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MK671:
MARKETING ANALYTICS
CUSTOMER PROFITABILITY
INTRODUCTION
1. Metrics designed to simply count how many customers the firm serves
2. Introduces the concept of customer profit
3. Discuss how to calculate and interpret customer lifetime value
4. Explain prospecting decisions of CLV and makes the distinction between prospect and
customer value
5. Discusses acquisition and retention spending
5.1 CUSTOMERS, RECENCY, & RETENTION
Purpose: to monitor firm performance in attracting and retaining customers
These 3 metrics are used to count customers, track customer activity irrespective of the number of transactions
made by each customer
Customer Counts: these are the number of customers of a firm for a specified time period (much easier in
contractual [vs non-contractual] customers)
Recency: this refers to the length of time since a customer’s last purchase.
Retention Rate: this is the ratio of the number of retained customers to the number at risk
RULES OF THUMB
• Define the customer properly
• Not all customers are the same
• Where is the customer?
• Who is the customer?
5.2 CUSTOMER PROFIT (CP)
• CP is the profit the firm makes from serving a customer or customer group
over a specified period of time
• It is important because you will be able to understand if the relationship is
worth the investment
• CP → the difference between the revenues earned from the costs associated
with the customer relationship during a specified period
CP (CONT.)
• The overall profitability of the company can be improved by treating dissimilar
customers differently (80/20 rule… Table 5.1 & 5.2)
• Reward the top tier
• Grow the second tier
• Fire the third tier
5.3 CUSTOMER LIFETIME VALUE (CLV)
Purpose: to assess the value of each customer
• The central difference between CP and CLV is that CP measures the past and CLV looks forward.
• CLV is the dollar value of a customer relationship based on the present value of the projected future cash flows
from the customer relationship.
• CLV → encourages firms to shift their focus from quarterly profits to the LT customer relationships.
• NOTE: formula assumes that margins and retention rates are constant
CLV DEVELOPMENT
• CLV is the dollar value of the customer relationship to the firm
• Could be found using “cohort & incubate” method (cruise-ship customers)
• This works well when customer relationships are stationary—changing slowly over time.
• When value of relationships changes slowly, the value of incubated past relationships can be used
a prediction of the value of new relationships
• When value of the relationship changes rapidly, this simple model (formula) can be used to forecast th
value of those relationships:
• CLV ($) = Margin (S) *
Retention Rate (%)
1 + Discount Rate (%) – Retention Rate (%)
CLV (EXAMPLE)
An Internet Service Provider (ISP) charges $19.95/month. Variable costs are about $1.50 per
account per month. With marketing spending of $6 per year, their attrition is only 0.5% per
month. At a monthly discount rate of 1% what is the CLV of a customer?
Contribution Margin = Price – Cost
= $19.95 – $1.50 – ($6/12) = $17.95
Retention Rate = 0.995 ( very important)
Discount Rate = 0.01
Therefore,
CLV = $17.95 * (0.995 / (1 + 0.01 – 0.995)) = $17.95 * 66.33
5.4 PROSPECT LIFETIME VALUE (PLV) V CUSTOMER
VALUE
Purpose: to account for the lifetime value of a newly acquired customer (CLV) when making
prospecting decisions
Value expected from the prospect – cost of prospecting
Acquisition Rate (%) → The value expected from each prospect
Acquisition Spending ($) → the cost of acquisition spending per prospect
PLV ($) = Acquisition Rate (%) * (Initial Margin ($) + CLV ($)) – Acquisition Spending ($)
If positive, wise investment; If negative, do not invest; the figure is often very small
PLV EXAMPLE
A service company plans to spend $60,000 on an advertisement reaching 75,000 readers. If the
service company expects the advertisement to convince 1.2% of the readers to take advantage
of a special introductory offer (priced so low that the firm makes only $10 margin on this initial
purchase) and the CLV of the acquired customers is $100, is the advertisement economically
attractive?
Acquisition spending = $60,000 / 75,000 = $0.80 per prospect
PLV = 0.012 * ($10 + $100) – $0.80
= $0.52
Therefore, expected PLV is $0.52… * 75,000 = $39,000
PLV EXAMPLE (CONT.)
• Acquisition rate = 0.012 (1.2%)
• How many readers do we need to have to break-even?
• Break-Even Acquisition Rate =
Acquisition Spending ($)
Initial Margin ($) + CLV ($)
=
$0.80
= 0.007273
$10 + $100
Therefore Acquisition rate > 0.7273% in order for the campaign to be successful
5.5 ACQUISITION V RETENTION COST
Purpose: to determine the firm’s cost of acquisition and retention
These two metrics help the firm monitor the effectiveness of two important categories of marketing
spending
Questions to ask yourself:
At the current spending levels, how much does it cost the firm (on average) to acquire new
customers, and how much is it spending (on average) to retain its existing customers?
