Being smart about research
A key area to nail to succeed as a marketer is research - it forms the basis of the decisions that you take. There are several areas to be explored in MR, so as a starter I requested Jayant to help clarify some basic MR concepts, and introduce a few key frameworks (in the following posts).
I know Jayant from MICA, and he's spent the better part of this decade working in research, helping consumer clients form better decisions from their data at AbsolutData in Chicago. You can find him here.
Here's the first of three parts: Being smart about research:
I know Jayant from MICA, and he's spent the better part of this decade working in research, helping consumer clients form better decisions from their data at AbsolutData in Chicago. You can find him here.
Here's the first of three parts: Being smart about research:
Most marketers would have support
from their Insights and Analytics team. These teams could work parallel feeding
strategic insights at regular intervals or at times respond to Adhoc requests
from marketing teams. In majority of the organizations insights and analytics
team is an integral part of the marketing team itself, working closely on a
day-to-day basis. A successful marketer would always be a smart researcher as
well. This does not imply to have deep knowledge of techniques and
methodologies but a quick understanding of three aspects –
1. Explaining
a Brief: How do I frame my question to get the best answer?
For
illustration purposes, let’s us say the question is “Locate the positioning of
a new brand line extension in the market for brand X”. This statement can be
further dissected into three questions:
·
Current
Brand Saliency: What does brand X stand for? My extension can’t be too far away
from it?
·
White
Spaces: What are some white spaces around brand X that this new line extension
will target?
·
Immediate
Line Extension Positioning Opportunities: How can I prioritize these different
white spaces for my new line extension?
Now
see below how these questions are translated into simple requests for the
research brief
Jargon – What you speak?
|
Plain Spoken – what you actually
need?
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What you do research wise?
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Current Brand Saliency
|
What is the
perception of brand X in the market?
|
See on which
brand attributes does brand X lead against competition
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White Spaces
|
Which
attributes are currently not owned by any brand?
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Identify attributes not owned by any brand
(i.e. all brands have 50% or lower association)
|
Immediate Line Extension Positioning Opportunities
|
What is the
perception of brand X on the “white spaces”
|
Locate which
attributes the line extension can position itself on
|
2. Suggesting
methodologies: What scale/scope of research do I need?
We will cover these topics in details one by
one in later posts. Here is quick cheat sheet of different methodologies for
the key business questions you may be thinking about
Pricing
|
Existing
SKUs/ Brands
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Nielsen data: Look at the Average
Selling price versus competition
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Market mix Modelling: See the
impact of price changes: Price sensitivity graphs
|
||
Price Pack Architecture: Price sensitivity
graphs
|
||
A/B Testing (this is mostly in
Ecommerce domain)
|
||
New
Product
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Price Sensitivity Monitor
|
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Gabor Grangers method
|
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Conjoint (Optimization)
|
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Profit Pool Analysis
|
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Product/
Portfolio
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Logo/
Design
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Qualitative: Concept/Copy Testing
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Quantitative: Concept/Copy Testing
|
||
Features/
Attributes
|
Usage and Attitude Study (also
called A&U, U&A etc.)
|
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Habits and Practices Study (e.g.
diary based, sometime through panels etc.)
|
||
Trade Off Exercise: Conjoint
(Optimization)
|
||
Promotions/
Campaigns
|
New campaign
|
Qualitative: Concept/Copy Testing
|
A/B Testing (this is mostly in
Ecommerce domain)
|
||
Conjoint (Optimization)
|
||
Existing
campaign
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Market Mix Modelling
|
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Ad tracking
|
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LINK test (there are several such
trademark studies available through traditional research providers)
|
||
Lift Calculation (Mostly in the
tech/digital and E-Commerce domain)
|
||
Medium/
Platform (TV, Digital, OOH etc.)
|
Market Mix Modelling
|
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Attribution Modelling (Mostly in
the tech/digital and E-Commerce domain)
|
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Place/
Medium
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Traditional
Channels
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Channel Optimization
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Market Mix Modelling
|
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Nielsen Dashboards: Nielsen data
can provide time series growth and distribution metrics
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E-Commerce
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Attribution Modelling (Mostly in
the tech/digital and E-Commerce domain)
|
You will notice that segmentation isn’t
mentioned here – because it’s an expensive and elaborate exercise to do. You
don’t sound smart by suggesting segmentation as a first solution to a business
problem. Any brand manager would have access to the profiles of key
segments in the market. A good segmentation is valid for 2-4 years depending
how mature the market is. The more mature/stable the market, the longer
segmentation is valid (e.g. US/Europe are mature markets versus Vietnam/India
which are emerging markets).
3. Reading
data: Know how to read data?
Reading data across different types of
consumers is called ‘Cuts’ or ‘Banners’. Some key cuts that you should always
keep in mind are as follows –
TOTAL
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Key
Consumer Segments
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Brand
Users
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Demographics
|
||||||||
All Respondents
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Seg A
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Seg B
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Seg C
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…
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Own Brand
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Comp 1
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…
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Age, gender, ethnicity, region, etc.
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New Users
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Loyal Users
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Lapsers
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|||||||||
A.
Total number is generally misleading, e.g.
·
There are consumers aged 18-20 yr and then there
are some older ones at 50-55 yr
·
The average age would be 30 yr
·
In reality – there isn’t any consumer aged 30,
so reading averages is dangerous
B.
Be careful in reading demographic data. E.g.
Reading across ethnicity is pretty common in US, while it is unethical in EU
C.
Reading across consumer segments is important,
as brands take decisions by keeping in mind only the “Key Strategic Segment”,
remember – you can’t target all
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