Chapter 10 digital business trans chaffey 2019 7e trang 2

DIGITAL BUSINESS AND E-COMMERCE MANAGEMENT

Sprint Planning

Meeting

Backlog
Daily Scrum

Refinement

Meeting

Sprint Review
Meeting

Sprint
Retrospective
Meeting

Figure 10.5

Scrum meeting

Sprint planning
To select what work needs to be done, prepare the sprint backlog with the team (and how much
time it will take to do the work) and work to a four-hour time limit for a two-week planning
sprint. During the first half of the sprint, the team agree what product backlog items need to
be considered and during the second half the development team establish the tasks required to

deliver the backlog items (called a sprint backlog).

Daily Scrum
Every day during a sprint, the team hold a stand-up meeting of no more than 15 minutes. The
meeting should happen at the same time, in the same location, every day; team members come
prepared and each person answers three questions:

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• What did I do yesterday that helped the development team meet the sprint goal?
• What will I do today to help the development team meet the sprint goal?

• Do I see any impediment that prevents me or the development team from meeting the
sprint goal?

Box 10.7

Using Scrum in marketing teams

If you are going to be implementing agile methods into a marketing department, these

are some of the key things to consider:
• Review meetings can take too long for teams of eight or more people.
• A month is probably too long for a marketing sprint because of the unpredictability
of schedule demands.

• Task estimating takes time to learn.
Marketing projects should be executed in 'mini development cycles', each lasting no
more than a month.

Any impediments identified in the Daily Scrum are recorded by the Scrum Master and dis

played on the team's Scrum board, with someone designated for working toward a resolution
(outside of the Daily Scrum). Detailed discussions should not happen during the Daily Scrum.

Sprint review and retrospective
During the sprint review, the team reviews the work that was completed and the planned work

that wasn't completed.
At the sprint retrospective, the team answers two questions:

1 What went well during the sprint?
2 What could be improved in the next sprint?
They then identify and agree continuous process improvement actions (see Box 10.7).

Developing agile marketing campaigns
Programmatic marketing
Automated bidding on advertising inventory in real time, for the opportunity to show
an ad to a specific customer in a specific context.

A good 'rule of thumb' is to use a marketing 70:20:10 rule:
. 70% ofyour marketing should be planned activity;
• 20% of your marketing should be automated marketing that responds to various actions

ofthe user (such as programmatic marketing);

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• 10% of your marketing should be entirely agile - reacting to news and events as and when
they happen.

In order to achieve this, the right resources are needed - a creative team who can generate
sharable content quickly - the correct tools in place to listen to social media feeds/provide alerts
to relevant topics and a culture that is open to ideas and experimentation (i.e. one that is not
risk-averse).

Social media is a perfect medium for agile marketing because of its real-time responsiveness.
Figure 10.6 shows a Specsavers advert, with text added to reflect the Brexit Referendum re

sult using Specsavers 'should have gone to Specsavers' slogan.

42102

We look You listen
Our hearing tests now include video technology

LEAVE

I Stay

Specsavers

Audiologists

Should havegone to Specsavers
JCDecaux

Figure 10.6

Specsavers Advert
Source: Joe Doylem/Alamy Stock Photo

The growth hacking process
There are five key pillars to achieving growth hacking success:
1 Product/market fit (create an MVP - minimum viable product)
2 User data analysis
3 Conversion rate optimisation

4 Viral growth
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5 Retention and scalable growth.

1 Product/market fit (create an MVP
- Minimum Viable Product)
Product/market fit is a product or service offering that perfectly satisfies the need of a particu
lar user segment, which creates a loyal and passionate user base.

Traditionally, marketers are given a finished product and it's their job to generate sales. In

the world of growth hacking, a different approach is taken... instead, the product build phase

should be entered as quickly as possible with a minimum viable product (MVP). This is a basic
(or beta) product without any 'bells and whistles'. Marketers are enlisted during this initial
product development phrase to help put the MVP in front of potential customers to gain feed
back. This is done by running surveys, testing and iterating to improve the product.
Sustainable growth is only possible if a large group of people consider the product or service
a 'must have'. This is difficult to achieve without user feedback and product improvement be
fore officially launching the product to upscale the business. The idea of 'fail fast, fail cheap'
and improvement via constant experimentation can be seen in the Instagram mini case study.

