Industry size of wallet

1. A method for modeling consumer spend by industry, comprising:

calculating, by a computer-based system for modeling consumer spend comprising a processor and a tangible, non-transitory memory, a size of wallet for each consumer in a plurality of consumers, wherein the size of wallet is calculated by a method comprising:

modeling, by the computer-based system, spending patterns using individual and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data;

estimating, by the computer-based system, credit-related information of each consumer based on tradeline data of each consumer, previous balance transfers of each consumer, and the model of consumer spending patterns to arrive at estimated credit-related information, wherein the credit-related information comprises a spend amount associated with the individual consumer; and

offsetting, by the computer-based system, the previous balance transfers from the estimated credit-related information;

calculating, by the computer-based system, a share of wallet for each consumer;

determining, by the computer-based system, a subset of the plurality of consumers whose share of wallet is above a given percentage of their size of wallet;

determining, by the computer-based system, an industry size of wallet for each consumer in the subset using a fixed weighting factor and a graded weighting factor in conjunction with lifestyle variables comprising a location rank, a length of each consumer's tenure with a credit bureau, each consumer's gender, and each consumer's household size, wherein the graded weighting factor varies, in accordance with the value of at least one of the lifestyle variables; and

deriving, by the computer-based system, a correlation between an industry size of wallet of a given consumer and one or more characteristics of the given consumer using the industry size of wallet for the consumers in the subset.

2. The method of claim 1 , wherein:

calculating a share of wallet comprises calculating a share of wallet associated with a given financial institution; and

determining a subset of the plurality of consumers comprises identifying consumers whose share of wallet associated with the financial institution is greater than approximately 90%.

3. The method of claim 1 , wherein determining an industry size of wallet comprises:

determining the amount of spend within the industry using one or more accounts associated with a financial institution; and

equating the amount of spend within the industry with the industry size of wallet.

4. The method of claim 1 , wherein the characteristics of the consumer include at least one of:

total size of wallet of the consumer;

residence location;

credit bureau tenure;

age;

gender;

household size; and

number of active transaction cards in a household of the consumer.

5. The method of claim 1 , wherein deriving a correlation comprises:

identifying consumers having substantially similar industry sizes of wallet; and

examining spend habits of the identified consumers to ascertain common characteristics that influence spend in the industry.

6. The method of claim 1 , wherein the industry is one of a travel industry, a restaurant industry, or an everyday spend industry.

7. The method of claim 6 , wherein the industry is an airline industry, a lodging industry, or a vehicle rental industry.

8. The method of claim 1 , further comprising:

developing a model based on correlations between the industry size of wallet and the characteristics of the consumer.

9. The method of claim 8 , wherein developing a model comprises:

assigning a weight to each of the characteristics.

10. The method of claim 9 , wherein the characteristic is a residential location, and assigning a weight comprises:

computing an average industry size of wallet per zip code using a zip code and industry size of wallet for each consumer in the subset; and

assigning a weight for each zip code based on the average industry size of wallet per zip code.

11. A method of targeting consumers, comprising:

calculating, by a computer-based system for targeting consumers comprising a processor and a tangible, non-transitory memory, a total share of wallet associated with a financial institution for one or more consumers;

estimating, by the computer-based system, an industry size of wallet of each consumer, wherein the industry size of wallet of each consumer is calculated by a method comprising:

modeling, by the computer-based system, spending patterns using individual and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data;

estimating, by the computer-based system, credit-related information of each consumer based on tradeline data of each consumer, previous balance transfers of each consumer, and the model of consumer spending patterns to arrive at estimated credit-related information, wherein the credit-related information comprises a spend amount associated with the individual consumer; and

offsetting, by the computer-based system, the previous balance transfers from the estimated credit-related information;

calculating, by the computer-based system, an external size of the industry size of wallet of each consumer using a fixed weighting factor and a graded weighting factor in conjunction with lifestyle variables comprising a location rank, a length of each consumer's tenure with a credit bureau, each consumer's gender, and each consumer's household size, wherein the graded weighting factor varies in accordance with the value of at least one of the lifestyle variables; and

targeting, by the computer-based system, one or more consumers having a relatively high external size of the industry size of wallet and a given minimal total share of wallet with offers to increase their industry share of wallet associated with the financial institution.

12. The method of claim 11 , wherein estimating an industry size of wallet of each consumer comprises:

calculating an industry size of wallet using characteristics of the consumer that are indicative of industry spend.

13. The method of claim 12 , wherein calculating an industry size of wallet of each consumer comprises:

assigning a weight to each characteristic of the consumer based on an industry size of wallet model; and

totaling the weighted characteristics for the consumer to produce an industry size of wallet for the consumer.

14. The method of claim 11 , wherein calculating an external size of the industry size of wallet of the consumer comprises:

subtracting industry spend of the consumer associated with the financial institution from the industry size of wallet of the consumer.

15. The method of claim 14 , wherein calculating an external size of the industry size of wallet of the consumer further comprises:

determining the amount of industry spend of the consumer associated with the financial institution from internal records of the financial institution.

16. The method of claim 11 , wherein targeting one or more consumers comprises:

targeting the consumer with an offer for a new product to encourage new spending with the financial institution.

17. The method of claim 11 , wherein targeting one or more consumers comprises:

targeting the consumer with an incentive to increase spending on an existing product associated with the consumer with the financial institution.

18. A computer readable storage medium bearing instructions, the instructions, when executed by a processor for modeling consumer spend by industry, cause said processor to perform operations comprising:

calculating, by the processor, a size of wallet for each consumer in a plurality of consumers, wherein the size of wallet is calculated by a method comprising:

modeling, by the processor, spending patterns using individual and aggregate consumer data, including tradeline data, internal customer data, and consumer panel data;

estimating, by the processor, credit-related information of each consumer based on tradeline data of each consumer, previous balance transfers of each consumer, and the model of consumer spending patterns to arrive at estimated credit-related information, wherein the credit-related information comprises a spend amount associated with the individual consumer;

offsetting, by the processor, the previous balance transfers from the estimated credit-related information;

calculating, by the processor, a share of wallet for each consumer;

determining, by the processor, a subset of the plurality of consumers whose share of wallet is above a given percentage of their size of wallet;

determining, by the processor, an industry size of wallet for each consumer in the subset using a fixed weighting factor and a graded weighting factor in conjunction with lifestyle variables comprising a location rank, a length of each consumer's tenure with a credit bureau, each consumer's gender, and each consumer's household size, wherein the graded weighting factor varies in accordance with the value of at least one of the lifestyle variables; and

deriving, by the processor, a correlation between an industry size of wallet of a given consumer and one or more characteristics of the given consumer using the industry size of wallet for the consumers in the subset.