Choose the Market Research Type that Meets Your Needs
Next time you wonder what type of market research to conduct, I invite you to ask yourself where
the particular problem at hand belongs: Awareness, Targeting, Acquisition or Retention. Then take
a look at the Relevance Wheel to find the approach that will help you answer your specific
questions.
If you choose your method carefully market research can give you a big advantage over your
competition.
Analysis of your research in terms of what statistical information is presented is a common and
powerful exercise. Statistical analysis allows the researcher to view data to determine trends,
frequency, relationships and commonality. Most statistical methods used in business are known as
inferential statistics. That is, using a sample to infer the results across the rest of the population. If
one group of people behave/think this way, then it’s likely that everyone else will. Commonly used
statistical analysis measures:
Mean, median and mode
Variance and standard deviation
Probability Testing hypotheses
Correlation
Regression analysis (to predict future results) Mean median and mode
The mean (or average) of a set of data values is the sum of all of the data values divided by the
number of data values. The median of a set of data values is the middle value of the data set when
it has been arranged in ascending order. That is, from the smallest value to the highest value. The
mode of a set of data values is the value(s) that occurs most often. Variance and standard
deviation: are measures of how spread out a distribution is. They measure how variable a group of
results are. Standard deviation is a measure of how spread out your data is. Computation of the
standard deviation is a bit of a process.
Compute the mean for the data set
Compute the deviation by subtracting the mean from each value
Square each individual deviation
Add up the squared deviations
Divide by one less than the sample size
Take the square root
Example You are being asked to look at a local coffee shop. The owner wants to make some
decisions about the business and doesn’t know where to start. Among other things you decide to
survey 10 people about the coffee and take a score out of five. You get your results and find that
the mean is three.
You do however need to understand the spread of these figures. The results mean something
quite different given that there are a few fives and ones. Finding the standard deviation shows us
how spread out the numbers are. This helps us determine what is really going on. If you only relied
on the mean, three, you would think things were fine, slightly above average. Ones however
suggest problems, knowing the spread is very useful. You can obviously see the spread on such a
small sample, but imagine you surveyed 10 000 coffee drinkers.
Variance = the average squared deviation of each number from its mean
Probability – the measure of how likely it is for something to happen.
Testing hypotheses – A hypothesis is an assumption or proposition that a researcher makes about
some characteristic of the population being investigated?
What is the problem?
What is your expectation?
Create a model to measure the data against
Use your model to guide data collection
Compare your expectation with the information at hand (data)
Use the data to modify your model
Use the information and model to make predictions about the hypothesis
Correlation: the degree and type of relationship between two variables in which they vary together
over a period of time.
For example, the Grade Point Average (GPA) achieved at school in relation to the hours of
television watched.
Regression analysis
Predicts the outcome of an event (the dependent variable) based on interactions between other
related drivers (the explanatory variables). It is important to note that it is difficult to be sure as to
what are explanatory variables (or causal), and what happen to be coincidence. Causality is difficult
to establish. Just because A increases at the same time as B decreases, does not prove a
relationship between the two. For example, you can reasonably predict sales volumes by looking at
marketing expenditure and the number of sales representatives your company employs. As
marketing expenditure and sales rep numbers increase, you can reasonably expect a
corresponding increase in sales volume.
What performance hypotheses did you come up with?
Does this mean that salary, gender or tenure cause performance? No, not necessarily. There are
many possible explanations for the suggestions of relationships. For example:
Barbara gets paid more than anyone else in the group. She may be the Sales Director, and
therefore have more responsibilities as well as a smaller sales region than the rest of the
team
Janet, while earning the least amount and has the second lowest performance %, may only
work part time
Henry may have the region with the largest client base, and therefore does not need to
work particularly hard to make sales
You will need to make assumptions when analysing the data you gather. Part of the research
process is ‘sanity checking’ your assumptions with others. Peer and expert review is critical to your
success.
When checking your assumptions validity, ask yourself…
Can I demonstrate a clear relationship between the data and my conclusions?
Is there any other explanation for this relationship?
Can I explain how I came to my conclusion?
Do my assumptions relate to my research objectives?
Quantifying Customer Value19
A Value Proposition answers 3 central questions:
Is our offering better?
In what ways is it better and how much better is it?
What is that worth to the customer?