PayPal co-founder and technology start-up investor Peter Thiel believes that:
If a product requires advertising or salespeople to sell it, it's not good enough.
Most tech start-ups take the 'traditional' approach of building a product and then seek
funding to assist with bringing in new users via sales and marketing. However, technology
start-up investors require 'proof-of-concept' before releasing funding, so that key marketing

metrics such as cost of acquisition and month-over-month growth can be provided to prove
sustainability.
The model in Figure 10.7 shows each stage of the start-up process and how customer feed
back is an important part of finding a business model that works.
The perfect target market for a start-up is a small target audience served by few or no com
petitors because trying to enter a large market already served by competing companies will

erode profits.
One of the main aspects of the products we use on a regular basis is that we're hooked on
them. How often do you use platforms such as Facebook and Twitter and/or products like your
iPhone or iPad? Eyal (2014) is an expert on applied consumer psychology; he has developed a

model that helps people build better products and achieve product/market fit. The 'Hook Can
vas' can be seen in Figure 10.8. Designing a habit-forming product has four parts: trigger, ac
tion, rewards and investment. We will look at each of these in more detail next.

Trigger
Triggers come in two types - external and internal. External triggers are embedded within
information and tell the user what to do next (e.g. click a link in an email). Internal triggers
are when a product becomes aligned with a thought, emotion or pre-existing routine - i.e. cap
turing moments with friends/family via photos and sharing them on Facebook. Once internal

triggers become part of people's routine behaviour, the habit is formed.

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Listen to customers
During customer development, a start-up searches for a business
model that works. If customer feedback reveals that its business

hypotheses are wrong, it either revises from or "pivots" to new
hypotheses. Once a model is proven, the start-up starts executi
building a formal organization. Each stage of customer

development is iterative: A start-up will probably fail several times
before finding the right approach.

SEARCH

EXECUTION

2

2

CUSTOMER

CUSTOMER

CUSTOMER

COMPANY

DISCOVERY

VALIDATION

CREATION

BUILDING

PIVOT

2

1

3

4

Founders translate

Start-up continues

The product is

company ideas

to test all other

refined enough

from start-up mode,

into business

hypotheses and

to sell. Using

with a customer

model hypotheses,
test assumptions

tries to validate

its proven

development team

customers' interest

hypotheses, the

searching for

about customers

start-up builds
demand by rap

answer, to functional

create a "minimum"

through early
orders or product
usage. If there's no

its model.

viable product"

interest, the start

idly ramping up
marketing and

to try out their

up can "pivot" by

sales spending

proposed solution

changing one or

and scales up

on customers.

more hypotheses.

the business.

needs, and then

Business transitions

departments executing

Figure 10.7

Lean start-up

Source: />
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Action
After a trigger comes the intended action. There are two pulleys of human behaviour -moti
vation and ability. Although motivation is a widely contested topic in psychology, this model
is based on the fact there are three core motivators driving our desire to act: seek pleasure
and avoid pain; seek hope and avoid fear; and seek social acceptance and avoid rejection. It is
worth considering that negative emotions, such as fear, can be powerful motivators. The other

'pulley' links to usability, i.e. the ability of the user to take action easily. Companies such as
Pinterest, Instagram and Snapchat have simplified online content creation and sharing, they
have used modern technology to take out steps. Fogg (n.d.) describes six elements of simplic

ity: time, money, physical effort, brain cycles (level of mental effort/focus needed to take an
action), social deviance and non-routine (how much it matches or disrupts existing routine).
To put this in context, Google has reduced the amount of time and cognitive effort required to
find information.

The HOOK Canvas

REWARD

TRIGGER

1. What internal trigger is

4. Is the reward

the product addressing?

2. What external trigger

fulfilling, yet leaves the
user wanting more?

getsthe userto the product?
5. What "bit of work" is done

3. What is the simplest

to increase the likelihood of

behavior in anticipation

returning?

of reward?

INVESTMENT

ACTION

Figure 10.8

The Hook model

Source: www.slideshare.net/nireyal/hooked-model/135

Rewards
The variable reward phase is when users are rewarded by solving a problem, thus reinforcing
their motivation for taking the action in the first place. Variable schedules of reward are a pow
erful way to hook users. Research has shown that levels of dopamine surge when the brain is
expecting a reward and introducing variability multiplies the effect, activating the parts ofthe
brain associated with wanting and desire. Lotteries and slot machines work on this premise.

