Data Science for Marketing

Recommender Systems – the start of marketing personalization

collaborative filtering recommendation system

Recommender systems research study has incorporated a broad variety of artificial intelligence methods consisting of machine learning, data mining, user modeling, case-based thinking, and customer satisfaction, among others. Personalized suggestions are a vital part of numerous online e-commerce applications such as, Netflix, and Spotify. This wealth of practical application experience has supplied the motivation to researchers to extend the reach of recommender systems into new and challenging areas. The purpose of this unique issue is to take stock of the current landscape of recommender systems research study and recognize instructions the field is now taking. This post supplies an overview of the current state of the area and presents the different articles in a particular concern.

Learn more about recommender systems

Personalization – How much do you understand your customer?

All marketing activities that are organized for us, all the offers that we receive on our emails, apps, banner ads, billboards on the bus stations, hidden messages in the last movie you watched or message to buy pairing product. All these marketing activities try to show that they understand your preferences. Or they try to persuade you that the product they represent is the product you are looking for.

What personalization can mean to your organization?

How can Marketing use Data Science and AI?

marketing data science

Data Science is a field that extracts meaningful information from data and helps marketers in discerning the best insights. These insights can be on different marketing elements such as client intent, experience, habits, etc. that would assist them in efficiently optimizing their marketing strategies and derive optimum income.

How can Data Science help Marketing?

Increase Marketing ROI with Multi-touch Attribution Modelling

Advertisers typically realize a 15% – 44% improvement in marketing ROI with advanced multi-channel machine-learning algorithm. Using advanced machine learning techniques for marketing can give you true insight on performance all down to channel, campaign and device-level.

Implementing advanced machine learning models for marketing attribution will give you many advantages.

How to gain advantages using machine learning for multi channel attribution modeling?

Modeling marketing multi-channel attribution in practice

multi channel attribution.png

What is the next step I need to take to close his deal? What will this customer ask for next and how can I drive it to him? What is the shortest path to close a deal?
How much do all my marketing and sales activities really cost? How much does one action or marketing channel costs?
All these are common questions marketing and sales are facing with on daily bases.
Luckily, there is an answer.
Knowing how much you spend on each of the marketing and sales channels and activities is from essential importance for your business success.

Learn how to model marketing multi-channel attribution in practice

Web Analytics – Why is SEO important?

Building a strong brand in today’s word is a real challenge.
The web, the globalization, the widespread knowledge, the easily accessible information and lot more is expanding the competition from your neighborhood to the whole wide world.This highly competitive environment puts high standards for the success of the business. Building a website with a nice graphic is not a fancy feature anymore, but a standard. However, the fancy website will mean nothing to your business without the well-expected visitors.

Why SEO is important? by Kire Hajba