十二 072016
 

As technology evolves, so do the c-suite roles related to technology. In particular, the roles of Chief Digital Officer and Chief Data Officer – both referred to as CDO – have seen rapid changes. This post will document the changes I've observed in these two roles, and answer questions I've heard as our customers have been navigating the […]

Rise of the CDO reflects the rising role of data was published on SAS Voices.

十二 062016
 

The cybersecurity challenge exemplifies how global threats have evolved and how governments must combat them. For all the complexity of the Cold War, the United States defense officials knew the nations that posed the biggest threat. The world is much different today. As General Michael Hayden (ret.), former Director of the National Security […]

Cybersecurity: A conflict of old and new was published on SAS Voices.

十二 062016
 

As data-driven marketers, you are now challenged by senior leaders to have a laser focus on the customer journey and optimize the path of consumer interactions with your brand. Within that journey there are three trends (or challenges) to focus on:

  • Deeply understanding your target audience to anticipate their needs and desires.
  • Meeting customers’ expectations (although aiming higher can help differentiate your brand from the pack).
  • Addressing their pain points to increase your brand's relevance.

customer journey

No matter who you chat with, or what marketing conference you recently attended, it's safe to say that the intersection of digital marketing, analytics, optimization and personalization is a popular subject of conversation. Let's review the popular buzzwords at the moment:

  • Predictive personalization
  • Data science
  • Machine learning
  • Self-learning algorithms
  • Segment of one
  • Contextual awareness
  • Real time
  • Automation
  • Artificial intelligence

It's quite possible you have encountered these words at such a high frequency, you could make a drinking game out of it.drinking-game

There’s a lot of confusion created by these terms and what they mean. For instance, there is hubbub around so-called ‘easy button’ solutions that marketing cloud companies are selling for customer analytics and data-drive personalization. In reaction to this, I set off on a personal quest to research questions like:

  1. Does every technology perform analytics and personalization equally?
    • What are the benefits and drawbacks to analytic automation?
    • What are the downstream impacts to the predictive recommendations marketers depend on for personalized interactions across channels?
    • Should I be comfortable trusting a black-box algorithm and how it impacts the facilitated experiences my brand delivers to customers and prospects?
  2. Do you need a data scientist to be successful in modern marketing?
    • Is high quality analytic talent extremely difficult to find?
    • How valid is the complaint of a data science talent shortage?
    • How do I balance the needs of my marketing organization with recent analytic technology trends?

Have I captivated your interest? If yes, check out this on-demand webcast.

It's time to dive in deep and unleash on these questions. During the video, I share the results of my investigation into these questions, and reactive viewpoints. In addition, you will be introduced to new SAS Customer Intelligence 360 technology addressing these challenges. I believe in a future where approachable technology and analytically-curious people come together to deliver intelligent customer interactions. Analytically curious people can be data scientists, citizen data scientists, statisticians, marketing analysts, digital marketers, creative super forces and more. Building teams of these individuals armed with modern customer analytics software tools will help you differentiate and compete in today's marketing ecosystem.

marketing ecosystem

 

tags: artificial intelligence, Context-aware, customer intelligence, customer journey, Data Driven Marketing, data science, digital marketing, Digital Personalization, machine learning, marketing analytics, Predictive Personalization, Real time Automation, segment of one, Self-learning algorithms

Customer analytics: Think outside the black box was published on Customer Intelligence.

十二 062016
 

As data-driven marketers, you are now challenged by senior leaders to have a laser focus on the customer journey and optimize the path of consumer interactions with your brand. Within that journey there are three trends (or challenges) to focus on:

  • Deeply understanding your target audience to anticipate their needs and desires.
  • Meeting customers’ expectations (although aiming higher can help differentiate your brand from the pack).
  • Addressing their pain points to increase your brand's relevance.

customer journey

No matter who you chat with, or what marketing conference you recently attended, it's safe to say that the intersection of digital marketing, analytics, optimization and personalization is a popular subject of conversation. Let's review the popular buzzwords at the moment:

  • Predictive personalization
  • Data science
  • Machine learning
  • Self-learning algorithms
  • Segment of one
  • Contextual awareness
  • Real time
  • Automation
  • Artificial intelligence

It's quite possible you have encountered these words at such a high frequency, you could make a drinking game out of it.drinking-game

