Ad networks in Digital marketing

Understanding ad networks.

Objectives:
The objectives of ad networks in digital marketing are to :

1.Find websites with advertising space to sell. 

2.Connect website to companies. 

Definition:

Display advertising networks are like a middleman, connecting businesses who want to advertise, with websites with ad space to sell. 

Need of ad networks

contacting the site directly to work out the details can get pretty time consuming. This is where

display advertising networks come in. 

Functions of ad networks

1. They handle both the buying and the selling of displayads, linking businesses to websites that want to sell advertising space.
2. Another way to think of it is as a marketplace that brings businesses and websites together, helping manage the transactions. 

Examples of ad networks

1. Google Display Network or 
2. Yahoo, 

Process:

• Step1: offer businesses looking to advertise ad space on websites.
• Step2:Websites that offer these ad  spaces can become a part of these networks. 
• Step3: set minimum prices for how much money they expect for showing ads.
• Step4: business can then bid for the spots it wants throughout all the websites in that network, deciding how much they’re willing to pay. 
• Step5 :Buyers and sellers are connected every single time pages are loaded, and the ads that win the right to fill the ad spot are shown.

Audience Targeting:

Target specific audiences through two main routes: the topics of the web pages where the ads appear, and general information about the people viewing the content. 

Financial management:

Another thing networks do is handle the money involved. Buying and selling ads happens

every second of every day, and the networks collect money from businesses and pay the

websites that show the ads. 

Data management:

advertising networks collect and share data with businesses.

Data analysis:

1. Number of times ads are shown, 
2. Number of times ads clicked on,
3. Cost of advertising
4. Tracking of websites

Machine learning for Managers Part-1 Introduction

Introduction to Machine learning for managers

Before we explore the world of machine learning, we need to understand a few basics of Machine learning.

This will bring you in the comfort zone as many managers lack engineering knowledge.

Input:

The raw data required for the analysis. This data may be from survey, observations, or from data sets. 

Marketing: Customers purchased data in a Big bazaar retail out let for a month..

Human Resources: Employees training data for last ten years.

Finance: Stock price of FMCG products in last five years on a daily basis.

Operations: The cost of packaging for multiple orders from multiple customers in the firm for last two years.

Algorithm:

Machine learning field works on the algorithms to identify the predicted value. Thus, it is necessary for us to know basics of algorithms. These are set of rules a manager introduces to get the forecast value.

Let us understand with some examples;

Marketing: 

A manager would like to know the best sales promotion tool for his online marketing campaign in different markets. The set of rules are as follows:

Step1; START

Step2; Define promotional too used by a company Search ads, display ads,

Step 3; Define different markets: Panaji, Mumbai, Bengaluru, Delhi, and Chennai.

Step4; Define revnue from diffrent markets.

Step 5: Define cost of promotion types in different markets.

Step 6; Calculate Revenue per market per campaign.

Step 7: Calculate the best ROI for a promotion on a market wise

Step 8; STOP.

Human  Resource Management:

A human resource manager would like to know influence of educational qualification in completing a particular project. This will help them in recruiting a better candidate next time. The rules can be;

Step1: START

Step 2: Extract the education qualification data from employee records say CS, IS, Mechanical, Civil etc...

Step 3; Collect project time taken by each employee.

Step 4: Identify the project difficulty level easy, medium and difficult.

Step 5: identify the best combination of difficulty, qualification and optimum time.

Step 6: STOP.

Finance:

A finance manager want to construct a portfolio of stocks to recommend his clients. The algorithm can be set as below:

Step 1; START

Step 2 : Collect P/E ratio of all NSE stocks and set range of expectation.

Step 3: Collect EPS data for all NSE stocks

Step 4: Collect dividend announced by companies in last five years.

Step5: Define sector names ( Automobile, Bank, IT etc..0

Step 6: Calculate top 10 companies a sector wise on P/E, EPS and Dividend.

Step 7; Collect predicted growth rate for each sector.

Step 8; Divide company in Step 6 based on STEP 7 paramter.

Step 9; STOP