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Email Marketing Statistics to Guide your Email Strategy in 2018 - EmailMonks

Email Marketing Statistics to Guide your Email Strategy in 2018 - EmailMonks | The MarTech Digest | Scoop.it
Before you plan your marketing strategy for 2018, have a look at a list of email marketing statistics and facts handpicked and compiled by EmailMonks.
CYDigital/marteq.io's insight:

Email Marketing Statistics to Guide your Email Strategy in 2018 - EmailMonks

 

Many more stats when you click through.

 

This news comes to you compliments of marketingIO.com. #MarTech #DigitalMarketing

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The Four Statistical Concepts Every Online Marketer Should Know - Kissmetrics

The Four Statistical Concepts Every Online Marketer Should Know - Kissmetrics | The MarTech Digest | Scoop.it
1. The Pareto Principle
You have probably already heard about the Pareto principle. You may know it as the 80/20 rule as it states, according to Wikipedia, that “for many events, roughly 80% of the effects come from 20% of the causes.”

2. The Law of Large Numbers
The law of large numbers tells us that if you repeat a random experiment often enough, the average of the outcomes will converge towards the expected value.

3. Relative and Absolute Numbers
Imagine reading about a new drug that reduces the risk of getting a dangerous disease by 25%. At first, this might sound very promising but does it really tell us what the real benefit of taking the new drug is?

Let’s assume 20 in 1,000 people get the disease without the drug. By taking the drug, this number is reduced to 15 in 1,000 people. While this is indeed a 25% relative drop, we should also consider the absolute reduction.

4. Simpson’s Paradox
Simpson’s paradox, as stated on Wikipedia, is the name of a paradox “in which a trend appears in different groups of data but disappears or reverses when these groups are combined.”
CYDigital/marteq.io's insight:

CT for the impact on online marketing.

 

BTW: missing is Schrodinger's cat.

 

Visualize your Marketing Stack. marketingIO will analyze your marketing technology and deliver a visual of your MarTech Stack. Free. Go here: http://go.marketingio.com/stack_analysis 

 

#MarTech #DigitalMarketing

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A Marketer’s TL;DR on Bayes Theorem - Gartner

A Marketer’s TL;DR on Bayes Theorem - Gartner | The MarTech Digest | Scoop.it
The probability of A happening in a world where B has happened is equal to … (The probability of B happening in a world where A has happened) times (The probability of A happening at all) divided by (The probability of B happening at all).

The probability of a click happening on an impression is equal to … (The probability that type of impression caused a click in the past) times (The probability of a click happening at all) divided by (The probability of that impression happening at all).

We are in the realm here of conditional probability. Bayes was interested in the likelihood that some event would happen given that something else had happened. In our case, that a click would happen given that a particular impression occurred.
CYDigital/marteq.io's insight:

FYI...

 

MarTech requires constant optimization to continually squeeze ever improving performance. No time for continual CRO? Contact us. #MarTech #DigitalMarketing

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7 Epiphanies Needed to Intuitively Grasp Statistical Significance - Kissmetrics

7 Epiphanies Needed to Intuitively Grasp Statistical Significance - Kissmetrics | The MarTech Digest | Scoop.it
Epiphany #1: Large sample sizes dilute eccentricity
Epiphany #2: P-values are trade-offs between certainty and experiment length
Epiphany #3: Small differences in conversion rates are near impossible to detect. Large ones, trivial.
Epiphany #4: You destroy a test’s validity by pulling the plug before its preordained test-duration has passed
Epiphany #5: “Relative” improvement matters, not “absolute” improvement
Epiphany #6: “Statistically insignificant” does not imply that the opposite result is true
Epiphany #7: Any tests that are run consecutively rather than in parallel will give bogus results
CYDigital/marteq.io's insight:

CT for the devil in the details.

 

Email your comments to joe_rizzo@marketingIO.com. I’ll publish it here. marketingIO: One Source for All Marketing Technology Challenges. See our solutions.  

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A Refresher on Statistical Significance - HBR

A Refresher on Statistical Significance - HBR | The MarTech Digest | Scoop.it

marketingIO: One Source for All Marketing Technology Challenges. See our solutions

CYDigital/marteq.io's insight:

I only scooped top-line to shake the dust off, but more when clicking through.

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A Refresher on Regression Analysis - HBR

A Refresher on Regression Analysis - HBR | The MarTech Digest | Scoop.it
You probably know by now that whenever possible you should be making data-driven decisions at work. But do you know how to parse through all of the data available to you? The good news is that you likely don’t have to do the number crunching yourself (hallelujah!) but you do need to correctly understand and interpret the analysis created by your colleagues. One of the most important types of data analysis is regression.

Regression analysis is a way of mathematically sorting out which of those variables does indeed have an impact. It answers the questions: Which factors matter most? Which can we ignore? How do those factors interact with each other? And, perhaps most importantly, how certain are we about all of these factors?