There’s a lot of confusion created by these terms and what they mean. For instance, there is hubbub around so-called ‘easy button’ solutions that marketing cloud companies are selling for customer analytics and data-drive personalization. In reaction to this, I set off on a personal quest to research questions like:

  1. Does every technology perform analytics and personalization equally?
    • What are the benefits and drawbacks to analytic automation?
    • What are the downstream impacts to the predictive recommendations marketers depend on for personalized interactions across channels?
    • Should I be comfortable trusting a black-box algorithm and how it impacts the facilitated experiences my brand delivers to customers and prospects?
  2. Do you need a data scientist to be successful in modern marketing?
    • Is high quality analytic talent extremely difficult to find?
    • How valid is the complaint of a data science talent shortage?
    • How do I balance the needs of my marketing organization with recent analytic technology trends?

Have I captivated your interest? If yes, check out this on-demand webcast.

It's time to dive in deep and unleash on these questions. During the video, I share the results of my investigation into these questions, and reactive viewpoints. In addition, you will be introduced to new SAS Customer Intelligence 360 technology addressing these challenges. I believe in a future where approachable technology and analytically-curious people come together to deliver intelligent customer interactions. Analytically curious people can be data scientists, citizen data scientists, statisticians, marketing analysts, digital marketers, creative super forces and more. Building teams of these individuals armed with modern customer analytics software tools will help you differentiate and compete in today's marketing ecosystem.

marketing ecosystem

 

tags: artificial intelligence, Context-aware, customer intelligence, customer journey, Data Driven Marketing, data science, digital marketing, Digital Personalization, machine learning, marketing analytics, Predictive Personalization, Real time Automation, segment of one, Self-learning algorithms

Customer analytics: Think outside the black box was published on Customer Intelligence.

十二 062016
 

After a bit of a delay, the JMP Blog has moved over to the JMP User Community. Stop by and check it out!

We missed you these past few weeks, but we look forward to sharing lots of posts -- starting with today's post by data visualization expert and R&D director Xan Gregg, about parallel coordinate plots.

P.S. We are still working on the look and feel of the blog in its new home, so please pardon us while we continue to fine-tune.

tags: JMP Blog

The post JMP Blog has moved! appeared first on JMP Blog.

十二 052016
 

Data quality initiatives challenge organizations because the discipline encompasses so many issues, approaches and tools. Across the board, there are four main activity areas – or pillars – that underlie any successful data quality initiative. Let’s look at what each pillar means, then consider the benefits SAS Data Management brings […]

The post How SAS supports the four pillars of a data quality initiative appeared first on The Data Roundtable.

十二 052016
 

A common practice in traditional marketing is to first choose a target market to focus on. You then align your organization’s strategies and messaging to create a campaign in that target market. But what happens when it becomes clear that the campaign you created isn’t working? How agile are you in terms of adjusting on the fly and adapting to the needs of your prospective customers?

The challenge

A campaign we ran at SAS targeted small to medium-sized businesses, or SMBs. We needed to come up with tailor-made messaging that would be distinct from similar campaigns we were launching targeted at larger, enterprise-level companies. To do that, we highlighted what we thought wedata-analysisre business needs, language and case studies that would resonate with the SMBs.

But after the program launched and began, the results were disappointing. We saw lower-than-expected results for performance metrics including click-through rates and conversions. So we tweaked the messaging, offers and program structure to improve results. After crunching those numbers, the results came in – the campaign was still floundering.

We were now forced to take a fresh look. What had we done wrong? On reflection, we came upon an even more telling question: Did we actually need to separate SMBs from larger organizations? We started with an underlying assumption that the SMB market should be treated differently. Had that been a mistake?

The approach

To help guide us forward, we selected a roster of key performance metrics to analyze:

  • E-mails sent.
  • Open rates.
  • Click-through rates.
  • Opt-out rates.
  • Conversions (those who filled out registration forms to receive the promoted asset).
  • Lead-generated SSOs (an internal measure of conversions that we identify as leads that later progress to become sales opportunities).
  • Rate of completed leads to SSOs.

We then looked at how the SMBs responded to the SMB-specific campaign compared to how they responded when they received the enterprise-level messaging.

