﻿ Search Example #3 - Multiple products simultaneously

# Example #3: Searching for Multiple Products Simultaneously

For illustration, let’s make the search space a bit more complex. Let’s assume that RCA is interested in offering two different television sets. What are the best two offerings to bring to the market to optimize revenue for RCA, again under the previous assumptions of competition and market size?

If we employ an interpolation step value of 10, there are 31 unique prices to investigate over the \$300-\$450 range. With two products to search simultaneously, the total possible combinations is equal to (3x3x2x2x31)2 = 1,245,456.  With speedy simulation methods like Share of Preference or First Choice, this would still take about 1 hour to search using Exhaustive Search.

Speed and IIA Concerns

Although Exhaustive Search guarantees finding the global optimum, when it takes too long to be feasible in practice, much faster heuristic searches such as Grid or Genetic may be used.  Although Grid and Gradient do not guarantee the globally optimal result, they often still find the optimal result or one extremely close to it.

Another consideration with this example is that two products provided by RCA would likely cannibalize themselves in the real world.  Simulation methods like Share of Preference are not as preferred as Randomized First Choice when cannibalization issues (also red-bus/blue-bus, or IIA) are a concern.

Randomized First Choice is much slower than the First Choice or Share of Preference methods, so we'll use the following settings for this example to keep things running quickly:

Randomized First Choice with just 20 iterations per respondent

Grid Search

Specifying the Products

Product #1

Product #2

Product #3

Product #4

Product #5

Product #6

JVC

25" screen

Mono sound

No blockout

No PIP

\$300

JVC

26" screen

Stereo sound

Blockout

No PIP

\$350

Sony

26" screen

Stereo sound

No blockout

PIP

\$350

Sony

27" screen

Surround sound

Blockout

PIP

\$450

RCA

1-3

1-3

1-2

1-2

=Range(300,450,5)

RCA

1-3

1-3

1-2

1-2

=Range(300,450,5)

Simulation Method: Randomized First Choice (click the icon to specify that 20 iterations should be used per respondent)

Range Behavior: Search - Grid, optimizing Revenue (click the icon to specify Revenue as the single objective)

The optimal 2 products found in terms of maximizing revenue for JVC (net revenue across both products) are as follows:

Product #5

Product #6

RCA

27" screen

Stereo sound

Blockout

PIP

\$315

RCA

27" screen

Surround sound

Blockout

PIP

\$350

We can see that as with the one-product optimization solution, an RCA at about \$350 with all the best features for screen size, sound quality, channel blockout and PIP is found (product #6 above). In addition, a bit more revenue might be derived if a very similar television were simultaneously offered with the lesser quality Stereo Sound for \$35 less (product #5 above), rather than with Surround Sound.

Recall that the best one-product simulation (Search Example #2) achieved revenue of about \$169MM, whereas the revenue for a two-product offering from RCA is about \$197MM.

All the simulations conducted this point have lacked the important element of cost information. We don’t know whether RCA could stay in business manufacturing and selling these two television sets. Even though we have specified a revenue maximization strategy, RCA may very well lose money on every unit sold if the total costs for these television sets exceed the prices indicated.

As we’ve emphasized before, having cost information is key to obtaining the most value from product optimization searches. Without key cost data, the search results often return trivial answers that are not in the best interest of a company. In the next example, we’ll extend this example by including cost information and conducting a profitability search.