MArketing analytics unit 1 ppt

1. 18AIE328T - Marketing Analytics
UNIT I
Department of Computational Intelligence
School of Computing
SRM Institute of Science and Technology

2. UNIT I - Syllabus
• Introduction to marketing analytics,
predictive analytics, and Big Data
• What consumer wants
• Basics of a statistical software package: How
to import, clean, and manipulate data for
analysis
• Linear regression for prediction
• Price elasticity and pricing strategy
• Intertemporal dynamics
• Methodology: Empirical identification
• Application: Price promotion
• Dynamic pricing
• Methodology: Modelbuilding & optimization
• Application: Ridesharing & seat-booking
platforms
• Interpretation & Consumer Segmentation
• Segmentation through customer analytics
• Targeting through customer analytics

3. Need for Marketing Analytics
• Customers today have a lot of options to choose from. With so many alternatives,
they have also become wary of new products or services. While a quality item is
necessary, the marketing teams also need to engage customers to try out the
company’s products or services in offer.
• For this, analytics tools help in targeting customers properly based on their
interests rather than relying on demographic cohorts
• Marketing analytics helps the team serve the right customer at the right time
and in the right manner.

4. Marketing Analytics
• Marketing analytics is the study of data to evaluate the performance of a
marketing activity. By applying technology and analytical processes to
marketing-related data, businesses can understand what drives consumer actions,
refine their marketing campaigns and optimize their return on investment.
• Purpose-To gauge how well your marketing efforts are performing, measuring
the effectiveness of your marketing activity. To determine what you can do
differently to get better results across your marketing channels.
• For example, to understand how a recent blog post is performing, a marketing
analyst could look at its page views and other web analytics over its first 30 days
and compare the data you see to the first-30-days performance of similar blog
posts you've published in the past.

6. Three components
• Analyzing the present: Marketers need to assess marketing analytics from
current campaigns and activities in order to get a clear picture of where the
marketing activities stand and to compare them to past campaigns.
• Reporting on the past: Marketing departments also rely on reported marketing
data analytics at the completion of campaigns, focusing on information such
as lead conversion, customer lifetime value, and sales funnel churn rate.
• Predicting for the future: Finally, marketing departments rely on marketing
analytics to plan future projects. This type of data analytics in marketing will
include lead scoring, targeted content distribution, and upselling readiness and
relies on datasets as well as modeling and AI.

7. PREDICTIVE ANALYTICS
• In the marketing context, predictive analytics refers to the use of current and/or
historical data with statistical techniques (like data mining, predictive modelling,
and machine learning) to assess the likelihood of a certain future event.
or
• Predictive analytics is a form of analysis that uses past data to predict marketing
trends and scenarios. By leveraging the old data with predictive AI, you can create
a more optimized marketing strategy and drive better decisions.
• Predictive analytics can be used to streamline operations, boost revenue, and
mitigate risk for almost any business or industry, including banking, retail,
utilities, public sector, healthcare, and manufacturing

8. PREDICTIVE ANALYTICS – Cntd…
Real World Examples of Predictive Analytics in Business Intelligence
• Identify customers that are likely to abandon a service or product. ...
• Send marketing campaigns to customers who are most likely to buy. ...
• Improve customer service by planning appropriately. ...
• First, identify what you want to know based on past data.

9. Top Marketing Analytics Tools

10. Big Data in Marketing
Why it’s Important, Where it’s Going, and How to Get Started
• In marketing, big data comprises gathering, analyzing, and using massive amounts
of digital information to improve business operations, such as:
• Getting a 360-degree view of their audiences. The concept of “know your
customer” (KYC) was initially conceived many years ago to prevent bank fraud.
KYC provides insight into customer behavior that was once limited to large
financial institutions
• Big data analytics provides the business intelligence you need to bring about
positive change, like improving existing products or increasing revenue per
customer.