The results

To our surprise, SMBs responded more strongly to the enterprise-level campaign (see the table below). Our assumption had been proved wrong. So we adjusted by closing the SMB-specific campaign and retargeted the SMBs with our enterprise-level messaging.

adele-table

The takeaway for us was a reminder that we can’t afford to let our assumptions about the market hinder our ability to adjust to customers’ needs. In this situation, we relied on the power of analytics to provide the answers about what people wanted rather than continue in a losing cause.

You can best meet customers along their decision journey by relying on advanced analytics to increase the quality of a marketing campaign by using scoring, optimization and predictive capabilities. The standard spreadsheet-based reports that marketers used to rely on to see how their campaign performed have now shifted to interactive visualization dashboards to track the efficacy of their campaign, while making changes on the fly when necessary to ensure a campaign is reaching its potential. The biggest difference is that marketers now have these tools at their disposal. We no longer have to submit requests to the IT department to get this information.

==

Editor’s note: This post is part of a series excerpted from Adele Sweetwood’s book, The Analytical Marketer: How to Transform Your Marketing Organization. Each post is a real-world case study of how to improve your customers’ experience and optimize your marketing campaigns.

tags: Campaign Management, customer analytics, customer insights, customer journey, marketing campaigns, midmarket, smb

How analytics empowers campaign agility was published on Customer Intelligence.

十二 052016
 

A common practice in traditional marketing is to first choose a target market to focus on. You then align your organization’s strategies and messaging to create a campaign in that target market. But what happens when it becomes clear that the campaign you created isn’t working? How agile are you in terms of adjusting on the fly and adapting to the needs of your prospective customers?

The challenge

A campaign we ran at SAS targeted small to medium-sized businesses, or SMBs. We needed to come up with tailor-made messaging that would be distinct from similar campaigns we were launching targeted at larger, enterprise-level companies. To do that, we highlighted what we thought wedata-analysisre business needs, language and case studies that would resonate with the SMBs.

But after the program launched and began, the results were disappointing. We saw lower-than-expected results for performance metrics including click-through rates and conversions. So we tweaked the messaging, offers and program structure to improve results. After crunching those numbers, the results came in – the campaign was still floundering.

We were now forced to take a fresh look. What had we done wrong? On reflection, we came upon an even more telling question: Did we actually need to separate SMBs from larger organizations? We started with an underlying assumption that the SMB market should be treated differently. Had that been a mistake?

The approach

To help guide us forward, we selected a roster of key performance metrics to analyze:

  • E-mails sent.
  • Open rates.
  • Click-through rates.
  • Opt-out rates.
  • Conversions (those who filled out registration forms to receive the promoted asset).
  • Lead-generated SSOs (an internal measure of conversions that we identify as leads that later progress to become sales opportunities).
  • Rate of completed leads to SSOs.

We then looked at how the SMBs responded to the SMB-specific campaign compared to how they responded when they received the enterprise-level messaging.

The results

To our surprise, SMBs responded more strongly to the enterprise-level campaign (see the table below). Our assumption had been proved wrong. So we adjusted by closing the SMB-specific campaign and retargeted the SMBs with our enterprise-level messaging.

adele-table

The takeaway for us was a reminder that we can’t afford to let our assumptions about the market hinder our ability to adjust to customers’ needs. In this situation, we relied on the power of analytics to provide the answers about what people wanted rather than continue in a losing cause.

You can best meet customers along their decision journey by relying on advanced analytics to increase the quality of a marketing campaign by using scoring, optimization and predictive capabilities. The standard spreadsheet-based reports that marketers used to rely on to see how their campaign performed have now shifted to interactive visualization dashboards to track the efficacy of their campaign, while making changes on the fly when necessary to ensure a campaign is reaching its potential. The biggest difference is that marketers now have these tools at their disposal. We no longer have to submit requests to the IT department to get this information.

==

Editor’s note: This post is part of a series excerpted from Adele Sweetwood’s book, The Analytical Marketer: How to Transform Your Marketing Organization. Each post is a real-world case study of how to improve your customers’ experience and optimize your marketing campaigns.

tags: Campaign Management, customer analytics, customer insights, customer journey, marketing campaigns, midmarket, smb

How analytics empowers campaign agility was published on Customer Intelligence.

