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EyeForTravel, LondonHenrik Imhof4.5.2017

The Interaction of Revenue Management and Pricingin Car Rental

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Disclaimer

The simulation results in this presentation are just for illustration purposes. They are not based on real data, nor do they reflect the pricing strategy of Sixt.

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SIXT OFFERS DRIVING PLEASURE IN MORE THAN 100 COUNTRIES: FROM 1 MINUTE, 2 DAYS UP TO 5 YEARS.

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Should Revenue Management and [dynamic] Pricing be distinguished?

„cost plus“ pricing … in travel

Ressources:unit costs /

opportunity costs

...

...

markup

cost

Revenue Management:- Supply- TOTAL demand

Pricing:- Product differentiation- Extra services required

by customer- Price Strategy per

CHANNEL AND PRODUCT

Does your organisation / system account for that split?

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Room for improvement?

feedbackloop

forecast

optimise

availability& price

analytics

operate

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2

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4

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elasticity estimation per product / channeltotal volume of future bookings, no-shows

calculate opportunity costsadd / move capacity

set controls

assign cars

reaction to changing trends / strategies

- Interaction of Capacity Management / RM and Pricing?- Where are the biggest opportunities?- Data science?- „Classical“ RM stuff?

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Pricing:demand curve

direct costs

elasticitymarket observationhistorical sales…

RM: opportunity costs

capacity management

future demand(across all channels/products)remaining capacity

add / move capacity

PricingRM

futuredemand

(per channel/prodcut)

Interaction of RM and PricingWhat can go wrong?

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Interaction of RM and PricingWhat can go wrong?

Pricing:demand curve

direct costs

elasticitymarket observationhistorical sales…

RM: opportunity costs

capacity management

future demand(across all channels/products)remaining capacity

add / move capacity

PricingRM

futuredemand

(per channel/prodcut)

setting a (slightly) wrong price

setting a fixed price for a period,ignoring capacity (cost) updates

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Quantfying pricing error: fixed costs per unit, no limits on capacity (text-book…)

- corresponds to long term planning decision

- price error of -10% [+10%] results in profit loss of 4% [2%]

- also applies to typical RM (system) decision on a micro time scale:

- small amount of booking time - small“ capacity changes- constant opportunity costs per unit

Ealsticity -3, unit costs 100

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The other extreme: fixed capacity at zero (variable) cost

last moments of the booking horizon… if you were to set a fixed price

- Need to be 100% right about the price?!

- In reality: booking curve tells you quicklywhether your price was right.„Threshold steering“

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Constant price for a certain period- robust within certain limits

Is this a real life situation?- Lacking the capability of dynamic pricing

update, driven by remaining capacity

In between: bid price kicks in… increasing capacity costs

parameters as before

16015014013012010510410310210110099989080502020202020

unitc

osts

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Impact of errors

Stochastic simulation, using Dynamic Programming

elasticity and capacity costs similary to earlier slides. Stochastic demand adjusted so that ca. 90% utilisation are reached in optimal control case.

Simulated Pricing errors:

a) at each time step, apply random price error of +/-10% (uniform distribution).

b) systematically, price 10% too low [too high].

For a) & b), the reference value is defined as the optimum price giventhat a) / b) is to be applied for the remaining booking horizon, i.e.the capacity impact of future (wrong) pricing is fully taken into account.

Simulated RM errors:c) the price is set once for the whole booking horizon. Optimum determined by variationof that price.

Combined Pricing & RM errors:d) apply a systematic error of -10% [+10%] on the optimum fixed price of scenario c).

Results, as compared to optimum profit:

a) 99.5%

b) 98.1% [98.9%].

Stochastic process reduces the(already small) impact.

c) 98.2%

d) 90,3% [93,2%]Stochastic doesnot help!

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Risks & Challenges

Two ways of looking at these results

Avoiding errors:

Small pricing errors have limited impact – aslong as capacity forecasts are dynamicallyadapted all the time.

Small pricing errors have devastating effect ifbase price is not adjusted dynamically tochanges in capacity.

Seeking new opportunities:

Small uplift opportunity by market / elasticityestimation alone-> seek new ideas.

Get the basics of RM / capacity control right(if you have not done so, yet…)!

related results by Lämmle (2012): Shuttling cars andleaving price fixed can achieve almost as much asdynamic Pricing. (The combined performance isbetter still, though.)

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Getting the basics right in car rental RM

A typical day

typical figures, for some cases including Franchise partners, exact values may vary.

capacity andno showforecast

transferoptimisation

fleet mix optimisation

integrate RM and channelmix pricing

1.000 in-fleetsSupply over 2.000 stations

20.000 reservations, many „last minute“for 50 different groups

8.000 unplanned returns3.000 cancellations and 2.000 no-shows

7.000 one-way-rentals2.000 Walk-Up‘s

4.000 planned transfers

plus: delivery+collection / recalls / damages+repairs / defleet due to mileage …

autocontrol

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New opportunities

customer type / preferences /

historytaylored

offer

Potential for additional revenuethrough targeted offers:- product choice / presentation, - upsell incentive,- additional services.Often much higher than through priceoptimisation alone.

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Sixt Group KPI’s – Long-term profitable growth

1) Figures 2013 adjusted for “at equity” consolidation of joint ventures

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Consolidated financial statements 2010-2015 – Key figures [EUR m]1)

OPERATING REVENUE EBIT EBT

177.3

2012

167.7

2011

189.8

2010

156.2

2015

221.8

2014

199.2

2013 2015

185.2

2014

157.0

2013

137.6

2012

118.6

2011

138.9

2010

102.3

Leasingrevenue

2015

Rentalrevenue

Otherrevenue

1,939

420

1,377

142

2014

1,645

417

1,120

108

2013

1,50596

2012

1,426

383

954 1,016

89

2011

1,373

394

896

83

2010

1,328

404

807

117

393

- update -EBT 2016

218.3

2016