11. • Brand awareness is another way big data can have a significant impact on
marketing. Aberdeen Group’s Data-Driven Retail study showed that “data-driven
retailers enjoy a greater annual increase in brand awareness by 2.7 times (20.1%
vs. 7.4%) when compared to all others.”
• Improved customer acquisition is another great benefit that big data brings to
marketing. A McKinsey survey found that “intensive users of customer analytics
are 23 times more likely to clearly outperform their competitors in terms of new
customer acquisition.”.

12. Uses of Big Data in Marketing
1. Building Better Customer Relationships
• Knowing the customer and their preferences can enable the marketing team to
understand the decision-making of the customer before choosing any particular
brand. This can enable the marketing team to make the customer journey suitable
and smooth.
2. Appropriate Brand Positioning
• Big Data eases the brand or product positioning by just being the source of various
categorizations and groupings. Having data about the growth and customer base of
the brand can help the businesses inadequately positioning their brand in the
market among the right customers.

13. 3. Optimizing Prices
• Big Data can enable the companies to have the details about the price of the
competitors and inflation rates over the years, also this can help the companies
understand the purchasing power of the users of the brand so that they can subtly
stick to that without incurring any losses.
4. Designing Campaigns and Advertisements
• Big Data is also collected from social media, considering that the marketing team
can have a look at what is being in trend so that they can adapt the same for their
marketing strategy.

14. Three types of big data for marketers
• Customer data helps marketers understand their target audience. The obvious
data of this type are facts like names, email addresses, purchase histories, and web
searches. Just as important, if not more so, are indications of your audience’s
attitudes that may be gathered from social media activity, surveys, and online
communities.
• Financial data helps you measure performance and operate more efficiently. Your
organization’s sales and marketing statistics, costs, and margins fall into this
category. Competitors’ financial data such as pricing can also be included in this
category.
• Operational data relates to business processes. It may relate to shipping and
logistics, customer relationship management systems, or feedback from hardware
sensors and other sources. Analysis of this data can lead to improved performance
and reduced costs.

15. Real-life examples of big data in marketing
• Elsevier uses big data to streamline a marketing calendar
• Elsevier is the world’s largest provider of scientific, technical, and medical
information, publishing 430,000 peer-reviewed research articles annually.
• Big data and a multi-cloud environment provide an efficient way to closely track
journals and books throughout their lifecycle and more effectively schedule
resources to streamline production and support marketing. Those articles come
from a wide variety of resources across the global organization. Combining big
data from multiple clouds and sources across the globe merges many regional
marketing efforts into a single global marketing message strategy.

16. • DMD Marketing Corp. outperforms competition 3x with big data
• DMD Marketing Corp. offers the only authenticated database available that can
reach, report, and respond to the dynamic digital behavior of more than six million
fully opted-in U.S. healthcare professionals. To date, DMD has deployed more
than 300 million emails and 30,000 email marketing campaigns.
• Given that marketing emails to healthcare professionals is a very competitive
commodity business, big data gives DMD a way to differentiate. Using cloud-
based big data integration tools, DMD refreshes email data every day, rather than
every three days, which helps the company outpace the competition with 95%
email deliverability.

17. Challenges of big data in marketing
1. Incorporating Complex Data into the Customer Journey
2. Surplus Data
3. Streaming data sources
4. Breakdown of the Data

18. Companies using Big data for marketing

19. What Consumer Wants
• The basic customer’s wants and needs are the primary driving force for taking
action to engage with your brand and buy your products or services.
• A consumer's wants usually reflect the desired preferences for specific ways of
satisfying a need. Thus, people usually want particular products, brands, or
services that satisfy their needs in a specific way. A person is thirsty but wants
something sweet, so perhaps they choose a Coke.

20. Consumer needs
Product Needs
1. Functionality : Customers need your product or service to function the way they need in order to
solve their problem or desire.
2. Price: Customers have unique budgets with which they can purchase a product or service.
3. Convenience: Your product or service needs to be a convenient solution to the function your
customers are trying to meet.
4. Experience : The experience using your product or service needs to be easy — or at least clear
— so as not to create more work for your customers.
5. Design : Along the lines of experience, the product or service needs a slick design to make it
relatively easy and intuitive to use.