To be useful in sales conversations, a good Value Proposition boils the answers to these questions
down to the two to four ways that our offering is better than the competition and what that is
worth to the customer. A good Value Proposition quantifies these differentiators in a way that is
clear and simple, cutting through technical complexity.
19 Source: Leverage Point, as at https://www.leveragepoint.com/blog/quantify-customervalue/
quantifying-customer-value-good-data/, as on 4th September, 2017.
A good framework helps provide clear thinking in quantifying customer value:
What are we offering?
To what customer segment?
Compared to what reference point?
Our offering’s positive differentiation creates value for that customer that can be highlighted and
articulated as value drivers.
It doesn’t take a rocket scientist or an MBA to set up a value driver. Common sense and a desire to
understand the customer’s business are usually enough. The quantified value of a differentiated
product or offering is built using the following elements:
A formula
Data to quantify our product’s claimed benefit
Data about market conditions and the customer’s operations
Let’s look at each of these elements in turn to understand how to start and how agile marketing
results in ongoing improvement.
Value Driver Formulae. A good value driver formula is based on an understanding of the
customer’s business, turned into a formula through straightforward arithmetic. Good product
managers start with simple questions:
How does my offering reduce my customer’s costs?
How does my offering increase my customer’s revenue and profitability?
How does my offering reduce customer risk and what is that worth?
The value driver formula picks out key elements of the customer’s market and operations as a
basis for quantifying value, using data that capture product claims to provide a clear comparison
between a competitive reference point and our offering. These formulae are invariably created by
teams who generate hypotheses which they test with experts, experienced sales reps and/or
selected customers. Formulae usually start simple. They often evolve to a more detailed and
analytical form as the team gets deeper answers from their tests, understanding the customer
better. Simplifying and suppressing detail is necessary to prepare the Value Proposition for sales
use and effective communication.
The formula itself is a way to present important information. It can be displayed in a way that
highlights key data and shows clear calculations transparently. Or it can be complex, obscure, hard
to follow, shown with too much information or include irrelevant detail. An agile marketing process
should consider how formulae are presented, test alternate approaches and improve the
presentation of the Value Proposition based on sales experience. Suppressing detail can help
improve the clarity of most conversations. Structuring formulae and initial formula display simply to
support these conversations is usually good.
Value Proposition design is even better when it also supports the transition to drill-down
discussions, possibly involving our own subject matter experts. Those conversations are more
effective in building customer consensus when detailed build-up of summary variables and the
sources of information are accessible even if they were not visible on the surface. Great cars have
simple controls that the average driver can use confidently but also have great engineering under
the hood that is appreciated by good mechanics.
Product Claim Data. Scientists and management consultants love well-designed statistical research.
Studies engage their intellect. Evidence-based decisions are their mantra. Study design is an
integral part of their training. Performing extended studies can be part of their full employment
plan. Extreme examples can be found among drug developers, conditioned by FDA approval
requirements, who only find comfort in randomized, multi-center, double-blind, placebo
controlled, clinical trials. Trials that take years to perform, cost millions of dollars and sometimes
deliver ambiguous results.
For value conversations, product claims data rarely need to be this rigorous or this expensive. Start
with an initial hypothesis of quantified product benefit. Even the most meticulous development
studies start with quantitative hypotheses before they test anything.
Next have an internal conversation with the development team or the technical team to test and
refine the hypothesis. Then talk to a beta customer or an early adopter. These conversations
usually transform an initial hypothesis into data “based on anecdotal evidence” at a minimum. With
a modest amount of additional process and presentation, the data become “based on case
studies,” “based on customer interviews” or “the results of a survey.” More detail on the sources of
the data (even if they are anonymous) bolsters credibility. If needed, these data can help inform
well-designed market research or outcomes studies.
None of these data sources adheres to the exacting methodology of scientific development
studies. Even in a medical context, the vast majority of customer value claims don’t require an
expensive study. Yet these data sources support a product claim and support good conversations
about a product’s differentiation and what that is worth. More importantly, using hypotheses and
approximate data as a basis for conversation helps to refine the data themselves through
feedback, identify credible data sources and build the quality of the Value Proposition with
experience.
