Teori Pricing Analisis

Theory of Pricing Analytics

Theory of Pricing Analytics

• Bagaimana manager menentukan harga produk yang mereka jual?

• Beberapa perusahaan besar memiliki tim tersendiri untuk menentukan harga.

•  Penting untuk memeriksa historical pricing dan data penjualan.

Historical Data The best price

Helps to determine

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Apa yang membuat konsumen memutuskan untuk membeli barang? Apa yang membuat konsumen memilih suatu produk diantara alternatif lain?

Penggunaan term consumer utility untuk merepresentasikan value sebuah produk untuk konsumen.

Willingness to Pay

• Adalah harga maksimum dimana konsumen bersedia untuk membeli sebuah barang.

• Contoh: utility sekaleng coca-cola adalah $1 dan utility sekaleng pepsi adalah $0.90 Harga sekaleng coca-cola = $0.75 Harga sekaleng pepsi = $0.50 Konsumen akan membeli produk yang

memaksimumkan the remaining utility setelahmengurangi dengan harga belinya.

Sehingga konsumen akan membeli sekalen gpepsi karena:

($0.90 - $0.50) > ($1.00 - $0.75)

Consumer Search Cost

• Konsumen seringkali paham bahwa brand atau harga alternatif tersedia dilokasi atau channel yang berbeda.

• Contoh: konsumen tahu bahwa ada toko lain di seberang jalan yang menjual sekaleng pepsi seharga $0.40. Apakah konsumen maumencari pepsi tersebut dan menyimpan $0.10?

Konsumenakanmembelipepsidari store yang sama (bukandi store yang berbedasepanjang:

($0.90 - $ 0.50) > ($.090 - $0.40 – search cost)

Search cost merepresentasikankerumitanmencaridanmembeliprodukdilokasi lain.

What makes pricing challenging? •  Utilitas konsumen bisa berubah sewaktu-waktu. •  Faktor-faktor yang dapat mempengaruhi contohnya

adalah season, cuaca, dan sebagainya. •  Different consumers typically have different WTPs

and different search costs (termed “heterogeneous customers).

•  Sehingga the practice of pricing analytics tidak hanya berkembang dari historical pricing performance namun juga realita-realita tersebut.

“In practice, there is always uncertainty about whether a given price is the ‘right’ price yet still, approximating the optimal price is the goal.”

Pricing Analytics adalah sebuah proses yang berulang menggunakan historical price/data sebagai penyesuaian harga untuk memaksimumkan profit.

Caranya! menganalisis trade-off dari harga, volume, dan biaya.

Price analytics telah bergerak menuju term price optimization ->optimisasi berarti mungkin untuk menganalisissebuahharga yang dapat memaksimumkan profit berdasarkan reasonable degree of confidence.

•  Pendekatan pricing analytics ->memformulasikan pricing problems yang bisa diatasi dengan teknik-tenik standar, dimana yang dibutuhkanadalah: ▫  A price-response function yang mendeskripsikan

bagaimana konsumen diekspektasikan untuk merespon pricing actions. ▫  An objective function yang menekankan apa yang

ingin dicapai, misal, maksimisasi profit. ▫  A set of constraints yang mehambat apa yang bisa

dilakukan. (contoh: kemampuan/kapasitas, ketersediaan modal, etc)

The Pricing Analytics Process

Historical Price/Demand Data •  Menunjukkan seberapa banyak demand yang terjadi

for given time period, a given location, &a given price.

•  Care must be taken to ensure the accuracy. •  Contoh: pengujian perlu diadakan untuk melihat

suatu pattern dan apabila ada outlier, perlu diuji apakah outlier tersebut perlu dimasukkan ke data set atau tidak.

•  Contoh: pada sales data, ada saat dimana produk out of stock namun permintaan pada waktu tersebut tidak dicatat.

Estimating and Choosing the Best Price-Response Models • Historical price/demand data selain dapat

menjadi representasi bagus sebagai past consumer demand, juga dapat digunakan untuk mengestimasi alternatif dari price-response functions.

• Alasannya ->belum diketahui price-response function manakah yang menyediakan representasi terbaik dari permintaan konsumen.

Set Objective Function and Constraints and Optimize the Prize • Ketika price-response function sudah

diestimasi, fungsi objektif & hambatan telah ditentukan, optimisasi harga untuk setiap segmen pasar akan typically straightforward.

Optimal Price

Price that results in the best value of objective

function

Required resources remain within the

limit specified

Execute Pricing

•  Perusahaan bisa saja perform exceptionally well pada setiap langkah di proses pricing analytics tetapi jika “the right prices are not presented to the right set of consumers in the right way”, seluruh proses akan otomatis menjadi gagal untuk menyampaikan valuenya.

