Why Analytics Requires Managerial Judgment

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Del curso dictado por Northwestern University

Leadership Through Marketing

calificaciones

Northwestern University

Leadership Through Marketing

calificaciones

Curso 4 de 6 en SpecializationOrganizational Leadership

The success of every organization depends on attracting and retaining customers. Although the marketing concepts for doing so are well established, digital technology has empowered customers, while producing massive amounts of data, revolutionizing the processes through which organizations attract and retain customers. In this course, students will learn how to identify new opportunities to create value for empowered consumers, develop strategies that yield an advantage over rivals, and develop the data science skills to lead more effectively, allocate resources, and to confront this very challenging environment with confidence.

De la lección

Leadership’s Role in Analytics with Florian Zettelmeyer

Why the data explosion in marketing has made an understanding of Big Data and Analytics essential for Leadership through Marketing.

Overview of Modules 3 and 43:06

The Moneyball Story Revisited4:44

Why Analytics Requires Managerial Judgment5:41

Why Analytics Requires Process and Incentive Changes7:58

Why Analytics Has To Be Problem Driven5:00

Why You Need a Working Knowledge of Data Science4:54

Conoce a los instructores

Greg Carpenter
James Farley/Booz Allen Hamilton Professor of Marketing Strategy, Director of the Center for Market Leadership, Faculty Director, Kellogg Markets and Customers Initiative (KMCI)
Kellogg School of Management
Florian Zettelmeyer
Nancy L. Ertle Professor of Marketing, Director, Program on Data Analytics at Kellogg
Kellogg School of Management
Sanjay Khosla
Adjunct Professor of Executive Education
Kellogg School of Management

[MUSIC]

The first reason that analytics is a leadership problem is that analytics

requires managerial judgement.

To see this, you have to understand that data science does not equal truth.

There's a lot of room for judgment in the data science that drives analytics.

You see analytics fundamentally speaks to management decisions, and

management decisions are inherently political.

There are different agendas and there are winners and there are losers.

And because of that, before long you're going to be in a situation where team

A reports back to you with result A backed up by data science team A.

And team B will report back to you with result B backed up by data science team B.

And guess what?

Result A and result B will not be the same.

And the problem is, you can't call your resident data scientist to help you figure

out who's right, because there are already six data scientists in the room and

they all disagree with each other.

And so it falls on you to decide.

You have to be able to make the judgement about who to believe.

But that is not the only reason why analytics requires managerial judgement.

I was recently invited to an executive retreat for a major company.

One of the executives got up to present, and

he told us that his company had recently been able to close the loop

between online advertising and offline sales.

Now, this is very hard to do in this industry and so there was a great

deal of interest in the room about how online advertising affect sales.

The executive then shows a graph like this and

he says, what you see here is the probability that a customer

buys a product in our industry during, I think a 90 day period,

depending on what ad they saw on a search engine, for example Google or Yahoo.

If consumers did not see any ads related to a product in our industry

on search engines, he says.

Their probability of buying in a 90 day period was 0.7%.

This is what you see in the left column.

If they had seen ads only from retailers,

the probability of buying in a 90 day period was 3%.

This is what you see in the second column from the left.

If they had seen ads only from manufacturers,

the probability of buying went up to 5%.

And if they had seen ads from both retailers and

manufacturers, their probability was 14%.

That's what you see in the right column.

And then the executive said, we learned two things from this.

First, search engine advertising really works, and second,

retailer and manufacturer advertising are compliments, not substitutes.

This means if you do both in isolation you get a worse result

than if you do them together.

In other words, 3% plus 5% is less than 14%.

Now there was an excited buzz in the room.

I mean, these were new findings,

and the executives in the room talked about how to use this information.

This went on for about 15 minutes until somebody suggested perhaps,

it would be a good idea to put out a press release about these findings.

The conversation went on a little longer, and then somebody pointed at the left

column where consumers had received no ads and asked, so

why would somebody not see any product related ads on a search engine?

And the answer, of course, is that these people did not search for

the product on the search engine.

And then pointing to the right column, he says, so

why would somebody see ads from retailers and manufacturers in our industry?

Well, the answer is that these consumers search for

the product and perhaps a location, which

then would have triggered keywords that both manufacturers and dealers bid for.

And then this person says, so what we have shown

is that if you are not interested in buying this product, and

he points to the left column, you don't buy the product.

And if you're very interested in buying this product, and

he points to the right column, you do buy this product.

And indeed, this graph says nothing about whether search engine advertising

works and whether retailer and manufacturer advertising are compliments.

And the reason is that we don't know whether the higher likelihood of purchase

is due to the fact that the people who saw retailer and

manufacturer advertising were inherently more interested in purchasing the product.

Or whether the fact that they saw search advertising made them

more interested in buying the product.

It took the executives in the room 15 minutes and a prompt to find this problem.

It should have taken them 15 seconds instead.

But it is hard to find problems like these

unless you have managerial judgement in analytics.

And this matters because executives get bombarded with more and

more evidence of this type.

[MUSIC]

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