Marketing 101: Sampling Plan

In the last Marketing 101 post we examined common Contact Methods for acquiring Primary Data.  I listed three traditional methods: telephone, mail, and focus groups.  The fact of the matter is that online technologies have completely changed how we as marketing directors and CMO's do our jobs.  I truly believe this is for the better.  It so so much easier to collect the Primary Data we need via online methods, and it tends to be more cost effective than offline methods.  However there are also challenges to online methods, and some of the same issues exist when it comes to the reliability of the data we collect.

Whether it's online or offline, if we're not doing focus groups, we're usually using surveys to collect Primary Data.  Surveys give us the opportunity to draw conclusions about different groups of consumers by studying a small statistical sample of the total consumer population.  The "sample" is the key.  A sample is usually defined as a segment of the population selected for our research that will represent the larger population as a whole.  Whether or not a sample is good enough to make observations with, depends on how we've designed it.

Designing a sample is a three step process:
1) Decide who you are going to survey
2) Decide how many you are going to survey
3) Decide how you are selecting the participants in the sample

Let's examine each of these three steps a little more.

1) Decide who you are going to survey
First, you have to decide who you are actually going to survey.  In more statistical terms, we are asking "what is the sampling unit"?  Any group of people can be used as a sampling unit.  What I mean here is children, adult women, men, etc.  Your sampling unit should be determined by the target groups in your survey, and the data you have about your target customers. If you don't know who your target customer is, then you have some research to do first.  Choosing the wrong sampling unit will waste your time and your money.  It will give you data that you cannot use, because the results from that group will be irrelevant.

2) How many should be surveyed (what is the sample size)
When we are asking ourselves "how many should be surveyed", what we are saying here is "what is the sampling size?"  Sample sizes that contain more people usually give us increased accuracy. For you statistical junkies, there are certain facts of mathematical statistics that describe this, such as the law of large numbers and the central limit theorem.  To keep this simple for our discussion, larger samples will give you more statistically reliable results than smaller sample sizes.  However, larger sample sizes will cost you more money.  Do not assume that you need to attempt to sample an entire population segment (which would take forever, and be almost impossible).  Usually less than 1 percent of a population segment will provide statistically reliable results.  There is a down-side to Probability Samples: cost.  Depending on your Contact Method, larger samples will result in drastically higher costs.  When cost is a factor, then researchers turn to Non-Probability samples.

3) How should the people in the sample be selected?
What we are asking here is, "What is the sampling procedure we are going to follow?"  There are two different types of samples we can choose from: Probability Samples, and Non-Probability Samples.  Probability Samples give each population member (a.k.a. a potential participant) a possible chance of being included in the sample.  Because you are not sampling the entire population, probability samples will always contain margin for error.  The larger the margin of error, the less trust you can place in the data you have that is supposed to represent your "population".  Larger samples give you less margin of error, and less margin of error lets you trust your data more.

There are three different types of Probability Samples:
Simple Random Sample
Every member of the population has a known and equal chance of selection.

Stratified Random Sample
The population is divided into mutually exclusive groups (such as age and race) and random samples are drawn from each group.  Basically, you are splitting your population into defined groups, and then sampling each of those groups.

Cluster (area) Samples
The population is divided into mutually exclusive groups (such as blocks, and they are relatively homogeneous) and the researcher draws a simple random sample of each group.

Non-probability sampling is much less expensive than doing Probability Sampling, but the results are of limited value, because the data is less reliable.  Non-probability samples should be used with caution. Non-probability sampling techniques cannot be used to deduce generalizations from the sample to the general population. Any generalizations created from a non-probability sample MUST be filtered through the researcher's knowledge (and yours) of the customer population being studied.

There are three different kind of Non-probability samples:

Convenience Sample
In a Convenience Sample, the researcher selects the "easiest", most convenient to locate member from the immediate population to obtain research data from.  I would consider this one of the most hap-hazard methods.  There is practically nothing you can generalize about the data you obtain, other than considering it a "snapshot" of a a particular group, at a particular time, at a particular place.

Judgment Sample
In a Judgment Sample, the researcher will use his/her judgement to select the the people sampled.  Immediately you have to ask...how good is their judgment in selecting a good candidate?  Again, the data that you obtain from this sort of sampling is just not reliable for generalized conclusions, but it may be interesting to use as a "snapshot".

Quota Sample
Probably the worst method I can think of, a Quota Sample is simply a researcher grabing enough people to meet a quota requirement for sampling participants.  Stay away from this sort of methodology.  Your data is practically useless.

