What to action on in a Page that changed

A page is very important in e-tail. It is not any other webpage. The more the visits to a page, the more important it is in the purchase path - and the higher is the impact that can be delivered.

No wonder merchandising teams are trying out different versions of the page. These are usually based on intuition & experience. It is the job of the analytics teams to work with the merchandising teams to ensure these decisions are based on data.
Unfortunately analytics role today in the world of simple web tools is just about providing visits and leakage information for the page OR running a simple A/B test (resulting in 2.5% upside which when annualized becomes a big number). The role of analytics should be much more than analyzing upside and annualizing the results of the A/B tests. Analytics should be able to get back and say what drove the upside/downside. I am presenting an approach that will help the same.

1. Starting Point: Make a list of levers that the merchandising teams have in changing the page
The levers fall into 2 broad buckets
a. Layout Changes
What changed in the layout? Some sample list could be
i. Rotating Banner instead of a static one OR position of Banner Changed
ii. New Links added, Old links removed
iii. Links rearrranged etc.

b. Feature Changes:
i. Page links to a page with a new feature

Step 2a: Create a segment to isolate the entries to the page
Step 2b: Create separate segments to isolate next clicks to pages from the page X
This is where it is imperative that you have a real web analytics tool at hand. You cannot do this in basic tools like SiteCatalyst or CoreMetrics or Discover. You need Insight/Visual Sciences

Step 3: Repeat Step 2 for the page 'before' and 'after' if you are doing an anachronistic analysis or repeat for the versions of the page being tested

Step 4: Tabulate as follows:


Cursory look reveals that for all landings on the page,
* Overall conversion of page improved
* Next clicks were fairly similar
* Bounces were little reduced
* Newly added Page E doing well
* Page A with changed features doing well

Now this can be translated as per our step 1 framework as follows:

Clearly upside in conversion is coming from the new Page E visits, from changed features on Page A & increased visits to Page A that has higher conversion. So business knows what improved

Advantages of my method.

1. Upside might not be coming from the changes to Page X that is being tested but rather because it leads to page Y with a new feature.
In such cases, my method captures the source of the changes
2. In cases where the page does not work, the merchandising & Design teams know what exactly to go and fix

If you need the raw xls for the analysis or help in how to set these things up in a web analytics tool like Insight, get in touch with me at rkirana@gmail.com

Comparison of ODG Vehicle Effectiveness

Continuing on the last post: http://webanalyticsnuggets.blogspot.com/2010/06/online-marcom-vehicle-characteristics.html
that provided an overview of each ODG vehicle, in this post we compare the different ODG vehicles on parameters like:
a) Costing Method
b) Cost of Compaign
c) Primary Effectiveness Factors
d) Tertiary Effectiveness Factors

Here we go :)

				Effectiveness Factors
SI#	Online Marcom Vehicle	Costing Method	Campaign Cost ($)	Primary	Secondary
1	Banner	Per Clickthrough	High	$ Spend	Choice of Site where banner placed, Messaging on the banner, Landing Page Matches Offer on Banner
2	High Impact Placement	Per Clickthrough	Very Very High	$ Spend	Choice of Site where HIP placed, Messaging on the HIP, Landing Page Matches Offer on HIP
3	Email	Close to 0 for sending email; Creative & Modelling Cost	Low for execution	Number of email drops	Choice of right audience; Sending right offer to right audience
4	Affiliates	Per Purchase, Usually 5-10% of the purchased amount is retained by affiliate	Medium - payment hits margins hard on purchases	Offer attractiveness	Choice of site where offer placed, Landing page showing same offer as what was placed
5	Product Listings	Per Purchase, Usually 5-10% of the amount is retained by the lister	Medium - payment hits margins hard on purchases	Offer attractiveness	Choice of site where offer placed, Currentness of Offer
6	Paid Search	Per Click	Medium	Keyword Choice & Placement	Effective Bidding Strategy with Google/Yahoo (Don't bid on keywords that you can migrate to natural search); Effective Landing Pages
7	Traditional Mass Media e.g: Newspaper Ads, Print, TV	Per Advertisement (e.g: for an ad on the front page, in minutes for TV advertisement)	High	Tracking Difficult	Intuition of the traditional marketing/brand manager :) ;)
8	Social Media & communities (SMAC)	Free for ads on your site, Pay for ads on other site	Medium	Create effective SMAC pages	e.g: Good Facebook Site for your company that others want to visit

Online Marcom Vehicle Characteristics

So you are the Marcom manager trying to understand which vehicle to spend money on?
Or the marcom analyst trying to understand what are the factors that drive each marcom vehicle? Then this post is for you.