Monitor and Evaluate Performance • Market feedback terjadi pada dua level: ▫  Pertama, immediate feedback menjadi analisis

alternatif. Efek dari most recent actions harus dimonitor sehingga immediate action dapat diambil. ▫  Level kedua dari feedback mengupdate parameter

dari price-response functions. Cth: jika penjualan dari beberapa produk lebih lambat daripada yang diekspektasi, mungkin mengindikasikan 1 pasar yang lebih cepat = more price responsive sehingg afuture market-response curve bisa disesuaikan.

The Price-Response Function

Linear

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Copying or posting is an infringement of copyright. [email protected] or 617.783.7860.

THEORY OF PRICING ANALYTICS 7

seller as a function of the price offered by that seller (Figure 1.2). This contrasts with the concept of a market demand curve, which specifies how an entire market will respond to changing prices. The distinction is critical because different firms competing in the same market face dif-ferent price- response functions that are the result of many factors, such as the effectiveness of their marketing campaigns, customer- perceived differences in quality, product differences, and location. Hence, the price- response function may be different even for the same product if the product is sold through different channels or outlets.

In a perfectly competitive market, the price response faced by an indi-vidual seller is a vertical line at the market price. If the seller prices above the market price, her demand drops to zero. If the seller prices below the market price, her demand is equal to the entire market. In a perfectly competitive market, the seller has no pricing decision— the price is set by the operation of the larger market.

The price- response functions that most companies face are not neces-sarily linear over the entire price range as shown in Figure 1.2, but they do demonstrate some degree of smooth price response over the entire range of possible prices. As price increases, demand declines until it reaches zero at some satiating price. As price decreases, the demand approaches the maximum market size for that product and location.

Figure 1.2. Linear price- response function.

Uniform

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8 PRICING SEGMENTATION AND ANALYTICS

Choosing the Best Price- Response Function

When choosing a price- response function to fit to the historical price/demand data, you are implicitly making assumptions about customer behavior. It is worthwhile to understand these assumptions so that we can judge which candidate price- response function is appropriate for the application. The most important of these assumptions involves the dis-tribution of the consumers’ WTP. For pricing analytics, we assume that consumers’ WTP has a known distribution, w(p), across the entire popu-lation of consumers. Exactly how consumers’ WTP is distributed across the potential customer population is an important decision in the pricing analytics process. Figure 1.3 shows a distribution of WTP where consum-ers are evenly distributed across the entire range of possible prices. Here, P is the maximum price consumers are willing to pay for the product/service they are requesting.

Note that 0 ! w(p) ! 1 for all nonnegative values of p. Let D = d(0) be the maximum demand achievable. In concrete terms, D is the sales expected to materialize at a price of zero and reflects the market size. We can derive the price- response function, d(p), from the WTP distribu-tion via

d(k) = the demand expected to materialize at a price p = k

= the number of people who have WTP greater than price p = k

Figure 1.3. Uniform WTP distribution.

Normal Distribution

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shown in Figure 1.5. The reason it is called a bell- curve distribution is because the graph of its probability density function looks like a bell. The normal distribution is the most common of the bell- shaped distributions. If P and k were $20 and $10, respectively, one could interpret the WTP distribution shown in Figure 1.5 as saying that there are few consumers with a maximum WTP of $15 or more for the product represented. On the other hand, there is a large percentage of the consumer population with a maximum WTP between $5 and $15.

Now that we have explored a few WTP distribution assumptions, we can explain how the choice of a WTP distribution results in different price- response functions.

Common Price- Response Functions

Choosing and estimating the right price- response function is arguably the most difficult part of any pricing analytics project. In this chapter, we present four commonly used functions. We save for the next chapter a discussion of exactly how to estimate the functions and how to choose the best function.

Figure 1.5. Density of demand at price k from a normal WTP distribution.

Common Price-Response Functions

•  d(p) = D + m . p, • WTP distribution: uniform

Linear Price-Response Function

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Linear Price- Response Function

The linear price- response function shown in Figure 1.6 can be repre-sented by the same familiar equation used for linear regression:

d(p) = D + m . p, (1.1)

where D represents the intercept term, p is the independent variable, and m = –D/P is the slope for 0 < p < P. It is commonly used in practice mainly because it is easy to estimate by applying simple linear regression on the historical price/demand data.

Constant- Elasticity Price- Response Function

A second commonly used price- response function is the constant- elasticity function, which has a point elasticity (defined in the next section) that is the same at all prices. It is based on exponential WTP distribution. The price- response function is

d(p) = C . pε, (1.2)

where C > 0 and ε are parameter values that are estimated by fitting equa-tion (1.2) to the price/demand data. Some sample constant- elasticity price- response functions are shown in Figure 1.7.

Figure 1.6. Linear price- response function.

