The document provides an overview of marketing analytics, including defining marketing analytics, key elements and capabilities, impact, and getting started with analytics. Some key points: - Marketing analytics is the process of identifying valid performance metrics, tracking them over time, and using the results to improve marketing. The goal is to measure progress toward objectives. - Key elements include people, steps, tools/technology, inputs and outputs. Capabilities include understanding performance and reporting it externally. - Impacts can include optimizing brand recognition, content, channels, customer understanding, and predictive intelligence. - Getting started involves assessing readiness, reviewing objectives, and establishing metrics like website, social media, email, and digital advertising metrics.

1. MARKETING ANALYTICS
Presented by:
Deep J. Gurung
Assistant Professor
Department of Commerce
CHRIST (Deemed to be University)
Main Campus, Bengaluru (India)

2. Defined
• Marketing analytics is the process of identifying
metrics that are valid indicators of marketing’s
performance in pursuit of its objectives, tracking
those metrics over time, and using the results to
improve how marketing does it work.
▫ Analytics are both the process and the collective
output of that process—performance information with
the ideal use as a management tool.
▫ Metrics are the “atomic unit” of analytics. The
marketing analytics process consists of creating a
series of metrics or measurements in specific areas.

3. Components from definition…
• Valid indicators:
▫ There are many things about marketing’s work
and results that are measurable.
▫ The analytics process must determine which
metrics have meaning and best represent the
value that marketing creates for the organization.

4. • Pursuit of objectives:
▫ The analytics process is ideally built to measure
progress toward a set of objectives.
▫ The objectives come first, followed by an
identification of the relevant performance metrics.

5. • Tracking metrics over time:
▫ The analytics process isn’t about taking a random,
one-time snapshot of a performance
measurement, but tracking measurements over
time to monitor trends and direction of
performance.

6. • Improve how marketing works:
▫ There are several reasons a marketing
organization might implement an analytics
process, such as accountability or justification of
resources, but the noblest and ultimately most
valuable reason is to improve its performance.

7. Key Elements of Marketing Analytics
• People: The marketing analytics process is
created, executed, and managed by people who
own it. In most marketing organizations, the
process owner is the chief marketing officer
(CMO) or the marketing director.
• Steps: The marketing analytics process consists
of a sequence of steps.

8. • Tools and technology: While the marketing
analytics process isn’t necessarily complex, tools
and technology help marketing organizations
deliver greater value faster than they ordinarily
might.
• Input and output: Data feeds the process, with
insights and decisions as the output of the
process.

11. Marketing Analytics Capabilities
• Understanding marketing’s performance. For
internal use, the metrics an analytics process tell
marketing how it is performing.
• Reporting marketing’s performance. For
external use, the output of the analytics process,
or at least part of it, is useful to show the rest of
the organization that it is getting a return on its
investment in marketing.

12. Impact of Marketing Analytics
• Brand recognition: Understand the mindshare
your brand enjoys and the sentiments customers
have toward it.
• Content: Know with certainty which of your
marketing content is most widely consumed,
shared, and produces the best conversion.
• Channel optimization: Compare performance of
various marketing channels, such as email or
pay-per-click, to improve their performance or
invest only those that perform the best.

13. • Customer understanding: Gain a deeper
understanding of customer behavior to better
understand their needs and preferences.
• Predictive intelligence: Accurately predict early
in the buying cycle which customers will buy and
when.

14. Getting Started with Analytics
• Assessing Organizational Readiness
• Reviewing Objectives
• Establishing Metrics
▫ Website: traffic sources, most visited pages, search
ranking, visits or unique visits, time of site or page,
bounce rate, and exit pages are all potentially
important for measuring website performance.
▫ Social media: social network reach (followers, fans,
subscribers, or contacts), shares/retweets,
engagement, posts, and referral traffic. Your social
media metrics should include your blog.

15. ▫ Email marketing: database or list size, number of
sends, open rate, click-through rate, and bounces
are all standard metrics for email marketing.
▫ Digital advertising: this includes pay-per-click and
other forms of paid, digital media. Key metrics
could include impressions, click-through,
inquiries, landing page views, conversion rate,
opportunities identified, revenue generated, and
program ROI.