21. Consumer needs
6. Reliability : The product or service needs to reliably function as advertised
every time the customer wants to use it.
7. Performance : The product or service needs to perform correctly so the
customer can achieve their goals.
8. Efficiency : The product or service needs to be efficient for the customer by
streamlining an otherwise time-consuming process.
9. Compatibility : The product or service needs to be compatible with other
products your customer is already using.

22. Consumer needs
Service Needs
1. Empathy
When your customers get in touch with customer service, they want empathy and
understanding from the people assisting them.
2. Fairness
From pricing to terms of service to contract length, customers expect fairness from a
company.
3. Transparency
Customers expect transparency from a company they're doing business with. Service
outages, pricing changes, and things breaking happen, and customers deserve
openness from the businesses they give money to.
4. Control
Customers need to feel like they're in control of the business interaction from start to
finish and beyond, and customer empowerment shouldn't end with the sale. Make it
easy for them to return products, change subscriptions, adjust terms, etc.

23. Consumer needs
5. Options
Customers need options when they're getting ready to make a purchase from a company.
Offer a variety of product, subscription, and payment options to provide that freedom
of choice.
6. Information
Customers need information, from the moment they start interacting with your brand
to days and months after making a purchase. Businesses should invest in educational
blog content, instructional knowledge base content, and regular communication so
customers have the information they need to successfully use a product or service.
7. Accessibility
Customers need to be able to access your service and support teams. This means
providing multiple channels for customer service. We'll talk a little more about these options
later.

24. Akio Morita (late CEO of Sony) once said:
• Our plan is to lead the public with new products rather than ask them what kind of
products they want. The public does not know what is possible, but we do.
• So instead of doing a lot of marketing research, we refine our thinking on a
product and its use and try to create a market for it by educating and
communicating with the public.

25. Statistical software
• Statistical analysis software solutions are tools for collecting and
analysing data to provide insight into patterns and trends.
• The five basic methods are mean, standard deviation, regression,
hypothesis testing, and sample size determination.

26. Basic statistical tools
• Some of the most common and convenient statistical tools to quantify such
comparisons are the F-test, the t-tests, and regression analysis. Because the F-
test and the t-tests are the most basic.
• Excel is the widely used statistical package, which serves as a tool to understand
statistical concepts and computation to check your hand-worked calculation in
solving your homework problems.
• https://www.wallstreetmojo.com/statistical-analysis/
• R can be downloaded from here:
• http://cran.csiro.au/

27. TOP STATISTICAL TOOLS
1. SPSS (IBM)
2. R (R Foundation for Statistical Computing)
3. MATLAB (The Mathworks)
4. Microsoft Excel
5. SAS (Statistical Analysis Software)
6. GraphPad Prism
7. Minitab

28. HOW TO IMPORT DATA STATISTICAL SOFTWARE
To import a CSV data file into SPSS
• begin by clicking File > Open > Data.
• In the Open Data window, change Files of type to "CSV (*. csv)".
• Locate your file and click on it to select it, then click OK
• SPSS Tutorials: Importing Data into SPSS – LibGuides
• https://libguides.library.kent.edu/spss/importdata#:~:text=To%20import%20a%20
CSV%20data,select%20it%2C%20then%20click%20OK.

29. To import a CSV data file into R
• You can create comma-separated-value (CSV) data files from Excel. For a
particular worksheet, you select File > Save As and after you select the folder as to
where to save the data, you select the CSV option for the Save File Type that is
under your choice of file name. Note that the file extension of these data will
be .csv, not .xlsx.
• A direct way of importing your data that are in a CSV format is with the following
command:
dat <- read.csv("your.path/filename.csv", header=TRUE)
• https://www.geeksforgeeks.org/importing-data-in-r-script/