Never select a sampling method without taking the time (as you always should) to weigh the needs you have when collecting Primary Data.  Always take into consideration your time-frame and your budget, and always try to be as objective as possible when you are evaluating the Primary Data you have obtained.



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Marketing 101: Primary Data Collection - Research

In this edition of the Marketing 101 series we will take a quick look at Primary Data collection.  So far we have been discussing data that is considered secondary.  Secondary data was collected by someone else.  Whether it was your sales department, or a comScore research report that you purchased, it was created by someone else other than your department.  It did not involve any of your department interacting with existing and potential customers to collect data.  It was not collected with your marketing objectives in mind.

There is nothing wrong with secondary data.  You cannot perform any Market Intelligence without secondary data.  It is a great and necessary starting point for any of your research.  Secondary data is critical when you are defining the problems and objectives that are the focus of your Marketing Intelligence initiatives.  However in most cases, you will need to collect primary data of some kind in order to have the information you need to make real decisions.

What is Primary Data?
Primary Data is research that has been conducted by your organization, first hand. It is also known as Field Research.  It is usually more reliable than secondary data, because it is usually more accurate since you collected it yourself.  Primary data is specific and relevant to your products and services. However, Primary Data is often very time consuming to collect, and usually costs more to create than purchasing secondary data reports. You must take special care when collecting primary data.  It needs to be relevant, current, and as unbiased as possible.

Primary Data is relevant when it directly applies to your company's products and services.  It is relevant when it relates to the problems you are trying to solve, and the marketing goals of your organization.  Primary Data is current when it is recent, and directly corresponds to the profile of your customers TODAY.  Primary Data is unbiased when your subjects have been honest and open during data collection.  When constructing your Primary Data collection plan, you must consider research methods, contact methods, the sampling plan, and your research instruments.

Research Methods consist of observation, surveys, and experimentation.  Contact Methods typically consist of mail, phone, personal interaction, and various online methods.  Sampling Plans take into account units, size, and procedures.  Research Instruments typically consist of questionnaires and other mechanical instruments.  Let's start with a quick discussion of Research Methods.  There are three typical ways that Primary Data is collected in marketing: observation, surveys, and experiments.

Observation
Observation is the collection of Primary Data through observing people, their actions and the situations they are in.  Observation may be the easiest research to do.  Typically, observation is also the most cost effective method.  Observation can also give you data that people aren't usually willing to tell you themselves, such as their feelings, emotions, attitudes or the motives behind their buying decisions.

How does observation work?  It's extremely simple.  Take a restaurant franchise owner.  He may be planning on opening another location.  He may also have little or no money to pay for marketing research.  However a lot of the data he needs he can collect himself.  He can get into his car and drive around town, observing the traffic patterns.  He can see where his clientele goes to shop.  He can see what time the traffic appears.  He can call real estate agents and ask them for lease prices for different properties.  He can drive around and look for areas that don't have his type of restaurant, looking for areas of little competition.  He can do all of this for just the cost of the gas in his car.  You can do this yourself.

Surveys
Surveys are the most common method of collecting Primary Data.  Surveys are the best way to get the descriptive information that you need for your marketing intelligence.  Simply put, surveys collect data by asking other people a series of questions about their personal knowledge, emotions, attitudes, preferences, and buying behaviors.  Surveys can provide you a wealth of data.  There is always a golden nugget, a piece of data that can give you the insight you need to figure out the direction of your next campaign.

However, there are drawbacks to the data you collect via surveys.  Often people just don't recall some of the information that you are asking for, and as a result, they are unable to answer the questions.  Therefore the response that they give will not be the complete truth, it may be something that they feel you want to hear.  Sometimes people are unwilling to provide information that they might deem "private".  This prevents completely truthful responses, and it skews the data that you are analyzing.  If the responses seem too good to be true...they just may be.  

Experimentation
Primary Data can also be collected via experimentation.  Experimentation is the practice of gathering data by selecting matched groups of people, giving them different treatments or scenarios, controlling related factors in their environments, and checking for differences in their responses.  Experimentation gives us what we call "causal" data.  Causal data helps us explain cause and effect relationships.  Experimenting helps us try to answer "why" someone is doing something, and what influences their buying behavior.

A common example of experimentation is price testing.  To the buyer, price will be the final emotional factor that determines whether or not they will give us their hard earned money.  Depending on the product and market segment, price may be the most important factor.  How do you know what price is the right price?  You have to test it.  Many companies will test certain prices when collecting primary data on a new menu item that is being developed.  How do you think McDonalds knows how much to charge for a Big Mac?  They tested how much they can charge for that Big Mac, looking for that magic number that will provide the most sales and the most profit.

In my next post we will continue this exploration of Primary Data by examining different contact methods.



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