Results and Predictions

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En provenance du cours de University of Pennsylvania

Customer Analytics

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University of Pennsylvania

Customer Analytics

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Cours 1 sur 5 dans Specialization Business Analytics

Data about our browsing and buying patterns are everywhere. From credit card transactions and online shopping carts, to customer loyalty programs and user-generated ratings/reviews, there is a staggering amount of data that can be used to describe our past buying behaviors, predict future ones, and prescribe new ways to influence future purchasing decisions. In this course, four of Wharton’s top marketing professors will provide an overview of key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices including Amazon, Google, and Starbucks to name a few. This course provides an overview of the field of analytics so that you can make informed business decisions. It is an introduction to the theory of customer analytics, and is not intended to prepare learners to perform customer analytics. Course Learning Outcomes: After completing the course learners will be able to... Describe the major methods of customer data collection used by companies and understand how this data can inform business decisions Describe the main tools used to predict customer behavior and identify the appropriate uses for each tool Communicate key ideas about customer analytics and how the field informs business decisions Communicate the history of customer analytics and latest best practices at top firms

À partir de la leçon

Predictive Analytics

Once you’ve collected and interpreted data, what do you do with it? In this module, you’ll learn how to take the next step: how to use data about actions in the past to make to make predictions about actions in the future. You’ll examine the main tools used to predict behavior, and learn how to determine which tool is right for which decision purposes. Additionally, you’ll learn the language and the frameworks for making predictions of future behavior. At the end of this module, you’ll be able to determine what kinds of predictions you can make to create future strategies, understand the most powerful techniques for predictive models including regression analysis, and be prepared to take full advantage of analytics to create effective data-driven business decisions.

Introduction to Predictive Analytics2:14

Asking Predictive Questions3:43

Regression Analysis, Part 1: The Demand Curve5:18

Regression Analysis, Part 2: Making Predictions5:06

Beyond Period 210:11

Making Predictions Using a Data Set8:54

Data Set Predictions: Mary, Sharmila, or Chris?10:23

Probability Models10:39

Implementation of the Model16:42

Results and Predictions5:20

Rencontrer les enseignants

Eric Bradlow
Professor of Marketing, Statistics, and Education, Chairperson, Wharton Marketing Department, Vice Dean and Director, Wharton Doctoral Program, Co-Director, Wharton Customer Analytics Initiative
The Wharton School
Peter Fader
Professor of Marketing and Co-Director of the Wharton Customer Analytics Initiative
The Wharton School
Raghu Iyengar
Associate Professor of Marketing
The Wharton School
Ron Berman
Assistant Professor of Marketing
The Wharton School

Well, thanks for joining me for our journey through the world of predictive

analytics and I know I can't do justice to the whole world of predictive analytics.

It's a big world and it's changing over time, but I just wanna give you some of

the highlights, some of the words, some of the concepts, some of the patterns.

And if you think about it,

I've been doing a lot of this during [LAUGH] our time together.

Talking about the past and how it's gonna give us some insight about the future.

And actually, if you think about it right now,

we're exactly half way through our customer analytics course.

So, I think it's a good time to look back at everything we've covered so far and

give you a little bit of a roadmap of what's to follow.

So what have we done?

Well, my colleague Rigo Angar has spent a whole lot of time talking about data.

All the different kinds of data that's available.

The kinds of descriptive analysis that you can do with that data.

The kinds of insights that you can draw immediately from the data, basically,

as soon as you get it in.

And that's great and for a lot of businesses,

that's gonna address a lot of the critical questions that they have.

And in many cases, you can stop right there.

But in many cases, the questions that you're asking,

the decisions that you need to make are gonna be about the future and

that's where predictive analytics comes in.

That's where models come in.

So what do we mean by a model?

These days, we throw that word around a lot and

we don't even think about what it literally means.

It's just something we use to kind of do stuff with data and

maybe make some predictions, but let's step back and think of what model.

Think about a model airplane or a model ship,

what are you trying to do when you're building a model?

Well, you're looking at something that's very real, very complex.

Something that you can't possibly capture every aspect of it, but

you just wanna capture the key aspects of it.

So that when we look at this thing, it's doing a pretty good job of representing

something that is really complex and that's why we're building a model.

Again, it's not just a means towards an end,

it's often an end unto itself is trying to capture the reality and you know what?

Customers making decisions over time, firms interacting with them.

Customers interacting with each other is a very, very complex phenomenon.

So one of the reasons why we build the models is to try to really do justice

to that behavior.

We don't necessarily wanna capture every nuance of it,

we wanna capture the important stuff.

Cuz once we capture the important stuff, it's gonna give us that confidence to

make statements about what we think will happen in the next period or beyond and

that's been the focus of what this particular session has been.

So try not to lay out this dichotomy between models,

regression models, data mining, all the things that guru spoke about.

That are really, really effective,

if we wanna make statements about who's gonna do what in the next period?

And again, for many, many decisions, that's all you need.

Those kinds of models, that kind of thinking has really dominated marketing

analytics for a long time and I think will continue to do so.

But today, as we get richer data, greater computational capabilities.

And more importantly, an imperative for

management that we need to look beyond just the next period.

We need to start thinking about different kinds of models.

Models that are better suited for the long run.

Maybe we're even sacrificing something in the short run as we build these things,

but we wanna really capture what's worth capturing.

Spent a lot of time talking about it with you.

And again, I tried to downplay the models.

I'm proud of them, happy to give some metaphor to have you understand what these

models are all about, but

it's really the insights that arise from them more than the models themselves.

Now sometimes when I talk about models and I talk about predictions and

I'll show some of the kinds of pictures that I've shown you here and

I say, hey, look how good these forecasts are.

And a lot of people will look at them and say, yeah, they're nice, but so what?

And I hear that question and it's kind of deflating.

It's like come on isn't the model good enough for you?

And the real answer is in many cases, no it's not.

We need to understand what decisions to make on the basis of those predictions and

I haven't really told you anything about that.

I have just told you about what the patterns are likely to look like,

if people keep flipping their coins and so on.

What the future might look like?

But as managers, it's our job to change the future or

at least to leverage what we understand and project about the future.

And that's where we're gonna start to move from predictive analytics

to prescriptive analytics.

Having some idea of what the true underlying process is and

what the future might look like.

How do we layer on top of that some optimization?

So we start to understand,

which message do we send to which customer at which time.

And lots and lots of other prescriptive questions that

manager are asking and academics like myself.

Or more importantly,

my colleague Ron Berman are spending their lives trying to answer.

And after Ron is done talking about prescriptive analytics,

then we'll bring in my colleague, Eric Bradlow to tie a ribbon around the whole

thing by giving a bunch of nice case studies and

examples of descriptive, predictive and prescriptive in action.

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