Digital Traffic Attribution & User tracking : A Basic Framework

Schematics of the Attribution technology:  on digital marketing.

The last time I wrote on attribution. Got a few requests on how the attribution technology should work. Thought I should BLOG it down for people to develop own technology, rather than hiring it at exorbitant pricing.

Things required before getting to develop the product.

1. Knowledge on Cookie, super cookie.
1. Cookie length Vs Cookie Validity
2. Referring URL
3. Traffic Quality 
1. Source 1 Vs Source 
2. Paid Vs Free 
4. Business Rules
1. First Referrer vs Last Referrer
2.  Many referrer 
5. Multi Channel Digital Channel 
1. Media 
2. Search 
3. Affiliate 
4. Mailer 
5. Social
6. Direct

Why is attribution so important on the multi channel digital marketing strategy. Some of the reasons for this are as follows.

1. Large media monies are being spent to get customers on the online space for
1. Brand popularity
2. Transaction
3. Reach
4. Frequency
5. Top of the mind recall etc etc
 
2. To provide the logical information to marketer about what channel is working
3. Performance is the key for a digital marketing campaign to be measured.
4. To know what worked best for the brand/ product / service on the digital channel.

One of the most interesting things to know is the fact that most companies that are in the eCommerce space in India don’t have this in place and don’t seem to have any possible solution to develop it.

Issues a marketer faces in attribution if the technology is not good are as follows:-

1. Multiple refers show us as claimants to a particular interaction
2. Allocation become an issue if the traffic provider is a paid one
3. User journey cannot be defined and hence measured at various steps
4. Budgeting for next campaign becomes an issue as you don’t know what worked the best for you.

So here goes my attempt into developing a schematic to a digital marketing attribution/ user tracking technology.

The landscape 

The advertisers landscape for attribution

The user Journey


Stage 1 : Research. 

The main thing to notice over here is the fact that the user might choose to engage with the brand using many channels or only one, but for a fact he will use two or three channels during his research work before buying a product . So it is important to allocate this user initially to the source of his first visit. Then with the same cookie id keep on updating his profile, add various data points like IP address(this way you can build the users profile over a period of time, city, service provider etc etc.)  


Now once the users moves from this bucket into the consideration set, you can add the  following data to the same cookie id. 


Stage 2/3 : Consideration / re-evaluation 

Product being the main, keep on adding the products that he browses thru, along with date &  time of the visit (again adding more to his user profile). Within this set of data, I am sure you would also capture the source of the user, the referrer, the publisher etc.
The source data / referrer data will be helpful later when we will look at the sales attribution. (Which the topic is all about). 

Stage 4 : Purchase  

In the sales stage of the user journey, just add the product purchased data to his profile in the same cookie id.


This is important as this is also one of big mistake large eCom companies do when they are doing cookie tracking on repeat visitor, they continue to show even the purchased product in banners when the users is being re-targeted on re-targeting networks out side the site or they even keep on throwing up the same product in the consideration set when the user is on the site again, thus adding nuisance value to the user's website journey.


Store the information both ways, If the user has purchased the product then store all the cookie id information into his login id., for future reference and his loginid details into the cookie id, enabling you to target or re-target him better. Next time the user pops onto your site or on your re-targeting network.


Important thing to note over here is that users who either delete cookies or come back after the expiration of the cookie validity period can be found if they again log into the system, enabling advertisers to offer a more customized product offering to them. 


Coming back to attribution of sales to the media channel.


Now that we have the details no the user and the road he used to travel to the site.

Now based on the business logic. first referrer wins or last referrer wins scenario you can attribute sales. 

Also if you organization believes in fairly compensating all  the parties in the user journey then also this data set would help in doing so.

This technology if developed can enable the advertiser to also do the following.

1. Fire tracking pixels of media channels based on business logic.
2. De-dupe sales data across media channels.
3. Find effective data for more efficient buying on the digital media.
4. provide some piece of mind to its eCom managers, doing Multi channel.

Will be happy to receive your feedback on the same, or if you feel I have missed out on some thing(s) please send me a mail or comment, will try to help and resolve your attribution issue.