There are three ways a product can heighten a user's search for variable rewards:

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• rewards of the tribe - gratification from others;

• rewards of the hunt - material goods, money or information;
• rewards of the self-mastery, completion, competency or consistency.
Investment
This is the last phase of the Hook Canvas: before users create mental associations that activate

automatic behaviours, they need to first invest in the product. This links to a psychological
phenomenon called the escalation of commitment - the more users invest time and effort into

a product or service, the more they value it. Therefore, this stage is about asking users to do a
bit of work-investment is generally in the form of asking the user to give some combination of
time, data, effort, social capital or money. Company's such as Giffgaff have utilised this phase
well, by asking for user-generated content for its knowledge base (see the section on 'Artificial
virality', p. 588).

More information about user behaviour and what drives customer engagement can be
found on Eyal's website, www.nirandfar.com.

One product that has hooked millions of people is Instagram. The photo- and video-sharing
social network has a team that are conversant in psychology as much as technology. Many

people have made using the app a part of their daily routines - forming a connection between
the need to capture images of things around them and using the app on an ever-present mo
bile device.

For many people, Instagram started off as a brief distraction (i.e. to relieve boredom), only
to become part of a regular routine. The fear of losing a special moment instigates a pang of
stress, which triggers Instagram users to open the app and alleviate the pain by capturing a
photo. Also, because the app is a social network, it dispels boredom by connecting users with
others, sharing photos and swapping banter. It also lessens or stops the pain of 'fear of missing
out'.

Instagram's journey to product/market fit is an interesting one, as you can read in Mini case
study 10.8.

Mini case study 10.8

Instagram

Instagram

Figure 10.9

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Instagram
Source: tanuha2001/Shutterstock.com

Instagram started life called Brbn, named after a whisky. Originally launched as a
location-based iPhone app, it allowed users to check-in at particular locations, make fu
ture check-in plans, earn points for hanging out with friends and post pictures of their
meet-ups.

It wasn't very successful. All of its features confused users. To tweak the app, they
looked at the user analytics and found that most people were using it to share photos.

Based on the usage data, they scaled down the product and focused on its photo
sharing infrastructure. They also looked at the competition - Hipstamatic had great fil
ters but photo-sharing was difficult, and Face-book was great for social networking but

not photo-sharing. They decided to build something in between.
After months of experimentation and prototyping, they released a simple photo
sharing app called Instagram.

2 User data analysis
The Instagram mini case study highlights the importance of user data analysis.
One of the key aspects of growth hacking is to find user patterns and test/optimise activities
that are linked to growth. However, one of the key challenges faced by start-ups is that there is

so much data available it is becoming increasingly difficult to understand how the data can be
used to create actionable insights.
User data analysis should be a mix of quantitative and qualitative research and a business
should develop a systematic method to feed into business insights.

Main areas of user testing
The five main areas of user testing are:
1 Technology analysis, such as conversion rate per browser
2 Heuristic analysis, such as relevancy, distraction and online value proposition

3 Web analytics, such as flow reports
4 Qualitative surveys, such as exit surveys

5 Usability testing, such as user session videos.
The information gained from this type of analysis can then be used to test hypotheses
relating to user growth and to validate ideas. This process is essential to finding 'non-norm' so
lutions to achieve growth in a short amount of time.
Another important tool (available in Google Analytics) is cohort analysis. Instead of looking
at cumulative totals or gross numbers, data are broken down into the performance of each

group of customers (a cohort) that comes into contact with the product independently. This
method helps companies understand customer flows, which provides more predictive power

than traditional gross metrics.
However, one of the main challenges facing a start-up during the launch stage is having

enough customers to provide meaningful data. This is why product/market fit is so important
- so that the product initially 'sells itself'.

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3 Conversion rate optimisation
User data analysis is not an isolated approach to growth hacking - it links to every stage in the
cycle, from product/market fit to retention. The information collected from data can then be
used for conversion rate optimisation (CRO), to help build an effective growth engine (as shown
in Figure 10.10).

