Analyzing an Experiment: Betty Spaghetty

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Marketing Analytics

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University of Virginia

Marketing Analytics

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Organizations large and small are inundated with data about consumer choices. But that wealth of information does not always translate into better decisions. Knowing how to interpret data is the challenge -- and marketers in particular are increasingly expected to use analytics to inform and justify their decisions. Marketing analytics enables marketers to measure, manage and analyze marketing performance to maximize its effectiveness and optimize return on investment (ROI). Beyond the obvious sales and lead generation applications, marketing analytics can offer profound insights into customer preferences and trends, which can be further utilized for future marketing and business decisions. This course gives you the tools to measure brand and customer assets, understand regression analysis, and design experiments as a way to evaluate and optimize marketing campaigns. You'll leave the course with a solid understanding of how to use marketing analytics to predict outcomes and systematically allocate resources. For more information on marketing analytics, you may visit; http://dmanalytics.org. You can also follow my posts in Twitter, @rajkumarvenk, and on linkedin; www.linkedin.com/in/rajkumar-venkatesan-14970a3.

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Marketing Experiments

Ever wonder how much you have to cut prices to drive the most sales? Or which advertisement copy is more effective in customer conversion? Do an experiment! Experiments allow you to understand the effectiveness of different marketing strategies and forecast expected ROI. This week, we'll explore how to design basic experiments so that you can assess your marketing efforts and invest your marketing dollars most effectively. We'll help you avoid a gap between your test results and field implementation, and explore how web experiments can be implemented cheaply and quickly. By the end of this module, you'll be able to design and conduct effective experiments that test your marketing campaigns--and then use the results to make future marketing decisions.

Designing Full Factorial Web Experiments4:02

Designing an Experiment: Etch A Sketch2:49

Analyzing an Experiment: Etch A Sketch8:22

Analyzing an Experiment: Betty Spaghetty5:25

Projecting Lift2:40

Calculating Projected Lift: Betty Spaghetty6:15

Pitfalls of Marketing Experiments: Betty Spaghetty3:14

Maximizing Effectiveness: Nanoblocks8:11

Takeaways: Marketing Experiments1:41

Rencontrer les enseignants

Rajkumar Venkatesan
Ronald Trzcinski Professor of Business Administration
Darden School of Business

Let's look at another TV advertising campaign that

Ohio Art ran for one of their newest toys, Betty Spaghetty.

Just like Etch A Sketch,

we have few videos of the TV advertisements that Ohio Art tested.

We have provided links on YouTube.

Why don't you go watch them,

and when you're back let's see what happened.

Welcome back, hope you liked the ads.

Now let's see what Ohio Art did with these ads.

So Arizona was the test state.

This is the state where the ads were run.

The ads feature two of the stock keeping units,

or skews as it's commonly called,

of Betty Spaghetty, Color Crazy and Go Go Glam.

The test period, the time when the ads were run,

was June 17-17 July.

And the number of units they sold were

1.8 and 2.2 in total for the stores per week.

Now they had a control state.

So California was the control and Arizona is the test.

The sales of Color Crazy and Go Go Glam in California were 0.3 and 1.2.

So from here, we should be able to calculate the lift

of sales provided to Betty Spaghetty by these TV ads.

So how would we do that?

So we would take 1.8+2.2 / 0.3+1.2.

So how much is this going to be?

As you know, I'm a bit slow with arithmetic.

So, let's see what happens here.

10, and 4 over 1.5.

Okay, whew, that was a bit scary over there but that's okay,

let's see what that means.

That's about 267%.

It's 1.8+2.2 / 0.3+1.2.

So one of the things if you notice that is different with

the Betty Spaghetty from Etch A Sketch is that in

Etch A Sketch you had sales of Etch A Sketch before the experiment.

In Betty Spaghetty, we have sales only during the experiment.

Now this is a practical reality when you do experiments in reality, right?

Betty Spaghetty was not in the stores before the TV ads were run,

so you really didn't have any pre-existing sales data.

So it's really difficult to compare this lift to any base,

or to control this lift for any pre-existing differences between Arizona and California.

That's just the way it is,

we have to deal with it and then figure out and make

a decision because that's the reality of the situation that Ohio Art faced.

Now with lift of 267%,

what do we have to do next?

That's right, we have to think about

the break even lift that is necessary for the national campaign.

Now just like Etch A Sketch,

let's walk through this.

Now the first things first,

is to see what the Ad budget was.

It was about 3 million.

Now Betty Spaghetty sold for about $15.

Retail percent margin was 36% which means the retailer keeps 36% off the $15.

The price Ohio Art is selling the products to the retailer is 9.6 and we

get that by taking 15X(1-36%).

Now the manufacturer contribution margin,

this is how much Ohio Art can keep off the 9.6, is about 58%,

and that is equal to $5.5 so that will been 9.6 times 58%.

The break even units would be,

break even units is equal to 3 million over how

much Ohio Art makes from the sale of a single Betty Spaghetty.

So that would be 5.5 over here.

The 3 million is the ad budget and that gives us

538,000 units that Ohio Art has to sell to recover the ad budget.

Now let's pause here,

think about this and figure out whether

the 267% lift is going to give Ohio Art these break even units.

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