What is Data Analysis?

What is data analysis exactly? Try to define the phrase, data analysis and you’ll get different answers depending on the discipline or industry of the person you are asking.

A scientist or sociologist may define it as a body of methods used to help describe facts (based on observation), detect patterns, develop explanations or test a hypothesis.

A marketing manager may define it more closely to the Wikipedia article, “Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making.”

Ask a mathematician or a statistician and they may well tell you that to define data analysis one must first define statistics, which, may be defined as a set of methods used to collect, analyze present and interpret data.

So again we ask the question, “What is Data Analysis?”  We define it as the process of taking raw information (data) and creating something understandable and useful, that either supports or rejects a specified goal.  In other words, when the CEO asks, “Should I or can I do what I want to do?”; analysis of the available data will support either a yes or no answer.

Analyzing Data For The Real World

Although raw data, expressed as a numerical result, describes values that relate to questions, in the real world the analysis of this data is not about the numbers.  Rather it is about translating those numbers into something meaningful that relates to a real world question.

Your analysis may indicate trends or show an average, median or mean. It can identify groups or sub groups with common characteristics or differences among those groups.  Data analysis can show the rate of growth or the rate of decline.  When translating these numbers into an answer for a real world question, a skilled analyst can present information in such a way as to significantly influence a decision maker to move in a certain direction.  However, using the same data, it is possible to make a strong case for going in the opposite direction. This apparent contradiction is not found within the data; rather it is found within the motivation, background and bias of the analyst and/or within the political and economic constraints of the analyst’s organization or client.

Analysis Presents a Position

Ultimately, data analysis is used to present an argument.  Should the company promote product A?  Should it raise prices?  Can we sell more of product A if we introduce change B? These types of ‘Should we’, ‘Can we’ questions require the support of data that has been collected, analyzed and presented in as unbiased a manner as possible.  The data that is collected is rarely, if ever, a Should we, Can we question.  Most often the Should we, Can we questions are answered in response to data collected relating to Who, What, When, Where or How questions. (Who buys hair spray?  What is the median income of our clients? Where do they shop? When do they shop? How do they shop? etc.)

Bias and Mistake

It’s up to the analyst to interpret and present the data from the Who, What, When, Where and How questions to the decision maker in such a way as for the decision maker to answer the Should we, Can we questions.  For the reasons stated above, presenting a wholly unbiased interpretation is nearly impossible. Often it is not the process of analysis that is at fault but rather the conclusions drawn from the information (or how they are presented) that lead to the failure of a product launch or ad campaign.  A primary culprit leading to erroneous conclusions is mistaking correlation for causation.  The classic example: roosters do not cause the sun to rise!    There are, however, numerous techniques and tools analysts use to minimize errors but those require additional explanation not suited for this post.

What is Data Analysis Then?

So we ask one more time, “What is Data Analysis?”  I would argue that data analysis is simply the interpretation and presentation of the validity or invalidity of data as it relates to a question posited by a decision maker who needs to understand and rely on the information presented by the analyst in order to take action on that question.

In other words, data analysis is the telling of the data’s story. It is a story that began with the questions, “Should we or Can we do what we want to do” and ends with the answer, “That depends on your interpretation”.

4 Main Types of Segmentation in Market Research Analysis

Segmentation is the process of dividing potential markets or consumers into specific groups.  Market research analysis using segmentation is a basic component of any marketing effort. It provides a basis upon which business decision makers maximize profitability by focusing their company’s efforts and resources on those market segments most favorable to their goals.

There are four main types of segmentation used in market research analysis: a priori, usage, attitudinal and need.

a priori (most commonly used)

a priori is defined as relating to knowledge that proceeds from theoretical deduction rather than from observation or experience. For purposes of market research analysis this means making certain assumptions about different groups that are generally accepted as pertaining to that group.  For example, deducing that adults over 50 are not as tech savvy as twenty somethings is a safe assumption based on the reasoning that high tech devices were not as widely available to the older generation than they are to the younger. However, be careful to check your assumptions since they can change over time. In 30 years, that statement may no longer be true.

Usage Segmentation (also used frequently)

Usage segmentation is completed either by decile or pereto analysis. The former splits the groups into ten equal parts and the latter distributes according to the top 20% and the remaining 80%. Usage segmentation helps to drill down more deeply then a priori because it indicates which priori group is the heaviest user of your product.

Attitudinal (Cluster analysis)

Using cluster analysis to create customer psychological profiles is difficult because it is limited by the input data used.  Demographic data is the least helpful, whereas preference data (scaling) is better suited toward this type of analysis.

However, once a usage segmentation is created, it’s exceptionally helpful to know the motivating factors behind the purchasing decisions of the heaviest users of your product.

Needs Based Segmentation

Needs based segmentation is the concept that the market can be divided based on customer need.  This type of analysis is used to develop products that sell rather than trying to sell products a business developed.

Needs based segmentation uses conjoint analysis to separate the groups according to functional performance.  Using cluster analysis, it’s goal is to determine the driving forces behind the performance data.

Knowing which segmentation to use is often as critical as the analysis itself because it is driven by cost and the stated business goals of the decision makers.

Presenting Analytics to the C-Level Executive

For many analysts, presenting your data analysis and reporting to C-Level executives is challenging and often frustrating. The company invests a lot of time, money and effort in the process. The data is clean, the proper conclusions are drawn and the information is communicated and visually supported by charts, graphs and tables in a bang up presentation.  So why is it often ignored or given little weight in the decision making process?

The answer is found in the disparity between the mind set of the analyst and that of the executive.  The analyst looks at data, cleans it, looks at it again, thinks about the data, thinks about the problem, looks at the data again and comes to a conclusion.  The decision is driven by the data, not by the analyst. It is very logical.  Not so with C-level executives.

To say that C-level execs make decisions based solely on their gut feeling or past experience is to do them a disservice.  Although this often appears to be the case, most execs make decisions, whether they realize it or not, based on the axiom, what’s good for the company is good for me provided (this is important) the decision is one I can personally benefit from (increase in power, acknowledged success, etc.) or one I can deflect if the decision proves detrimental. Data analysis and logic have little to do with it.

As an analyst you have a greater chance of C-level execs accepting your data analysis and reporting if you understand these 5 rules.

  1. Less is more.  State your conclusion first but don’t say how you came to that conclusion.
  2. Stay on point. Don’t provide information that doesn’t directly support your conclusion.
  3. Numbers are boring. Execs will only “hear” a number that has a dollar sign in front of it.  So don’t use them.
  4. Speak in plain English.  No jargon. If you must use a technical term explain it briefly and succinctly (without using other terms to describe that one!)
  5. Give’em what they want, leave’em wanting more. Present charts and graphs only if the exec asks for them (have them in a packet if presenting to a group).

Although the data is clear, let the exec ask questions that leads them to the conclusion you already know. It empowers them, allowing them to decide what’s best for the company rather than having the data force their hand. Being heard will empower you.