16. Summary
• https://www.youtube.com/watch?v=-
oHGG5jJPHQ

17. Marketing Analytics Process
• Step 1: Identify Metrics
▫ Social media gives us likes, shares, posts, tweets, and
other metrics.
▫ Email marketing generates opens, bounces, click-
through, subscribes, and more.
▫ Websites let us track visits, unique visits, referral
sources, search terms, and much more.
▫ There are conversion rates, content downloads, views,
and a seemingly endless stream of metrics at the
disposal of the modern marketing team.
▫ Identifying meaningful metrics is very difficult if there
is no marketing strategy or set of related objectives.

18. ▫ Effectiveness metrics would include conversion
rates, a behavioral indicator that means a prospect
voluntarily took the desired action that moves
them further down the sales funnel.
▫ Effectiveness metrics help marketing measure its
impact.

19. Step 2: Data Collection
• DATA TYPES
▫ Contact information (name, email, mobile)
▫ Demographic indicators such as age, gender,
address, marital status.
▫ Behavioral indicators such as purchase
preferences, preferred medium of communication
& brand engagement, etc.
•

20. • DATA SOURCE
▫ Website
▫ Social media
▫ Landing pages
▫ In-store tablets
▫ Marketing tools capturing user behavior

21. Step 3: DATA PREPARATION
• DATA CLEANING
▫ Remove duplicate data
▫ Ensure consistency in formatting of the data
 Age is defined in same units – years/months
 Gender is Male/Female across the file
▫ Update missing data
▫ Contact customer and get missing information
▫ Find similar profiles in your database and
estimate
▫ Analyze outlier data separately

22. • PRECAUTIONS
▫ Do not use average/median values to fill empty
spaces
▫ Personal Biases to fill missing data can result in
significant errors
▫ Do not run math operations on abstract data
 Abstract data such as City names (Mumbai,
Hyderabad, Bangalore) are assigned numbers 1,2,3
and then averaging may reveal 2 as the most
common city. (Huge mistake in analysis)

23. • Taking the data and, through inspection and
analysis, turning it into actionable information.
• When analyzing marketing metrics, what
marketers should do is understand the current
state, compare it to the ideal state, and then do
root-cause determination to explain any
differences.

24. Step 4: Analyze the Metrics
• ANALYTICAL TECHNIQUES
• RFM (Recency, Frequency, Monetary)
▫ Will help you identify your best customers
• LTVC (Life Time Value of a Customer)
▫ Will help you evaluate customer cost of
acquisition
• Segmentation
▫ Will help you run targeted marketing campaigns

25. RFM (Recency, Frequency, Monetary)
RFM analysis is a marketing technique used to
determine your best customers quantitatively by
using information about:
▫ Recency - How recent was the purchase
▫ Frequency - How often does the customer
purchase
▫ Monetary - How much has the customer spent

26. • RFM method: Ranking
• BENEFITS OF RFM:
▫ Reach out to your best customers and make
▫ them feel special
▫ Make them your brand ambassadors
▫ Align your marketing expenses better

27. Life Time Value of a Customer
• LTVC (Lifetime value of your customer) is a
great way to identify how much value your
customer will bring to you over his/her lifetime.

28. Benefits of LTVC
• Determining the right amount of money to
invest in acquiring a customer
• Analyze customer acquisition strategy and
solidify your marketing budget

29. Segmentation
• Customer Segmentation is the subdivision of a
market into discrete customer groups that share
similar characteristics

31. • RESULTS
• Identifiability
▫ Are you able to easily differentiate between
segments
• Substantiality
▫ Are your clusters big enough
• Accessibility
▫ Are you able to reach your customers
• Stability
▫ Will these clusters remain stable with time
• Actionable
▫ Are the segments helping with marketing
direction

32. Step 5: Take Improvement Actions
• OBVIOUS: The improvement actions are
obvious
• COMPLICATED- Example
• The analysis shows that the same strategy was in
use for all PPC campaigns: a unique landing web
page was set up for those who clicked on the ad.
The offer or “call to action” conversion on the
landing page was similar to previous campaigns
that did perform well.