This approach is basically using structured testing to improve website effectiveness. Growth
optimisation is moving from data, to insight and then to money. User data analysis is needed
throughout this CRO process, so that activity can be prioritised.
Generally, a company should use a minimum sample size of 250 to test changes for CRO. They

also need to think about business cycles - for example, if your weekend traffic is very different,
ending a test by excluding that segment would make your sample unrepresentative.
Key CRO elements
There are three main conversion rate optimisation elements:

1 Tools - insights, creating pages, personalisation, campaign and automation
2 People - insight, management, creative execution, test set-up, implementation, out
source

3 Process - planning and creating new ads and content, optimising old ads and content.

According to Eisenberg et al. (2011), there are 30 key optimisation factors to consider (see
Table 10.2).

Box 10.8

Heuristic analysis

According to Phillips (2016), a heuristic-based analysis approach for e-commerce
companies would include the following:

1 Determining conversion rate for

different device types
A helpful place to start is to determine whether the conversion rate for orders is
different by device.

2 Segmenting critical conversion rates by
key dimensions to understand differences,

such as by marketing channel
You may segment conversion rate by paid search on the mobile versus paid search
on the desktop.

3 Identifying the bounce rate on your landing pages
Product pages, category pages, brand pages and the home page can be landing

pages for email, search and display advertising campaigns. Ensuring that these]pages
perform as effectively as possible by testing the creative is important.

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Identifying the exit rate on important pages
The exit rate is the percentage of people who leave the site on that page. If you

notice large exit rates on certain pages, such as login pages, purchasing pages, ship
ping pages and order summary pages, then you can consider testing them.
Source: Extract from Ecommerce Analytics by J. Phillips (2016)

Understand

Prioritize

Test &

Visitors

Planning

Analyze

Figure 10.10

The conversion optimisation loop

Source:

www.slideshare.net/seanellis/cro-preso-for-growth-hackers-conf-nov-2013

ellis-updated-28050686/7
Steps_to_Better ConversionsTwitter_SeanElliswwwGrowthHackerscom 7

Table 10.2

30 Key optimisation factors

Element

Details

Planning
• WIIFM: What's in it for me?

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Unique

value proposition/campaign

proposition

. The buying decision
. Categorisation
. Usability
. Look and feel
• Searchability

• Layout, visual clarity and eye tracking
.

Structure

• Purchasing
• Tools

• Error prevention

• Browser compatibility
• Product presentation
.

• Load time

• AIDAS (scent)

. Trust and credibility
• Navigation/user oflinks
Momentum

• Product selection/categorisation

. Up-sell/cross-sell
. Calls to action/forms
• Point of action

. Security and privacy

• Persuasive copywriting
. Content
• Headlines

• Readability
Communication

• Use of colour and images
• Terminology/jargon
.

'We-We'

Test

(customer-focused lan

guage)
. Features like reviews

Source: />
There are seven main areas that can help improve website conversion and sales:
1 A/B testing and multivariate testing
2 Having a structured approach

3 Customer journey analysis (covered in Chapter 8)
4 Copy optimisation

5 Online surveys/customer feedback
6 Cart abandonment analysis
7 Segmentation (covered in Chapters 7 and 8).

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Parizek (2013) has produced a conversion rate optimisation maturity model (see Figure
10.11), which is based on seven key pillars:

• People - the quality and quantity of a team is essential.
.

• Knowledge - this is aligned with people. CRO starts with online marketing basics, having

.

an overview of what e-commerce is, how traffic generation works, what web analytics is
and how to read reports and take actions. The next stage is to add online testing knowl
edge, principles of user experience, web analytics knowledge and copywriting skills. It is
impossible for a single person to be an expert in all those areas, so a well-acting team is
needed.

• Activities - there are various quantitative and qualitative activities that can be run to
understand customers better. The higher the quality and frequency, the better the out
come.

• Tests of strategy and frequency - one of the main CRO activities is A/B and multivariate
testing (discussed next in this chapter). The maturity of a company's testing processes
is extremely important: tests can be executed on an ad hoc basis; or a more mature ap
proach will plan and execute in a testing roadmap. Or, better still, tests are run in an iter
ative manner.

• Processes - the overall CRO processes in a company are another important asset. Do

key departments cooperate smoothly? How about communication and politics within
the company? Are deliverables such as testing roadmaps, testing summaries and learn
ing overviews recorded and used? All of these are variables that influence CRO results
significantly.

