But there is another kind of distribu on — be er known as dispersion — which shows how a
variable is dispersed/spread with respect to its central tendency.
DATA VISUALIZATION
2) Rela onship
Trees grow taller as they get older in the early years. That's a rela onship between two
variables — height and age.
height = f(age)
In another example, the price of a house depends on the number of beds, number of
bathrooms, loca on, square footage etc. This is a rela onship between one dependent and
many explanatory variables.
price = f(beds, baths, loca on, area)
DATA VISUALIZATION: distribu on, rela onship, composi on, comparison
The principles of data visualiza on
Centuries ago scien sts didn't have cameras to take pictures of distant galaxies or ny
bacteria under the microscope. Drawings were the primary medium to communicate
observa ons, ideas and even theories. In fact, the ability to draw abstract ideas and objects
was an essen al skill for scien sts.
We cannot take pictures of the distribu on variables or their correla on. Instead, we
communicate them through drawings and illustra ons — aka data visualiza on — through
modern tools and technologies.
A dataset contains one or more variables, and we can visualize each and their interac on with
others in mul ple ways. Which visualiza on to choose depends on data and the kind of
informa on we want to communicate. However, fundamentally, they are of four different
kinds:
Distribu on of a single variable
Rela onship between two variables
Composi on of a single or mul ple variables
Comparison between different categories/individuals
1 Distribu on
An important concept in sta s cs and data science is distribu on. Distribu on generally refers
to the probability of occurrence of an outcome. In a distribu on of 100 coin flips how many
will get heads and how many tails? Frequency distribu ons like this are presented in
histograms or curves.
If you look at a dataset just as numbers, there is no way to iden fy these rela onships. But in
fact, you can, without going into complex sta s cal analysis, with the help of a good
visualiza on.
Rela onship between age and height of a class of students
3) Comparison
The third cornerstone of data visualiza on is Comparison. This kind of visual material
compares mul ple variables in datasets or mul ple categories within a single variable.
Let's check out the following two visuals:
Visuals to show comparisons
The one on le compares a variable (salary) between two groups of observa ons (scien sts vs
lawyers) on a bar chart. The right panel is also a comparison chart — in this case, comparing a
variable (GDP)between two groups (UK and Canada) but along a me dimension.
4) Composi on
Have you heard about stacked bar charts? But I'm sure you know what a pie chart is.
The purpose of these charts is to show the composi on of one or more variables in absolute
numbers and in normalized forms (e.g. percentage).
Composi on charts are some of the old school visualiza on techniques that nowadays have
limited use cases (do you really need a pie chart to show a composi on of yellow 10% and red
15%?). Nevertheless, some mes they can present informa on in a visually aesthe c and
familiar, vintage fashion.
Composi on plots: Stacked bar chart (le ) and pie chart (right)
Priori ze Your Data Analysis to Achieve Long Term Success
Real meaning of an “Insight”
As an analyst it is important to priori ze your analysis to have long term success. We see
majority of our analysts and data scien sts busy with repor ng and data prepara on. And
they get minimal me to provide solid fresh insight which can show a business impact.
Proper insight should have followings
Analysis and insight
Recommended ac on
Predicted impact
What can I do with an insight?
Each analysis should start with a business problem otherwise you will end-up nowhere with
your effort. In parallel to that you should know exact areas where you can add value to the
business. Below are the 3 ares you can provide recommenda on and make an impact to the
business with marke ng analy cs
Campaigns recommenda on
UI/UX recommenda on
Product recommenda ons
Filter Your Analysis Requirements
to explain a framework to manage your me or mul ply it. ( How To Mul ply Your Time). This
framework can be adopted to priori ze your analy cs work to become analy cs ninja not a
person who do data puking. Below are the 3 key filters you can used to pick an analysis.
Urgency : How soon does it ma er — If the required analysis me sensi ve then you have to
priori ze it. E.g. Campaign is running for two weeks and campaign team about to spend
$20,000 within 14 days. Proper site search / landing page analysis could save them $5000
within the same week. Jump on it an do it because that opportunity is not going to come
again.
