An overview of marketing metrics and analytics, what they mean and how they can be used to improve ROMI and performance.

1. Metrics in a Nutshell
Dr. Debra Zahay-Blatz
Professor and Chair of Marketing and Entrepreneurship
Thanks to Stukent for some of this material, the Roberts and Zahay
Text, and other sources as noted

2. Agenda
• Q & A
• Simulation results to date
• Data Analytics
• Marketing Metrics
• Web Analytics

3. Q & A

4. Simulation

5. Project Metrics
• Performance measurement: describe the metrics
you will use to evaluate performance.
• Customer Lifetime Value (CLV), Return on
Investment (ROI).
• Performance metrics must match precisely to
marketing objectives
• Be specific—what metrics, how they will be
collected

6. Copyright © 2011 Pearson Education, Inc. Publishing as Prentice Hall 4-6
What are Marketing Metrics?
Marketing metrics are the set of measures that
helps marketers quantify, compare, and interpret
marketing performance.
Kotler and Keller, A Framework for Marketing
Management, 6e, 2016

7. The Measurement Process
Goal
Measurement
Diagnosis
Correction

8. Analytics and Metrics in
Marketing
Data
Analytics
Uses Internal,
External and
Modelled data
Uses statistics
and predictive
modelling
Tries to predict
results of
marketing actions
Web
Analytics
Focuses on
company web site
Seeks to track
conversion/other
actions
Aids making web
site changes to
increase
effectiveness
Marketing
Metrics
Focus on Sales
and Marketing
Objectives
Relies upon
Internal company
information
Used to make
broad financial
decisions
Zahay, Digital Marketing Management, 2015

9. Data Analytics

10. Marketing Databases are
Carefully Crafted
External
Modeled
Internal
Zahay, Digital Marketing Management, 2015

11. Marketing Metrics

12. Marketing Metrics Framework
Sufi, 25 Essential Marketing Metrics, 2011

13. Product and Profitability
Analysis
Kotler and Keller, A Framework for Marketing Management, 6e, 2016

14. Market Metrics vs. Customer
Metrics
Sufi, 25 Essential Marketing Metrics, 2011

15. Google’s Search Engine Market
Share Remains Constant
15

16. Ford’ Share of My Garage
FORD
CHEVY
What is Ford’s Share of Wallet?
A. 50%
B. 100%
C. Don’t Know

17. Sales or Market Metrics
• Sales Growth
• Market Share
• Sales from New Products
Customer Readiness to Buy
Metrics
• Awareness
• Preference
• Purchase Intention
• Trial rate
• Repurchase rate
Kotler and Keller, A Framework for Marketing Management, 6e, 2016

18. Customer Metrics
• Customer complaints
• Customer satisfaction
• Ratio of promoters to
detractors (Net Promoter
Score)
• New-customer gains
• Customer losses
• Customer Churn
• Retention Rate
• Customer Lifetime Value
• Customer Equity
• Customer Profitability
• Return on Customer
• Share of Wallet
Communication Metrics
• Spontaneous (unaided)
brand awareness
• Advertising Awareness
• Reach Frequency Gross
rating points
• Response Rate
Distribution Metrics:
Stocking, Average sale, etc.
Kotler and Keller, A Framework for Marketing Management, 6e, 2016

19. Return on Investment/Return on
Marketing Investment
ROI
(Return – Investment)
Investment
ROMI
Gross Profit – Marketing Investment
Marketing Investment
http://www.marketingmo.com/campaign
s-execution/how-to-calculate-roi-return-
on-investment/

20. Return on Investment/Return on
Marketing Investment
ROMI
Gross Profit – Marketing Investment
Marketing Investment
ROMI
Search Campaign: Value – Costs =
Costs
http://www.marketingmo.com/campaigns-
execution/how-to-calculate-roi-return-on-
investment/

21. Round 1 Results ROMI
Value – Costs = (30555-8118)/8118 =
Costs 2.76 x 100 = 276% ROMI

22. Customer Lifetime Value
• Starbuck’s Coffee
– Average Customer Spend =$5.90
– Average customer visits (4.2 x per week)
– Average Cuosmter Value = $24.30/week
– Average Annual Spend = 52 x $24.30 =
– Customer Lifespan = 20 Years
– CLV = 20 x 52 x $24.30 = $25,272
https://blog.kissmetrics.com/wp-
content/uploads/2011/08/calculating-ltv.pdf

23. Web Analytics

24. http://www-01.ibm.com/software/marketing-solutions/benchmark-hub/
Mobile Traffic Spikes on Weekends

25. Desktop Conversion Rates are Higher
http://www-01.ibm.com/software/marketing-
solutions/benchmark-hub

26. Desktop Order Values are Higher
http://www-01.ibm.com/software/marketing-
solutions/benchmark-hub

27. Web Analytics Focus on the Internet:
Retail Site
Visits 12,483
Conversions 512
Total Revenue $17,541.12
$1.41
4.1%
$34.26
Conversion Rate
AOV: Avg Ord Value
RPV: Rev per Visit
Stukent Instructor Materials, 2015

28. By Channel: Same Retail Site
Organic
Search
Paid
Search Direct
Display
Ads Referral
Social
Media Email
Visits 2,129 1,430 141 2,238 4,140 792 1,613
Conversion
s 72 80 13 55 68 58 166
Total
Revenue $1,492.43 $5,181.94 $445.38 $2,204.44
$2,518.4
9 $1,377.44
$4,321.
00
Organic
Search
Paid
Search Direct
Display
Ads Referral
Social
Media Email
Conversio
n Rate
3.4% 5.6% 9.2% 2.5% 1.6% 7.3% 10.3%
AOV $20.73 $64.77 $34.26 $40.08 $37.04 $23.75 $26.0
3
RPV $0.70 $3.62 $3.16 $0.99 $0.61 $1.74 $2.68

29. Conversion Funnel For Online
Hotel Site
Rates & Availability
2,347,892
Review Itinerary
367,495
Guest Info
103,471
Confirmation
72,498
Revenue: $23,464,220
Room Nights: 143,285
1. What is the conversion rate?
72,498/2,347,892 = 3.09%
2. Calculate each micro-conversion rate.
Rates & Availability
2,347,892
Review Itinerary
367,495
Guest Info
103,471
Confirmation
72,498
367,495/2,347,892
= 15.7%
103,471/367,495 =
28.1%
72,498/103,471 =
70.1%
Stukent Instructor
Materials, 2015

30. A Simple Estimate: CPC BEP
(Cost Per Click Break Even
Point)
6/10 = .60
https://www.google.com/search?q=cost+per+click+break+even+calculator&r
lz=1C1CHMO_enUS559US559&oq=cost+per+click+break+even+calculator&a
qs=chrome..69i57.6225j0j7&sourceid=chrome&es_sm=93&ie=UTF-8

31. We Can Spend More Per Click
Than We Are and Still Break
Even
9.53/26.61 = 2.54 and
we are spending less
than that
30555 (Value) – 8118 (Cost) = 22437 Gross Margin
22437 (Gross Margin)/843 (Clicks) is $26.61 Estimated Gross Profit per transaction
or per Click

32. Summary
• Databases are created in the company and can
be used for predictive modelling
• Marketing Metrics can help with product and
Customer Decisions on a both a product/market
and customer level
• Web analytics allow the marketer to make
detailed decisions on a daily basis

33. Contact Information
• dblatz@stedwards.edu
• 512-448-8645
• @zahay

34. Thank you
St. Edward’s University
Thank you.
St. Edward’s University
Thank you.
St. Edward’s University
Thank you.
St. Edward’s University
Thank you.
St. Edward’s University