Methods, systems, apparatus and articles of manufacture to generate projection weights for a panel

A computer implemented method to reduce panel imbalance errors, the method comprising:

• identifying, by executing an instruction with a processor, a retailer in a geographic region indicative of shopping bias;
• identifying, by executing an instruction with a processor, households of interest in the geographic region having combined panel data, the combined panel data representing core panelist data and auxiliary data;
• calculating, by executing an instruction with a processor, potential spending of the households at one or more stores of the retailer, the potential spending based on observed spending data;
• and
• calculating, by executing an instruction with a processor, projection weights for the combined panel data based on (a) the potential spending at the one or more stores and (b) demographic representation data of the combined panel data.

The computer implemented method as defined in 16, wherein calibrating the potential spending includes:

• calculating potential sales of the one or more stores based on the potential spending of the households of interest and other potential spending of other households in the geographic region;
• calculating potential expenditures of regions of interest in which the households of interest and the other households are located based on the potential spending;
• aligning the potential sales to observed sales of the one or more stores in connection with satisfaction of a first threshold value;
• and
• aligning the potential expenditures to respective observed expenditures of the regions of interest in connection with satisfaction of a second threshold value.

A tangible machine-readable storage medium comprising instructions which, when executed, cause a processor to at least:

• identify a retailer in a geographic region indicative of shopping bias;
• identify households of interest in the geographic region having combined panel data, the combined panel data representing core panelist data and auxiliary data;
• calculate potential spending of the households at one or more stores of the retailer, the potential spending based on observed spending data;
• and
• calculate projection weights for the combined panel data based on (a) the potential spending at the one or more stores and (b) demographic representation data of the combined panel data.