Sponsor - this is usually a high-ranking employee who is an advocate of CRO, trusts the

team and fights for budget. They support CRO efforts and share the plans and results
with senior management, if the team are unable to.
• Tools - tools need to be in place to conduct analyses and tests. There are many different

tools available to do this - such as web analytics, heatmaps, surveys, feedbacks, targeting
and testing tools. In general, the more mature a company's CRO efforts are, the more so

phisticated the tools.

A/B and multivariate testing

Often site owners and marketers reviewing the effectiveness of a site will disagree and the only

method to be certain of the best-performing design or creative alternatives is through design
ing and running experiments to evaluate the best to use. Matt Round, then director of person

alisation at Amazon, speaking at the E-metrics summit in 2004, said the Amazon philosophy,

described further in Case study 10.2, is:
Data trumps intuition.
A/B testing and multivariate testing are two measurement techniques that can be used to
review design effectiveness to improve results.

A/B testing
In its simplest form, A/B or AB testing refers to testing two different versions of a page or

a page element such as a heading, image or button. Some members of the site are served
alternately, with the visitors to the page randomly split between the two pages. Hence it is

sometimes called 'live split testing'. The goal is to increase page or site effectiveness against key
performance indicators including clickthrough rates, conversion rates and revenue per visit.

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A/B or AB testing
Refers to testing two different versions of a page or a page element, such as a heading,

image or button, for effectiveness.

Level5

Level4

Data-driven

Level3

Optimization as a

Level2
Level1

Optimization using

Starting with

Regular Online Testing
and Optimization with

key online

clear plans

marketing asset

as-hoc Online

Optimization

Testing

PEOPLE | Online marketing generalist |

| Part-time conversion

II

Level 2

Level 1
basics

II copywriting
Level1
Advanced traffic and

|

| conversion report analysis

|

||

|Online testing

1

& FREQUENCY | 1-2 tests per quarter

PROCESSES | None

TOOLS | Web analytics tools

11 Level 3

team

|| Level 4

| 1-2 tests permonth

|| Random/ad-hoc

|| Level1

11

II segmentation

Customer analytics

11.Businessanalytics
11. MBA

JL

11 Level 4

|| Multichannel analysis and

Basic segmentation and

Advanced segmentation and
T
targeting
11. Extensive UX research
|• Personalization

11

optimization

1:1 Personalization

11. Data mining
|| 360° business analysis

IIIterative testing

II. Disciplined testing

1. 2-3 testsper month

11.3+tests permonth

11. 6+ tests per month

|| Regular and standardized

|| •Optimized

|| Super-optimized

Il Level 2

11 Level 3

| Customer survey and

||

|| Customer experience

feedback
Heatmaps and screen

management tools
Personalization tools
T

recording tools

TIME
|| Head ofOnline

|| Managementskills

11 Advanced analytics

|

tools

Advanced analytics including

11 Level 3

survey analysis

II. Regular and planned testing

I. Ad-hoc testing

I

Targeting knowledge
• Excellence in UCD/UX

11 Customer feedback and
targeting
IlCompetitor analysis

| | Online testing and targeting

SPONSOR None

team

11 Level 2

I

UX principles and testing

TESTING STRATEGY 1- No testing strategy

Il

|

ACTIVITIES I. Basic traffic and conversion!
|
Sales
report monitoring

II.Large conversion optimization

Il

• Deeperknowledge about
CRO, UX and analytics
Content management and

|| Conversion optimization

report analysis

|| Smallconversion optimization

|

KNOWLEDGE Basics of online marketing |

|| Full-time conversion
II optimization specialist

optimization specialist

CRO is in your
company's DNA

Il Director level

r

11.VP level

Level 4

|| Personalization automation
tools

II. Multichannel analytics and
II

Optimization tools

10

|| Entire organization

Figure 10.11

Conversion rate optimisation maturity model
Source: />
When completing A/B testing it is important to identify a realistic baseline or control page

(or audience sample) to compare against. This will typically be an existing landing page. Two
new alternatives can be compared to previous control, which is known as an ABC test. Different

variables are then applied, as in Table 10.3.
An example of the power of A/B testing is an experiment Skype performed on its main top
bar navigation, where it found that changing the main menu option 'Call Phones', to 'Skype

Credit' and 'Shop' to 'Accessories' gave an increase of 18.75% revenue per visit (Skype were
speaking at the 2007 E-metrics summit). That's significant when you have hundreds of mil
lions of visitors! It also shows the importance of being direct with navigation and simply de
scribing the offer available rather than the activity.

