M-commerce is the next best thing to work on. Now the problem is how we diagnose which ad to be placed with which user. This brings to a thought pondering exercise. Here are some golden rules:
1. Find out the social habits of the person through social networking sites
The social networking platform is a good site for diagnosing the basic needs of the person. The forums he uses, the forum he maintains or creates, his social image as presented- all this should give enough pointers to know about the character sketch of the person.
2. Find out the spending habits of the person through the credit card usage
Basically we are trying to find out if he is an impulsive buyer or a logical negotiator. The number of withdrawals from the ATM in terms of frequency and volume should give the nature of his spending. Any company would like to target more of the impulsive buyers, then ending up at the dead end of rationality.
3. Find out the social strata of the person through personal data
This gives the basic economic strata he is in for. The classes define the spending habits as well as the segmentation of the customers on the basis of the income of money.
4. Find out the surfing habits of the person
This leads to the specific ad that a person needs to target to.
5. Find out his hobbies and interests
Obviously, a person with a strong habit would like to spend more on the specific mobile ads.
Mostly in other countries, these things can be tracked via SSN of the country. But in India, these are fragmented. Here comes the concept and use of UID.
The following segmentation of customers can be done based on age, income, surfing habits and interests.
By age, we can see that 0-15, 16-22, 22-32, 32-50, 50-65, 65 and above can be a good segment based on rationality.
Similarly, income levels of 0-3.6, 3.6 to 6.0, 6.0 to 10.0, 10.0 to 18.0, 18.0 to above can be a good segment.
Also, surfing habits can be dumped using the database and then BI tools can be used to run the efficient method of maximum pages hit in a week to find out the surfing habits. If a person buys more things like gadgets online, ads should be of that nature. Simple reasoning would be used to target those specific customers. Similar trend is found suitable in the credit card usage for a person. More of the cause is devoted to the interest levels of that person.
Once the proper segmentation is done, the buying habits can be used to determine the ads for that particular person. More specific and meaningful ads to generate more revenue per volume of ads sent.
Again, the effectiveness in case of companies doing the same has to benchmark against each other from total revenue and revenue per volume of the ads sent.