step change in web analytics

7/30/2019 Step Change in Web Analytics

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Step

Change

Web Analytics

in



Participate

This is a highly interactive session; request

all of you to participate with questions,

challenges & solutions…



Web Analytics 1.0

Click Stream data

Visits

Visitors

Geo Targeting

Average time spent

Funnel conversion

Landing page optimization Conversion rates….

In Brief we were looking at the What, When & where questions



What did we miss?



Advent of Web 2.0

User generated content

Content distribution through Rss & Xml

Rich internet applications

Non traditional browsers like iPhone, BlackBerry.

KPIs sans insight Demand for more insights rather than aesthetically

presented numbers/ Ratios.

Achieving marketing ROI with onsite optimisation &

behavior targeting



Change in how Web

Analytics is perceivedby SEM



Off-siteResources Email marketing

Affiliate programs

Behavioral Targeting

Paid search management

Banner advertising

Call center referrals

Search Engine Optimization

Offline marketing to web

In-store Web promotion

On-siteResources

On-siteResources

Registration

optimization

Site testing

Web analytics

Usability testing

Large gap in off-site and on-sitespending…

Off-siteResources

On-siteResources

LargeInvestment Gap



On-site engagement determinesconversion success

Off-siteMarketingSpending

On-siteExperienceDeterminesConversion

Rate

Campaign Traffic

Email marketing

Affiliate programs

Behavioral Targeting

Referred Traffic

Paid search management

Banner advertising

Call center referrals

Direct Traffic

Search Engine Optimization

Offline marketing to web

In-store Web promotion

Campaign Landing Pages Home Page

Successfulconversion

Conversion Process

Product Category Pages

Attrition

losses

Attrition

losses

CriticalEngagement

Layer



How automated 1 to 1 targetingworks:

Visitor arrives at your website

Visitor Profile

Repository

Call goes out toVisitor Profile

Repository

CMS (Serves content)

build profile

First-time visitor

retrieve profile

Repeat visitor

Self-learning

Predictive Modeling Engine

Optimal

content decision

sent to CMS

Contentlibrary



How did this visitor

arrive here?

Have they alreadyexpressed

what they want?

If we could answer a few questions, we coulddetermine what page to serve to eachcustomer

What is this visitor

doing now?

What

have they donebefore?

Where is this visitor

Located?

What is

their online

experience like?Offline Customer

Variables

When is this visit

occurring?How frequently &

recently have

they visited?

Highly

predictive

anonymous

visitor profile



Referring domainCampaign ID

AffiliatePPCNatural search

Search keywordsDirect/bookmark

Referrer Variables

What data is used to select therelevant offer?

Customer/prospectNew/return visitorPrevious Visit patternTools usage

Previous Product interestsSearchesPrevious online purchasesPrevious Campaign exposurePrevious Campaign responses

Site Behaviour Variables

IP addressCountry of originTime zoneOperating systemBrowser type

Screen resolution

Environment Variables

Offline CustomerVariables

Temporal Variables

Time of day

Day of week

Recency

Frequency



Lloyds TSB Initial Page



Profile A



Profile B



Profile C



Profile D



Targeting on the secure logoffpage



Temporal targeting

3:15 PM



1:45 AM



Why Web Analytics 2.0

is the inevitable response to the changing Internet

A reflection that:

Page views are becoming less relevant as afundamental measure on some sites

Quantitative data alone doesn’t tell us enough aboutvisitor engagement

The browser wars are starting over again, this timeon mobile devices

Available reporting mechanisms are increasinglyinadequate

The nature of measurement is changing rapidly



Web Analytics 2.0 is

(1) the analysis of qualitative and quantitative

data from your website and the competition,

(2) to drive a continual improvement of the

online experience that your customers, and

potential customers have,

(3) which translates into your desired outcomes

(online and offline).



Arrive at Insight

1. Clickstream — Typical web analytics.

2. Multiple Outcomes Analysis — All those objective outcomesneed to be measured to see if the site is really driving the desiredoutcomes.

3. Experimentation & Testing — In it’s simplest form, this means

A/B testing the design of your website, including text, graphics,buttons, banner ads, everything.

4. Voice of the Customer — The results can be tied back toanalytics data and may reveal customers’ true motivations.

5. Competitive Analysis — Your competitors may be runningcampaigns or launching products/features that are impactingyour site’s performance (could be either up or down).



Customer ExperienceManagement

The core value of CEM systems is the ability

to capture and report on every interaction a

visitor has with a site.

It is highly diagnostic as it helps to determinewhether the abandonment was audience or

application related.

Pinpoints the true source of the problem



Customer is still the king

Hence understanding the customer/ visitor

behaviour through both quantitative &

qualitative ways are critical.

Tools such as CEM, VOC & Click Stream

give us a complete view of our customer

behaviour.



Web 3.0

The real problem we would all eventually face is

Web 3.0 will be about mobile computing All the same problems …

On smaller screens … With different usability challenges …

Potentially without JavaScript and cookies …

But Web 3.0 will create unique opportunities Every request for information could be tied to a good

unique ID

Every request for information could be coupled with ageographic location



Web Analytics 3.0

Some new questions we’ll be able to ask with Web Analytics 3.0! Which of our stores was the visitor in or near when they came to our

site?

What offers do we have in the visitor’s neighborhood at work or athome?

Can the visitors location or demographic profile be used todisambiguate search?

Which ads work best based on the visitors phone browsing platformand time of day?

What message would be most appropriate given time of day,geographic location, and observed visitor behavior?

Web 3.0 will bring advertisers and marketers closer than ever totheir customers

And how will we help them take advantage of these newopportunities



Source

Improving Customer Acquisition through Analytics - Brent Hieggelke

CUSTOMER EXPERIENCE MANAGEMENTND WEB ANALYTICS From KPIs toCustomer Transactions - Eric Peterson

Multiplicity: Succeed Awesomely At Web Analytics 2.0! - Avinash Kaushik

step change in web analytics

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