Table 10.3

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A/B test example

Test

A (Control)

Test 1

Original page

B (Test page)
New headline, existing
button, existing body
copy

Existing headline, new

Original page

Test 2

button, existing body
copy

Existing headline,

Original page

Test 3

existing button, new

body copy

Control page
The page against which subsequent optimisation will be assessed. Typically a current

landing page.

Mini case study 10.9

Multivariate testing at National Express Group increases conver

sion rate

The National Express Group is the leading provider of travel solutions in the UK. Around
1 billion journeys a year are made worldwide on National Express Group's bus, train,
light rail and express coach and airport operations. A significant proportion of ticket
bookings are made online through the company's website at www.nationalexpress.com.
The company used multivariate testing provider Oracle Maxymiser to run an exper
iment to improve conversion rate of a fare selection page that was the penultimate step
in booking. The analysis team identified a number of subtle alterations to content and

calls to action on the page with the aim of stimulating visitor engagement and driving
a higher percentage of visitors through to successful conversion without changing the
structure of the page or National Express brand identity. In order to aid more effective
up-sell to insurance add-ons, changes to this call to action were also proposed.
It was decided that a multivariate test would be the most effective approach to de

termine the best-performing combination of content. The variants jointly developed by
Oracle Maxymiser and the client were tested with all live site visitors and the conversion

rate of each combination monitored. They tried 3,500 possible page combinations and
during the live test the underperforming combinations were taken out to maximise con

version rates at every stage.
At the end of the testing period, after reaching statistical validity, results showed
that the best combination of elements showed a 14.11% increase in conversion rates for

the page, i.e. 14.11% more visitors were sent through to the fourth and final step in the

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registration process, immediately hitting bottom-line revenue for National Express (Fig
ure 10.12).

Content

Maxybox A

combination

Maxybox B Maxybox C

Maxybox D

Maxybox E

Lift on
control

1

Variant 3

Variant 2

Variant 4

Variant 3

Variant 1

14.11%

2

Variant 3

Variant 3

Variant 4

Default

Default

14.09%

3

Variant 6

Variant 3

Variant 4

Default

Default

11.15%

4

Variant 3

Variant 3

Variant 2

Default

Variant 3

10.57%

Default content

Variant 3

Variant 2

Default

Default

Default

0.00%

Conversion rate uplift by page combination:

Page

1

14.11%

2

14.09%

11.15%

3

combination

10.57%

4

1

Default 0%

0%

2%

4%

6%

8%

10%

12%

14%

16%

Figure 10.12

Results of multivariate testing for National Express

Multivariate testing
Multivariate testing is a more sophisticated form of A/B testing that enables simultaneous
testing of pages for different combinations of page elements that are being tested. This enables
selection of the most effective combination of design elements to achieve the desired goal.
An example of a multivariate test is shown in Mini case study 10.9.

In order to achieve significantly higher increases in sales growth, companies need to com
plete six to seven A/B tests or multivariant tests a month. An example of how powerful this
technique can be is demonstrated in Mini case study 10.10.

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Forward path analysis
Reviews the combinations of clicks that occur from a page. This form of analysis is most
beneficial for important pages such as the home page, product and directory pages. Use

this technique to identify messaging/navigation combinations that work best to yield

the most clicks from a page.

Clickstream analysis and visitor segmentation
Clickstream analysis refers to detailed analysis of visitor behaviour in order to diagnose prob
lems and opportunities. Table 10.4 gives an indication of the type of questions asked by author
Dave Chaffey when reviewing clients' sites.

Path analysis
Aggregate clickstreams are usually known within web analytics software as 'forward' or 're
verse' paths. This is a fairly advanced form of analysis, but the principle is straightforward you seek to learn from the most popular paths.
Viewed at an aggregate level across the site through 'top paths' type reports, this doesn't

appear particularly useful as the top paths are often:
• Home page: Exit

• Home page: Contact Us: Exit
News page: Exit

.

Mini case study 10.10

How Obama raised $60 million by running an experiment

To demonstrate the power of conversion rate optimisation across all types of campaigns,
a simple experiment in December 2007 has actually changed the course of history.
Dan Siroker was the Director of Analytics for the Obama 2008 campaign; his job was

to use data to help make better decisions when running the campaign. He did this by
running an experiment to test two pages of the campaign splash page - the media sec
tion and the call-to-action button.

