of-truth product data solution: a product information management (PIM) software.

Using a PIM reduces the time your team spends in the weeds of data management. That, in turn, unlocks the delegation of data management that you so crave. It speeds up the time to market, too. Best of all, it unleashes your thought leaders to bigger projects. Those are just a few of the problems a PIM solves.

What comes next for your brand? Were these numbers easy for you to calculate? Were you surprised by how many collective hours are spent on product data optimization and management?

If you’re left wondering why it's important to optimize product data at all, don't even go there—product data management is too important in the landscape of today's digital commerce to let the world pass you by.

Tell us what you learn as you crunch these numbers. The more information we have about your own experience and your ROI from product data optimization, the better we can build our solutions and give brands like yours our top ideas on how to gain the biggest value (and ROI) from PIM technology.