In regression analysis, those factors are called variables. You have your dependent variable — the main factor that you’re trying to understand or predict. In Redman’s example above, the dependent variable is monthly sales. And then you have your independent variables — the factors you suspect have an impact on your dependent variable.


marketingIO: One Source for All Marketing Technology Challenges. See our solutions.

CYDigital/marteq.io's insight:

If you've forgotten or you just need a quicker refresher, here you go.

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8 Highly Reputable Resources for Compelling Marketing Stats - HubSpot

8 Highly Reputable Resources for Compelling Marketing Stats - HubSpot | The MarTech Digest | Scoop.it

iNeoMarketing.com bridges the gap between your MarTech and your in-house experience. Contact us.

CYDigital/marteq.io's insight:

FWIW: quite often there is duplicate data. Not always, but often. So if you're not suffering from Stat FOMO, pick 4-5 and you're fine.

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Insight’s Periodic Table of B2B Digital Marketing Metrics - Insight Partners

Insight’s Periodic Table of B2B Digital Marketing Metrics - Insight Partners | The MarTech Digest | Scoop.it


↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓↓

Receive a FREE daily summary

of The Marketing Technology Alert HERE

↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑↑

 

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20 Captivating Marketing Statistics that will Drive 2014 | WebDAM | #TheMarketingAutomationAlert

20 Captivating Marketing Statistics that will Drive 2014 |  WebDAM | #TheMarketingAutomationAlert | The MarTech Digest | Scoop.it
WebDAM Solutions created this infographic to illustrate how the marketing world perceives the trends that will drive efforts going into 2014.

 

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CYDigital/marteq.io's insight:

Scattered yet entertaining.


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[INFOGRAPHIC] 26 Digital Marketing Statistics You Shouldn't Miss - Optimind | #TheMarketingAutomationAlert

[INFOGRAPHIC] 26 Digital Marketing Statistics You Shouldn't Miss - Optimind | #TheMarketingAutomationAlert | The MarTech Digest | Scoop.it
26 Digital Marketing statistics you shouldn't miss with downloadable quick reference infographic.
CYDigital/marteq.io's insight:

More stats 'n facts to support your 2014 budgeting.


  • See the article at www.myoptimind.com
  • Receive a daily summary of The Marketing Automation Alert directly to your inbox. Subscribe here (your privacy is protected).
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  • iNeoMarketing drives more revenue and opportunities for B2B companies using marketing technologies. Contact us
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Mark H. Cohen's curator insight, April 6, 2015 11:47 AM

Excellent stats. 

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Big Data = Big Trouble: How to Avoid 5 Data Analysis Pitfalls - Search Engine Watch | #TheMarketingAutomationAlert

Big Data = Big Trouble: How to Avoid 5 Data Analysis Pitfalls - Search Engine Watch | #TheMarketingAutomationAlert | The MarTech Digest | Scoop.it
When trying to make sense of data, it's easy to fall victim to confirmation bias, irrelevancy, statistical insignificance, and causation vs. correlation and action vs. intent confusion. Here are solutions to these (and more) common analysis problems.


Summarized...


1. Confirmation Bias: You have a hypothesis in mind but you are only seeking data patterns that support it – ignoring all data points that reject it.

2. Irrelevancy and Distraction: Focusing on data that is irrelevant to the problem you are trying to solve or being distracted by data that isn't directly connected to your analysis goal. In the age of Big Data, this is doomed to happen more and more.

3. Causation vs. Correlation: Mixing the cause of a phenomenon with correlation. If one action causes another, then they are most certainly correlated. But just because two things occur together doesn't mean that one caused the other, even if it seems to make sense.

4. Statistical Significance: Using data sets that are too small to suggest a trend or comparing results that are not different enough to have statistical significance.

5. Action vs. Intent: Inferring the wrong intention based on the actions recorded in the data rather than the suggested intent.

6. Apples and oranges: Comparing unrelated data sets or data points and inferring relationships or similarities.

7. Poor data hygiene: Analyzing incomplete or "dirty" data sets and making decisions based on the analysis of that data.

8. Narrow focus/not enough data: Analyzing data sets without considering other data points that might be crucial for the analysis (for example, analyzing email click-through rate but ignoring the unsubscribe rate).

9. Bucketing: The act of grouping data points together and treating them as one. For example, looking at visits to your website and treating unique visits and total visits as one, inflating the actual number of visitors but understating your true conversion rate.

10. Simple mistakes and oversight: "It happens to the best of us."

CYDigital/marteq.io's insight:

The title reads 5, but there are actually 10. And the author provides a solution to the first five. Regardless of the status of Big Data in your organization, this is a must-review article! We're all statisticians now!


  • See the article at searchenginewatch.com
  • Receive a daily summary of The Marketing Automation Alert directly to your inbox. Subscribe here (your privacy is protected).
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  • iNeoMarketing drives more revenue and opportunities for B2B companies using marketing technologies. Contact us
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