Importance : How much does it ma er — Campaign team wanted to do a ribu on analysis to
improve budget alloca on on mul channel campaigns. In this case, do analysis and provide
recommenda on to op mize exis ng campaigns based on a ribu on models. Which can
mul ply the return on ads.
Significance : How long does it ma er — If there is an analysis which can help you to achieve
sustainable growth and save/make money every month then do it. E.g. GA analysis done to
iden fy trending product to market could be a good analysis where you can use every
week/month/year. Read the complete guide to do that analysis “Google Analy cs Guide to
Finding E commerce Products with Higher Demand”. These are analysis, you don't see huge
impact immediately but it can provide cumula ve impact in the long run.
Way to scale your analysis
Eliminate ad-hoc and repe ve repor ng work which can't add value or unable to take ac on
to make an impact to the business. Some me you can't avoid the report because your HIPPO
wanted that report weekly/daily basis. If you really can't avoid a report then move in to
second step and automate the report. Ini ally you might have to spend me on automa ng it
but it will save me for you in the long run. Apart from that, you can add a recommenda on
along with the automated report to provide few ac on items. Assume that you are sending a
daily revenue report but that report shows last week revenue has a drop of 20%, this is a good
opportunity for you to show your analy cs skills and find the problem with the revenue
report.
Once you have the analysis which can provide the recommenda ons to an eCommerce
customer, simply write a R code where you can plug to any customer and derive the insight
within few mins. Even though you automate it, you require human eye to go through and
verify that the report is relevant to the business also it would be handy if you can add
comments to increase the value of your report. Comment should come with a person who
understand dynamics in the business strategy, products and service and bit of psychology.
Finally train people to read a report or do an analysis considering business strategy, products
and service, and user psychology.
What make you a successful analyst
Below process help analyst to climb the ladder quickly. You can do 100 analysis and come up
with recommenda on but all these recommenda ons are hypothesis unless you experiment
and quan fy the business impact.
Start with the problem > Analysis > Ac ons Items > Experiment > Learning
You should analyse all the experiment you have done regardless of the results. There are
opportuni es even with a loosing experiment. Once you have significant no of experiments
under your belt, you will get the ripple effect and the results are extraordinary.
Search Data, Trends & Analy cs: Catching the Pulse of a Market and its Consumers.
The benefits of search data go beyond SEO-focused keyword research. It is a valuable resource
for both market and product research, too. In fact, it can even be integrated into all research
types and intelligence analyses where listening to and decoding the consumer's voice plays an
important role.
WHY SEARCH DATA CAN BE USED AS A KEY SOURCE OF INSIGHT AT THE STRATEGY LEVEL.
Why Search Data
It would have been prac cally impossible for me to reach the number of analy cs
professionals Krista had in her study, so I decided instead to use publicly available search data
and see if any noteworthy trends would emerge.
At that point in me I was actually already thinking about the poten al benefit of this type of
analysis — but for a different reason. Covid-19 was at its peak, and it was becoming obvious
that the pandemic was strongly impac ng consumer behaviour. Finding ways to look closer at
how consumers search for products online and which new search pa erns are emerging could
offer useful insights.
On the other hand, Fishtown Analy cs (another term with close-to-meteoric rise) released a
popular open-source tool that contributed to its rapid growth.
2. Rising stars in analy cs
It's easy to keep the focus only on the terms and topics that have the largest volumes of
traffic. However, valuable insights can be found anywhere within the keyword universe. A
number of analy cs so ware vendors — in some cases, we could think of them as niche
players — have grown very consistently, even though some literally started from zero.
An interes ng insight is that three out of the six vendors in the graph belong to the category
of 'privacy-first' analy cs, which seems to be a reac on to all the privacy legisla on and
discussions that followed over the course of the last few years. It is a strong signal that the
new privacy landscape can create significant opportuni es for new players in the analy cs
space.
3. Fastest-growing terms in Analy cs jobs and training
Moreover with increasing barriers in the tracking of user behaviour, any addi onal data
sources that echo the consumer's voice, become highly useful. I hypothesised that search
data could also provide another complementary angle to the original analy cs trends
research.