How many more times is it to acquire a new customer compared to retaining an existing one?
ACQ. V RET. COST FORMULA
• The firm’s average acquisition cost is the ratio of acquisition spending to the number of customers
acquired.
Average Acquisition Cost ($) =
Acquisition Spending ($)
Number of Customers Acquired (#)
• The average retention cost is the ratio of retention spending directed toward a group of customers to
the number of those customers successfully retained
Average Retention Cost ($) =
Retention Spending ($)
Number of Customers Retained (#)
EXAMPLE
During the past year, a regional pest control service spend $1.4 million and acquired 64,800
new customers. Of the 154,890 customer relationships in existence at the start of the year,
only 87,957 remained at the end of the year, despite about $500,000 spent during the year in
attempts to retain the 154,890 customers.
Acquisition cost:
Retention cost:
1,400,000 = $21.60 per cust.
64,800
500,000 = $5.68 per cust.
87,957
Thus, it costs about
4x more to acquire
a new customer
than it is to retain
an existing one
CHAPTER 1: THE BOLD
NEW WORLD OF WEB
ANALYTICS 2.0
ANALYTICS IN AN ONLINE SPACE
• Cost per click estimates (not sure I believe some of these)
• This is often done in a bidding process
• What would you want to know when making your bids?
• How can we apply what we’ve already learned here?
• Is the only value in an advertisement achieved when the consumer clicks?
• Online analytics were one of the major causes of marketing’s shift to quantitative
decision making
INTRODUCTION
• The importance of Web Analytics is rising as more and more emphasis is being placed on the digital
landscape
• Clickstream → What
• Multiple Outcomes Analysis → How much
• Experimentation & Testing → Explains why
• Voice of Customer → complements the why
• Competitive Intelligence → What else
• Like an onion from Clickstream being the outermost layer and Competitive Intelligence being the
second to last layer before reaching…. INSIGHTS! (most important tool)
CLICKSTREAM
• Click-level data is basic and straightforward
• Collecting, storing, processing, and analyzing click-level data
• Helps measure pages & campaigns by looking at
• Visits
• Visitors
• Time on Site
• Page Views
• Bounce Rate, etc.
MULTIPLE OUTCOME ANALYSIS
• Most impactful thing you will do with web analytics is to tie outcomes to profits
• A website attempts to deliver 3 types of Outcomes:
• Increase Revenue
• Reduce Cost
• Improve Customer Satisfaction/Loyalty
EXPERIMENTING & TESTING
• HiPPO → Highest Paid Person’s Opinion
• They often dictate the content (i.e. The Kardashians), but may not understand the customer experience
• Quick results
• Failing can be cheap
• Higher returns
VOICE OF CUSTOMER
• VOC is important for more direct feedback
• Feedback is coming from consumer in the target market
COMPETITIVE INTELLIGENCE
• Understanding your competitors better will make for better strategy
• Highlight competitive advantage
• Free information about what competition is doing
• Digital diagnostics
• Algorithms (web crawling) = digital gold
CONCLUSION
• Tools to maximize success for each aspect discussed
• With increasing digital marketing importance, understanding how to get the most from digital
world is critical for success
• Understanding how and when to use metrics will make you distinguished
• Digital marketing is growing (exponentially), so either get onboard or miss the train
CH. 10 & 3: WEB METRICS
INTRODUCTION
• Web-specific metrics are important because:
• Communication + Engagement
• Sales with real-time feedback on effectiveness
• Data rich
• Data is only as good as the individual’s analyzing skills
T echnology
Acceptance
M odel
GETTING INTO IT
• Definitions:
• Metric → a quantitative measurement of statistics
describing events or trends on a website
• Key Performance Index (KPI) → a metric that
helps you understand how you are doing against
your objectives
• Objectives need to be SM ART
• Specific
Creating a great metric
1. Uncomplex
2. Relevant
3. Timely
• Measureable
• Achievable
• Relevant
• Time-bound
4. Instantly useful
EXAMPLE
WEB METRICS LIFECYCLE PROCESS
VISITORS & VISITS
PAGEVIEWS
IMPRESSIONS & CLICKS
CLICKTHROUGH RATE
BOUNCE RATE
“Exit rate”
Good vs Bad rates
• Measures the percentage of sessions where Time on Site
was less than five seconds
• This shows if the visitor was “engaged” with the material
• Two important bounce rates: entire site & top landing
pages
ORDERS, SALES, & LEADS
CONVERSION METRICS
• Hover around 2%
PURPOSE OF SITE
ENGAGEMENT → CRM
• Creating website experience that draw favorable attention or interest.
