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.
