You would have heard of its largest pure-play analytics services forum based out of Bangalore ticket merchant is the second-largest ticket online ticket marketplace in Australia bouncing noble a US bookstore MIT University is one of the largest educational institutes in Australia I think most of you would be familiar with some of the problems that I've worked on are around customer loyalty offer recommendation engines which are on personalization campaign optimization around range optimization.

What kind of products do to put in the store to maximize traffic funnel optimization this is all around digital marketing conversion forecasting what drives conversion how to improve conversion from lead to customer decision trees how do customers go about making decisions when buying something online that's some problems for you now let's talk about the contents, so we will talk about and a quick introduction to analytics, and then we'll venture into marketing in the 21st century we will talk about some use cases the problems which are pertinent in marketing in today's world.

Then we'll spend some time talking about one of the problems in marketing through a case study in the last 20 minutes I'll take questions from you during this session whenever you have any questions feel free to type them in the chat box and towards the end of the session I'll try and answer as many as I can all right let's get started so start with an introduction to analytics so in very very simple words the analytics is the process of going from data to decisions right and here you know I would like to ask you when you think of decisions what kind of decisions sorry I have nice can you hear me can you please type in the châteaux if you can hear me fine.

So here I would want to you now ask you what kind of decisions are we talking about you feel free to type on the chat box some of the decisions that come to your mind which you think are required to be taken their businesses or organizations any ideas yep drug scenarios pricing decisions yep, so I think as quite a few of you have mentioned some of the decisions that you could be taking in in businesses, or you know which customers should I target with marketing which social media platforms should advertise on what product should I place in my stores or what should be the price of my new product how to improve my customer satisfaction or how do I improve my organization's productivity there's examples very very few examples of you know a lot of decisions that are taken daily by businesses now to take those decisions what kind of data do you think is yep.

Some of you have suggested yeah it could be you know I'm looking at in the customer profiles there are pens you could be looking at customer product purchases what kind of products are purchasing demographics like some of you said age and employee working activity you know and then you can look at product sales data you can look at macroeconomic trends what is happening in the economy in general what are the wage rates unemployment GDP all those things you can look at online user behavior where our customers spending time and you know what content are they engaging with right again this is this these are some of the examples of you know what kind of data you can use to get to those decisions.

It's a very quick overview of what analytics is now let's talk about some basics nowhere if you think of you know how does data get generated at the end of the day everything starts from human behavior that you are you know swiping your credit card in stores you are purchasing online you are engaging with the content you're looking at ads all those are human behaviors and in the in today's world that behavior can be tracked and that results in data.

Data is nothing but the digital footprint of human behavior once you have the stored data then you can basically you know put them into some format which is easy to read and analyze from that data you can you know analyze it in many many different ways like you would have heard of you know machine learning techniques so you can kind of basic analysis so basically the whole field of data science and analytics you can apply those techniques and why do you apply them you want to identify patterns and extract insights from them and using those insights you take decisions and actions and what happens after that reinforces the human behavior and this cycle keeps going right.

Basically is the basic cycle of Anna dicks hey guys I'm getting some comments that my voice is not clear can you just let me know if you can hear me fine just by typing on the chatbox thank you all right so now this is a very very basic overview of what analytic cycle looks like now let's try and put this into the slightly more structured way a slightly more sophisticated way and that is where you see all the different layers of analytics right.

Let's start at the top what happens here is decision support all the business leaders of the organizations are making decisions daily what happens in this layer you know they identify focus areas they ask key questions you know take decisions to identify actions put those actions into operations operationalize them and then you know measure performance the next layer is about visualization this is where people collaborate you know to answer key questions look at the outputs extract insights define problems and then you know to discuss and shape the recommendations that are that than give in to decision support leaders at the top what is the next layer that is where data science comes.

This is where all the solutions happen where you analyze identify patterns extract insights from data using statistical and machine learning techniques for that to happen what you need is data that is ready for analysis now this is a very very you know important layer in this whole a whole stack where you consolidate data you cleanse it you transform it create new variables get it ready for analysis so that you know data science can happen in a meaningful way and at the bottom of it are data systems these are all the data warehouses you know data lakes the cloud virtualization ETL architecture all those things right and the bottom is where you captured it as the data sources right.

Now if you think about this in any organization you would have probably seen in yours as well you know it's it's continuous cycle where you can top-down where business leaders say this is my focus area and these are the questions I want to answer and then you basically execute bottom up saying you know go from data to support those decisions and this cycle of creation and consumption continues now this is this is what you know the analytics layers look like this is a the overall holistic view of you know what analytics entails this point in time again guys if you have any any questions please continue typing in the chat box all right let's move to a slight distinction between you know what is analytics and what is data science and this confusion is is widespread in the industry I feel having seen it from close quarters so I think that the key differentiation here is the data science is a part in analytics where you need to look at you know three broad skill sets now what you see on the on the left on the top left is the computer science and technology skill set.

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