AI for Marketing & Growth #1 – Predictive Analytics in Marketing

AI for Marketing & Growth #1 – Predictive Analytics in Marketing

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Welcome to our brand new AI for Marketing & Growth series in which we’ll get you up to speed on Predictive Analytics in Marketing! This series you-must-watch-this-every-two-weeks sort of series or you’re gonna get left behind..

Predictive analytics in marketing is a form of data mining that uses machine learning and statistical modeling to predict the future. Based on historical data.

Applications in action are all around us already. For example, If your bank notifies you of suspicious activity on your bank card, it is likely that a statistical model was used to predict your future behavior based on your past transactions. Serious deviations from this pattern are flagged as suspicious. And that’s when you get the notification.
So why should marketers care? Marketers can use it to help optimise conversions for their funnels by forecasting the best way to move leads down the different stages, turning them into qualified prospects and eventually converting them into paying customers.
Now, if you can predict your customers’ behavior along the funnel, you can also think of messages to best influence that behavior and reach your customer’s highest potential value. This is super-intelligence for marketers!

Imagine if you could not only determine whether a lead is a good fit for your product but also which are most promising. This’ll allow you to focus your team’s efforts on leads with the highest ROI.

Which will also imply a shift in mindset. Going from quantity metrics, or how many leads you can attract, to quality metrics, or how many good leads you can engage.
You can now easily predict your OMTM or KPIs in real-time and finally push vanity metrics aside.
For example, based on my location, age, past purchases, and gender, how likely are you to buy eggs I if you just added milk to your basket? A supermarket can use this information to automatically recommend products to you
A financial services provider can use thousands of data points created by your online behaviour to decide which credit card to offer you, and when. A fashion retailer can use your data to decide which shoes to recommend as your next purchase, based on the jacket you just bought.
Sure, businesses can improve their conversion rates, but the implications are much bigger than that. Predictive analytics allows companies to set pricing strategies based on consumer expectations and competitor benchmarks.
Retailers can predict demand, and therefore make sure they have the right level of stock for each of their products.

The evidence of this revolution is already around us. Every time we type a search query into Google, Facebook or Amazon we’re feeding data into the machine. The machine thrives on data, growing ever more intelligent.

To leverage the potential of artificial intelligence and predictive analytics, there are four elements that organizations need to put into place.

1. The right questions
2. The right data
3. The right technology
4. The right people

Ok.. let’s look at some use cases of businesses that are already leveraging predictive analytics.

Other topics discussed:

analytics case study
artificial intelligence
big data
deep learning
demand forecasting
forecasting sales
machine learning
predictive analytics in marketing
data mining
statistical modelling
predict the future
historical data
AI Marketing
machine learning marketing
machine learning in marketing
artificial intelligence in marketing
artificial intelligence
Machine learning


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