Four buttons and six different media types were tested (three images and three
videos); the metric to measure success was sign-up rate (number of people who signed
up divided by number of people who saw a particular variation).
The test was run using Google Website Optimizer and was a multivariate test (i.e.
they tested all of the combinations of buttons and media against each other at the same
time).
Staff believed that 'Sam's video' would be the best media. However, all of the videos

did worse than all of the images.
This page had a sign-up rate of 11.6%, against the original sign-up rate of 8.26%
(40.6% improvement). This equated to 2.8 million email addresses and an additional
$60 million in donations. Key lessons learned:
Every website visitor is an opportunity - take advantage of this through website
optimisation and A/B or multivariate testing.

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• Always question assumptions - videos were the most popular media but they didn't
perform.
• Experiment early and often - small incremental changes can generate aggregated
marginal gains.
Source: />simple-experiment/

Table 10.4

A summary of how an analyst will interpret web analytics data. GA is terminology for
Google Analytics (www.google.com/analytics), one of the most widely used tools

Analystquestion

Typical web analytics re
port terminology

How successfulis the siteat

Conversion goals (GA)

Diagnosis ofanalyst used to improve performance

.Is engagement and conversion consistent with other.
achievingengagement and

sites in the sector?

Bounce rates (GA) Pages/

outcomes?

visit (GA)

Whereare visitors entering

Top entry pages

the site?

Top landing pages (GA)

. What are maximum engagement and conversion
rates from different referrers?
• How important is the home page compared to other
page categories and landing pages? Does page popu

larity reflect product popularity?
. Check that messaging and calls to action are effective

on these pages
• Assess source of traffic, in particular keywords from

search engines, andapply elsewhere
.Arethefull range of digital media channels relevant
for a company represented?
What are sources of visitors
(referrers)?

Referrers Traffic sources

• Is the level of search engine traffic consistent with

Filters set upto segment

the brand reputation?

visitors

. What are the main link partners driving free traffic

(potential for more)?
• Is page popularity as expected? Are there problems

with findability caused by navigation labelling?
What is the most popular
content?

. Which content is most likely to influence visitors to

Top content (GA)

outcome?

. Which content is most popular with returningvisi
tors segment?

• How popular are different forms of navigation, e.g.
top menu, sidebar menus?
Which are the most popular
findability methods?

• What are the most popular searches? Where do
Site search (GA)

searches tend to start? Are they successfully finding
content or converting
to sale?

. Are these as expected (home page, About Us page,
transaction completion)?

Where do visitors leave the
site?

Top exit pages (GA)
. Arethere error pages (e.g. 404 not found) that cause
visitors to leave?
. How can attrition

in conversion funnels

be im

proved?
Which clickstreams are taken?

Path analysis Top paths (GA)

. What does forward path analysis show are the most
effective calls to action?

• What does reverse path analysis indicate about the

pages thatinfluence sale?

Clickstream analysis becomes more actionable when the analyst reviews clickstreams in the
context of a single page - this is forward path analysis or reverse path analysis.

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On-site search effectiveness

On-site search is another crucial part of clickstream analysis since it is a key way of finding
content, so a detailed search analysis will pay dividends. Key search metrics to consider are:
• number of searches;
.

.

.

average number of searches per visitor or searcher;

% of searches returning zero results;

• % of site exits from search results;
• % of returned searches clicked;
• % of returned searches resulting in conversion to sale or other outcome;
• most popular search terms - individual keyword and key phrases.

Reverse path analysis
Indicates the most popular combination of pages and/or calls to action that lead to a

page. This is particularly useful for transactional pages such as the first checkout page
on a consumer site; a lead-generation or 'contact us' page on a business-to-business site;

an email subscription page or a call-me-back option.

Visitor segmentation
Segmentation is a fundamental marketing approach, but it is often difficult within web an
alytics to relate customer segments to web behaviour because the web analytics data aren't
integrated with customer or purchase data, although this is possible in the most advanced sys
tems such as Adobe Analytics, Sitecore and Mixpanel.

