Metrics_ How to Improve Key Business Results - Martin Klubeck [47]
Quantitative Data
Quantitative data usually means numbers—objective measures without emotion. This includes all of the gauges in your car. They also include information from automated systems like automated-call tools, which tell you how many calls were answered, how long it took for them to be answered, and how long the call lasted.
The debate used to be that one form of data was better than another. It was argued that quantitative data was better because it avoided the natural inconsistencies of data based on emotional opinions. Then the quantitative camp argued that someone could rate your product high or low on a satisfaction scale for many reasons other than the products’ quality. Some factors that could go into a qualitative evaluation of your service or product could include:
The time of day the question was asked
The mood the respondent was in before you asked the question
Past experiences of the respondent with similar products or services
The temperature of the room
The lighting
The attractiveness of the person asking the question
If the interviewer has a foreign accent
The list can go on forever. Quantitative data on the other hand avoids these variances and gets directly to the things that can be counted. Some examples in the same type of scenario could include:
The number of customers who bought your product
The number of times a customer buys the product
The amount of money the customer paid for your product
What other products the customer bought
The number of product returns
The proponents of quantitative information would argue that this is much more reliable and, therefore, meaningful data.
I’m sure you’ve guessed that neither camp is entirely correct. I’m going to suggest using a mix of both types of data.
Quantitative and Qualitative Data
For the most part, the flaws with qualitative data can be best alleviated by including some quantitative data—and vice versa. Qualitative data, when taken in isolation, is hard to trust because of the many factors that can lead to the information you collect. If a customer says that they love your product or service, but never buy it, the warm fuzzy you receive from the positive feedback will not help when the company goes out of business. Quantitative data on the number of sales and repeat customers can help provide faith in the qualitative feedback.
If we look at quantitative data by itself, we risk making some unwise decisions. If our entire inventory of a test product sells out in one day, we may decide that it is a hot item and we should expect to sell many more. Without qualitative data to support this assumption, we may go into mass production and invest large sums. Qualitative questions could have informed us of why the item sold out so fast. We may learn that the causes for the immediate success were unlikely to recur and therefore we may need to do more research and development before going full speed ahead. Perhaps the product sold out because a confused customer was sent to the store to buy a lot of product X and instead bought a lot of your product by mistake. Perhaps it sold quickly because it was a new product with a novel look, but when asked, the customers assured you they’d not buy it again—that they didn’t like it.
Not only should you use both types of data (and the accompanying data collection methods), but you should also look to collect more than one of each. And of course, once you do, you have to investigate the results.
You may believe qualitative measures are more obvious indicators. Yet even when we ask a customer if she is satisfied with a product, and she answers emphatically, “yes,” her response doesn’t mean she was truly satisfied. The only “fact” we know is that the respondent said she was satisfied.