As I work on a data set to help answer some question from my original post on customer satisfaction. I wanted to to do a quick case study on Apple and their journey to differentiation themselves from other hardware manufacturers in the early 2000s.
The first thing I wanted to check was whether the change to customer satisfaction for Apple corresponded with an increase in their stock's performance. As you can see in the chart below the market began to value Apple much more highly as it differentiated itself from its peers. It is impossible to say whether the differential satisfaction caused the increase in valuations, was just a part for the increase, or was merely correlated, but if you accept the hypothesis that differential customer satisfaction drives differential performance the data are hard to ignore.
This relationship seems to hold true as well when you model the stock price indexed to 1995 against a couple of customer satisfaction metrics:
|Apple Customer Satisfaction Relative to Industry||.0053||Yes|
|Apple Customer Satisfaction||.00008||Yes|
Assuming you accept the argument that Apple's differential performance was at least partially caused by the increase in customer satisfaction the next step is to try to understand what caused the increase in satisfaction and whether that insight can be applied to other businesses. Based on what I know of Apple my hypothesis was that the increased customer satisfaction was likely driven by a combination of product design, branding, and possibly the ability to get help and try the products at Apple stores. Below are the high-level proxy metrics I used for the modeling exercise. I also lagged the metrics and used transformations of them:
|Potential Driver||Proxy Metric(s)||Notes|
|Product Design||R&D Expense||Directional over time as the impact of this probably has a long tail. Also, success rates of projects are important|
|Branding||Advertising Expense||Does not measure effectiveness of advertising in driving a brand that customer's like, but is at least a measure of investment|
|Apple Store Availability||# of Apple Stores||Missing data for 2002-2004; did a straight line computation from 2 stores in 2001 to 116 in 2005. This is probably a proxy for a renewed focus on the interaction between Apple and its customers|
When modeling these metrics up to a two-way interaction with a target of Apple's overall customer satisfaction you get the following results:
|Interaction between Advertising Expense & the # of Apple Stores||.00510||Yes(ish)|
|Advertising Expense||.3406||No, but included in the model because it is a part of a significant interaction|
|Accrued Marketing Expense||.0801||Yes(ish)|
|# of Apple Stores||.000051||Yes|
As you can see in the table above the data suggest there was not a single "silver bullet" in Apple's case that drove higher customer satisfaction, rather a combination of factors were correlated with the increase. Of course, this only proves that these metrics are correlated, but this is probably the best we can hope for until an organization is both willing to drive a negative experience and to share that they did that along with the data.