• Understanding if someone enjoyed their experience on your website or if they begrudgingly
spent time there while having a problem.
… the role of the marketer is to be able to distinguish between the two
Degree. The degree of (+) or (-) engagement from low- to high- involvement
Kind. Customers can be positively or negatively engaged with a company or product (i.e.
engagement, sympathy, trust, pride, etc.)
EFFICIENCY & PRODUCTIVITY METRICS
EFFICIENCY & PRODUCTIVITY METRICS
(CONT.)
EFFICIENCY & PRODUCTIVITY METRICS (
EFFICIENCY & PRODUCTIVITY METRICS
(CONT.)
EFFICIENCY & PRODUCTIVITY METRICS
(CONT.)
EFFICIENCY & PRODUCTIVITY METRICS
(CONT.)
OTHER WEB METRICS
• Can use Abandonment Rate for leads and signing up for
an email newsletter
10 OF THE MOST IMPORTANT GOOGLE
ANALYTICS METRICS TO TRACK
Each group needs to assess 2 of the metrics mentioned.
TESTING & EXPERIMENTING/
EMERGING ANALYTICS
EXPERIMENTS
• Experiments are designed to give us causal data!
• Correlations do not imply causation – even if we find that two things are related, we
can’t say they are causally related unless we have experimental data
• Spurious correlations
• Usually used to test hypotheses – it is hard to conduct “Exploratory” experiments
TERMS
• Independent Variable – the variable which is manipulated by the researcher (the thing we
change to see if it has an effect)
• Dependent Variable – the variable which we measure, often to see if our manipulation had
some effect
• Control Variable – variables which are kept the same across conditions so as to not
influence results, or that have their effect accounted for to isolate the IV-DV relationship.
• I want to see if there is a difference in average test scores between a class that I have do
the MBTN modules and a class where they do not do the modules.
• What is the IV? What is the DV? What would be some variables which might need control?
REQUIREMENTS
• 3 required things to have a “True” experiment
• Proper time sequence – the IV has to be manipulated prior to the DV being measured
• Evidence of association – we should have some evidence that the IV and the DV are
related
• Control of confounding factors – all other variables which might influence the IV-DV
relationship should be controlled by either being held constant or eliminated.
ONLINE TESTING & EXPERIMENTING
Reasons:
• Reduce guess work
• Ability to be selectively bold
• Quick assessments (only slowed down by own traffic)
• Customer engagement
• Cheap
A/B TESTING
• It is a technique for testing two or more versions of a page on your website
• Each page is unique and visually different from the control
• Select desired outcome, monitor, and apply
• Pros: easy, cheap, quick, & easily communicated
• Cons: knowing the difference-making features (if too many applied)
MULTIVARIATE TESTING
• When considering making big changes with certain features
• Javascript coding (Grasshopper [Javascript coding app])
• Techniques:
• Full factorial- test all combinations of the pages that might occur as a result of your experiment
• Partial factorial- test fewer combinations and infer results for what might happen with other
combinations
TESTING HEURISTICS
• Fix those with high bounce rates
• Focus on checkout, registration, and lead submission pages
• Optimize the number and layout of ads
• Test different prices and tactics (careful with price!)
CONTROLLED EXPERIMENTS
• Direct cause-and-effect relationships
• Impacts of cross-channel and multichannel campaigns – can test individual channels and the
effect of multiple contacts
• Strategy-customer matching
• Can impact qualitative constructs
• Practice research habits
CONCLUDING TESTING & EXPERIMENTS
• Have a culture of testing and experimentation
• Start with hypothesis… try to stay away from exploratory/grounded theory unless deliberately exploring
• Don’t get too carried away… be specific
• Measure multiple outcomes
• Match test with specific goal
• Analyze data and communicate information
CH#9
EMERGING ANALYTICS
• Digital marketing – social media is changing the most rapidly
• Paid Media: SM Adv., Paid Search MK, Display Adv., Affiliate MK
• Owned Media: Website, SM Profiles, Email MK
• Earned Media: SM Sharing, Direct Traffic, SEO, Press Coverage
• Path: Acquisition → Engaged → Loyalty
• Create a Plan: Objective → Plan Content → Distribute/Promote Content → Measure
QUESTION FOR THE CLASS
• Do you think that people are more easily persuaded to try products because they follow those
companies through social media?
