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

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

Hey and welcome to our brand new series in
which we are going to get you up to speed on Artificial Intelligence for Marketing & Growth Predictive analytics is a form of data mining
that uses machine learning and statistical modeling to predict the future. Based on historical data. Applications in action today are all around
us already. For example, banks are being using predictive
modelling to approve or decline your credit cards and personal loans. But it’s not only that. Is also used for weather forecasting, recommendation
engine, spam filtering and fraud detection. So why should marketers care? 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. This will also allow you to going from quantity
metrics to quality metrics, which leads to focus more time on. 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 based on the jacket you
just bought, can use your data to decide which shoes to recommend as your next purchase. based on historical behaviour that other customers
have had in the past. But the implications are much bigger than
that. Retailers can predict demand, and therefore
make sure they have the right level of stock for each of their products. Every time we type a search query into Google,
Facebook or Amazon we’re feeding data into the machine, growing ever more intelligent. To leverage the potential of artificial intelligence
and predictive analytics, there are four elements that organisations need to put into place. First of all:
You need to ask the right questions. Which questions am I trying to ask with my
predictive analytics? Which Metrics am I trying to forecast, which future behaviour am I trying to predict? You need a sound hypothesis to actually test. The second one i having the right data
We’ve come a long way in terms of data availability it’s been said that 90% of all of the world
data has been generated in the last two years. But we still need complete and clean data
sets to arrive to plausible conclusions. It’s important you figure out what data
is available to you and whether it will be sufficient to answer your questions convincingly. Third of all, you need the right technology
Whether or not a particular software is right for the problem you are trying to solve
And finally is the right people. Without the right people, it’s impossible
to pose the right questions Let’s look at staff retention at IBM IBM is using predictive analytics to retain
its employees and come up with possible solutions to forego high turnover. By uploading a structured data file, Watson
can spot the common factors in employee dissatisfaction. This then feeds into a ‘quality score’
for each employee, based on their predicted likelihood of leaving IBM. This is what we call “People Analytics” Next let’s look at supply chain optimization at Walmart Walmart takes data instantaneously from its
systems and incorporates it within its forecasts to assess which products are likely to go
out of stock and which have actually underperformed. Combined with behavioural data from its customers
online, this provides a huge amount of data points to help Walmart prepare for increase
or decrease product demand. Forecasting this, allows Walmart to personalize
its online presence, targeting customers with specific products based on their predicted
likelihood of making a purchase. We’ll go more into more depth on predictive analytics in our next episode. Don’t forget to hit the subscribe button
and be notified when episode #2 is available!


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