AI in Digital Marketing
Hi Guy, how’s life. Hope you guys are doing fine. Tough times global lockdown I hope and I believe all of us will come out of this together. Stay strong stay inside make use of this time.
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Ok, now let’s talk about how AI in digital marketing. In the previous video, application of AI in marketing, covered how the core concepts in AI are being used in marketing.
This video specifically talks about how AI is being applied in digital marketing.
The face of marketing is changing. Digital marketers must shift their focus from top of the funnel to full funnel marketing.
Pirate Metrics — a term coined by venture capitalist Dave McClure . He categorizes the metrics, a startup needs to watch as acquisition, activation, retention, revenue, and referral — AARRR.— gets its name from the acronym for five distinct elements of building a successful business.
These five elements don’t 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 a 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.
We have identified a number of business goals that AI can help you achieve at each stage.
At the Acquisition stage the function is – Generate attention through a variety.
of means, both organic and inorganic.
Relevant metrics are – Traffic, mentions, cost per click, search results, cost of acquisition, open rate
A.I. Marketing Goals – content marketing, landing page testing, campaign optimization, conversion rate optimization, lead scoring, competition and trend analysis, predict sales, optimise product pricing, programmatic media buying, segmentation and clustering for targeting, personalisation.
At the Activation stage the function – Turn the resulting drive-by visitors into users who are somehow enrolled
Relevant metrics are – Enrollments, signups, completed onboarding process, used the service at least once, subscriptions.
A.I. Marketing Goals – personalization, psychographic segmentation, behavioural segmentation
At the Retention stage the function is Convince users to come back repeatedly, exhibiting sticky behaviour
Relevant metrics are – Engagement, time since last visit, daily and monthly active use, churns.
A.I. Marketing Goals – predict churn, customer care chatbot, sentiment analysis, visual social listening, personalization
At the Revenue stage the function is Business outcomes (which vary by your business model: purchases, ad clicks, content creation, subscriptions, etc.)
Relevant metrics are – Customer lifetime value, conversion rate, shopping cart size, click-through revenue
A.I. Marketing Goals – predict and maximise customer lifetime value, recommender systems, market basket analysis
At the Referral stage the function is Viral and word-of-mouth invitations to other potential users
Relevant metrics are – Invites sent, viral coefficient, viral cycle time.
A.I. Marketing Goals – predict will the user recommend your product
You need to build a funnel for each stage and analyse if machine learning can help you optimise your funnel for each stage.
I have prepared a diagram illustrating the data science architecture that you can build for your business. Data from each stage should go through this process so you could leverage the power of AI and make better decisions about your business.
Collect and store data
They say data is the new oil. Without data you cannot really use machine learning. The first step toward gaining insights is to collect and consolidate your data in a central location. Choose 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 feedback or any offline marketing campaign, all this data will be stored in your CRM.
They are plenty of CRMs available in the market like Salesforce, Hubspot, Zoho etc.
The right CRM for you will depend 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, you learn how to generate a unique id for each customer and pass it in your CRM.
The next step is to transform your data for analysis, which includes cleaning and reformatting to provide consistency in big datasets. You want your analysts to be able to clean up data with little to no coding—for example, through a visual tool that can scale and run distributed transformations. Google Dataprep and IBM Data refinery can help you do just that.
After you save your cleaned data, you can begin analysing it for insights. Data mining, predictive and prescriptive analysis can help you drive insights to take actions 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, Google Cloud AutoML
Can help you create complex machine learning models without any code. All the four companies offer full service custom modelling machine learning platforms. Soon I am 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 modelling or even more:
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 represent the uncovered relationships in the dataset. 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 dataset. Mostly used for Market Basket Analysis and recommender systems.
In data mining, classification is considered an instance of supervised learning, i.e., learning where a training set of correctly identified observations is available. Classification is the problem of identifying to which of a set of categories a new observation belongs, on the basis of a training set of data containing observations whose category membership is known. An example would be assigning a customer into “high risk” or “low risk” classes or assigning a diagnosis to a given patient.
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 (clusters). In marketing, clustering is used for creating various kinds of the segment for better marketing.
Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might be the estimation of
some variables of interest at some specified future date.
Regression analysis is widely used for prediction and forecasting. In data mining, the regression analysis is a statistical process for estimating the relationships among variables. Most commonly, the regression analysis estimates the conditional expectation of the dependent variable given the
independent variables, i.e., the average value of the dependent variable when the independent variables are fixed. In marketing regression is used to predict a number like a customer lifetime value, predict marketing mix, predict sales etc.
F. Sequence Discovery
Sequential 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 closely related. In marketing, this could be used for predicting customer buying behaviour.
The purpose of data visualisation is to communicate information clearly and efficiently via statistical graphics, plots and information graphics. Effective visualisation helps marketers analyse and reason about data and evidence. It makes complex data more accessible, understandable and usable. Data visualisation combines technical and artistic aspects of data analysis.
Three popular tools that can help you visualize your data are Google Data Studio, Tableau and Power BI.
This will be the end of the video hope you learned something new today. Let me know in the comment section if you liked the video. If you want to learn more about AI for marketing I reckon you join the email list this is the link, it is also in the description. Get an early bird offer and exclusive content right in your inbox.
That’ll be all see you in the next one.
Let dive deep and look at how AI and machine learning is making life easy for digital marketers.
We have identified top digital marketing skills where AI will empower marketers the most.
SEO, Search Engine Marketing, Social Media Marketing, Web Analytics, Email Marketing, Content Marketing, Influencer Marketing, Conversion Rate Optimisation, Tools Based Marketing, Lifecycle Marketing Automation.
Let’s briefly discuss them. Stay tuned my next videos will cover some of the most important skills in detail.
We can look at how AI is impacting SEO from two angles:-
First, how Google is using AI to improve user experience
Second, how digital marketers could use AI to make their sites rank higher.
We are only interested in the second angle. You will find plenty of video discussing the 1st.
These are the most prominent areas in SEO where AI is helping digital marketers.
- Keyword Research
- Topic Discovery
- On-page SEO
- Off-page SEO
- Technical SEO
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