Online Marcom Vehicles can be classified into 2 buckets
Bucket 1: Those that are influenced PRIMARILY by SPEND

Banners:
Example: On a little known newssite www.ndtv.com there is a banner on the right (linking to electronics giant Siemens) and a banner on the top (linking to travel site tripadvisor.in)
You are likely to get more visits from the sites you place banners on WHEN you spend more


High Impact Placements (HIPs)
High Impact Placements (HIPs) are banners on 'extremely high' traffic sites like msn.com
Below is banner for netflix on msn.com; NetFlix will get more visits by advertising on msn.com as compared to ndtv.com; However the costs of this are huge and by the time you go to msn.com, the netflix HIP is probably gone !!

Bucket 2: Those that are influenced PRIMARILY by ACTIONS

Email:
Email is a marcom vehicle heavily influenced by the time of drops. Usually a mining algorithm run in the backend scores prospects on their likelihood to respond/make a purchase - and targeted emails are sent to this sample
These are heavily influenced by the drops - If there are a large number of drops in a particular week say 'days of deals' visits from email would be huge

Emails cost nothing to send - the only cost involved is cost of making the creatives



Bucket 3: Those that are influenced PRIMARILY by OFFER ATTRACTIVENESS

Affiliates:
Mr X has a popular blog on computers. Samsung wants to advertise there.
There are 25 other similar blogs by folks like Mr X :). Can a large company like Samsung actually go and negotiate rates/service adherence with all these 25 folks.

Now there are many other sites where Samsung would want to advertise. E.g: Places where folks get coupons e.g: Couponmountain.com,.....etc

In these cases, the companies would tie up with an affiliate network site like www.linkshare.com, www.befree.com, www.commissionjunction.com. Linkshare, BeFree, CommissionJunction - one of these would be contracted by a giant like Samsung to handle affiliate marketing. These sites would receive a portion of the payment that the end users get.

Example: Say somebody used a coupon from www.couponmountain.com and made a purchase from Samsung (ASsuming Samsung contracted Linkshare as its affiliate vendor and CouponMountain is one of the many that Linkshare engages) worth $150, out of the approximately 5-10% commission that couponmountain gets which would be between $7 and $15, a small portion would go to Linkshare.


Product Listings:
Product Listings are sites that carry your list of offers - pricing comparison sites.
Example is Deals2buy.com -> They have daily changing deals of many companies one below the other.

In deals2buy site, today, there are offers of both Samsung & canon - competitors - one below the other



In both Product Listings & Affiliates, the primary driver is "offer attractiveness" - it maybe features or maybe price


Bucket 4: Those driven primarily by PLACEMENT and CHOICE

Paid Search:
Google - "no evil" - for the user of the product - for the one that wants to see search results. But definitely not "no evil" for the advertiser. Google monetizes its wonderful search engine through advertisements.

Example: A search on "used cars" yields lot of results. However there are 2 portions marked in red, one above the search results and one on the right - called "sponsored links" -> this is an example of PAID SEARCH



The ones in the red have actually paid to be there. The ones that bid the most got on top and similarly the ones below.
If you are a seller of used cars like carwale.com, your traffic from paid search is going to be determined by:
a) Placement: Where you were placed
b) Choice: What keyword you bid on

Example: might have been better for you to have bid on a keyword of suzuki used car versus ford used car in India


Bucket 5: Traditional Marcom Media

These include TV advertisements (No way to track effectiveness easily and I am dead against using traditional mass media for e-comm sites - Ibibo.com does this and I hope somebody drives it to them :)

Also include catalogs, print media etc mailed to these folks.

No way to track effectiveness and also very expensive. I am against these

Bucket 6: New-age Social Media & Communities
These include the world of facebook, twitter, orkut......

Marcom Attribution Problem

Online Demand Generation/Marcom spends form an important portion of the Internet Marketing budget. The budget has to be spent wisely to ensure that the right funnel inputs get rewarded. Rewarding wrong vehicles can result in lower revenue/value for the company. Optimizing marcom spend is necessary to ensure firm value is maximized.


Case 1: Which vehicle should get the credit in the multi-visit multi-vehicle case?
Peter visited www.evogear.com on Monday from a banner advertisement
He visited on Tuesday from an email advertisement
He visited on Thursday from paid search
Finally he came directly on Friday by typing URL in the browser and made a purchase.
Who should we give the credit to?

Case 2: Upto how many days after visit should a marcom vehicle get credit?
Suppose Peter came from an affiliate site to www.evogear.com on Monday March 1st.
Suppose he bought on March 15th. Should you pay the affiliate site?

Affiliates usually charge 4-5% of the revenue that results from purchase and the payment model varies from 30 - 90 days after first visit depending on the equity between site and affiliate (Porter here :) -> Supplier Power/Vendor Power)

Knowing how your purchasers usually buy and what vehicle touchpoints they take is an important step to undertake before paying the ODG vehicles - that gives you good negotiating power and helps strike good deals
I will tackle case ii) in this post and case i) in a separate one.


Tackling case ii) - What is the ideal number of days to consider?

To find: How many purchases happen 1, 2, 3, 4, .........n days after the person came from Campaign. Say I want to find effectiveness of affiliates

Method: Usage of latency feature in Omniture Insight/Visual Sciences. Will outline this in a separate post