30. • To import a CSV data file into Python
• A simple way to store big data sets is to use CSV files (comma separated files).
• CSV files contains plain text and is a well know format that can be read by
everyone including Pandas.
• In our examples we will be using a CSV file called 'data.csv'.
• Example
• Load the CSV into a DataFrame:
import pandas as pd
df = pd.read_csv('data.csv')
print(df.to_string())
• https://www.geeksforgeeks.org/python-read-csv-using-pandas-read_csv/

31. DATA CLEANING
• Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly
formatted, duplicate, or incomplete data within a dataset.
• When combining multiple data sources, there are many opportunities for data to be
duplicated or mislabeled. If data is incorrect, outcomes and algorithms are
unreliable, even though they may look correct.
• There is no one absolute way to prescribe the exact steps in the data cleaning
process because the processes will vary from dataset to dataset. But it is crucial to
establish a template for your data cleaning process so you know you are doing it
the right way every time.

32. HOW TO CLEAN DATA
What are the methods of data cleaning?
1. Remove duplicates.
2. Remove irrelevant data.
3. Standardize capitalization.
4. Convert data type.
5. Clear formatting.
6. Fix errors.
7. Language translation.
8. Handle missing values.

33. Cleaning data
Data Problem Possible Solution
Missing data
Exclude rows or characteristics. Or, fill blanks with
an estimated value.
Data errors
Use logic to manually discover errors and replace.
Or, exclude characteristics.
Coding inconsistencies
Decide upon a single coding scheme, then convert
and replace values.
Missing or bad metadata
Manually examine suspect fields and track down
correct meaning.

34. DATA MANIPULATION
• Data Manipulation Meaning: Manipulation of data is the process of manipulating
or changing information to make it more organized and readable. We use DML to
accomplish this.
• What is meant by DML? Well, it stands for Data Manipulation Language or a
programming language capable of adding, removing, and altering databases, i.e.
changing the information to something that we can read. We can clean and map
the data thanks to DML to make it digestible for expression.

35. In Excel, How Do You Manipulate Data?
• Formulas and functions – Addition, subtraction, multiplication, and division are
some of the basic math functions in Excel. You need to know how to use these
Excel-critical features.
• Autofill in Excel-When you want to use the same equation across several cells,
this feature is useful. One way of doing it is to retype the formula. Another way is
to drag the cursor to the cell’s lower right corner and then downwards. It will help
you simultaneously apply the same formula to several rows.

36. In Excel, How Do You Manipulate Data?
• Sort and Filter- Users can save a lot of time when analyzing data by sorting and
filtering options in Excel.
• Removing duplicates-There are often chances of replication of data in the process
of collecting and assimilating data. In Excel, the Delete Duplicate feature can help
remove duplicate spreadsheet entries.
• Column splitting, merging, and merging-Columns or rows in Excel may often be
added or removed. Data organization often requires integrating, splitting, or
combining multiple datasheet

37. LINEAR REGRESSION
• Linear regression is the most commonly used method of predictive analysis. It
is used to predict the value of a variable based on the value of another
variable.
• It uses linear relationships between a dependent variable (target) and one or more
independent variables (predictors) to predict the future of the target.
• Linear regressions can be used in business to evaluate trends and make
estimates or forecasts. For example, if a company's sales have increased steadily
every month for the past few years, by conducting a linear analysis on the sales
data with monthly sales, the company could forecast sales in future months.

38. Linear regression is one of the most
commonly used predictive modelling
techniques. It is represented by an
equation 𝑌 = 𝑎 + 𝑏𝑋 , where a is the
intercept, b is the slope of the line. This
equation can be used to predict the value
of a target variable based on given
predictor variable(s).
https://www.tutorialspoint.com/r/r_linea
r_regression.htm
https://www.geeksforgeeks.org/linear-
regression-python-implementation/

39. Price elasticity and Pricing strategy
• What is price elasticity in pricing strategy?
• Price elasticity of demand is used to measure the relationship between price
and demand, and how changes to one will affect the other. All products will
have different responses in consumer demand to price changes. Therefore, it's
critical to understand those differences when making important pricing decisions.
• If a product is elastic, that means changes in price will have a higher impact on
demand. Typically, products with multiple substitutes and low brand loyalty will
have a high elasticity. For instance, tissue paper at the grocery store, where
shoppers can easily compare prices on multiple brands.

