- [Narrator] Hello, everyone.
Welcome to this week's video lecture,
which is on market research.
So this will help
you once you've identified your target market.
How can you get information from prospective customers?
How do you learn
more about what is going on in the industry?
We're gonna learn how to do basic market research.
So here's our agenda, how to do marketing research?
Gonna look at the basic motivation.
Then I wanna walk you through a model of types of knowledge
that I have found to be very,
very effective and very enlightening
as far as how do businesses and other organizations manage
and use the information that they have.
We're gonna talk about the two main types
of data collection, secondary and primary
and then I will walk you through the homework for this week.
Here is your homework and you will be doing this based
on the research that you learn about here,
as well as using the resources
that our reference librarian Ms. Magi has presented.
So in part one, I want you to use at least two
of the library resources that she mentioned to create
a rich description of one segment that you think
would be a good target for your product
or service or organization.
And by answering the questions that you'll see
on the next slide.
So first, what information did you glean from each source?
Next, what are the demo, psychographic
and spending patterns of each segment?
And what insights did you gain
to help you reach these folks?
And then think about the degree to which did they sort
of can paint a common picture,
or were there lot of discrepancies?
Get ready to discuss this in class.
And then for part two,
really building on what we're about to do here, which are,
what are the unanswered questions?
What methods would you use?
What do you hope to gain and note
that you won't actually be doing the research,
but I want you to make a case
of why these are the right methods
for the data or the information that you need.
And that fill a gap that are left
after you've done your secondary research.
So why do research?
It can be very time consuming.
It can cost you time and money.
It is imperfect that you will never get perfect data,
but so why do you do it?
Our textbook author Crane says
"The most successful entrepreneurs are the best informed."
And this can be done in fairly quick and inexpensive ways.
So some of the methods that you will learn
here and especially relying on the data sources
that Ms. Magi presents, you can get really valuable
and useful information without expending
a whole lot of effort.
Here is a long sort of laundry list of what you can learn.
Some of the questions that you might want
to answer with your research.
Who's interested in my product or service?
Learn about your competitors.
What are some similar products that people are buying?
What do they like or not about them?
How much they'll pay?
How much they'll buy?
What are the benefits that they get?
What's the most attractive features?
Some behavioral questions like where, when and how often,
and where do they get information about new products
or services, which can let you sort
of get into their ear in a sense?
I want to walk you through a series
of categories of knowledge.
And this comes from some very influential work
that I've read that I have built
on and actually done some research on.
There are three types of knowledge
and here I'm gonna describe them and then the next slide,
I'll say a bit more about how they can be applied.
So the first kind of knowledge is explicit knowledge,
and that is knowledge that can be written or spoken.
So it tends to be fairly context independent
and it's knowing a fact knowing that, or knowing what.
And an example of this is how to make cold cereal.
It tends to be fairly straight forward.
Most of us probably wouldn't even need it.
And a key thing here is we would all basically do
it the same way you take your cereal,
you pour it into a bowl, you take your milk,
you pour that in, you put a spoon in and you eat.
It's fairly straightforward and anybody
that could read that you could just write this down,
or you could speak it to them and they could grasp
it pretty easily.
The next thing is tacit knowledge that
which you can only do by experiencing or by doing it.
So another example is how to ride a bicycle,
that nobody is born knowing how to ride a bicycle.
I could tell you how and explain how,
but still that first time that somebody gets onto a bike,
they really are not able to do
it until they learn their own way
until they get the feel for it.
Another example is while cold cereal
is pretty straightforward,
people are gonna be pretty successful their first time.
Making hollandaise sauce is a more complex thing.
And I don't know if any of you have ever tried
to make hollandaise sauce,
but I bet you that if you did the very first time
that you made it, and maybe the first few times you ended up
with very buttery, scrambled eggs.
And only by getting a feel for it,
by doing it, by observing, by getting the experience of it,
can you learn how to really make hollandaise sauce?
And this is a sense of knowing how.
Finally there is co-created knowledge.
This cannot be spoken
it can't even be gained by doing it yourself.
This tends to be the kind of knowledge that's needed
as we'll see in the next slide
to address very complex issues.
And they are very context specific.
And it's how do people sort of work together to innovate
and collaborate together to solve really complex issues
and to generate answers that do not exist yet.
So they can't be written,
they're not just something you do yourself,
but they can only be gained by collaboration
with others and by experimentation.
So I hope that the next slide will say
a bit more about that.