Economic and Operating Data. It doesn’t take an MBA or a CPA to quantify the effect of a product
on a customer’s business. Initial economic, market and operating data can almost always be
generated quickly through some combination of publicly available sources, sales rep knowledge
and expert knowledge. Where there are gaps in the data from these sources, a plausible initial
hypothesis is a good place to start. Testing v1 with beta customers and early adopters helps
improve the quality of the data. Often it also helps identify customer segments, situations and
competitors where a modified version of the Value Proposition with distinct data makes sense.
Public sources, interviews, surveys and case studies can be usefully supplemented with more
formal market research and outcome studies. But waiting for formal studies to start the customer
dialog misses an opportunity. Most customers are willing to engage in conversations specific to
their needs and situation. That includes providing specific data. Customers are often trying to
figure out whether to invest time and internal political capital in a purchasing process. Having a
better understanding early of value specific to a customer’s business helps the customer make
better decisions. It also helps the sales rep qualify the account as a target.
In practice, there are two barriers to having conversations about customer business specifics:
Too many empty blanks. Some Value Propositions are designed with a long list of
questions before any estimated Customer Value is available for a conversation. These
Value Propositions are rarely successful. Sales reps have to spend extra prep time getting
answers before their customer call. Worse still, reps have to spend scarce customer time in
cycles of discovery without knowing the payoff. Customers get tired of answering
questions without understanding why. Some customers stop answering questions
altogether, perhaps out of concern for confidentiality or perhaps because they get
suspicious as to why the rep is asking.
The best Value Propositions make it possible to have a value conversation with no customer
specifics, usually based on a combination of benchmark data and other customer experience. They
start with a value answer and highlight a few pieces of benchmark data where it is easy and natural
for the customer to react by providing their own specifics. This process of incremental tailoring and
customizing in a series of conversations allows for gradual customization of a Unique Value
Proposition without creating the customer experience of a trip to the dentist to for a complete
check-up. It also allows the conversation to go only as deep as necessary. And it facilitates early
value conversations by the sales rep that work seamlessly with the later involvement of technical
pre-sales or subject matter experts in the customer value conversation.
Insufficient confidence. The true causes of this problem are harder to diagnose. Confidence
shortfalls could arise from any or all of the following concerns by a member of the
customer-facing team:
o Data quality
o Data relevance to the specific customer
o How to find and defend the data source
o How to change the data
o Whether changing the data will destroy the value
o How to use value based on benchmark data in a conversation
o How to get more specific with the customer about their data
o General lack of skills and confidence
Apart from the last concern, these are all addressable with good Value Proposition design, good
content, good training and well-communicated motivation coming from other reps’ experiences
and successes. Value selling can become a viral habit of sales with good Value Propositions and a
good adoption plan.
With customer engagement comes feedback, including customer data that can help improve the
general Value Proposition and customer-specific data that can be useful to other reps in other
sales situations. Ultimately, easy access to general value propositions and relevant customerspecific
value propositions make the next sales conversation better. An agile approach makes this
repository of useful tools better and better.
Great Value Propositions help sales teams win. Value Propositions are useful in a number of ways
to accelerate sales and improve profitability. Not only are perfect data unnecessary, but requiring
exact data can become an excuse, creating a barrier to implementation and improved
performance. Sales professionals use Value Propositions in some ways and circumstances that
don’t require specific customer data at all. Where customer-specific data and strong product
claims support the sales process, agile methods make it possible to start with plausible
approximate or benchmark data and continue to improve the quality of the Value Proposition and
underlying data quality based on customer conversations. Supporting those conversations and
learning from them helps to translate specific customer experience into general sales success.
Representing Data in Charts20
You’ve got your data, you’ve made some sense of it, and now it is time to communicate your
results. Great! This article will provide examples of many types of charts and graphs and explain
how to pick the best one for your data depending on the message you want to convey.
Choosing a type of chart depends first and foremost on what kind of data you have and what you
want to express. I find that charts and graphs are typically used to convey one of the following:
comparisons/relationships, distribution, trends, composition, flow/process, or location.
- Comparison/Relationship Charts – Pretty self explanatory, right? You have data on two or more
variables and you want to show them together, probably to show a correlation or pattern of some
type. Examples might include MPG of three different cars, average heights according to race,
etc. Bar charts and line charts, or combinations of the two, are very commonly used for the
purpose of comparison.
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