40. • However, if a product is inelastic, pricing changes will have a relatively low
impact on demand. Take for example, pain reliever at a convenience store. If
someone pulls into a gas station to find relief for a headache, they aren’t going to
balk at a $0.10 increase and drive to the next store.
• The Price Elasticity of Demand (PED) is calculated by dividing the percentage
change in quantity demanded by the percentage change in price. As a formula it
is written thus:
Price Elasticity of Demand = % Change in Quantity Demanded / % Change in Price

41. • Example:
• As an example, if the quantity demanded for a product increases 15% in response
to a 10% reduction in price,
Price Elasticity of Demand = % Change in Quantity Demanded / % Change in Price
The price elasticity of demand = 15 / 10 = 1.5.

42. Price elasticity and Pricing strategy
Pricing strategy?
• A pricing strategy is a model or method used to establish the best price for a
product or service. It helps you choose prices to maximize profits and
shareholder value while considering consumer and market demand

43. • Most Common Pricing Strategies
• Cost-plus pricing
Calculate your costs and add a mark-up.
• Competitive pricing
Set a price based on what the competition charges.
• Price skimming
Set a high price and lower it as the market evolves.
• Penetration pricing
Set a low price to enter a competitive market and raise it later.
• Value-based pricing
Base your product or service’s price on what the customer believes it’s worth.

44. Empirical analysis
• Empirical evidence is information that can be gathered from
experience or by the five senses.
• Empirical analysis is an evidence-based approach to the study and
interpretation of information. In a scientific context, it is
called empirical research.
• Example: when studying if high education helps in obtaining better-
paying jobs
• Empirical analysis requires evidence to prove any theory.
An empirical approach gathers observable data and sets out a
repeatable process to produce verifiable results. Empirical analysis
often requires statistical analysis to support a claim.

46. Price Promotion
• Promotional pricing is a sales strategy in which brands temporarily reduce the
price of a product or service to attract prospects and customers.
• By lowering the price for a short time, a brand artificially increases the value of a
product or service by creating a sense of scarcity.
• Promotional pricing can help with customer acquisition by encouraging cost-
conscious shoppers to buy. It can increase revenue, build customer loyalty, and
improve short-term cash flow.

47. The most common promotional pricing types include
1. BOGOF (buy one get one free)
2. Seasonal sales promotions
3. Discounts
4. Flash sales.
• Based on specific pricing objectives and business strategy, you can also consider
multi-buys, loyalty programs, conditional sales, free shipping, or gifts.

48. What are methods of promotion?
• The promotions mix is often divided into four categories: advertising,
digital selling, sales promotion and public relations. Each of these
promotion methods has their own role in generating revenue for a company.
• Promotional methods
• television.
• radio.
• print, eg local and national newspapers.
• leaflets and flyers.
• social media.
• blogs.
• banner and pop-up adverts.
• websites.

49. Dynamic pricing
• Dynamic pricing — also known as surge pricing, demand pricing, or time-based
pricing — is a strategy where businesses adjust the prices of their offerings to
account for changing demand.
• For instance, an airline will shift seat prices based on seat type, number of
remaining seats, and time until the flight.

50. Types of dynamic pricing?
• The goal of dynamic pricing is to allow a company that sells goods or
services over the Internet to adjust prices on the fly in response to market
demands.
1. Dynamic pricing based on groups.
2. Dynamic pricing based on time.
3. Cost-plus pricing.
4. Competitor-based pricing.
5. Value-based pricing (price elasticity)
6. Price skimming.- Initially high price, then lowers it over time
7. Bundle pricing: multiple products or services as a package
8. Penetration pricing: offering a lower price during its initial offering

51. What is Marketing Optimization?
• Marketing optimization describes the process of collecting and analyzing data to
measure and report campaign performance accurately.
• When done right, marketers can gain a clear picture of the effectiveness of each
aspect of a marketing campaign. This information enables informed decisions
about what touchpoints and ads are working and which they may need to tweak.
Why Is Marketing Optimization Important?
• A robust marketing optimization program is essential to ensuring you are spending
your marketing dollars as efficiently as possible. Whether your budget is
expanding or shrinking, you want to know you are getting the best possible return.

