Predictive Marketing 1: The Secret to Boosting Business? Data Driven Insights!
have you ever wondered how the marketing
industry has evolved recently let's take Amazon as an example in 2019 Amazon launched
A+ content allowing brand owners and sellers to enhance their product details with rich media
content of high quality images infographics and engaging texts allowing potential customers to
have more comprehensive product features and benefits using data and statistics A+ content is
proven to be able to improve customer satisfaction and potentially increase sales today with
the emergence of big data analytics tools like SQL python spark and others marketers
can explore deeper into customer behavior and buying Trends this allows businesses to better
understand their target audience and run highly cost-effective targeted ads on platforms like
Facebook and Instagram these opportunities were not available with traditional marketing methods
what I'm going to tell you in this video will transform your knowledge that marketing Trends
are not always about sales but rather it's more toward understanding customers through data so
in this video first I will briefly explain some challenges of traditional marketing second I will
discuss how predictive marketing can create an impact on business through customer lifetime
value finally I'm going to share how machine learning and Predictive Analytics can be used to
improve retention if you stay till the end of the video I will share my tips on how prescriptive
analytics can be used to make informed business decisions based on my past experience along
with future Trends and Predictive Analytics in the first part of this video let me explain
the concept of traditional marketing before the Advent of predictive Marketing in the past few
decades marketing strategies relied heavily on traditional methods such as brochures newspaper
ads TV commercials face-to-face meetings and phone calls traditional marketing focuses on high
sales product oriented and large distributed media however these approaches had limitations
especially with the absence of customer behavior and historical data one major drawback of
traditional marketing was the timeconsuming nature there was no immediate feedback or real-time
tracking of campaign success forcing businesses to wait for an extended period to measure
results however modern marketing techniques prioritize agility and Rapid experimentation
the key is to fail fast and succeed Faster by continuously learning from iterative experiments
and adapting to uncertainty this approach enables businesses to quickly test and refine ideas
increasing the chances of success and reducing the risk of failure to illustrate this point
let's consider Apple's iPhone products Apple has released 42 different iPhone models over the
past 15 years with new products being released every year Apple continues to release new models
because many people are attracted to the newer features introduced with each iPhone model Apple
listens to what customers want through data and incorporates those features into new models from
the front-facing selfie camera on the iPhone 4 in 2011 to the touch ID fingerprint feature on the
iPhone 5S in 2013 and now with larger screens faster 5G networks and advanced cameras on the
recent iPhone 12 these constant improvements have made Apple's position as the most Innovative
company in the world with special thanks to customer data in the second part of this video
Let's introduce predictive marketing with the goal to understand customer Behavior through day dat
and to overcome challenges faced by traditional marketing the main goal of predictive marketing
is to acquire grow and retain the most profitable customers by analyzing historical customer
Behavior data buying patterns and demographics to make predictions about future outcomes one purpose
of customer data analysis is to calculate customer lifetime value or so-called clv that is measuring
the expected revenue or profit from customers over their entire relationship with a business from
the graph example we can see that only 20% of customers generate most of the business Revenue
therefore predictive marketing will look for customer behavior and patterns corresponding
to these 20% highly profitable customers to calculate clv formula let's say one customer has
an average order value for $200 per pair of shoes this customer purchases five pairs per year
for the next8 years therefore the clv of this customer would be $8,000 to understand average
order value frequency purchase rate and customer lifetime requires multi-dimensional customer data
analysis to capture a complete customer Journey using statistics multi-dimensional analysis
studies customer Behavior Beyond purchases looking at factors like acquisition Source product
category and demographics it tests hypotheses about things like big purchases in smaller cities
or whether summer purchases lead to repeat orders data analysis helps businesses refine their
understanding of customers at different angles after analyzing customer behaviors and calculating
clv there are two types of customers first is high value or high clv and second is low value or low
clv customers High clv indicates loyal customers while low clv suggests short-term customers
companies need to adjust strategies to improve customer retention and increase brand loyalty
these strategies will be explained in the upcoming section using Predictive Analytics in the final
part of this video developing effective customer retention can utilize Predictive Analytics
understand churn rate for example 10% that is the percentage of customers who stop using
a service within a time period use Predictive Analytics like logistic regression and decision
trees to identify high-risk churn customers a probability value greater than 0.5 indicates
likely churn the goal of predictive models later will repopulate high churn risk customers
to be grouped together allowing for improved segmentation rather than just random segmentation
prior modeling a classification algorithm is used to create a confusion Matrix which summarizes
the performance of a machine learning model The Matrix shows true positives true negatives
false positives and false negatives it is important to accurately predict both positive
and negative cases when predicting churn with an accuracy of at least 80% this helps distinguish
between high-risk and lowrisk churn customers in addition High Precision is required to ensure
prediction stability over time now we already know how to predict highrisk churn customers the
next question is what business should do to lower the churn this can be done using prescriptive
analytics using computational Tools in partial dependence modeling to suggest ways businesses can
lower churn and make informed decisions let's take a look at the insurance agency recruitment use
case example in this graph the best cutoff age of successful recruiters would would be 38
years old by looking at the initial highest peak of the probability curve between productive
age of 30 to 50 years old to summarize predictive marketing has transformed the business mindset
from product Focus to customer Centric this shift is attributed to the data driven nature
of predictive marketing which enhances business efficiency by accurately targeting the right
customers additionally predictive marketing enables businesses to continually innovate
based on customer data the future of predictive marke will be centered around delivering
hyper-personalized marketing messages to Target audiences based on real-time data as seen
in the example of Netflix users receive customized images of their favorite TV series tailored
to their individual preferences and viewing history this level of personalization is achieved
through analyzing past customer interactions with the platform in my next video I will go into
more detail on how to improve customer lifetime value using customer data involving acquiring
the right customer and improving their data quality thank you for watching this video if you
found this information valuable please subscribe so you won't miss my future content on
data and analytics stay tuned and take