The way this works is you can see explicit,
tacit and co-created on the left column of boxes
that as we move from explicit down to tacit to co-created,
these are better suited for the complexity of the problem.
So explicit knowledge is very good
for solving simple problems.
Like if your faucet leaks go underneath and turn
it until it stops or something like that, you know,
put tab A in slot B.
Tends to be very straightforward
and since we would all basically do this the same way,
this has very low or no strategic value.
So if you make your way in the world in a job
that just uses explicit knowledge,
chances are it's not going to be a very well paid
or a very satisfying job.
All the way at the bottom is co-created knowledge.
So this type of knowledge is needed
to address wicked problems.
So wicked problems are those which are extremely complex
that we don't even agree on the starting
or ending point that really are too complex
for any one individual to solve.
And sustainability is an example
of this that we don't even agree exactly
what it looks like, we don't know where it starts,
there's no sort of absolute end-point that we all agree
on and to achieve sustainability and our economy
in our society it's not something
that you can do on your own.
It's something that we can only do collectively
and figure out how to do it as a group.
So co-created knowledge then is very context specific
where explicit knowledge is one size fits all.
We all basically make cold cereal the same way,
but to create a partnership which helps to increase
the sustainability of an entire supply chain or industry,
everybody needs to be involved and the way
that you do that is very context specific.
It's a one size fits one solution.
And as a result of that, it can't be stolen,
it can't be copied and then for that reason,
it has very high strategic value.
And one last example that I think might help.
So thinking about music, one thing I love,
and hopefully a lot of you do too.
So explicit knowledge is like sheet music.
It will say, these are the notes that you should be playing,
this is the timing, this is the tempo,
this is whether you're playing loud or soft.
And it's basically all written down, but the first time,
unless you are exceptionally good, the very first time
that you sit down with your instrument or voice
and go through it, you probably won't do it exactly right.
So that's where tacit knowledge comes in.
So you practice, you get better
at that and certainly I would imagine the 10th
or 100th time that you play through a piece
that the better you will be, that you will learn your way.
How do you interpret and express
what the author of the sheet music was hoping for?
And finally, co-created knowledge is like playing in a band.
It's not just sheet music, it's not just you learning
how to play your instrument well,
it's how to play with others.
When do you hold back?
When do you play more forcefully?
How do you hear what everybody else is doing?
How do you blend?
How do you make your sound fit
in with what everybody else is doing?
And for those of you who have ever been in a band
or orchestra, like that's why we rehearse
so that even though everybody might know that the piece
can read the sheet music explicit knowledge
and can play their own part, tacit knowledge,
it still takes time of collaborating and experimenting
and rehearsing until as a group you're really able
to play the piece well.
So now I'm going to walk you through the different types
of data and some sort of how to's and tips for doing it.
So there are two major types of data.
There are secondary and primary.
So secondary is that which has already
been collected by others.
So it's reports or data sets or things like that.
They already exist very often,
it's the first step in what you do
so you go out and you see, what do we already know?
What's already been done?
What data are already available?
Get your mind around it, make it so you're not reinventing
the wheel, not doing research that somebody else
has already done.
And since this is written down,
this is all explicit knowledge.
And then there is primary data,
which is collected by you or your team.
It tends to fill in specific gaps of secondary data
so while secondary data is explicit,
it's a sort of one size fits all.
Primary data, you really hone in on the specific needs
of your organization or your partnership.
And it's done with others is tends to ideally
be co-created knowledge.
So, in addition to the library resources
that Ms. Magi presents here are some data.gov specific,
which is the U.S. government data.
You can go to specific agencies like census.gov
if you want census data, you can go to USDA.gov,
that's a place that I go a lot for data
about food production and consumption and so forth,
farm viability, things like that.
If you want data about the state of Vermont,
there is vermont.gov.
So these are all government agency,
the data sources where these agencies are charged
with collecting and maintaining and reporting out
on data in specific areas.
There's also much other secondary data books
and periodicals.
There's lots and lots of trade publications.
There are databases which are often available for purchase.
InfoUSA is one that I've used in my past work to sort
of find a list of specific types of businesses
in a geographic area.
So maybe all the restaurants in Burlington or in Vermont.
If I'm interested in a B2B model of selling
some sort of product to restaurants.
There's also many websites.
Mintel has really great sort
of market intelligence research in many, many areas.
You may also wish to read blogs or podcasts,
technology blogs, trendsetters blogs,
or podcasts about your industry
or one that you are interested in.
So that's the end of part one,
please go to part two and watch that.
Thank you.