Building a Yield Model, Part 1

In an earlier article, I wrote about what I call a yield model and how this simple concept can help you focus your efforts to analyze customer data and yield big benefits to your business. In that article, I focused on what and why, but some of you have asked me to elaborate on how. I’m happy to oblige!

A brief review: A yield model is a series of calculations aimed at measuring a prospect’s progress through the major steps involved in making a sale. It helps you determine how effective you’re being at getting a person in the door, through the site, and on her way to generating revenue — and it helps you determine what steps need improvement. A small percentage improvement to a single step can lead to exponentially more revenue.

The yield model also helps you focus your efforts where you’ll get the biggest results. Your yield model can be as high level or as detailed as you want it to be — summarizing all of your marketing efforts for a certain period or focusing on a single marketing campaign.

We’ll start with a hypothetical one-month yield model for an online store, then break down the steps and look at how to address each one.

You can modify this model to fit your business, such as to accommodate content or ad-supported sites. I’ve also seen yield models that add marketing expenses at the top, and end with return on investment (ROI) on marketing dollars. Just adapt it whatever way makes sense, so it will work for your business.

A word of caution: This is a very simple example of how to get started using a yield model. Every business is different and you will surely run into challenges and complications along the way, many of which are not addressed here. Keep it simple and focus on getting something down on paper before you try to perfect it.

Step 1: How many people saw your ads?

The number of people your marketing efforts reach plays a key role in determining the effectiveness of your campaigns. It’s the denominator in calculating your response rate. We start here, at the wide end of the funnel, so we can effectively measure what it takes to push people through to the other end.

Figuring out how many people saw your ads may be simple or more difficult, depending on the type of marketing you’re doing and how much data is available. You may need to estimate. Estimating is fine as long as you are consistent (and logical) in your methods.

If you’re creating a yield model to measure one or more online marketing campaigns, you probably have a lot of data on hand. With a banner ad or email campaign, for example, you’ve got a pretty good idea how many people saw it because you know how many impressions you purchased and are probably getting reports on the number of impressions delivered. For you analysts who are measuring the results of someone else’s campaigns, check with the marketer; the data is available.

Online advertising still has some unknowns, though. What percentage of impressions were delivered to people who had already seen the ad? How many of those impressions were delivered to people who were already customers rather than people who had never heard of your site?

The above are examples of the kinds of obstacles you might think you need to resolve before you complete an initial yield model. Don’t get distracted! You can’t fine-tune your yield model if you never complete a first pass. Save the complications for later and just try to get something down on paper now.

In the offline world, it gets a bit trickier but is not impossible. With snail mail, you know how many pieces were dropped. TV and radio ads come with an estimate of how many people will see or hear the ad. I’ve even seen a yield model for a roadside billboard; the billboard company provided an estimate of how many cars drive by each day.

Don’t worry right now about how many of the recipients actually opened your letter or how many radio listeners are already your customers. Just start with what you know. Estimating the number of people who heard about your site will always be a challenge and will always present accuracy issues. The point is this step doesn’t have to be perfect to be effective, particularly if you are consistent in your estimating methods. If you try it and find you just can’t live with the ambiguity, skip this step.

An additional challenge is worth mentioning. Many of you analyzing customer data are not the same people running customer acquisition campaigns. Therefore, you may not have access to details about every campaign. If your efforts to obtain the data fail, you can still create a yield model. Make an estimate for step one, and put a big question mark next to the resulting calculation. A completed, although partially hypothetical, yield model is like an unfinished puzzle and is a great tool for motivating others to cooperate in your quest for data.

In my next column, we move on figuring out how many people visited your site because of your marketing efforts. Some businesses take this step so seriously they’ve built custom tracking systems to figure out where every single visitor came from. What a dream… a company that takes analyzing its data that seriously. Until next time, good luck with step one.

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