33. A/B testing
• two versions of a landing page to visitors.
• The A version is the control page and the B version
has some variation.
• The variations might include different call-to-action
text, graphics, a different page layout, color scheme,
or any conceivable change.
• The goal is to find what variations perform the best.
Both pages are presented to visitors, and the
analytics are monitored closely.
• The visitors themselves determine the winning page.

34. • Repetition of the analytics process is necessary
to produce consistent and sustained
improvement.

35. Marketing Dashboards

36. Definition
“A dashboard is a visual display of the most
important information needed to achieve one or
more objectives, consolidated and arranged on a
single screen so the information can be monitored
at a glance.”
-Stephen Few

37. • Why You Need a Dashboard
▫ Marketing dashboards are important because they
render the large volume of data a marketing
analytics process can produce into a meaningful,
understandable and actionable summary.

38. • Dashboard distills the most important results of
the marketing analytics process into an easily
digested visual summary.

39. Keys to Success with Dashboards
• If the dashboard(s) that marketing publishes
become the primary influencer of the opinion of
marketing, the marketing team cannot afford to
get this wrong.
• Ideal selection of metrics
• Accurate Data: The collection of marketing
analytics data often requires tuning, tweaking,
and testing methods across several
measurement cycles to get it right and gain
confidence in the data.

40. • Sharing and collaboration

42. Executive level marketing dashboards
• Marketing program metrics: overall performance
Indicators such as revenue generated and current
budget status.
• Customer program metrics: customer acquisition
costs, customer retention rate, customer lifetime
value, Net Promoter Score, and so on.
• Lead generation metrics: new leads by channel, cost
per lead, conversion rates, opportunities created,
and so on.
• Website metrics: traffic sources, top pages, unique
visitors, bounce rate, time on site, and so on.

43. Supporting marketing dashboards
• Demand generation metrics: a dashboard for
reporting on the performance of campaigns and
metrics associated with the flow of leads from
capture to qualification and conversion.
• Content marketing metrics: a dashboard for
tracking the content creation, publication, and
consumption process. The content marketing
dashboard should include metrics for volume as
well as engagement, such as sharing.

44. • Social media metrics: a dashboard for tracking
the activity, engagement, and reach of social
media marketing efforts across all social media
networks in use.
• Public relations metrics: a dashboard for
tracking media mentions by frequency, source,
topic, type, tone, impressions, and impact
(response).
• Events marketing metrics: a dashboard for
tracking events by type, cost, participation,
leads, and revenue generated.

45. • Digital marketing metrics: a dashboard for
tracking the results of digital marketing efforts
such as impressions, landing page views,
conversion rates, leads captured, and revenue
generated.
• Web marketing metrics: a dashboard for
tracking website effectiveness through traffic
sources, page views, unique visits, referrals,
keywords, search rank, and other key metrics.

46. Tools and Technologies
• Marketing Analytics Technology
• Demand Metric defines marketing automation
as the strategies, processes, and software
technology that enable marketing departments
to automate, measure, and improve the
performance of strategies, activities, and
workflows.

47. Marketing Analytics Tools
• Strategic: tools and systems for business
intelligence, customer intelligence,
understanding buying behavior, advanced
attribution, and predictive analytics. Strategic
marketing analytics are those that help provide
direction to the marketing function.

48. • Operations and logistics: tools and systems for
managing, testing, and optimizing a web
presence, mobile, multichannel campaign
performance, demand, and geo-modeling.

49. The Definitive Guide to Predictive
Analytics for Retail Marketers
• Behavioral clustering: helps better understand
how customers behave while purchasing.
• Product/category-based clustering: segments
customers into groups based on which products
they purchase. This insight lets marketers make
more intelligent choices about which offers to
extend to customers.

50. • Brand-based clustering: identifies brand affinity
groupings that customers have. For example,
customers who prefer brand A also prefer brand
C, but not brand B.
• Predictive lifetime value: predicts future lifetime
values of customers. Useful for setting spending
parameters on costs of new customer
acquisition.