52. 4 Steps to Start Optimizing Your Marketing Strategy
1. Collect High-Quality Data
• The quality of the data you gather matters. Otherwise all the analysis become fails
2. Analyze Performance
• Once you have collected the relevant data and are confident it meets your quality
standards, the next step is to analyze what you have gathered. The analysis step is
no time for manual spreadsheets and marketing intuition.

53. 3. Make Adjustments
• The goal of collecting and analyzing this high-quality marketing data is to enable
you to make meaningful adjustments to your campaigns. The solution you've
chosen for data analysis will make appropriate recommendations.
4. It's an Iterative Process
• Marketing optimization is not a "one and done" proposition. It's an iterative
process that you must continually engage in. In succeeding rounds of improving
your marketing data and making adjustments, you will validate the previous
round's changes and set yourself up for additional optimization.
• You should repeat steps one, two, and three ad infinitum.

54. Ride sharing & seat-booking platform
• Ride-sharing refers to the common use of a motor vehicle by a driver and one or
several passengers, in order to share the costs (non-profit) or to compensate the
driver (i.e., paid service) using billing information provided by the participants
(for profit).

55. Top Ride sharing platforms
1. Uber: The Rideshare Giant
2. LYFT: Your Friend with a Car
3. GoJek: Going Beyond Rideshare
4. Careem: Dominating the Middle East & North African Market
5. Ola: Rideshare & Car Rentals
6. Via: The Smaller Alternative
7. BlaBla Car: Share Your Commute
8. Bridj: Moving Large Groups
https://www.netsolutions.com/insights/best-ridesharing-apps/

56. Customer Segmentation
• Customer segmentation is the process by which you divide your customers up
based on common characteristics – such as demographics or behaviours, so you
can market to those customers more effectively.
Or
• What is consumer segmentation? Consumer segmentation is the practice of
dividing a customer base into groups of individuals that are similar in specific
ways relevant to marketing, such as age, gender, interests, and spending
habits.

57. The Importance of Market Segmentation
• The Importance of Market Segmentation

58. Types of Market Segmentation

59. 1. Demographic Segmentation
• Demographic segmentation is one of the most common forms. It refers to splitting
up audiences based on observable, people-based differences. These qualities
include things like age, sex, marital status, family size, occupation, education
level, income, race, nationality and religion.

60. 2. Behavioral Segmentation
• Dividing your audience based on behaviors they display allows you to create
messaging that caters to those behaviors. Many of the actions you might look at
relate to how someone interacts with your product, website, app or brand.
• Some types of behaviors to look at include:
• Online shopping habits
• Actions taken on a website
• Benefits sought
• Usage rate
• Loyalty

61. 3. Geographic Segmentation
• Geographic segmentation, splitting up your market based on their location, is a
basic but highly useful segmentation strategy. A customer’s location can help you
better understand their needs and enable you to send out location-specific ads.

62. 4. Psychographic Segmentation
• Psychographic segmentation is similar to demographic segmentation, but it deals
with characteristics that are more mental and emotional. These attributes may not
be as easy to observe as demographics, but they can give you valuable insight into
your audience’s motives, preferences and needs. Understanding these aspects of
your audience can help you to create content that appeals to them more effectively.
Some examples of psychographic characteristics include personality traits,
interests, beliefs, values, attitudes and lifestyles.

