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So far, we’ve talked about the theoretical background of price elasticity in Part 1: How to use them to steer pricing decisions and the practical side of price elasticity in Part 2: Practical implementation and best-in-class dashboards. Today we widen our perspective to include pricing analyses and metrics. Price elasticity is part of that bigger picture of analyses and, as described in the first parts of this Master Class series, when determining to change your price on a product you will need to keep in mind multiple viewpoints, not price elasticity alone.
Why do we need more information besides price elasticity?
Setting ‘the best’ price for a product is not just optimizing the revenue for one product. Previously, we highlighted the need to track the influence of price changes on your own or competitor products; however, one should also keep in mind the positioning of products and brands when changing prices. A mid-range brand increasing their prices might suddenly compete with different brands other than their current competition and even different consumer preferences and brand image.
All these factors should be considered, which is why a pricing study involves more than just a price elasticity curve and determining the best point on that curve.
What are some useful pricing and assortment related dashboards to keep in mind?
Price Range Analysis
One of the closest analyses related to price elasticity is price range analysis. While price elasticities will help you indicate what the effect of certain price changes is on other brands, the price range analyses focus more on the actual pragmatic range of products that have already been seen in the past. Just because a TV can sell hundreds of pieces at a low price, doesn’t mean you or your competitor will do this.
During regular market trends it is reasonable to assume competitors will not go over 10% over/under their previous highest or lowest price as that would typically spark a lot of risk and uncertainty.
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The bottom table shows additional KPIs that give background information, allowing you to see
Insights Plotting the existing price ranges will show more clearly what price points have already |
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Assortment
Assortment is among the most important analyses, and easier ones to include, when you are already doing pricing studies (which commonly focus on electronic point of sales data). You can use that same data to make a ‘virtual shelf’ by dividing all products into price ranges and plotting the product performance (sales or revenue) against those ranges. Commonly, this is done with either price bands (or price tier) bar or column charts (see example below) or with bubble charts where the bubbles represent the products.
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Price tier or price band chart for a typical market The sales are divided into 5 segments (bars) based on price range indices (in this case they
Background The light blue brand is active in Premium and Upper Mainstream, while not having much It is important to note that these charts are not the actual shelf space, but rather the performance |
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What are some interesting analyses you can do with these dashboards beyond their use in relation to price elasticity analysis?
In addition to looking at your current brand/product position, you could also calculate how much volume you might need to gain in one of the price tiers to get a certain market share, and with that, plan to target a specific brand or product currently performing in that segment. For example:
Your company is a newcomer in the market (popcorn) and has set itself the target to have 10% market share in the premium segment this year. You estimate you can sell 1000 products in this first year. The total popcorn market is 10.000 products, meaning at first glance you think this should be achievable. However you are targeting the high-end market, which is only 2.000 products sold on a yearly basis. Suddenly this means that you are aiming to sell 1000/2000 = 50% of the premium segment. That is quite the achievement! To know if this is possible, you need to check the brands currently selling in that premium segment (for example with the price tier chart listed above) to see which brands you need to target.
Doing this exercise with the correct information and dashboards make it instantly clear if the goals are achievable or not, and what the result of certain actions such as promotions are.
Relationship to other KPIs and areas:
The examples given today only scratch the surface of how analyses (especially pricing related) are related and require different views and dashboards to conclude.
That presents us with a big question: how can we define the impact of decisions and compare these with other possible actions? If it is financially possible to either reduce the price of a product or increase its distribution, how do we determine which action to take?
Even if you can calculate the distribution impact, how would you relate this back to sales? And how do you account for market changes?
In our next blog, we will take a step even further back and look at how to compare opportunities and threats; what KPI could give a suitable answer to the above scenario and should it be used in all analyses to compare impact on your revenue?
The above example dashboards are possible with almost any dataset. If you have sell-out data and would like to know more about how to implement price ranges and price elasticity insights, and to leverage those insights to increase your business, contact Harm today or download a sample report here!