However, all analytics systems have a capability for some segmentation and it is possible to
create specific filters or profiles to help understand one type of site visitor behaviour. Examples
include:

. First-time visitors or returning visitors

• Visitors from different referrer types including:
- Google organic
- Google paid

- Strategic search keyphrases, brand keyphrases, etc.
- Display advertising
. Converters against non-converters

• Geographic segmentation by country or region (based on IP addresses)
• Type of content accessed, e.g. are some segments more likely to convert? For example,
speaking at Ad Tech London '06, MyTravel reported that it segments visitors into:

- Site flirt (two pages or less)

- Site browse (two pages or more)
- Saw search results

- Saw quote

- Saw payment details
- Saw booking confirmation details.

Budgeting
To estimate profitability and return on investment of e-channels as part of budgeting, compa

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nies need to consider both tangible and intangible costs and benefits.
A similar approach can be used to calculating the ROI of enhancements to an e-commerce
site. Hanson (2000) suggests an approach to this that requires identification of revenue from

the site, costs from site and costs from supporting it via a call centre. These are related to profit
as follows:

Operatingprofit = Net income from sales - E-commerce site costs-Call-centre costs
Net income from sales = (Product price - Unit cost) x Sales - Fixed product costs
E-commerce site costs = Site fixed costs + ((% site support contacts) x Cost site support
contact x Sales)

Call-centre (CC) costs = CC fixed costs + ((% CC support contacts) x Cost CC support con
tact x Sales)
Different approaches for estimating costs are recommended by Bayne (1997):

. Last year's Internet marketing budget. This is assuming the site has been up and run
ning for some time.

• Percentage of company sales. It is again difficult to establish this for the first iteration of
.

a site.
.

Percentage of total marketing budget. This is a common approach. Typically, the per
centage will start small (less than 5%, or even 1%), but will rise as the impact of the In
ternet increases.

• Reallocation of marketing dollars. The money for digital marketing will often be taken
by cutting back other marketing activities.

• What other companies in your industry are spending? This is definitely necessary in
order to assess and meet competitive threats, but competitors may be over-investing.
.

Creating an effective online presence. In this model of 'paying whatever it takes', a

company spends sufficient money to create a website that is intended to achieve their
objectives. This may be a costly option, but for industries in which the Internet is having
a significant impact, it may be the wise option. A larger-than-normal marketing budget

will be necessary to achieve this.
• A graduated plan tied into measurable results. This implies an ongoing programme in
which investment each year is tied into achieving the results established in a measure
ment programme.

• A combination of approaches. Since the first budget will be based on many intangibles, it
is best to use several methods and present high-, medium- and low-expenditure options
for executives with expected results related to costs.
As a summary to this section, complete Activity 10.2.

Activity 10.2

Creating a measurement plan for a B2C company

Purpose
To develop skills in selecting appropriate techniques for measuring digital business
effectiveness.

Activity

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This activity acts as a summary to this section on digital business measurement.
Review Table 10.5 and assess the frequency with which metrics in each of the following
categories should be reported and acted upon for a sell-side e-commerce site. For each

column, place an R in the row for the frequency with which you think the data should be
recorded.

Table 10.5

Alternative timescales for reporting e-commerce site performance

Promotion

Behaviour

Satisfaction

Outcomes

Profitability

Hourly

Daily
Weekly
Monthly

Quarterly
Re-launch

In Chapter 1, we started this text with a case study of the world's largest digital business,
which has transformed the taxi industry. In this last chapter we offer the case of the world's

second-largest online retailer, showing how the growth hacking culture of test, learn, refine is
key to its success.

Case Study 10.2

Learning from Amazon's culture of metrics

Context
Why a case study on Amazon? Surely everyone knows about who Amazon is and what it does?
Yes, well, that may be true, but this case goes beyond the surface to review innovations in
Amazon's business and revenue model based on a historical review from its published annual

reports (United States SEC filings).
Like eBay, Amazon.com was born in 1995. The name reflected the vision of Jeff Bezos to

produce a large-scale phenomenon like the River Amazon. This ambition has proved justified
since, just eight years later, Amazon passed the $5 billion sales mark - it took Wal-Mart 20 years
to achieve this.

Vision and strategy
Amazon's mission statement centres around its customers, providing them with a place where
they can search and discover anything they may wish to buy online. This is a fairly generic
statement, but previous statements (i.e. from the SEC filings in 2008) are more specific.

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