十二 052016
 

A common practice in traditional marketing is to first choose a target market to focus on. You then align your organization’s strategies and messaging to create a campaign in that target market. But what happens when it becomes clear that the campaign you created isn’t working? How agile are you in terms of adjusting on the fly and adapting to the needs of your prospective customers?

The challenge

A campaign we ran at SAS targeted small to medium-sized businesses, or SMBs. We needed to come up with tailor-made messaging that would be distinct from similar campaigns we were launching targeted at larger, enterprise-level companies. To do that, we highlighted what we thought wedata-analysisre business needs, language and case studies that would resonate with the SMBs.

But after the program launched and began, the results were disappointing. We saw lower-than-expected results for performance metrics including click-through rates and conversions. So we tweaked the messaging, offers and program structure to improve results. After crunching those numbers, the results came in – the campaign was still floundering.

We were now forced to take a fresh look. What had we done wrong? On reflection, we came upon an even more telling question: Did we actually need to separate SMBs from larger organizations? We started with an underlying assumption that the SMB market should be treated differently. Had that been a mistake?

The approach

To help guide us forward, we selected a roster of key performance metrics to analyze:

  • E-mails sent.
  • Open rates.
  • Click-through rates.
  • Opt-out rates.
  • Conversions (those who filled out registration forms to receive the promoted asset).
  • Lead-generated SSOs (an internal measure of conversions that we identify as leads that later progress to become sales opportunities).
  • Rate of completed leads to SSOs.

We then looked at how the SMBs responded to the SMB-specific campaign compared to how they responded when they received the enterprise-level messaging.

The results

To our surprise, SMBs responded more strongly to the enterprise-level campaign (see the table below). Our assumption had been proved wrong. So we adjusted by closing the SMB-specific campaign and retargeted the SMBs with our enterprise-level messaging.

adele-table

The takeaway for us was a reminder that we can’t afford to let our assumptions about the market hinder our ability to adjust to customers’ needs. In this situation, we relied on the power of analytics to provide the answers about what people wanted rather than continue in a losing cause.

You can best meet customers along their decision journey by relying on advanced analytics to increase the quality of a marketing campaign by using scoring, optimization and predictive capabilities. The standard spreadsheet-based reports that marketers used to rely on to see how their campaign performed have now shifted to interactive visualization dashboards to track the efficacy of their campaign, while making changes on the fly when necessary to ensure a campaign is reaching its potential. The biggest difference is that marketers now have these tools at their disposal. We no longer have to submit requests to the IT department to get this information.

==

Editor’s note: This post is part of a series excerpted from Adele Sweetwood’s book, The Analytical Marketer: How to Transform Your Marketing Organization. Each post is a real-world case study of how to improve your customers’ experience and optimize your marketing campaigns.

tags: Campaign Management, customer analytics, customer insights, customer journey, marketing campaigns, midmarket, smb

How analytics empowers campaign agility was published on Customer Intelligence.

十二 052016
 
Kepler's third law for planetary bodies

A recent issue of Astronomy magazine mentioned Kepler's third law of planetary motion, which states "the square of a planet's orbital period is proportional to the cube of its average distance from the Sun" (Astronomy, Dec 2016, p. 17). The article included a graph (shown at the right) that shows the period and distance for several planetary bodies. The graph is plotted on a log-log scale and shows that the planetary data falls on a straight line through the origin.

I sometimes see Kepler's third law illustrated by using a graph of the cubed distances versus the squared periods. In a cubed-versus-squared graph, the planets fall on a straight line with unit slope through the origin. Since power transformations and log transformations are different, I was puzzled. How can both graphs be correct?

After a moment's thought, I realized that the magazine had done something very clever. Although it is true that a graph of the squared-periods versus the cubed-distances will CONFIRM the relationship AFTER it has been discovered, the magazine's graph gives insight into how a researcher can actually DISCOVER a power-law relationship in the first place! To discover the values of the exponents in a power-law relationship, log-transform both variables and fit a regression line.


How to discover a power law? Log-transform the data!
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How to discover a power law

Suppose that you suspect that a measured quantity Y is related by a power law to another quantity X. That is, the quantities satisfy the relationship Ym = A Xn for some integer values of the unknown parameters m and n and constant A. If you have data for X and Y, how can use discover the values of m and n?