63. Other Methods

64. 5 Benefits of Market Segmentation

65. • https://www.lotame.com/what-is-market-segmentation/

66. Customer Targeting
What is customer targeting?
• At its most basic, customer targeting is the act of reaching out to a portion of your
customer list to re-engage them and drive sales. Popular tactics for these
campaigns include direct mail and email. Social media and digital ads provide
newer avenues to connect with your customer base with more speed and precisio

67. Customer targeting challenges
• Low email response rates. Email open rates are around 20% and drive an average
of 2% CTR(Click-Through Rate). This means that 80% of the time, your email
is ignored. Despite this, email is an effective re-engagement channel that delivers
great ROI(Return Of Invesment)—but it’s best used in conjunction with other
tactics.
• Low match rates. Many of the existing customer targeting solutions have
insufficient or inaccurate identity graphs, and/or have issues successfully matching
shoppers across devices. This leads to low match rates (how many of your CRM
customers are identified online) and, therefore, a smaller audience to re-engage.

68. Customer targeting challenges
• Limited inventory reach. Solutions are often limited to the reach of their own
network, but shoppers are spending significant amounts of their time online
outside of places like social media and search engines. Reaching more shoppers in
more places means adding additional solutions.
• Limited creative optimization. In some cases, there is a limited amount of
creative formats available, or an inability to dynamically personalize creative,
which can hurt ad engagement rates.
• Manual campaign set up. Some customer targeting solutions require heavy
manual effort, from set up through analysis and optimization. This means that
brands and retailers are spending inordinate amounts of time without a guarantee
of improved results.

70. 8 proven customer targeting strategies
• Re-engage lapsed shoppers with top sellers. Reach lapsed customers using their
last purchase date, and segment as you see fit (i.e. 6, 12, or 24 months). Bring
them back to buy with ads featuring your top sellers.
• Connect with seasonal buyers. Reach your seasonal buyers when they’re most
likely to convert using recurring campaigns. Boost customer engagement by
targeting them with timely offers during seasonal sales periods. That customer
who bought earrings for Valentine’s Day last year? Maybe a personalized ad
showing a matching bracelet is what’ll get him to return to your site.

71. • Bring offline buyers, online. In many cases, a shopper’s first or only touchpoint
is with your brick-and-mortar store. A customer targeting campaign can drive
premium offline audiences online by offering personalized product
recommendations and special promotional codes to entice them visit your website.
• Target loyalty program members. Boost brand engagement and drive product
sales using paid display promotions that target premium/gold members with
exclusive offers based on their last purchase date.
• Upsell based on a previous purchase. Reconnect with your existing customers
by reaching out with offers for accessories related to an item they previously
bought, like a case or headphones, or service or warranty packages to go with the
laptop they bought a few days ago,

72. • Cross-sell based on a previous purchase. Don’t forget to also reach out to
previous buyers with offers for other products they might be interested in, like a
tablet or smart speaker for the customer who bought a laptop a few months ago.
• Communicate new products. Your frequent buyers are your best candidates for
purchasing again. Be sure to hit this group with offers promoting new products.
• Upgrade to a newer/better product. Boost new product sales by connecting with
customers who’ve purchased a product that now has a newer or better version
available.
• https://www.criteo.com/digital-advertising-glossary/customer-
targeting/#:~:text=What%20is%20customer%20targeting%3F,include%20direct
%20mail%20and%20email.

73. What consumer wants
• Customer needs are the things that customers require when
purchasing a product or service. Businesses must find out about their
customer’s needs in order to be successful.
• MARKETING ANALYTICS TOOLS
• https://improvado.io/blog/marketing-analytics-tools
• https://www.techtarget.com/searchbusinessanalytics/definition/Goo
gle-Analytics
• https://hevodata.com/learn/best-marketing-analytics-tools/
• Free tools
• https://watchthem.live/marketing-analytics-tools/

74. Using Python
• https://www.youtube.com/watch?v=-hww0evBtZM
• https://www.youtube.com/watch?v=ak6JedM85Ew
• Project ideas
• https://www.projectpro.io/article/marketing-analytics-project-
ideas/591
• https://towardsdatascience.com/data-science-project-marketing-
analytics-data-driven-solutions-72d050084642
• https://careerfoundry.com/en/blog/data-analytics/data-analytics-
portfolio-project-ideas/
• https://www.coursera.org/learn/marketing-analytics-project