The Factors Affecting How Severely Sales Will Decline When the Price Goes Up

The prime factors are how much buyers need the product and the ratio of the price increase to the buyers’ income

From Pixabay

By David Grace (Amazon PageDavid Grace Website)

For many products, as the price increases the sales volume decreases, so sellers want to know at what point the dollars lost because of the reduction in sales volume will exceed the increase in gross revenues derived from the higher price.

Almost every seller wants to raise its price until it reaches the point where it will generate the most revenue.

If every consumer of every product were identical, it might be possible to predict the reduction in sales volume resulting from a specific increase in the price of a specific product, but because the consumers of every product have different levels of

  • Desire for the product
  • Need for the product
  • Minimum Required Usage of the product
  • Willingness to accept substitutes for the product
  • Availability of acceptable substitutes
  • Net income

it is impossible to craft one algorithm that will accept a product’s price increase as its input and accurately spit out the percentage total sales decline that will result as its output.

Machine Learning

It would be interesting to apply machine learning to this issue, for example, input all the demographic information on 100 zip codes scattered across the country and then increase the price of gasoline, milk, ice cream, hamburger, toilet paper and bananas above a base price by 10%, 25% and 50% for a month each, record the sales volume changes and then decrease those prices by the same percentages below the base prices for another month each, record those sales volumes, and then feed all that data into a machine learning system and see if it could predict the actual sales volumes of those products in a hundred new zip codes in response to identical pricing changes.

Short of that, all we can do is play with subjective ratings to try to come up with very inaccurate predictions of the level of increase or decrease in sales based on each particular market’s demographic and historical data.

Factors Affecting A Sales Decline In Response To An Increase In Price

The Consumer’s Financial Pain From A Price Increase

The sales decline from a price increase varies in relation to the size of the price increase as a percentage of the consumer’s daily take-home pay above the poverty line.

A $25 increase in the cost of one day’s gasoline usage is a significant incentive to decrease the number of gallons purchased to a consumer whose daily net pay above the poverty line is $75, but it is negligible incentive to decrease purchases to a consumer whose daily net pay is $500 above the poverty line.

Suppose that a consumer’s take-home pay is $36,000/year; $3,000/month; $100/day.

The poverty line is about $35/day Minus $100 = $65/day

The ratio of the price increase to the consumer’s take-home pay after deducting the poverty-line daily income is:

Price Increase / (Take-Home Pay — Poverty Level Income)

The Need For The Product

How vital is the product to its consumers?

Put differently, how able AND how willing are the consumers of that product to reduce or eliminate their consumption of that product?

A Must-Have-ALL Of The Product — Insulin

If the product is a life-saving drug like insulin, its consumers both

  • (1) won’t be able to do without it and
  • (2) will be unable to materially reduce the quantity they purchase.

This means that a huge increase in the price of insulin will likely cause only a small drop in sales.

A Must-Have-SOME Of The Product — Gasoline

If it’s gasoline, drivers will pay a much higher price, but they may be able to reduce the quantity they consume so there will likely be a sharper drop off in gasoline sales than in insulin sales for the same cost-per-day increase in price.

A WANT-To-Have-Some Of The Product — Beef

If it’s beef, consumers will want it, but they will be able to live without it, so they will be more willing to materially reduce their consumption in the face of a much higher price.

Are There Cheaper Alternatives?

For a diabetic there are no alternatives to insulin, so insulin consumers will have no option but to keep buying it at pretty much the same level.

There are some less desirable alternatives to a driver’s consumption of gasoline: eliminating non-essential trips; riding a motorcycle or scooter; taking public transit, etc.

There are several alternatives to buying beef: switching to pork or chicken; switching to casserole or stir-fry dishes that have smaller amounts of beef; buying the same quantity of cheaper cuts of beef.

Need & Want Differ With Geography And Custom

The answers to these questions often vary by geography and culture. The average consumer’s level of desire to eat beef is greater in Tulsa than it is Santa Barbara.

The average consumer’s need to purchase natural gas to heat their home is higher in Fargo, North Dakota than it is in Orlando, Florida.

Playing With The Numbers

I decided to try to assign a “necessity” rating to a few products which would combine

  • (1) How much its consumers needed it
  • (2) How able and willing they are to use a cheaper alternative and
  • (3) How able the consumer would be to materially reduce their consumption of the product.

How Vital Is The Product To Its Consumers?

On a scale of 1 (Vital) to 10 (Unimportant), let’s say that the need/desire for these products is as follows:

Completely Subjective Importance Level

How Closely Other Products Can Replace The Original

On a scale of 0 (unavailable) to 10 (highly available) how available is an acceptable cheaper substitute:

Completely Subjective Availability Of Alternate Products Rating

How Much Can The Consumer Reduce Their Volume Of Consumption?

On a scale of 0 (unable) to 10 (highly willing and able) the ability and willingness of the consumer to reduce consumption and/or switch to an alternate product

Completely Subjective Willingness To Reduce Consumption/Switch To An Alternative Product

Adding these three values together yields a Necessity Score times the Price Increase/Take-Home Pay percentage.

Completely Subjective Product Necessity Score Times The Price/Pay Ratio

What Do These Numbers Mean?

If only these numbers accurately represented the decline in sales to consumers whose daily take-home pay minus daily poverty-level income was $65 in the face of a 100% price increase for each of the above products.

So, we ask ourselves:

  • If insulin prices went up from $20/day to $40/day, would the sales volume to diabetics whose take home pay was $100/day actually decline by 3%?
  • If the price of a steak went up from $10 to $20, would the sales volume to people whose take home pay was $100/day actually decline by 34%?
  • For consumers whose take-home pay was $100/day, if the cost of gasoline went up from $10/day to $20/day would the sales volume of gasoline purchases by those consumers decline by half the percentage decline in the sales volume of chocolate-covered cherries purchased if the price increased by $5/box?

Almost certainly not.

These numbers are NOT a real, predictive tool. They are ONLY a tactic to make people think about how need, desire, product alternatives, income, and price interact to cause changes in the sales volumes of different products.

It’s all guesswork. Spit, gum and bailing wire.

Everyone is free to create their own formulas any way they think make sense, but without real-world empirical data they are all just mental exercises.

Short of aggregating machine-learning “black box” prognostications for every zip code in which the producer sells its product, it is very difficult to imagine a methodology that might even grossly predict by what percentage or by how many units a product’s sales volume will rise or fall in in a particular zip code in response to a specific decrease or increase in its price.

Trial And Error

But, without some rough estimation of how sales will change in response to a change in price, producers will not be able to accurately determine in advance if and by how much a change in price will result in an increase or decrease in revenue and profits.

So, prices go up week-by-week and the sellers watch their gross revenue figures for information on when to stop raising them.

Do you think the price increases for insulin were just an accident? According to GoodRX the average price for insulin increased from $.22/unit on January 1, 2014 to $.34/unit on January 1, 2019 (55% increase). As of October 1, 2021 it was $.31/unit (41% increase).

Week-by-week, dollar-by-dollar, the insulin manufacturers discovered that their revenue maximized at about a 41% price increase,

Large landlords, meat suppliers, lumber suppliers, gasoline refiners are just following in the insulin manufacturers’ footsteps with their own price increase experiments.

See my column noting that the link between low excess market capacity and large market-share sellers can result in all sellers increasing their prices to close to the monopoly price even though there are enough units available to meet all customer demand.

— David Grace (Amazon PageDavid Grace Website)

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