If you have been in the ad tech industry, you would often hear people say "Fraudsters are always a step ahead in the industry". Can we conclude if the fraudster in the industry is some of the ad-tech vendors itself? Consider solving the case below by following the conversation. Case : Three people form the advertising industry. The advertisers always tell the truth. The ad-tech vendor never tell the truth. The fraud detection vendor alternatively tells truth and lie. The world's renowned explorer questioned all of them. The advertiser, ad-tech vendor and the fraud detection vendor. Let the 3 people be Jack, John and James not in the same order. Explorer : Jack, which section of industry do you belong to? Jack : I'm an Advertiser. Explorer : John, to which section do you belong to? John : I'm an Ad-Tech Vendor. Explorer : Was Jack telling the truth? John : Yes. Explorer : James, to which section do you belong to? James : I'm an Advertiser Explorer : To which section does Jack belong to? James : Jack is a Fraud Detection Vendor. Now that you have heard the conversation above, can you tell which person is the Ad-Tech vendor? A) Jack B) John c) James Once you arrive to the answer, you would be able to get a bitter insight of the advertising industry today. Below find an interesting structure of Ad-Tech ecosystem, understand the bridging layers here that lead to fraud. 1. A layer of semi owned dummy mobile apps are needed to steal data inventory from real publishers, fraudulent publishers and ad networks. This allows the ad tech company to mix traffic, shadow the original traffic sources not allowing transparency across supply chain. 2. A layer of semi owned dummy mobile apps are needed to simulate human behavior through bots after reverse engineering fraud suites which are build internally or externally. We at AdIQ, are building an encrypted inventory matching technology which would help us expose this layer of dummy apps with legal addresses that ad tech vendors use in the industry to gain inventory and game the system now and then. We are also studying what personality traits make up these cyber criminals in the market and how they been in the industry for over the decade. "What is clear is that we need to cut through the chaos and focus what is best for our advertisers, not anything else."

Years have gone by and the Ad Tech industry has seen a whooping sense of consolidation and obliteration. According to Sloan Gaon, CEO of PulsePoint, ad tech leaders should double down efforts and embrace differentiation, specialization and verticalization if they would want to succeed. Analyst's forecast less than 250 net operating companies in ad tech in 2020 from over 1500+ companies in 2013. The domino effect of bad debt, ad fraud, technology upheaval and overheated evaluations within the industry has strong armed Ad Tech companies into the ultimatum, differentiate or die on the vine. Leading brands are moving more into the traditional way of advertising, McDonald's acquisition of Dynamic Yield to create more store walk-ins and drive-thru, trending menu items by geo-fencing or Walmart's acquisition of Polymorph to innovate it's digital advertising and draw more customers to the brand. While the list is endless, Sojern acquiring Adphorus, AirBnB purchased AdBasis. The industry is thrown back to the clear intent of brands looking to adopt, apply and integrate technology with data to effectively create real and transformative changes instead of driving fake acqusitions, MAU's and DAU's over unethically acquired inventories which has created an non transparent and non trust worthy ad tech market. As Jack Welch says, "Change before you have to". To successfully weather the storm, players should be wise to play a long term way keeping customer deliverable an utmost priority. The compound effect of poor profitability in an overcrowded market has left players to scout for working capitals from various sources by window dressing the balance sheets. Advertising needs to take into account the diversity of individuals in a generation to avoid being archaic and ineffective. - Dave Asch

Understanding Linear Programming Linear programming (LP) is a method for engineers or data scientists to find the best outcome of a business problem i.e maximum profit, minimum cost in a linear mathematical model. Each of the LP problem consist of following components : 1. Objective Function : Purpose behind LP i.e maximize profit, minimize loss , 2. Decision Variables : These are the controllable variables that influence the objective function, 3. Constraints : These are linear restrictions on decisions variables. Case Example : XYZ Pharmaceuticals XYZ Pharmaceuticals manufacturers two types of medicine with same salt : A and B. The manufacturer wants to maximize their weekly operational profit. $1 of profit per medicine A. $1.5 of profit per medicine B. Medicine A requires 1 hour of manufacturing labor and 2 hours of packaging labor. Medicine B requires 2 hours of manufacturing labor and 1 hour of packaging labor. Each week, XYZ has only 100 hours of manufacturing labor and 100 hours of packaging labor available. Lets Build the Objective Function, Decision Variables and Constraints Let x be the of medicine A produced and y be the medicine B product in the week Objective Function : Max(z) = 1x + 1.5y Decision Variables (Subject to) 1x + 2y <= 100 (Available Manufacturing Hours) 2x + 1y <=100 ( Available Packaging Hours) Constraints : x >= 0 & y >= 0 Loading & Solving the Problem Statement in PuLP Discussing Solution We see that the optimal solution for production of Medicine A & B to return maximum profit is 33.33 units weekly for both A & B to maximize the profit up to 83.33 units. We can even plot the illustrative graph using matplotlib library in python. There are many commercial optimizer tools, but having hands-on experience with a programmatic way of doing optimization is invaluable.