Measuring consumer switching costs in the television
industry
by Oleksandr Shcherbakov
October 4, 2016
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Switching Costs
In many markets, consumers are reluctant to switch
Examples:
Banking
Telecommunication
Electricity
Paid-TV
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Switching Costs
Consumers repeat purchases from the same provider
Some reasons:
Provider consistently oers the best price-and-quality products
Exit/termination fee (monetary costs)
Installation fee (monetary costs)
Too much hassle to switch (utility costs)
Brand loyalty (utility costs)
Switching costs: additional monetary and utility costs of switching to an
alternative provider
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Switching Costs
Anecdotal evidence of switching costs
Survey results for paid-TV:
Approx. 80% of satellite subscribers report high levels of satisfaction
with their service (Nielsen Media Research survey, 1997).
By contrast, a dramatically lower percentage (45%) of cable
subscribers are satised.
Yet, only 10% of cable TV consumers indicated that they are very
likely to switch to satellite service (Chilton Research Services Survey,
1997).
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Switching Costs
What are the eects of switching costs on competition?
Quantifying switching costs is important to answer the following questions:
1. Do markets become more competitive due to rms' incentives to invest
in their customer base?
2. When do rms start extracting rents (harvesting) from locked-in
customers?
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Switching Costs
Approach
Step 1. Develop a
dynamic
empirical framework that identies and
estimates customer switching costs using
market-level data.
Identication requires separating the following eects from
aggregate statistics :
i. Consumer heterogeneity in preferences
ii. Switching costs
Step 2. Simulate counterfactual and compare dierences in the optimal
policy of cable TV providers under static and dynamic monopoly
and duopoly structures.
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Switching Costs
Outline of Talk
1. Overview of Paid-TV Industry
2. Model (dynamic):
i. Flow Utility
ii. Dynamic Programming
iii. Purchase Probabilities (random logit) and Market Shares
3. Data
4. Identication
5. Estimation algorithm (nested loops)
6. Results
i. Switching Costs
ii. Counterfactual simulations
7. Conclusion
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Switching Costs
Paid-TV Industry
Before early-1990s, cable TV providers were
local monopolies.
Since then, a Direct Broadcast Satellite (DBS) service was launched
and there is new entry into the market.
Competition is unusual because cable TV providers set prices and
quality locally, whereas DBS providers set them at the national level.
Products are vertically dierentiated and more expensive bundles
uniformly include all the channels from low quality bundles - used to
construct scalar quality measure.
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Switching Costs
Users face substantial switching costs
Customers face the following costs when switching providers:
Up-front installation fees
Equipment purchases
Hassle costs (e.g. installation appointments, obtain landlord's
permission to install satellite dish, etc.)
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Switching Costs
Model
They propose a dynamic model of consumer behaviour in a market with
switching costs.
Denitions:
t
- time period (one year).
g ∈ {o, c, s}
- outside option (free over-the-air TV), cable and
satellite services
pgjt
- monthly subscription fee
qgjt
- quality of program content measured as a weighted average
total no. of channels (more weight given to more costly channels)
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Switching Costs
Consumer's Flow Utility
Consumer's ow utility
Ui (ait , ait−1 ) =
from
service
g
is:


q̄gt ) + ξgt +εigt
−ηig · 1(ait−1 6= g ) + |δ̃ig (p̄gt , {z
}
if ait = c, s,


otherwise
mean utility,δig
εiot
where
ηig denote switching costs that are known to consumers
ait ∈ {o, c, s} - consumer i 's choice of provider
ξgt - unobserved quality shock to econometrician but observed by consumers
(similar to BLP)
εigt - i.i.d. random taste shocks for providers; follows EVT1 distr.
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Switching Costs
Dynamic Programming
The Bellman equation for a consumer's dynamic maximisation problem is:
Vi (Ωt , ait−1 , εit ) =
max {Ui (ait , ait−1 ) + βE [Vi (Ωt+1 , ait , εit+1 )|Ωt , ait , εit ]}
ait ∈{o,c,s}
s.t. Ωt denote current service characteristics and any factors aecting future
characteristics. It evolves according to a rst-order Markov process.
