do you know the strategies that successful
businesses use to attract customers and generate repeat orders Starbucks for example
implemented a Rewards program that effectively doubled customer satisfaction through the use
of loyalty cards which offer points for each purchase Starbucks improves customer loyalty and
collects valuable data on individual preferences this data is then utilized to create personalized
marketing campaigns ultimately contributing to the brand sustained Success Through through
customer lifetime value or profitability calculation one loyal Starbucks customer can
contribute 14k USD considering the extensive number of loyal customers they have Envision the
substantial Revenue that Starbucks has generated so far customers are an invaluable asset in any
business whether or not they are profitable the key question then becomes how can we improve the
value of unprofitable customers or those with low customer lifetime value in this video I I will
address how to enhance customer profitability by first acquire the right customers second
collecting customer data and building customer profiles third grow and retain the existing
customers through advanced analytics make sure that you watch the video Until the End
as I will be sharing my personal experience and enhancing customer lifetime value within the
insurance industry additionally I will share how Netflix has successfully expanded its customer
base over the last decade without any further delay let's be in the first part of this video
improving clv that is customer lifetime value or customer profitability begins with effective
customer acquisition that is the process of acquiring new customers and nurturing long-term
relationships for conversion one crucial aspect of customer acquisition at least based on my
previous experience in the insurance industry is establishing multi- Channel Partnerships with
other businesses such as e-commerce platforms and offline retail store stores through these
Partnerships these businesses can provide valuable customer information such as names
addresses and phone numbers to enhance marketing leads the insurance company has implemented a
promotional offer of free insurance protection although limited to basic coverage for 3 months
in exchange for this benefit customers are kindly requested to participate in a brief survey that
encompasses straightforward questions about demographics and vehicle ownership the ultimate
objective of this initiative is to ga relevant information and enhance our understanding of our
clientele once the compliment insurance coverage period has expired the insurance company will
introduce its paid products marking the start of the actual conversion process predictive
models utilize survey responses and conversion status to make predictions about conversions
through variable importance analysis these models identify the key predictors that can
efficiently Target potential customers such as those who prefer credit cards for favorite
payments this process greatly improves the new customer targeting acquisition for future
campaigns to be much more efficient in this section it is time to understand customer profiles
by proper data collection during data collection it is important to keep the following points in
mind First Data originates from various channels including mobile apps and social media platforms
second data cleansing ensures that the collected information is consistently formatted
thereby enable reliable analysis finally integrating data from multiple channels provides a
comprehensive and unified view for a more holistic understanding there are two primary categories
of data behavioral and demographic behavioral data is dynamic capturing changes over time and
includes information such as purchase history and claims records on the other hand demographic
data is static and represents customer identity encompassing characteristics like gender and
census data it is essential to differentiate between these two types of data When developing
an analytical framework and constructing precise predictive models the subsequent procedure
involves data cleansing by resolving issues of consistency such as standardizing date
formats and capitalizing letters this process also includes removing duplicate entries for
improved efficiency additionally missing values are filled in using the mean although in certain
cases median imputation is preferred to ensure robust statistical analysis the following step
after Gathering and refining customer data from multiple sources is to consolidate it by utilizing
a single identifier such as a customer national ID the data collected from the main database claims
and transactions can be unified this integration allows for aggregated data at a summarized level
for each customer providing an extensive amount of information now the attention needs to be
directed toward constructing customer profiles by post in essential inquiries first who are
the most valuable customers second what is the frequency order for the top 10% of customers
third what are the predicted profits for these valuable customers fourth which top brands
are sold to the top 10% of customers there will be more questions but it is recommended
to brainstorm a few impactful questions once answered the next step is to determine their
applicability for root cause analysis within non-profitable or low clv customers to build
effective Improvement strategies to summarize it is crucial to implement effective data collection
strategies this involves capturing highly detailed transaction level information for customer data
ensuring accuracy and consistency through high integrity measures additionally achieving
a unified view of customers across various channels is essential for complete representation
and Analysis by implementing these practices businesses can enhance their understanding of
cust customer behavior and effectively maximize customer lifetime value in this section I will
share an example highlighting the effectiveness of Netflix's recommendation engine in driving
customer growth and satisfaction Netflix data is generated from two primary sources first is
user Behavior consisting of watch History viewing duration and thumb likee platform interactions
second is movie content details such as release year genres ratings actors and directors this
information is gathered to enhance the overall user experience on the platform Netflix utilizes
two methods for movie recommendations the first method is collaborative filtering which suggests
content based on shared preferences with similar users for instance if user a enjoys action
movies user B will likely receive action movie recommendations the second method is content-based
filtering which recommends content based on an individual's previous viewing history this
approach personalizes future suggestions to cater to the user specific preferences Netflix
employs a personalized homepage alongside its recommendation engine allowing users to easily
access their favorite movies and TV series this userfriendly interface improves the overall
viewing experience by prioritizing content tailored to individual preferences finally Netflix
provides an individualized experience to its users by offering personalized movie recommendations
based on their unique preferences in viewing history this feature ensures that each viewer
receives tailored content suggestions that resonate with their interests for example if a
user is a child they will receive recommendations for children's movies while other users may
be recommended action movies Netflix strives to create a curated and enjoyable streaming
experience for all of its users Netflix's steady expansion over the last decade can be attributed
to its Advanced Data science capabilities which greatly enhance the overall customer experience
by prioritizing continuous Improvement Netflix is well positioned to sustain and further grow
its customer base in the future in summary to improve the clv that is the customer lifetime
value or profitability businesses must acquire the appropriate customers and gather relevant data
analyzing customer Journeys behaviors and profiles derived from data enables informed decisionmaking
on strategies with the help of machine learning Advanced analytics to further enhance long-term
customer relationships in the next video I'm going to share how machine learning in the
propensity model can be used to create impactful Solutions in marketing thanks for watching
my video don't forget to subscribe and like my video so that you will not miss information
about data and analytics until then take care