• Are they going to go on the value system that is on the same continuum as their friends?
• Example:
• Childhood friend
• Favorite Sports Team
• College friend
• Sephora/MAC
• High school friend
• Nike/Adidas
• What are some of your thoughts?
AREAS OF FOCUS FOR SOCIAL MEDIA
• Customer service improvement
• Reach new customers
• Provide product information & support
• Collect customer feedback
CONTENT
• Conversation Rate is an important indicator → engagement
• Visitor comments / posts
• Can apply to blogs, twitter, Youtube, Facebook, Instagram, etc. All posts are not created
equal, so may want to segment post type.
• Difficult to measure social media indicators as things are not as clear cut
• Looking to address one of the aforementioned areas of focus
THE RISE OF MOBILE
• What you want from your mobile analytics tool:
• Can you create segments from different platforms (i.e. iPhone v Android) and compare their behavior?
• Can you compare the performance of phones without JavaScript functionality with phones that have that
functionality?
• Can you look at two of the most popular screen resolutions and see the impact on Time on Site or
Conversions?
• Can you do the valuable segmentation and analysis you are accustomed to with your web analytics tool?
TRENDING FIGURES ON TOP SM APPS
• Number of Followers
• Reach
• Message Amplification – how much do others
spread your posts?
• Frequency
• Click-Through Rates
• Velocity – retweeted or
reposted?
• Conversions Rates
• Demand – are people
following you?
• Engagement
• Network Strength
• Ratings/Comments/Favorites (VOC)
• Activity
ADVANCED PRINCIPLES
ATTRIBUTION
• Process of assigning “credit” for a marketing outcome
• Purchases, sign ups, behavior, etc.
• Hard to say the impact of ads sometimes… For example
• So – does anyone feel like buying a Rav-4 tomorrow?
• Is anyone in the market for a new car?
• Why advertise like this for such a low frequency purchase?
• What about Coke and Pepsi advertising?
MULTI-TOUCH ATTRIBUTION
• Most people don’t see just a single marketing communication and then go make a
purchase
• This can be even more convoluted online where people might go to your site, leave
and do some research, come back in a month, see some ads on TV, hear and ad on the
radio, spend some more time on your site, then buy.
• So how can we figure out where our money is well spent?
• Hard to do well, and if you figure it out you will be well paid by whatever company you
work for
ONLINE ATTRIBUTION MODELS
• Ways of assigning credit for the purchase – lots of different ways to do this and no
one model is always the best – you will have to pick and choose which ones make the
most sense for your analysis
LAST CLICK
• Focuses on the most recent marketing campaign or click prior to purchase. Assigns
all ROI to that campaign
• Generally used as the standard method due to its simplicity and its focus on recent
marketing.
• Probably too simple, and may undervalue key marketing touch points.
FIRST CLICK
• The opposite of last click – attribution is given to the first marketing contact
• Same issues as last click, but focuses on the first contact as the valuable one.
• Can argue that without the initial contact, all subsequent efforts would have been
useless.
EVEN CLICK
• Attribution ($’s) is divided among all marketing contacts.
• If we have 7 contacts, we divide the attribution by 7.
• Doesn’t account for recency or primacy, but does capture the entire marketing
ecosystem.
• Also doesn’t factor in any sort of drop-off effects over time or other non-linear effects.
SPLIT CREDIT
• Credit is divided based on a heuristic
• Standard method: 50% of the credit is given to the last touch prior to purchase, and
the remaining 50% is split among all other touches
• Can help identify which marketing contacts are inciting purchase
• May still have issues with accuracy as 50% is fairly arbitrary
CUSTOM CREDIT
• Attribution is split based on some rule – spending %, contact time, whatever you want
• Usually based in the expertise of someone who has been in the industry for a while
• Can be arbitrary if not carefully assigned – which may result in wasted spending
HOW DO WE PICK?
• Run them all and compare
• Use model suggestions to try to improve marketing performance
• Use an experimental perspective to test competing theories to see if one
outperforms another over time
MEDIA MIX MODELING
• Tries to optimize by allocating budget $’s across different media channels
• Mirrors off-line media mix models
• Often done experimentally to see what happens
• Can assess marginal attribution – idea is you make a change and hold everything
else constant. Any differences are then due to the change.
• Similar to experiments, controlling the other variables is hard
• Also hard to isolate effects from other market conditions such as economic fluctuation
OFFLINE ATTRIBUTION
• What is the impact of offline advertising on online campaigns?
• Hard to measure
• Often why offline ad …
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