51. • Propensity to engage: predicts how likely it is for
a customer to take certain actions, such as
clicking on a link in a promotional email
message.
• Propensity to convert: predicts the likelihood
that a customer will respond to call-to-action
offers extended to them via email, direct mail, or
other means.
• Propensity to buy: identifies customers who are
ready to make a purchase, allowing marketers to
trigger those purchases with a special offer, or
market more aggressively to those who aren’t
ready to buy.

52. • Upsell recommendations: helps increase the
average size of order by predicting premium
products or greater quantities in which a
customer might have interest.
• Cross-sell recommendations: at the time of
purchase suggests other products that are
frequently purchased together.
• Next sell recommendations: after a customer has
already purchased a product, suggests products
that are likely next purchases.

53. TRADITIONAL PRICING
• Marketers develop prices based:
• Cost to produce the product,
• Standard margins,
• Prices for similar products,
• Volume discounts
• Universal 10 percent price hike on everything.

54. Marketing Analytics & Pricing
3 Pricing strategies
• Cost Based Pricing
• Demand Based Pricing
• Value Based Pricing

55. Pricing Analytics
• Pricing analytics are the metrics and associated
tools used to understand how pricing activities
affect the overall business, analyze the
profitability of specific price points, and
optimize a business’s pricing strategy for
maximum revenue.

56. Turn Data into Profit
• Listen to the data
• Automate
• Build skills and confidence
• Actively manage performance

57. Four Metrices
• Willingness to pay (WTP) : sometimes
called price sensitivity, is the maximum
amount a customer is prepared to pay for your
product or service.

59. • Feature value: relative preference analysis,
measures which features are more or less
important to customers relative to other features

60. • Average revenue per user (ARPU):
measure of the revenue generated each month
from each user.
• ARPU = Total MRR ÷ Total number of
Customers
• Monthly Recurring Revenue (MRR): all of your
recurring revenue normalized into a monthly
amount.

61. • Customer acquisition cost (CAC) and
lifetime value (LTV) : spend the right amount
to drive new customers to your service without
jeopardizing the revenue from that customer.
This is known as the LTV/CAC ratio

62. DYNAMIC PRICING
• Video
• https://www.youtube.com/watch?v=cKPaIsOQs
lo

63. Point of Sale (POS) data
• INTRO TO ePOS:
• https://www.youtube.com/watch?v=7VVKtPge-
zw
• Video
• https://www.youtube.com/watch?v=XWuwlCh
R8c4
• Video

64. • POS data collection is passive data collection
and used to help “predict user preferences based
on an historic profile of interactions with a
company or site.”
• Every time you use your POS, you are accessing
and creating multitudes of data points about
your customer, about buying habits, about the
market that you serve and about ecommerce
capabilities and potential growth areas for your
business.

65. Micro scale vs Macro Scale POS data
collection
• Point of sale data is data collected by a business
when a transaction happens.
• On a micro scale this includes any checkout at a
retail store, handheld POS hardware and even
QR or barcode scanners from apps.
• On a macro scale data is collected from groups of
retailers, like all ecommerce stores in a specific
niche, shopping mall data, or even city-wide
data.

66. POS solutions
• Terminal POS: Hardware and software solutions
that may include barcode scanners, cash
registers and app scanners.
• Cloud-Based POS: online POS systems, often
used in conjunction with existing hardware like
tablets or computers. Heavily utilized by online
stores and ecommerce websites.
• Mobile POS: normally used as payment
processing systems, usually adopted by small
business owners

67. Using/Leveraging POS data
• Optimize your inventory and stock levels
▫ Inventory counts
▫ Check stock levels at different stores
▫ Transfer stock
▫ Manage returns, etc.
▫ Automated reorder points
• Make staffing decisions

68. • Gain product and customer insights
▫ Product affinity (which items are frequently bought
together): Use this information when bundling
products and recommending related products at
checkout.
▫ Order history: Learn what specific customers like,
then make smart recommendations for future
purchases and retargeting campaigns.
▫ Sales by product: Identify sales trends by product
(or category) and dig deeper into those trends to find
out the WHY.
▫ Refunds, returns and exchanges: Find out which
items are being returned, what customers are buying
instead, and even who’s a serial returner.