One way is to use linear regression on the log-transformed data. Take the logarithms of both size and simplify to obtain log(Y) = C + (n/m) log(X) where C is a constant. You can use ordinary least squares regression to estimate values of the constant C and the ratio n/m.

Planetary periods and distances

For example, let's examine how a modern statistician could quickly discover Kepler's third law by using logarithms and regression. A NASA site for teachers provides the period of revolution (in years) and the mean distance from the Sun (in astronomical units) for several planetary bodies. Some of the data (Uranus, Neptune, and Pluto) were not known to Kepler. The following SAS DATA step reads the data and computes the log (base 10) of the distances and periods:

data Kepler;
input Name $8. Period Distance;
logDistance = log10(Distance);
logPeriod   = log10(Period);
label logDistance="log(Mean Distance from Sun) (AU)"
      logPeriod  ="log(Orbital Period) (Years)";
datalines;
Mercury   0.241   0.387
Venus     0.616   0.723
Earth     1       1
Mars      1.88    1.524
Jupiter  11.9     5.203
Saturn   29.5     9.539
Uranus   84.0    19.191
Neptune  165.0   30.071
Pluto    248.0   39.457
;

The graph in Astronomy magazine plots distances on the vertical axis and periods horizontally, but it is equally valid to flip the axes. It seems more natural to compute a linear regression of the period as a function of the distance, and in fact this how Kepler expressed his third law:

The proportion between the periodic times of any two planets is precisely one and a half times the proportion of the mean distances.

Consequently, the following call to PROC REG in SAS estimates the power relationship between the distance and period:

proc reg data=Kepler plots(only)=(FitPlot ResidualPlot);
   model logPeriod = logDistance;
run;
kepler2

The linear fit is almost perfect. The R2 value (not shown) is about 1, and the root mean square error is 0.0005. The table of parameter estimates is shown. The intercept is statistically insignificant. The estimate for the ratio (n/m) is 1.49990, which looks suspiciously like a decimal approximation for 3/2. Thus a simple linear regression reveals the powers used in Kepler's third law: the second power of the orbital period is proportional to the third power of the average orbital distance.

A modern log-log plot of Kepler's third law

Not only can a modern statistician easily discover the power law, but it is easy to create a scatter plot that convincingly shows the nearly perfect fit. The following call to PROC SGPLOT in SAS creates the graph, which contains the same information as the graph in Astronomy magazine. Notice that I used custom tick labels for the log-scaled axes:

title "Kepler's Third Law";
title2 "The Squared Period Is Proportional to the Cubed Distance";
proc sgplot data=Kepler;
  scatter y=logPeriod x=logDistance / datalabel=Name datalabelpos=bottomright datalabelattrs=(size=12);
  lineparm x=0 y=0 slope=1.5 / legendlabel="log(Period) = 3/2 log(Distance)" name="line";
  yaxis grid values=(-1 0 1 2 3) valuesdisplay=("0.1" "1" "10" "100" "1000") offsetmax=0;
  xaxis grid values=(-1 0 1 2) valuesdisplay=("0.1" "1" "10" "100");
  keylegend "line" / location=inside opaque;
run;
Kepler's Third Law: For a planetary body, the square of the orbital period is proportional to the cube of the mean distance to the sun

Remark on the history of Kepler's third law

This article shows how a modern statistician can discover Kepler's third law using linear regression on log-transformed data. The regression line fits the planetary data to high accuracy, as shown by the scatter plot on a log-log scale.

It is impressive that Kepler discovered the third law without having access to these modern tools. After publishing his first two laws of planetary motion in 1609, Kepler spent more than a decade trying to find the third law. Kepler said that the third law "appeared in [his] head" in 1618.

Kepler did not have the benefit of drawing a scatter plot on a log-log scale because Descartes did not create the Cartesian coordinate system until 1637. Kepler could not perform linear regression because Galton did not describe it until the 1880s.

However, Kepler did know about logarithms, which John Napier published in 1614. According to Kevin Brown, (Reflections on Relativity, 2016) "Kepler was immediately enthusiastic about logarithms" when he read Napier's work in 1616. Although historians cannot be sure, it is plausible that Kepler used logarithms to discover his third law. For more information about Kepler, Napier, and logarithms, read Brown's historical commentary.

tags: Data Analysis, History

The post Discover power laws by log-transforming data appeared first on The DO Loop.