Hi guys whats up, my name is our pet hope. You guys are doing fine in these tough times. Stay strong, stay safe. Make use of this time learn something new upscale yourself. Never stop growing, Im working on a course about AI for marketing and Im. Just about to launch it join the email list link is in the description to get super valuable content delivered right in your inbox. Also, you get an exclusive limited time, early-bird offered so join the email.
List now, okay, now lets talk about how AI is being used in digital marketing. My previous video application of AI in marketing talked about how the core concepts of AI are being applied in marketing, but this video specifically talks about how digital marketers can use AI. I am working and will come up with content on topics like AI for SEO, AI in social, AI, in content, AI for email, marketing, etc. But for now this video is
About the approach and the frameworks that digital marketers could use, if theyre looking to leverage air in fact, Im going to illustrate a detailed data science infrastructure that you could build for your own business, the face of marketing is changing. In my previous videos, I have already talked about how digital marketers should not focus on full funnel marketing, rather than just focusing on top of the funnel pilot matrix a term point by venture capitalist dave. Mcclure, he categorizes the metrics. A start-up needs to watch as
Acquisition activation retention, revenue and referral, also known as our I know, its funny, but it gets its name from acronym of five distinct elements of building a successful business. These five elements dont necessarily follow a strict order. Users may refer others before they spend money, for example, or may return several times before signing up. But the list is a good framework for thinking about how the business needs to grow at each stage. You need to perform a function, track relevant, actionable metrics and can also apply AI at
Each stage, if there is a clear use case, weve identified a number of business goals that AI can help you achieve at each stage. In the acquisition stage, the function is to generate attention through a wide variety of means, both organic and inorganic some relevant metrics that you should crack at this stage is traffic mentions, cost per click, search results, cost of acquisition open rates. How a I could help you at this stage is through content, marketing
landing page testing campaign optimization conversion rate optimisation lead scoring competition in trend analysis predict sales optimize product pricing programmatic media buying segmentation and clustering for targeting personalization then we move to the next stage which is activation at this stage the function is termed the resulting drive by visitor into users who are somehow enrolled relevant metrics that you can track at this stage are enrollments signups complete onboarding process use the service at least once subscriptions how AI can be leveraged at this stage is through personalization psychographic segmentation behavioral segmentation.
Then the next stage is of retention. The function here is convince users to come back repeatedly, exhibiting sticky behavior some relevant metrics that you should track our engagement time since last wizard, daily and monthly active users Jones. How I can help you at retention stage, is by predicting shown customer care, chatbots sentiment, analysis, visual, social, listening again personalization, then we move to the next stage, which is revenue. The function here is business outcomes, which vary by your business model like
purchase ad clicks content creation subscription etc and the relevant metrics that you should track our customer lifetime value conversion rates shopping cart size click-through News etc how a I can be leveraged at this stage is to predict and maximize customer lifetime value build recommender systems also you can leverage market basket analysis then the next stage is off referral the function here is viral and word-of-mouth invitations to other potential users.
relevant metrics that you should track are invites sent viral coefficient viral cycle time and how a I can be leveraged at this stage is well you can predict whether this user will recommend your product to another user or not you need to build a funnel at each stage and then analyze if machine learning can help you optimize that funnel or not whether machine learning has a clear use case and it can improve your performance at that stage or not Ive prepared a.
diagram illustrating data science architecture that you can use for your business the data from each stage should go through this process so that you can leverage the power of AI and make better decisions about your business the first step in your data science journey is to collect and store data they say data is the new oil without data you cannot really use machine learning the first step towards gaining insights is to.
collect and consolidate your data in a central location choose a technology that helps you collect information efficiently from your most important marketing channels and data sources as illustrated in the diagram your different marketing channels could be Facebook Instagram LinkedIn email campaigns custom campaigns data related to your app could be collected in firebase all this data could be collected or transferred to Google Analytics or a similar tool you will have to then transfer this data into a.
CRM as personally identifiable information cannot be stored in Google Analytics you would also like to collect data from other sources like your website CMS for order history and comments if you run any surveys or collect customer feedbacks or any offline marketing campaign all this data will be stored in your CRM there are plenty of CRMs of in the market like Salesforce up Squad.
Soho etc the right CRM for you depends on your budget and the functionality you need note you need to assign each customer a unique identity for better tracking and data analysis in the course that I was just talking about in the beginning of the video you learn how to generate a unique ID for each customer and pass it in your CRM then the next step in your data science journey is to transform the data for analysis which.
includes cleaning reformatting to provide consistency in big datasets you want your analyst to be able to clean up data with little to no coding for examples through a visual tool that can scale and run distributed transformations like Google Data prep and IBM data refinery it can help you do just that then the next step is to analyze after you save your clean data you can begin analyzing it for insights data mining predictive and prescriptive.
analysis can help you drive insights to take action in real time these techniques can help you improve the quality and trustworthiness of the data understand its semantics and provide intelligent business solutions tools like Amazon ml IBM Watson ml model builder Microsoft Azure ml Studio cool cloud auto ml can help you create complex machine learning models without any code all the four companies offer full service custom modeling machine learning platforms soon Im coming up.
with a list of AI tools offered by these companies in a separate video each data mining technique can perform one of the following types of data modeling or even more but these are the most important ones like classification in data mining classification is considered an instance of supervised learning that is learning with a training set of correctly identified observations is available classification is the problem of identifying to which of a set of categories the new observation belongs on the basis of a training set of data.
containing observation post category membership is known an example would be assigning a customer into high risk or low risk classes or assigning a diagnosis to work given patient clustering in data mining clustering is the task of grouping a set of objects in such a way that objects in the same group called a cluster are more similar in some sense or another to each other than to those in other groups other clusters in marketing clustering is used for creating various kinds of.
segments for better Marketing Association Association or Association rule learning is method that is used to discover unknown relationships hidden in big data rules refer to a set of identified frequent itemsets that represents the uncovered relationship in the data set the underlying idea is to identify rules that will predict the occurrence of one or more items based on the occurrence of other items in the data set mostly used for Market Basket analysis and recommender systems then we have regression regression analysis is.
widely used for prediction and forecasting in data mining the regression analysis is a statistical process for estimating the relationship among variables most commonly the regression analysis estimates conditional expectations of the dependent variable given the independent variable that is the average value of the dependent variable when the independent variables are fixed in marketing regression is used to predict a number like customer lifetime value predict marketing mix predict sales etc then we have forecasting its the.
process of making predictions of future based on past and present data and most commonly by the analysis of trends a commonplace example might be estimation of some variable of interest at some specific future date then we have sequence discovery sequence pattern mining is a topic of data mining concerned with finding statistically relevant patterns between data examples where the values are delivered in a.
sequence it is usually presumed that the values are discrete and thus time series mining is closed related in marketing this could be used for predicting customer buying behavior then we have visualized the purpose of data visualization is to communicate information clearly and efficiently wire statistical graphs plots and information graphics effective visualization helps marketers analyze and reason about data and evidence it makes complex data more accessible understandable and usable data visualization combines technical and artistic aspects of data analysis three popular tools that can help you visualize your data without any code or Google Data studio tableau and power bi.
thatll be all for today I hope you learned something new if you think so let me know in the comment section share this video with somebody you think will be useful for thatll be all thank you so much for watching today we will talk about how a i-n machine learning can be applied in marketing for each of its application Im gonna give you an inspirational story a use case or a case study.
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