Monday, October 21, 2019

Artificial Intelligence (AI) for Marketing 101

These days there is a lot of buzz in the marketing community about use of Artificial Intelligence (AI) and Machine Learning (ML) in marketing.

In this post I will cover some basics of AI that you need to know before you can explore how AI and ML can help you in your marketing efforts.

What is Artificial Intelligence? 

There are several definitions of Artificial Intelligence or AI. The simplest one to understand is from "Artificial intelligence (AI) refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect."

So in a nutshell AI refers to machine, which can learn and become intelligent like humans.

AI is an umbrella term that includes algorithms, concepts, tools, technologies etc. that perform these complex human like tasks.

One of such and widely used concept in AI is Machine Learning.  Keep in mind that all machine learning is AI but not all AI is Machine Learning as AI include much more than just Machine Learning (ML).

What is Machine Learning (ML)?

Machine learning is the practice of using statistics to parse large amount of data (structured and unstructured), find patterns in it, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task. (definition modified and adopted from: Nvidia).

Machine learning builds a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task. (source:

There are three major types of learning used to train these models - Supervised learning, Unsupervised Learning and Reinforcement Learning.

Supervised Learning

In supervised learning, the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs.

Example: Predict churn propensity of a customer.
You can provide training data that contains customers purchase data, past behavior data (input) and then each customer is labeled if they churned or not. Based on this data, model learns what purchase and behavior data will cause all the customers to be labeled as "Churn Risk" or "Not Churn Risk".

Unsupervised Learning

In unsupervised learning, the algorithm builds a mathematical model from a set of data which contains only inputs and no desired output labels. Unsupervised learning algorithms are used to find structure in the data, like grouping or clustering of data points. Unsupervised learning can discover patterns in the data, and can group the inputs into categories. (source:

Example: Uncover customer segments.
Unsupervised learning can help find various customer segments in your customer data using customer attributes, sales, onsite behavior etc.. This can then be used to drive better customer engagement and better marketing performance.

Reinforcement Learning

In Reinforcement learning, the agent (also called Machine, model or AI) is given a problem to solve and faces a game-like situation. It is given rewards for positive behavior and punished for negative behavior as it tries to solve the problem.. These rewards are provided by the developer of AI. The machine uses trial and error to come up with a solution to the problem. The developer does not provide the model any hints or suggestions for how to solve the game. It’s up to the model to figure out how to solve the problem and maximize the reward. The end goal is to make the model learn desired behavior that maximizes the total reward.

Example: Provide recommended products to customers.
Reinforcement leaning can be used to develop a online product recommendation engine.

Other Terms that you should be aware of

Structured Data

Data that can be organized in rows and columns such as Customer Demographics, Sales data, onsite behavior data etc.

Unstructured Data

Free form data such as word documents, call scripts, pdf, images etc. Anything that is not structured is classified as Unstructured data.

Marketing Uses of AI

There are several ways AI can be used in Marketing.  Here are some examples, this is not a complete list. I will add more articles in future to cover several use cases.
  • Customer Segmentation
  • Ad budget allocation across channels or by channel
  • Content creation
  • Chatbots - Which understands humans questions and then responds with appropriate response.
  • Churn Prediction/Customer Retention
  • Product recommendation engine
Hopefully this article provides some clarity to the confusion around AI in Marketing.

Your turn now. Are you using AI for marketing? If yes, how? If not then why not? What are the challenges. Let's talk.


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Thursday, October 10, 2019

Essential Excel Features & Functions for Marketing Analytics

Excel is one of the most used tool for data analysis by marketers, marketing analysts, web and digital analysts.
We ( have developed a course that will teach you Excel Functions and Features that you need for Marketing Analytics. You will also get several practical examples of how to use them for your analysis. Not only do we teach excel functions and features useful for Marketing, Digital and Web Analytics but also you will get cool ideas about what data you can analyze.

What you’ll learn
  • Understand various types of errors Excel throws and how to fix them
  • How to suppress the error or replace them with more meaningful data
  • Learn different ways of combining data from multiple columns
  • Understand different types of cell references.
  • Bulk delete blank rows to clean your excel sheet data
  • Find duplicate rows of data, highlight them and/or bulk delete them
  • Learn how to use VLOOKUP - Uses search engine (SEO) keyword data as an example.
  • Learn Pivot Tables - Example:  Digital Marketing Campaign Analysis
  • Find Optimal Campaign Budget Allocation with Excel Add-In Solver - This will change the way you allocate your marketing budgets.
Course requirements or prerequisites?
  • You should be familiar with the basics of Excel.
Who this course is for:
  • Anybody who wants to learn about some key features in Excel how to apply them to common data issues.
  • Marketing Analysts
  • Digital & Web Analysts
  • SEO analysts

Get this course on Udemy

or signup for Optizent Institute and get this course and several others with One Month Free Trial (Cancel anytime)

Tuesday, October 08, 2019

Search Engine Optimization with Google Analytics

There are several tools in the market for Search Engine Optimization (SEO). Some are free and some are paid. Google Analytics is one such tool, which is free.

I have developed an online course, that will teach how to use Google Analytics to get better at Search Engine Optimization (SEO).

Google Analytics provide lots of valuable information that you can use to your advantage and have higher ranking sites on Google, Bing etc.
This course also gives you an overview of Google Webmaster Tools also called Search Console and show you how you can get that data into Google Analytics.

What will you learn:
  1. Understand which Google Analytics reports to use to get insights to improve SEO
  2. Get insight into (not provided) keywords that show up in Google Analytics keyword report.
  3. Get a quick overview of Google Web Master Tools (Google Search Console) and connect it to Google Analytics
  4. Understand and use Landing Pages, Devices, Countries and Keyword reports from Google Search Console
  5. Learn how to compare various traffic drivers showing up in Google Analytics reports.
  6. Segment Organic traffic in your reports.
  7. Get a free SEO Custom Dashboard template that you can use to develop your own dashboard and use it right away and wow your boss.

or get access to all of Optizent courses (50% off First month) at