The Crisis in Chicken

A dive into the cost of living crisis’ impact on the general buying power of the average household across the UK.

In this blog, we show how geo-contextual indices can be used to demonstrate where the impacts on buying power have been greatest.

The impact the cost of living crisis has had on the price of chicken

Here at Kogenta, we believe in location based, geo-contextual solutions for Advertisers – the future of a cookieless world. However, we also see that the same geo-contextual data can have real life ramifications and even offer incisive, amusing and distinct insights on the everyday lives of the people of the United Kingdom.

Kogenta’s data portfolio hosts a wide range of granular attributes: KCI’s, or Kogenta’s Contextual Indices. The granularity of these attributes goes as far as considering Household Expenditure on particular food groups, for example – Chicken.

Many popular fast food chains have been through chicken-induced financial jeopardy in recent years, suffering with a lack of accessibility to poultry produce and therefore sustaining mass financial loss due to their economic dependence on the product. In 2021, popular restaurants such as KFC and Nandos, struggled significantly to keep their restaurants full of chicken. Zinger burger and wings without the zing or the wing anyone? Richard Griffiths, the chief executive of the British Poultry Council, stated that the members of his association were forced to “cut back weekly chicken production by 5-10%, year-round turkey production by 10% and Christmas turkey production to be cut by 20%.” The ramifications of the major delays in food processing were caused by a plethora of issues; such as Brexit, Covid and labour shortages. Griffiths went on to say that these issues leave the British public stranded and ‘being forced to rely on more imported food… this can only lead to a two-tier food system where high-quality British food may be out of reach for struggling consumers.’

Kogenta can see how these shortages have affected the country on a very real level. Our Kogenta Explorer Platform allows users to gain geo-contextual insights through the KCI data, leaving a clear picture of a certainly fractured society:

This image demonstrates an indexed view at the gross household expenditure on poultry products – where the national average is represented by a score of 100. Households in the lightest blue section spend up to 68% less than the national average. Similarly, the areas in the highest range indicate up to to 75% higher than the national average.

A first glance of the data evidently depicts a large disparity between the North and South. The regions with the highest household expenditure are, as suspected, predominated by London, the Home Counties, and the generally regarded as wealthier areas of the West Midlands – Warwickshire and Chesire, for example. Chicken may seem an odd choice to indicate the disparity of the poverty line and its very real effects, but upon further examination of chicken as a category of produce. Chicken historically connotes cheap, healthy, easy produce, and indeed is the main protein in the UK’s National dish – the humble Chicken Tikka Masala. Additionally, the commercial success of restaurants like the aforementioned KFC and Nandos delegates chicken as a fundamental staple of every UK household.

Using Kogenta’s data and geo-contextual mapping, it is also possible to view households in the United Kingdom exclusively by Household Income (HHI), of which we can see a similar pattern:

Here we have a correlation between the propensity to spend on chicken and areas with a lower HHI index, depicting a very similar heat map. So using the data, we can come to the probable and, unfortunately, predictable assumption that the cost of living crisis is undoubtedly hitting the poorest households the hardest.

The data we collect at Kogenta allows us to draw such specific comparisons, and even though these general assumptions may be predictable, the granularity of the data permits for a more specific analysis, picking out areas that under/over index, the extremes. This allows us to analyse and identify any surprising outliers that stand out from the national average.