Constant-Elasticity Price-Response Function •  d(p) = C . Pε • WTP distribution: Exponential

• 

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Both linear and constant- elasticity price- response functions are useful for local demand estimation but are often not realistic global price- response models. When global demand estimation over the entire range of prices is needed, we need a function that is based on the bell- curve WTP distribution shown in Figure 1.5. The price- response function that results from a bell- curve WTP distribution has a reverse S shape, as shown in Figure 1.8.

For price- response curves with a reverse S shape, there are diminishing returns when making large price decreases, as can be seen by the flatness of the curve in Figure 1.8 for prices on the left side of the graph. Simi-larly, the curve is flat for relatively high prices. Changes in price in the middle range, however, often results in significant changes in demand. One way to think about this is to imagine the population of Coca- Cola and Pepsi drinkers. There is a small segment of very loyal Pepsi or Coca- Cola drinkers that will not consider the other brand for any difference in price (the extreme left or right parts of the curve), while there is a larger segment of consumers that will change their purchase decision between the two if the price difference is large enough (the middle section of the curve). We will cover two of these reverse S- shaped functions here, the power and the logit functions.

Figure 1.7. Constant- elasticity price- response functions (C = 5,000).

Power Price-Response Function

•  d(p)= α⋅D / pβ +α • WTP distribution: Weibull

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Power Price- Response Function

The power price- response function is one function with the reverse S shape:

d(p) = ! . D/(p" + !), (1.3)

where D > 0, !, and " are parameter values estimated by fitting equation (1.3) to the price/demand data. The power price- response function is shown for different values of " in Figure 1.9. Higher values of " represent more price- sensitive markets. As " grows larger, the market approaches

Figure 1.8. Reverse S- shaped price- response function.

Figure 1.9. Power price- response functions.

Logit Price-Response Function

•  d(p)= C ⋅ea+b⋅p / 1+ea+b⋅p • WTP distribution: Gumbel

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Power Price- Response Function

The power price- response function is one function with the reverse S shape:

d(p) = ! . D/(p" + !), (1.3)

where D > 0, !, and " are parameter values estimated by fitting equation (1.3) to the price/demand data. The power price- response function is shown for different values of " in Figure 1.9. Higher values of " represent more price- sensitive markets. As " grows larger, the market approaches

Figure 1.8. Reverse S- shaped price- response function.

Figure 1.9. Power price- response functions.

Measures of Price Sensitivity

• Untuk menentukan segmen pasar dan untuk mengestimasi market response! Figure 1.1

• Menetukan market response adalah dengan menentukan berpara Perbahan harga produk, hasil dari perubahan demand

•  Slope price

• Change in demand is the result from change in price

• 

• Change in demand=slope.change in price

• Contoh d(p)= 10000-500.p Unit maksimal 10000, setiap kenaikan harga $1

akan menurunkan demand 500.

•  The slope of response function depends on the unit yang ditentukan oleh price dan Demand

•  Yang menentukan Price Sencitivity adalah price elasticity

•  Price Elasticity =

•  Jika elastisitasnya -2 adalah berarti setiap kenaikan 10 % harga akan terjadi penurunan demand sebesar 20 %

•  Jika elastisitasnya –0.6 berarti setiap penurunan 10 % harga akan mengakibatkan kenaikan permintaan 6 %

•  Point elastisitas yang lebih mudah

Ketentuan elastisitas

•  Selalu < 0 ( slope price response function selalu negatif)

•  Independent of unit . Misalnya harga harga gasoline ditentukan oleh gallon /$ atau Liter/ euro

•  Tergantung dari harga yang ditentukan •  Low elasticity • High elasticity

•  Tergantung dari waktu yang ditentukan, misalnya short run elasticity lebih rendah daripada long run elasticitykarena pembeli lebih fleksibel dalam menyesuaikan harga pada long run

•  Industry elasticity lebih rendah daripada Individual elasticity

Dampak Elasticity pada Revenue •  inelastic • Raising price , increase revenue

• Revenue independent of price

• elastic • Raising price, decrease revenue

Price Optimization

Price Optimization

• Objective Function Max prof (p) = Max (p-c).d(p)

Price Optimization Problem

• Demand: d(p) = 1000 – 50p • Max profit : Max profit(p) = (p-5).(1000-50p) = - 50p2 + 1250 p – 5000 0 = - 50p2 + 1250 p – 5000 0 = - 100p + 1250 p* = $12.5

Elasticity and Optimization

• Kombinasikan persamaan point elastisitas dan kondisi derivatif dari fungsi profit

• Rules :

Customer Segmentation dan Price Optimization • Konsumen berbeda dalam willingness to pay • Nilai dari price optimization lebih baik ketika

memasukkan price sensitivity yang dimiliki konsumen

• Contoh : grocery store

Summary

•  Perbedaan segmen berdasarkan price sensitivity • Optimalkan harga untuk masing-masing segmen

Teori Pricing Analisis

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