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## Modeling Customers Switching Between Brands – The General Case

This is the last installment of a four-part series.  The first three parts can be accessed by clicking on the links below.
Methods for Using Arrays Effectively

Modeling a Watershed with Arrays
Modeling Customers Switching Between Brands

Generalizing the Model

When I showed Steve Peterson (at Lexidyne) my brand switching model, he told me there is a more general version that separates the customer loss fraction from the fraction won by another competitor.  This has been presented in Pharmaceutical Product Strategy by Mark Paich, Corey Peck, and Jason Valant.

In my original formulation, the switching probability matrix was the product of these two variables.  However, in many practical cases, the data available comes from two different places and reflects these two separate components.  The revised model structure is shown below.

Instead of one composite switching probability, this model uses a switching out probability that is distinct from the switching in probability.  The switching out probability is a one-dimensional array that, for each product, contains the fraction of customers lost to rivals every time unit (in our case, month).  A sample for the five brands A, B, C, D, and E appears below.

 Brand Fraction Lost A 0.091 B 0.170 C 0.046 D 0.026 E 0.071

switching out probability

We can see from this table that Brand B is losing 17% of its customers to rivals each and every month!  Whoever is managing that product had better do something quickly.

The other side of the story has to do with which brand the customers are switching to.  The switching in probability matrix contains, for each brand, the fraction of lost customers that migrate to a rival brand.  Thus, each row of this matrix must add up to one (100% of lost customers).  A sample appears below.

 From\To A B C D E A 0.00 0.11 0.33 0.55 0.01 B 0.18 0.00 0.29 0.41 0.12 C 0.22 0.02 0.00 0.44 0.32 D 0.04 0.00 0.77 0.00 0.19 E 0.02 0.07 0.28 0.63 0.00

switching in probability

Note the diagonal will always be zero.

We can determine a lot of things from this table.  For example, brand B offers no competition to brand D, brand D is the biggest rival of all the other brands, and brand C is brand D’s biggest rival.

Given these probabilities, we can find the number of customers switching out with the equation:

switching_out_probability[Products]*Customers[Products]

We can then use the exact same formulation as previously for switching to:

switching_out[Products]*switching_in_probability[Products, Products]

and switching in:

ARRAYSUM(switching_to[*, Products])

Note that all customers that switch between two brands also pass through a stock called Customers Switching.  This was Steve Peterson’s idea:  If the equations in the model are correct and all rows of switching in probability add up to one, as they ought to, this stock will always be zero.  If it is ever non-zero, you immediately know there is something wrong with the numbers in switching in probability.  [Note:  Due to truncation errors during the simulation, the stock will not identically equal zero, so it has been globally scaled from –1 to +1 using the Range Specs.]

Expanding the Model

The structure given above models rivalry for existing customers quite well.  However, it does not include flows for new customers entering the market or existing customers leaving the market.  The full structure for acquiring and retaining customers looks like this (and is available by clicking here):

New customers are gained by each brand based on their respective market shares.  Each brand also has a fraction of customers who are lost completely to this market, who perhaps move on to other technology or just stop using that type of product.

Switching Based on Market Shares

There is one further variant to customer brand switching that may be useful in some circumstances (it is available by clicking here):

In this version, rather than using a switching in probability, the number of customers gained each unit time is determined by a dynamic overall market share, the one-dimensional array switching in market share.  Whereas the previous model (above) includes a mechanism whereby brand X explicitly gains customers from brand Y, this model bases switching on market share dynamics; if a customer decides to stop using brand X, they might then go searching for a replacement and would be more likely pick the market leader.  Note the switching in market share can be the same as the brand’s overall market share or, as shown, it can reflect the different market shares between new customers (market share) and switching customers (switching in market share).

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• Guest

Great post. I am curious, however, in how you would account for customers reducing spend across all brands? What if a customer is spending \$10 on Golden Grahams and \$8 on Life Cereal in the first period and \$8 on Golden Grahams and \$5 on Life Cereal in the second period. You would have overall contraction vs. switching, or do you somehow prorate this?

• kchichakly

This shows a way to model the effect of customers changing brands, presumably through consumer preferences and competition. It is explicitly looking at number of customers, not how much each customer is spending. As such, contraction in spending is outside the bounds of this model. However, once you know how many customers you have, you can multiply that by the average spend per customer per month to get your monthly revenue. In a contraction, the average spend per customer would drop based on some function you provide (using the STEP or RAMP functions or your own graphical function). You may also lose customers in a contraction, which you would implement by increasing the loss rate in the model above. It is likely during a contraction that you would also have a reduction in the customer influx.

• Mam

How do you change the matrix if there is category expansion. Eg, in time 0, there were 500 people in market and now 600. Especially if we know that new 100 people are buying brand A.

• kchichakly

Good question. New people arrive through the gaining customers inflow. To add 100 more for brand A at a certain time (let’s use month 5 for the example), you would change that formula as follows:

market_share[Products]*customer_influx + (Products = 1)*PULSE(100, 5, 10000)

[For version 9, use (ARRAYIDX() = 1) instead of (Products = 1).]

However, I do not think this is quite what you are looking for because the market shares/switching percentages are constant, so the model will still seek the distribution given in those switching constants.

To make the model dynamically vary the brand switching based on some criteria, you have to turn the constants in the table into equations for your new formulation. For example, you can use STEP to reduce A’s switching out probability at a certain time, and/or adjust market share (second model). Using the second model, you could adjust only the market share to show an increase in both new customers and switching customers choosing brand A (rather than using the PULSE above). Just make sure to adjust your equations so that the sum of the market shares is always one.

While stepping up the market share or pulsing in extra customers can be an interesting policy exercise or model sensitivity test, you normally would tie these probabilities and market shares to dynamically changing endogenous variables of the model, such as price, features, reputation, perception of value, etc.

• Esteban Ribero

Hi Karim,
Great post. Tks!
Shouldn’t the market share be directly
linked to the amount of customers for each product (example: customers for A/sum of all customers) and therefore be endogenous? It does not make much sense that we start with one market share and after the dynamics have played out we end up with the same market share. If you have more customers relative to your competitors at the end of the simulation you must certainly have a different market share. Please share your valuable
thoughts. Thanks!