W

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Step

Change

Web Analytics

in

Jaisri Chety

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 perceived

by SEM

Off-siteResources Email marketing

Affiliate programsBehavioral Targeting

Paid search managementBanner advertisingCall center referrals

Search Engine OptimizationOffline marketing to webIn-store Web promotion

On-siteResources

On-siteResources

Registration optimizationSite testingWeb analyticsUsability testing

Large gap in off-site and on-site spending…

Off-siteResources

On-siteResources

Large Investment Gap

On-site engagement determines conversion success

Off-siteMarketingSpending

On-siteExperienceDetermines Conversion

Rate

Campaign Traffic

Email marketingAffiliate programsBehavioral Targeting

Referred Traffic

Paid search managementBanner advertisingCall center referrals

Direct Traffic

Search Engine OptimizationOffline marketing to webIn-store Web promotion

Campaign Landing Pages Home Page

Successful conversion

Conversion Process

Product Category Pages

Attrition losses

Attrition losses

CriticalEngagement

Layer

How automated 1 to 1 targeting works:

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 visitorSelf-learning

Predictive Modeling Engine

Optimalcontent decisionsent to CMS

Content library

How did this visitorarrive here?

Have they alreadyexpressed

what they want?

If we could answer a few questions, we could determine what page to serve to each customer

What is this visitordoing now?

Whathave they done

before?

Where is this visitorLocated?

What istheir online

experience like?

Offline Customer Variables

When is this visitoccurring?

How frequently &recently havethey visited?

Highly

predictive

anonymous

visitor profile

Referring domainCampaign IDAffiliatePPCNatural search

Search keywordsDirect/bookmark

Referrer Variables

What data is used to select the relevant offer?

Customer/prospectNew/return visitorPrevious Visit patternTools usage

Previous Product interestsSearches Previous online purchasesPrevious Campaign exposurePrevious Campaign responses

Site Behaviour Variables

IP addressCountry of originTime zoneOperating systemBrowser type

Screen resolution

Environment Variables

Offline Customer Variables

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 logoff page

Temporal targeting

3:15 PM

1:45 AM

Why Web Analytics 2.0

is the inevitable response to the changing InternetA reflection that: Page views are becoming less relevant as a

fundamental measure on some sites Quantitative data alone doesn’t tell us enough about

visitor engagement The browser wars are starting over again, this time

on mobile devices Available reporting mechanisms are increasingly

inadequate 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 outcomes

need to be measured to see if the site is really driving the desired outcomes.

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 to analytics data and may reveal customers’ true motivations.

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

Customer Experience Management

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 determine whether 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 a

geographic 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 at

home? Can the visitors location or demographic profile be used to

disambiguate search? Which ads work best based on the visitors phone browsing platform

and 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 to their customers

And how will we help them take advantage of these new opportunities

Source

Improving Customer Acquisition through Analytics - Brent Hieggelke

CUSTOMER EXPERIENCE MANAGEMENT ND WEB ANALYTICS From KPIs to Customer Transactions - Eric Peterson

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

Questions

I would also cover the 3 step changes in detail in my blog - web-scapes.blogspot.com

If you want any clarification or want to post questions on the same please feel free to post it as comments in the blog as above or mail me at jaisrichety@gmail.com

Thank You