AnalyticsAnalyzing Customer DataBuilding a Yield Model, Part 4

Building a Yield Model, Part 4

At last, the revenue is coming in! Figure out why and how to get more.

This article is the final installment in a four-part series on how to build a yield model. The hypothetical model we’re using is for an online store. In part one, we identified how many people saw our ads. In part two, we figured out how many responded to our ads by visiting the site. In part three, we added the registration rate to identify how many people were poised to generate revenue. We’re tracking all of this at the campaign level, so we can compare campaigns — from the time people see your ads to the time a registered user makes a purchase.

We’re now ready to complete the model, adding in the fun part — the revenue generation components. From here, you’ll be able to track the start-to-finish impact of your marketing campaigns. You can monitor them over time to see if your campaign effectiveness is increasing or decreasing. You can identify areas where you need to improve. You can form hypotheses about possible causes for changes in yield. And you can compare campaigns of different types to trigger thinking about ways to capitalize on the best parts of each type of campaign.

In our hypothetical model, we’ve identified three components of the next step a registered user must take to generate revenue — purchase rate, order size, and price. Remember:

  • 10 million people saw your ads.
  • 200,000 responded (2 percent response rate).
  • 100,000 registered (50 percent registration rate).
  • 10,000 generated revenue (10 percent purchase rate).
  • On average, 2 items were purchased per customer.
  • On average, item purchased cost $20.
  • Revenue is $400,000.

I doubt I need to convince you at this point that adding purchase information to your yield model is a good idea. But let’s think about how you benefit from looking at a yield model in its entirety rather studying its components individually. The answer, as it has been in every article in this series, is that it helps you identify issues you couldn’t identify otherwise.

For example, if 100,000 people each from Campaign A and Campaign B took the time to register, why would Campaign B generate twice the revenue of Campaign A? Because you have the complete yield model in front of you, you’re able to see that Campaign A started with an unusually high registration rate. Because the ad pointed users directly to the registration page, many registered before they even browsed your products. This leads you to realize you weren’t really getting qualified registrations.

If you hadn’t looked at a full yield model, you might have started your investigation by looking at the demographics of the people who saw the ad or by checking to see if Campaign A folks had site problems. Or maybe your yield model reveals that Campaign B customers bought higher-priced items than Campaign A customers, which leads you in a totally different direction.

You’ll also be able to track changes over time, to understand the impact your business decisions have on your yield model. Did a decrease in purchase rate coincide with the release of a site redesign or a change to the registration page? Time to investigate those changes. Has purchase rate increased at the same time the registration rate decreased? Maybe you’re getting more repeat customers, or maybe you are attracting better quality leads.

When comparing yield models for individual campaigns or marketing methods, looking at purchase rate as part of a complete yield model can help you understand what impact different types of marketing have on your business. For example, your email campaigns may be your most successful in terms of driving traffic to the site, but they may result in a low purchase rate on a few small-ticket items. Or you may find that a banner campaign with a low click-through rate actually generates more revenue, because the resulting spenders place orders for large numbers of low-ticket items (or is it small numbers of large-ticket items?).

Here’s a side benefit. Looking at the complete yield model lets you forecast the revenue impact you’ll have if you make changes. There’s the obvious: calculating the increase in revenue you’d get from a change anywhere else along the line in the model. But you can also forecast the impact of changes to your marketing mix. Perhaps your marketing budget has been cut so you have to rely on cheaper marketing methods. Your yield model may help you build a case to prove that switching to the cheaper marketing methods will actually decrease revenue and return on marketing dollars. Or vice versa: You may realize you don’t need to spend as much money to get the revenue you need.

I use yield models religiously because they present a complete picture. I’ve outlined a very simple model for you here, but over time you may extend your model to incorporate all sorts of business activities, creating model branches for different customer actions. Perhaps you realize you lose a lot of people if they click “Help.” Creating a yield model for the steps that occur in the service process may reduce that problem. Maybe your help pages are confusing or they don’t answer the question completely. Perhaps people simply can’t find their way back or their shopping cart empties itself.

The possibilities are endless with a yield model. It’s especially helpful for explaining operational results to executives who are far removed from the everyday activity of generating revenue. But one of the most important things to remember is that yield models are not just for marketing and finance folks. In one company, the technical folks were motivated by the yield model because it helped them understand (finally!) that their efforts to roll out faster product launches and reduce site downtime were directly related to the results of our marketing efforts. Share it with every department. Just remember: It’s more important to complete an initial model than it is to perfect (or even identify) a single step.

Related Articles

Metrics to support 'your' digital monetization strategy

Analytics Metrics to support 'your' digital monetization strategy

1m Adam Singer
6 ways to increase your conversion rate using behavioral data

Analyzing Customer Data 6 ways to increase your conversion rate using behavioral data

7m Mike O'Brien
Influencer marketing: Eight tools to identify, track and analyze your brand's next biggest fan

Content Influencer marketing: Eight tools to identify, track and analyze your brand's next biggest fan

7m Tereza Litsa
Tools and tips for calculating the ROI of social media

Conversion & ROI Tools and tips for calculating the ROI of social media

7m Clark Boyd
How machine learning can help you optimize your website's UX

AI How machine learning can help you optimize your website's UX

7m Chris Camps
Why banks are becoming customer-centric organizations

Analyzing Customer Data Why banks are becoming customer-centric organizations

8m Al Roberts
How to achieve true omnichannel relevance

Analyzing Customer Data How to achieve true omnichannel relevance

8m Clark Boyd
How to use behavioral data to enhance your website's conversion rate

Analytics How to use behavioral data to enhance your website's conversion rate

8m Chris Camps