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stats 2018 State of Email Analytics: The Metrics Brands Measure and the Impact of Third-Party Analytics [PDF]

Brands use analytics to listen to what their subscribers and inbox providers are telling them. Without analytics, brands are deaf to both their cheers of happiness and their shouts of frustration. Based on our State of Email Survey of 3,000 marketers, our first-ever State of Email Analytics report takes a detailed look at the email metrics brands measure, the analytics tools they use, and the impact of the two of those on program performance. [...] [more] 
pardot.com    Intelligence, Study

discussion The Marketing Dashboard cartoon

“Not everything that counts can be counted, and not everything that can be counted counts.” I think this 1963 quote from William Bruce Cameron (often misattributed to Albert Einstein) touches on some of the potential pitfalls of marketing dashboards. In this era of data-driven marketing, dashboards can give marketers a false sense of security. They always appear authoritative, but the true value depends on what’s being measured. [...] [more]
marketoonist.com    Intelligence

tactics E-Mail: Die 5 häufigsten Fehler bei der Kennzahlen-Interpretation

Einer der riesigen Vorteile von E-Mail-Marketing ist die Verfügbarkeit einer Vielzahl von spannenden Kennzahlen. Erst diese Kennzahlen machen es möglich, die Newsletter einer Erfolgskontrolle zu unterziehen und eine Verbesserung Ihrer eMailings zu erreichen. Denn was man nicht messen kann, kann man auch nicht optimieren. Doch bei der Interpretation der Kennzahlen werden immer wieder Fehler gemacht. Die häufigsten Irrtümer haben wir hier für Sie zusammengefasst – damit Sie es richtig machen! [...] [more]
email-marketing-forum.de    Intelligence

tactics Profit-Maximizing A/B Tests [PDF]

arketers often use A/B testing as a tactical tool to compare marketing treatments in a test stage and then deploy the better-performing treatment to the remainder of the consumer population. While these tests have traditionally been analyzed using hypothesis testing, we re-frame such tactical tests as an explicit trade-off between the opportunity cost of the test (where some customers receive a sub-optimal treatment) and the potential losses associated with deploying a sub-optimal treatment to the [...] [more] 
arxiv.org    Intelligence, Test

tactics Empfehlungen in TensorFlow: Modell auf Daten aus Google Analytics anwenden

Dieser Artikel ist der dritte Teil einer mehrteiligen Anleitungsreihe, in der Ihnen gezeigt wird, wie Sie in der Google Cloud Platform (GCP) mit TensorFlow und der Cloud Machine Learning Engine ein auf maschinellem Lernen (ML) basierendes Empfehlungssystem implementieren. In diesem Teil erfahren Sie, wie Sie das TensorFlow-Modell auf Daten aus Google Analytics 360 anwenden, um Inhaltsempfehlungen für eine Website zu erhalten. [...] [more]
google.com    Customization, Intelligence, Webanalytics

discussion Warum Personalisierung nicht dem Kunden dient

Wie gut funktioniert Personalisierung heute schon aus Kundensicht? Jens Scholz, CEO des Personalisierungsspezialisten Prudsys, sagt: Auf einer Skala von null bis zehn stehen die guten Shops bei fünf bis sechs, der Durchschnitt eher bei drei bis vier. Er erklärt das Warum. [...] [more]
internetworld.de    Customization, Intelligence

discussion Die entscheidenden E-Mail-Engagement-Metriken

Die Verfügbarkeit detaillierter Echtzeit-Kennzahlen gehört zur DNA des Online-Marketings. Das E-Mail-Marketing bildet dabei keine Ausnahme. Geeignete Key Performance Indikatoren (KPIs) sind dabei essentielle Werkzeuge zur Bewertung, Steuerung und Optimierung der verschiedenen Online-Marketing-Kanäle. Besonders interessant sind dabei alle Kennzahlen, die das Engagement der User möglichst genau erfassen, denn Engagement ist der Schlüssel zum nachhaltigen ROI des E-Mail-Kanals. Generell sind User-[...] [more]
email-marketing-forum.de    Intelligence, Openingrate

discussion From 0 to 5: our journey of building an R-package ecosystem

At Emarsys, the data team uses R extensively for several different tasks: doing exploratory data analysis, producing reports, prototyping and building machine learning models to make marketing [...] [more]
craftlab.hu    Intelligence

stats Marketer email tracker 2018 [PDF]

Welcome to the DMA Marketer email tracker 2018. During this period of change for many in the marketing industry, with major legislative changes from the GDPR coming in to force in a matter of months, this report offers an opportunity to monitor the state of a key marketing channel. Email remains the central strand for any multi-channel marketing campaign, integrating seamlessly with both digital and physical channels. This report [...] [more] 
dotmailer.com    Intelligence, Multichannel, Study

discussion Yuval Noah Harari Explains Why the Secret to Surviving the Coming Tech Dystopia is Not What You Think

In his new book 21 Lessons for the 21st Century, historian Yuval Noah Harari (and bestselling author of Sapiens and Homo Deus) explains why your psychology—more than any skill or doomsday bunker—will determine your quality of life in the future. [...] [more]
gq.com    Intelligence, Marketing

discussion xkcd: Curve-Fitting Messages

Cauchy-Lorentz: "Something alarmingly mathematical is happening, and you should probably pause to Google my name and check what field I originally worked in." [...] [more]
xkcd.com    Intelligence

discussion Deep Neural Net with Attention for Multi-channel Multi-touch Attribution [PDF]

In this paper, we propose a data-driven multi-touch attribution and conversion prediction model denoted as deep neural net with attention for multi-touch attribution (DNAMTA) that outperforms other approaches in terms of both conversion prediction and attribution analysis. [...] [more] 
arxiv.org    Intelligence, Multichannel

discussion Making AI accessible for everyone

Our mission here at ReSci is to make artificial intelligence accessible and usable for brands. We believe that everyone should have access to the predictive capabilities that have made companies like Amazon and Netflix into the powerhouses they are today. So how did we seek to do this? We had proven data science models that worked, and we ran constant validation tests to prove it. But it was difficult for marketers to do something with this information, never mind attempting to apply it to marketing [...] [more]
retentionscience.com    Intelligence

tactics 3 Ways To Use Machine Learning To Become An Email All-Star

Machine learning is one of the most talked about “new” strategies and technologies in the industry today, but we often find that it is highly misunderstood — and many times, that’s our fault on the vendor side. It’s our goal [...] [more]
cordial.com    Intelligence

tactics Is RFM still king? A data science evaluation

Predicting and preventing customer churn has a strong impact on the success of e-commerce businesses. Many businesses understand the importance of churn and engineer a predictive model to analyse and identify churning users. There are various flavours to define user churn in e-commerce. One commonly used definition is “the probability that a user will cease buying from an e-commerce business in the future.” However, not all businesses have the resources to build, tune, and run a churn prediction mode [...] [more]
retentionscience.com    Intelligence, Segmentation
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