Sales Performance Forecasting

Identifying market potentials set against competitor pressure

IKEA India faced challenges in accurately predicting sales potential for home appliances and furniture due to limitations in existing data at the PIN code or city locality level.

KENTRIX provided a solution by mapping household/residential building data, including income, expenditure, and consumer lifestyle segmentation, into a geospatial sales value map. Using machine learning algorithms and consumer profile data, KENTRIX calculated expected annual spending on furniture and home accessories at a residential building level.

Thematic maps were created to visualize turnover and competitor pressure, helping IKEA India analyze sales outlets and identify optimal locations based on customer potential, competitor influence, and site accessibility.

The Challenge

IKEA India was looking to understand precise spending on home appliances and furniture category on residential building precision to assess their outlet locations’ catchment area sales potential. The company understands that PIN code or city locality–level data does not suffice to achieve here the precision of the sales prediction model output as desired.

The KENTRIX Solution

The KENTRIX household/residential building precise mapped information about income, expenditure, and consumer lifestyle affinity segmentation was modelled into a geospatial sales value map applying ML-driven calculations fed by consumer profile data in combination with purchase data in the category home appliances and furniture.

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On residential building precision, expected annual furniture and home accessories spending was calculated using the KENTRIX consumer profile data set. Thematic maps were created for the expected turnover visualization. 

With this, IKEA India sales outlets were analysed in a complex geospatial data equation using customer potential income, shopping affinities, consumer lifestyle affinity, competitor pressure, site accessibility, traffic times, etc.

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Competition sales locations were mapped, and competitor pressure thematic maps were created, overlaying over the potential maps and unfolding the next best location(s) to finalize. 

Other furniture brands and their outlet locations were rated and translated into a competitor influence map identifying competitor pressure on precise subsets of consumers.

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