Logic Model Level 3

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Del curso dictado por University of Pennsylvania

Business Strategies for Social Impact

64 calificaciones

University of Pennsylvania

Business Strategies for Social Impact

64 calificaciones

Curso 4 de 4 en SpecializationBusiness Strategies for A Better World

When is it good business practice to invest for social good? What are the most innovative and effective business strategies for developing positive social impact around the world? Designed by renowned Wharton professors Katherine Klein and Chris Geczy to help individuals, organizations, and investors bring about societal change, this course introduces the fundamentals of impact investing, and developing a business strategy that drives social impact. You’ll learn how to become a leader who cultivates purpose and inspires change, measure societal impact through evidence-based models, and invest in ventures effectively and meaningfully. By the end of this course, you’ll have a deep understanding of the realities of leading an organization with purpose, and be able to build successful strategies that bring impactful change to the world.

De la lección

Module 3: Impact Measurement

In this module, you’ll examine the criteria and standards of evidence developed to measure the true social impact of your organization. You’ll identify the Sustainable Development Goals (SDGs) and Environmental Social and Governance Criteria (ESG) to be able to better answer the question “What is the Impact of X?” By examining the Nesta Foundation Framework and its standard of evidence, you’ll explore the different levels of rigor for each logic model using real-life examples and learn to accurately assess your organization’s inputs, outputs, and impact. By the end of this module, you’ll be able to calculate the Social Return on Investment (SROI) of your enterprise and be able to strategize an effective plan for your business.

Introduction to Impact Measurement9:46

What is the Impact of X?3:39

Standards of Evidence7:00

Logic Model Level 15:08

Logic Model Level 210:53

Logic Model Level 37:22

Logic Model Level 418:44

Logic Model Level 57:28

Logic Model Level Q23:39

Conoce a los instructores

Katherine Klein
Edward H. Bowman Professor of Management
The Wharton School
Christopher Geczy
Adjunct Professor of Finance
Fianance

So at level three,

we don't just have output data.

We have outcome data.

Specifically, we have outcome data over time.

So, a reminder, what are outcomes?

Outcomes are the immediate changes that occur in the people, organizations,

communities that are the beneficiaries or

customers of your organization or business, right?

They're the immediate changes, the benefits,

that your organization or business wants to create.

An impact, impact is one step further.

It's about the long term consequences that occur in people,

organizations and communities that benefit from your company or organizations,

operations and products and services.

Okay. So, when we think about outcomes,

the key point is that we're hoping these outcomes are changing.

That outcomes are getting better for the beneficiaries of your organization or company.

That means that, we really need to have some pretty clear information about what were

outcomes before people or communities received your products and services.

What are those outcomes after?

We want to look at before versus after change.

And, why is this so important?

Well, if we don't actually have pre-data, well,

we might discover that people don't actually

change as a result of our products or services.

So, again, using a kind of exercise example, like,

maybe people who are really fit and loved to exercise,

sign up for your fitness program.

Your fitness program doesn't actually make them any more fit,

they were fit to begin with.

If you don't have the pre-data,

you don't actually know.

Was there a change in fitness?

It's possible there was no change in fitness.

Fitness looks pretty good afterwards.

But, hey, it looked pretty good before.

So, ideally, we would really like to measure outcomes before and after the intervention,

before and after your company or organizations work.

Sometimes, we actually don't have very good pre-data.

On the other hand, we may know a lot.

We may know that,

you know there was no electricity in this village before we came or

people really couldn't speak English before we came into this community.

So, we may have a strong sense of what preexisting conditions

were and we may be able to have some pretty strong confidence that,

look, we know that things have changed,

even if we didn't measure it beforehand.

Okay. So, ideally, you have outcome data and ideally you have outcome data before and

after people have received

your products or services or experience working in your organization.

What do we learn from outcome data even if you don't have impact data?

So, when we start to not just have outputs but actually have data on outcomes,

we learn a whole lot more about what people and

organizations are experiencing and gaining as a result of your work.

So, for example, are people actually using

that extra cycle that they bought or that you donated?

Are they using it to exercise,

and not just using it as a clothing rack?

Is the x-ray machine,

again that you sold or donated,

is the x-ray machine actually getting used,

or is it in a corner gathering dust?

That training program that you're giving,

does it actually result in people's knowledge improving?

Those are the kinds of things that we can learn from outcome data.

The organization Acumen provides an excellent example

of learning from outcome data in a quite efficient and practical approach.

They call this approach,

Lean Data Collection. Lean Data Collection.

So, Acumen is a nonprofit global venture fund,

that invests in the early stage enterprises that

deliver affordable goods and services to improve the lives of the poor.

So, Acumen provides investments in management to help grow companies that serve the poor.

Today, Acumen has invested

over 100 million dollars in over 100 companies around the world.

They're interested in understanding more about these companies' outcomes.

So, they collect lean data.

So, lean data is a really interesting approach.

The core idea here is quite simple.

Lean data really just means asking customers what they think.

Because the businesses that Acumen invests in

provide goods and services to low income customers,

talking to those customers about their perceptions,

their experiences, tells us a lot about the outcomes that these customers get.

So, how did Acumen do this?

What is this lean data approach actually look like?

They wanted, as I've said,

get data from customers.

They want to do this quickly and efficiently.

So, their goal is to make data collection quick, inexpensive and useful.

They've learned over time,

that they can do this pretty effectively if they do the following things.

First of all, they're really careful to create short surveys

and to collect data where possible using mobile phones,

using call centers, using online surveys,

anything they can do to make this easy for customers and cheap.

They also design surveys that really allow customers to be candid and direct,

like they want to know what these customers really think.

And, they design surveys to yield information that is actionable.

They're really going after what is information that these companies can

use to do a better job providing goods and services to the very poor.

So, Acumen has been able to use this approach and

learned some really practical things for the businesses they're investing in.

For example, they might learn,

well how poor are the people the business is serving?

Who gets the most value out of their products and services?

Are those people poor or richer, single, married,

male or female? Who's benefiting?

What do customers find most beneficial about the businesses' products and services?

What are their pain points?

What do they find annoying or frustrating about dealing with the business?

All of these things can be really important for helping the business grow its impact,

or at least the outcomes its customers are getting.

The lean data approach isn't the most rigorous approach to impact measurement.

It's only level three on our scale that goes from one to five.

But, it's a really practical approach.

It's a valuable next step,

especially in a field where we've spent an awful lot of

time gathering output data all the while saying,

"Wow, we wish we really knew more about outcomes."

Lean data starts to give us a sense of what our outcomes really are.

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