69. ASSORTMENT PANNING
Assortment planning in retail is when a store
optimizes:
▫ Visual merchandising,
▫ Store layout, and
▫ Product placement
for the most conversions.
• Product assortment planning happens by period,
whether daily, weekly, monthly, quarterly, or
some other cadence.

70. Importance of assortment
• Price point
• Shelf life
• Category proportions
• The width is the number or variety of different
product categories.
• The depth is the amount of product and brand
variation within an individual category

71. • Visual merchandising
• Store layout
• Promotions
• Reorder points
• Lead time

72. Assortment Planning
• Setting goals
• Historical data
• Product hierarchy
• Cross merchandise
• Capitalize on impulse buys
• Use of Planogram

73. PLANOGRAM
• A planogram is a diagram that shows how and
where specific retail products should be placed
on retail shelves or displays in order to increase
customer purchases.
• Planogramming is a skill used in merchandising
and retail space planning.
• A person with this skill is be referred to as a
planogrammer.

76. Analytics in Assortment Planning
• AirtableAnalyse2
• Aptos
• DotActiv
• First Insight
• Intelligence Node
• JDA Assortment
• Mi9 Retail

77. ASSOCIATION RULES
• An association rule implies that a particular
item is likely to occur given the presence of some
itemset.

78. Market Basket Analysis
• https://www.youtube.com/watch?v=sJWXgcZ-
E2g
• MBA

79. • Allows retailers to identify the relationship
between items which are more frequently bought
together
▫ Itemset: Groups of items are called itemsets.
▫ Confidence : It is the measure of uncertainty or
trust worthiness associated with each discovered
pattern.
▫ Support : It is the measure of how often the
collection of items in an association occur together
as percentage of all transactions. The number of
baskets that an itemset appears

80. EXAMPLE
• Customer 1: Bread, egg, papaya and oat packet
• Customer 2: Papaya, bread, oat packet and milk
• Customer 3: Egg, bread, and butter
• Customer 4: Oat packet, egg, and milk
• Customer 5: Milk, bread, and butter
• Customer 6: Papaya and milk

81. • Customer 7: Butter, papaya, and bread
• Customer 8: Egg and bread
• Customer 9: Papaya and oat packet
• Customer 10: Milk, papaya, and bread
• Customer 11: Egg and milk

82. Support
• Support: Percentage of orders that contain the
item set. In the example above, there are 11
orders in total, and {bread, butter} occurs in 3 of
them.
• Support = Freq(X,Y)/N
• Support = 3/11 = 0.27

83. Confidence
• Confidence: Given two items, X and Y,
confidence measures the percentage of times
that item Y is purchased, given that item X was
purchased.
• Confidence = Freq(X,Y)/Freq(X)

84. • Percentage of times that butter(X) is purchased,
given that bread(Y) was bought:
• Confidence (butter -> bread) = 3/3 = 1
• Percentage of times that bread is purchased,
given that item butter was purchased:
• Confidence (bread->butter) = 3/7 = 0.428

85. • Values range from 0 to 1,
▫ where 0 indicates that Y is never purchased when
X is purchased,
▫ and 1 indicates that Y is always purchased
whenever X is purchased.

86. LIFT
• Lift{X,Y} = lift{Y,X} = support{X,Y} /
(support{X} * support{Y})
• lift{butter, bread} = lift{bread, butter} =
support{butter, bread} / (support{butter} *
support{bread})
• lift{butter, bread} = lift{bread, butter}
=(3/11)/((3/11)*(7/11))
• lift{butter, bread} = lift{bread, butter} =1.571

87. Lift Value Interpretation
• Lift = 1; implies no relationship between X and Y
(i.e., X and Y occur together only by chance)
• Lift > 1; implies that there is a positive
relationship between X and Y (i.e., X and Y occur
together more often than random)
• Lift < 1; implies that there is a negative
relationship between X and Y (i.e., X and Y occur
together less often than random)

88. Reference:
Marketing Analytics Roadmap-
Methods, Metrics, and Tools
1st ed. edition
By Jerry Rackley
Appress
• ISBN-10: 1484202600
• ISBN-13: 978-1484202609