The Analy cs Trends Research
Method
I created and data-mined a keyword universe for Analy cs, looking for the fastest-growing
trends.
The method deployed was as follows.
· Create a keyword universe with 30,000 keywords containing the term 'analy cs' using
mul ple data sources for the keyword sugges ons.
· Retrieve the search volumes from Google for a period of four years.
· Apply sta s cal analysis to spot the key trends.
3 Trends in the Analy cs Industry Based on Search Data
A number of noteworthy findings naturally emerged from this analysis, which gives a good
indica on of the insights that search analysis can produce.
These are the fastest-growing analy cs tools, technology and vendors in mainstream analy cs
tech. Some of the tools, like Einstein and Spo fy analy cs, have grown in very consistent ways
over the course of the last four years.
Others, like Google Analy cs 4, follow a hockey s ck curve. It's o en worth looking at the
causes of the specific search behaviour for the various topics — in Google Analy cs 4, for
example, the surge in search interest was the result of a new product announcement.
It's easy to keep the focus only on the terms and topics that have the largest volumes of
traffic. However, valuable insights can be found anywhere within the keyword universe. A
number of analy cs so ware vendors — in some cases, we could think of them as niche
players — have grown very consistently, even though some literally started from zero.
This last trend illustrates the increasing demand for analy cs-related roles, such as analy cs
engineers and analy cs directors. Demand for specialised roles is followed by strong demand
for training and courses that qualify professionals to work in these roles. The high demand for
analy cs jobs and educa on is not a surprise in itself, but the pace of growth is quite
impressive — look at the rise of search interest for analy cs engineers and analy cs
bootcamps.
Search Data: Going from Tac cal to Strategic
I've used search and keyword data for a long me, mainly working with it from a tac cal
standpoint for keyword bidding, ROAS calcula ons, keyword rank tracking and keyword
research for SEO purposes.
While working on this analysis, it became obvious to me that search data can play a strategic
role when making business decisions involving products, markets and consumers. The analysis
above only scratches the surface, there is so much more to be explored when the terms
within a 'keyword universe' start to be grouped or clustered based on their seman c,
lexicographical or other case-specific a ributes. I have priori sed the use and analysis of
search data in my work since the analy cs trends study. Below, you'll find several ideas on
how to use search data strategically in your organisa on.
Ways to Use Search Data Strategically in Your Organisa on
1. Use it to observe the bigger picture of what's changing in an industry. The example in this
ar cle comes from the analy cs industry, but it could be any other industry or market. Search
data analy cs can be useful in iden fying rising or declining search interest in products,
markets or topics, while also helping businesses understand the specific pa erns in which the
change unrolls. Some terms, for example, can have a meteoric rise and fall compared to
others that grow slowly but surely over me. Understanding these pa erns is essen al when
deciding whether to invest in a new product or market for the long term or take advantage of
short-term opportuni es, i.e. marke ng campaigns that focus temporarily on topics that spike
in search interest for just a short me.
DATA VISUALIZATION: distribu on, rela onship, composi on, comparison
The principles of data visualiza on
Centuries ago scien sts didn't have cameras to take pictures of distant galaxies or ny
bacteria under the microscope. Drawings were the primary medium to communicate
observa ons, ideas and even theories. In fact, the ability to draw abstract ideas and objects
was an essen al skill for scien sts.
We cannot take pictures of the distribu on variables or their correla on. Instead, we
communicate them through drawings and illustra ons — aka data visualiza on — through
modern tools and technologies.
A dataset contains one or more variables, and we can visualize each and their interac on with
others in mul ple ways. Which visualiza on to choose depends on data and the kind of
informa on we want to communicate. However, fundamentally, they are of four different
kinds:
Distribu on of a single variable
Rela onship between two variables
Composi on of a single or mul ple variables
Comparison between different categories/individuals
1 Distribu on
An important concept in sta s cs and data science is distribu on. Distribu on generally refers
to the probability of occurrence of an outcome. In a distribu on of 100 coin flips how many
will get heads and how many tails? Frequency distribu ons like this are presented in
histograms or curves.
DATA QUALITY WORKS
DATAQWORKS
DATAQWORKS@GMAIL.COM