This is data that has equally split intervals

between each value.

One of the first steps to take in customer segmentation is to think about what

makes sense for your organization.

All customers are created equal.

If you’ve ever watched a show on Netflix or purchased a book on Amazon, then

you’ve seen the value of segmenting to predict and encourage future purchases.

This practice is called predictive analytics.

What is the purpose of customer segmentation?

Why is it important to estimate the size of each segment?

Are fiction customer based on real data obtained from customer segmentation

analyses, ethnographic, research, and interviews.

Unusual scenarios that include all the things a customer might do with software but

probably wouldn’t do most of the time are called what?

Look for themes and patterns that are relevant and based on behaviors and goals.

Well-developed personas guide decisions about design and functionality.

Prioritizing design elements and resolving design disagreement can be done in an

electronical way.

These will Constitute your “set character” the product or marketing teams should focus

on.

It’s common to use ________ ______for personas.

Customer segmentation is more than a simple process. It requires a combination

of facts and intuition to put yourself in your customers’ shoes.

A persona is a non-fictional person who represents the characteristics and goals

of a customer segment.

are usually the things you care

about but can't affect directly,

such as profitability, customer

satisfaction, and customer

loyalty.

becomes the task scenarios that you

simulate and measure during a usability test. It includes

the clicks, navigation paths, and browsing activities

found on websites and software.

preference data, opinions, desirability, branding, and

sentiments are usually captured in surveys, focus

groups, and usability tests.

Be sure you know that the

data you need exists, or that you’ll be able to collect

and analyze it.

To do the most with customer

analytics, you’ll want to gather data for each

customer, not aggregated data at product or company

levels.

Planning, collecting, and analyzing

data is only good if something is going to be done

about the insights.

Total revenue by product

is often at too high a level to understand

what’s driving purchases. In many cases,

you want to obtain customer transaction

data at the product level.

The first step is to organize the data in a way

that is simple to analyze. It can be helpful to

tabulate your data in a spreadsheet.

When your data is organized, you can estimate how many

customers are in a particular segment.

 Conducting a new survey

 Interviewing customers

 Observing

 Usability testing

 Secondary research

How much is it going to cost to

acquire new customers?

How much can you afford to spend

to get a customer who will continue to return for repeat business?

ROI = (CLV – Marketing cost per customer acquired) / Marketing cost per

customer acquired

These will constitute your “set character” the product or marketing

teams should focus on.

Look for themes and patterns that are relevant and based on behaviors

and goals. Look for patterns around:

 What they expect to accomplish with your product?

 How they go about achieving their goals?

 What their technical background or product proficiency is?

To determine which customer segments are economically

the most important to you, estimate the percentage of

customers in a specific segment who are going to

purchase your product within a specific time period and

the amount of revenue you expect from each sale.

A more advanced technique used to identify segments is

based on clustering algorithms such as cluster analysis,

factor analysis, and multiple regression analysis. These

techniques identify statistical patterns that are hard to

detect intuitively.

The next step is to refine your analysis by

“crossing” more than one variable.

One of the first steps to take

in customer segmentation is

to think about what makes

sense for your organization.

A new design, promotion, or feature may improve customers’ attitudes or sales,

but if the increase is small (something like a 5% increase), unless your

sample size is large enough, that difference won’t be distinguishable from

random fluctuations in the data.

Statistically significant essentially means that

the differences are not likely due to sampling error. However, statistically

significant does not mean the findings are noteworthy or important.

This is often guarded in

organizations because

it contains both sensitive

company and customer

data.

The “right”

analytics depend on the method. But one thing that

all good customer analytics have in common is that

they are meaningful to the customer.

becomes the framework for

testing experiences. It is the general pattern

customers exhibit when using your products and

services.

becomes the template for whom

you measure. It includes demographic data like

gender, age, geography, and income.

Looking for metrics that are meaningful to customers are not helpful.

Customer transactions and purchase data are popular sources for finding

baseline data.

These are some important things to consider as you prepare for your projects.

In this phase, you will use the collected data to show quantifiable improvements

in your metric.

10.This allows you to prevent waste and rework by putting in place system to reduce

the time between data collection and action.

a) Determining the correct sample size

b) Controlling the result

During this phase, you will be able to describe the customer’s current state and

identify the root cause of problems or insights for improvement.

can be directly

controlled or

manipulated.

This is interval data with a natural zero point.

This is data that has equally split intervals

between each value.

This includes data that has a natural ordering.

means essentially "in name only"

; if you

have a name, it belongs in this category.

measurements

countable items

Can be directly controlled or manipulated.

It is the central metric for testing better designs, ads, and campaign effectiveness.

What is a type of data that becomes the framework for testing experiences?

In managing the right measure, it is stated that, the right measure will identify .

It is largely involves turning customer actions and attitudes into data?

It is often the "why" beyond low satisfaction rates, higher sales, or high customer

turnover rates.

_________means essentially "in name only" if you have a name it belongs in this

category.

What level of data that has equally split intervals between each value?