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Fazit: Wie Analytics und CEM gemeinsam den Weg zu loyalen Kunden weisen (Kundenverständnis) . Ein positives Kundenerlebnis ist heute eine selbstverständliche Erwartung der Verbraucher. Um einen Kunden zu begeistern und dadurch zu binden, sollte ein starkes, analytisch gestütztes CEM (Customer Experience Management) im Unternehmen etabliert werden, das konsequent weiterentwickelt wird.
Like the fabled winds of song, new students come sweepin’ down the plains to the University of Oklahoma, with a little help from analytics. I recently had the opportunity to chat with Lisa Moore, Data Scientist at the University of Oklahoma, on her expanding use of predictive analytics. It had
Back in high school, you probably learned to find the intersection of two lines in the plane. The intersection requires solving a system of two linear equations. There are three cases: (1) the lines intersect in a unique point, (2) the lines are parallel and do not intersect, or (3)
Are you still learning about artificial intelligence (AI) and researching how it can be applied to your business scenarios? In a recent Harvard Business Review webinar, Editor of Special Projects & Research at HBR, Angelia Herrin, had an opportunity to discuss the value of AI to organizations with both Michael
Model Risk Management (MRM) ist im Grunde nichts Neues: Finanzinstitute nutzen seit Jahrzehnten Modelle für ihre Entscheidungsfindung. Seit Kurzem jedoch ist das MRM formalisierter und strenger. Regulatorische Anforderungen – zum Beispiel die gezielte Überprüfung interner Modelle (kurz: TRIM) der Europäischen Bankenaufsichtsbehörde (EBA) – nehmen Banken in die Pflicht, die Compliance
SAS programmers have long wanted the ability to control the flow of their SAS programs without having to resort to complex SAS macro programming. With SAS 9.4 Maintenance 5, it's now supported! You can now use %IF-%THEN-%ELSE constructs in open code. This is big news -- even if it only
SAS Visual Text Analytics provides dictionary-based and non-domain-specific tokenization functionality for Chinese documents, however sometimes you still want to get N-gram tokens. This can be especially helpful when the documents are domain-specific and most of the tokens are not included into the SAS-provided Chinese dictionary. What is an N-gram? An
Our company talks to utilities all over the world about the value of analytics and "the digital utility," and we share analytics use cases across: assets and operations; customers; portfolio; and corporate operations (see diagram below). In this third post of my four-part series, I'll highlight a customer analytics use case. Customer
Did you know that SAS has two on-site solar farms? At a combined 2.3 MW in capacity, SAS’ solar farms are located on 12 acres at world headquarters in Cary, NC. The photovoltaic (PV) solar arrays generate 3.8 million kilowatt-hours of clean, renewable energy each year, reducing carbon dioxide emissions
SAS enables you to evaluate a regression model at any location within the range of the data. However, sometimes you might be interested in how the predicted response is increasing or decreasing at specified locations. You can use finite differences to compute the slope (first derivative) of a regression model.
Atualmente, os dados são um dos ativos mais importantes das organizações. As organizações reúnem diferentes tipos de dados que são posteriormente processados e analisados para uma melhor compreensão da evolução das necessidades dos seus clientes. Os termos Business Analytics e Business Intelligence fazem parte da solução que ajuda as organizações a tomarem decisões baseadas
전 세계의 이목이 러시아에 향해 있습니다. 바로 2018년 국제 축구 대회 때문인데요. 오늘은 바로 이 국제 축구 대회에 대한 다양한 데이터를 시각화하고, 지난 대회 결과로부터 어떤 인사이트를 얻을 수 있는지 살펴보고자 합니다! 전 세계 축구팀들은 대회를 위해 얼마나 멀리 이동할까요? 데이터 시각화 리포트가 보이지 않으시면 클릭하세요. 위 보고서는 참가국들이 조별 리그 동안 얼마나 멀리
Künstliche Intelligenz wird meist mit Alexa und Siri oder Chatbots bei Banken und Callcentern assoziiert. Eher selten wird über das Potenzial der KI-Technologien in den Life Sciences und im Gesundheitswesen gesprochen – die Idee ist aber im Kommen. Das zeigt eine aktuelle Studie von Accenture: 74 Prozent der Führungskräfte in
前回は、SASの「Pipefitter」の基本的な使用方法を紹介しました。続く今回は、基本内容を踏まえ、ひとつの応用例を紹介します。 SAS Viyaのディープラーニング手法の一つであるCNNを「特徴抽出器」として、決定木、勾配ブースティングなどを「分類器」として使用することで、データ数が多くないと精度が出ないCNNの欠点を、データ数が少なくても精度が出る「従来の機械学習手法」で補強するという方法が、画像解析の分野でも応用されています。 以下は、SAS Viyaに搭載のディープラーニング（CNN）で、ImageNetのデータを学習させ、そのモデルに以下の複数のイルカとキリンの画像をテストデータとして当てはめたモデルのpooling層で出力した特徴空間に決定木をかけている例です。 In [17]: te_img.show(8,4) 以下はCNNの構造の定義です。 Build a simple CNN model In [18]: from dlpy import Model, Sequential from dlpy.layers import * from dlpy.applications import * In [19]: model1 = Sequential(sess, model_table='Simple_CNN') Input Layer In [20]: model1.add(InputLayer(3, 224, 224, offsets=tr_img.channel_means)) NOTE: Input
O mundo interconectado. Um mundo inteligente. Onde as empresas/marcas têm acesso a dados que permitem oferecer, aos clientes, soluções/produtos personalizados. Bem-vindos ao mundo (utópico?) da Internet das Coisas. Mas será que esta visão é viável? Como é que as empresas conseguirão atingir este cenário? Falámos com um dos especialistas na