Thursday, April 15, 2010

Building the ECRM Model: Variables of interest

Usual variables of interest in building an ECRM Model for a segmentation of customers would fall into the following buckets.

A) Source Information
* Did he come from a vehicle we spent marcom $ on OR did he come otherwise?
* What was the source that first got him to the site and what has he used subsequently?
* Which specific marcom vehicle/site did he come from? Which search keyword he used etc

Nail down all the source information

B) Visitor Profile
* How many visits has he made in the past?
* Was there a learning/purchasing cycle evident in the visit pattern? Was there something specific he was looking for? (e.g: Deal/Price Point/Specific Product .. etc)

C) Paths on the site
* What navigation methods in terms of pages seen were used on the site? - E.g: Navigation methods on the left/top bar, internal search, page - groups seen (e.g: Deals pages, Normal non-deals product pages), did he add to cart/save cart, did he try configuring, did he start checkout etc

D) Integrate with non-online channel data
Can we integrate his online information with some of his offline behaviors?
E.g: He might have visited the support site after buying in your commmerce site. The quality of support might determine if he purchases in the future.
Did he visit the retail store or did he try buying through a sales call?
Did he visit competitor sites? (Many of this information is now available on shared marketing sites)

E) Product Affinity
Analyze those who bought - transactional history - to understand product affinity - products likely to be bought together OR products that a segment likes. Increase cross-sell/up-sells based on these.

Based on these it is possible to build a robust eCRM model -> I am building one using SAS and advanced analytics. Will keep posted once I get interesting results or results that are publishable based on a standard methodology

Sunday, April 4, 2010

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