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Switching Costs
Purchase Probabilities
In each year (one time period), each consumer type chooses either cable,
satellite or the outside option. The conditional probability of choosing
service
g
when they were previously with
Pr(ait = g , ait−1 = k) = Pr
=
k
is:
−ηig · 1(g 6= k) + Vig + εigt ≥
−ηil · 1(l 6= k) + Vil + εilt , ∀l 6= g
exp(−ηig · 1(g 6= k) + Vig )
exp(Vi0 ) + exp(−ηic · 1(c 6= k) + Vic ) + exp(−ηis · 1(s 6= k) + Vis )
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Market Shares
Current period
predicted
market shares are given by:
sigt = Pr(ait = g , ait−1 = c) · sict−1 + Pr(ait = g , ait−1 = s) · sist−1
+ Pr(ait = g , ait−1 = o) · (1 − sict−1 − sist−1 )
and
aggregate
market shares:
Z
sgt =
sgt (p̄ct , q̄ct , ξct , p̄st , q̄st , ξst , sit−1 , ωi ) dGω,sict−1 ,sist−1 (ωi , sict−1 , sist−1 |θ)
|
{z
}
sigt
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Heterogeneity Moments
Predicted product-specic market shares for cable TV:
R
!
1 j = arg max {αip pgj 0 t + αiq qgj 0 t } sigt dGω (ωi , sit−1 |θ)
sj|g ,t =
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j 0 ∈Jgt
R
sigt dGω (ωi , sit−1 |θ)
Switching Costs
Data and Instruments
Cable TV:
Exhaustive information (e.g. market size, no. of subscribers, prices,
channel lineups) from 1992-2006.
Product-specic shares available at market level (a market,
n
is
dened as the no. of homes passed by a cable provider)
Satellite TV
Data collected from internet
Only total share of satellite rms are available and at a coarser level
(typically spans a few markets)
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Instruments for Price and Quality
E [pigt , qigt |ξgt ] 6= 0.
Therefore, need to instrument price and quality.
IV 1. Average prices and quality of other cable systems that belong to the
same multiple-system operator (MSO)
IV 2. Bargaining power of MSO, proxied by the no. of homes passed and
no. of subscribers of the parent company
IV 3. MSO average capacity level
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Switching Costs
Identication Strategy
Separating the eects of consumer heterogeneity (measured by random
coecients) and switching cost parameters from market level data is tough.
Therefore, he proposes the following strategy:
Identies
exogeneous shifters
switching cost parameters from
of the
previous period decisions (i.e. if switching costs do not exist, then
exog. changes in previous period should not aect current period's
decisions) E.g. cost-reducing innovations.
Identies
consumer heterogeneity from variation in observable product
characteristics
across
markets. Product-specic market shares provide
important information on the distribution of preferences.
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Estimation Strategy
The vector of parameter to estimate is:
(ᾱc , ᾱs , ᾱp , ᾱq , σαc , σαs , σαp , σαq )
Estimation Algorithm:
Inner Loop: Solves dynamic programming problem for
each
consumer type
and calculates aggregate market shares.
Middle Loop: Solves for
ξct
and
ξst
that match observed market shares to
the ones predicted by the model.
Outer Loop: Searches over the parameter vector for values that minimize
GMM objective function
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Solving for
ξct
and
ξst
Match model predictions for aggregate provider-specic shares to the ones
observed in the data, i.e. solve the following system of equations:
(
sct = sct (p̄ct , q̄ct , ξct , p̄st , q̄st , ξst |θ)
sst = sst (p̄ct , q̄ct , ξct , p̄st , q̄st , ξst |θ)
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Switching Costs
Moment Conditions
1.
E [ξ˜cnt |zcnt ] = E [ξ˜snt |zsnt ] = 0
2.
E [ucjt |It ] = 0
where
ucjt
(mean independence assumption)
is the measurement and approximation error between the
data and observed predictions of
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product-specic
market shares.
Switching Costs
Larger switching costs in satellite than cable services
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Switching Costs
Switching costs have nontrivial eects on mkt eqlbm
DM vs DD - DM oers 51% lower mean utility than DD (competitive
eects due to satellite entry)
DM vs SM - SM oers 89% lower utility (cable providers need to
lower prices to attract customers with high switching costs)
SD vs DD - SD oers 40% lower utility (if switching costs do not
exist)
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When do rms start harvesting?
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Conclusions
1. Switching costs are signicant and constitute approx. half of the annual
variable service costs for each service provider ($190 for cable; $240 for
satellite).
2. Professional installation costs only amount to 20% of switching costs,
with the remainder attributed to hassle costs.
3. From counterfactual simulations, they nd that due to switching costs,
cable rms have incentives to invest in customer base, hence increasing
utility.
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Switching Costs