Part 9 (2/2)

Custos are opinions-a qualitative measure of how satisfied your customer is Most qualitative collection tools consist of surveys and interviews They can be in the form of open-ended questions, s Even observations can be qualitative, if they don't involve capturing ”nu the number of strikes in baseball, or the number of questions about a specific product line When observations capture the opinions of the observer, we still have qualitative data

Many times, qualitative data is what is called for to provide answers to our root question Besides asking how satisfied your customers are, some other examples are: How satisfied are your workers?

Which product do your custoular or diet?

How fast do they want it?

Howto pay for your product or service?

When or at what hours do your customers expect your product or service to be available?

Do your workers feel appreciated?

No matter how you collect this data, they are opinions They are not objective data They are not, for the most part, even numbers You can take qualitative data and try to transpose the opinions into values on a Likert scale, for example But in the end, they are still opinions They'll look like quantitative data, but they are not

Some analysts, especially those that believe the custoht, believe that qualitative data is the best data Through open-ended questions these analysts believe you receive valuable feedback on your processes, products, and services Since the custo, what better analytical tool is there than to capture the customers' opinion on your products and services?

These analysts love focus groups and interviews Surveys will suffice in a pinch, but they lack the ability for analysts to observe the non-verbals and other signs that can help them determine the answer to the question, ”How satisfied are our custoanizational development books in recent years is First, Break All the Rules by Marcus Buckingham and Curt Coffman (Simon & Schuster, 1999) This book is built on the analysis the authors performed on qualitative data The reviews sell potential readers by proers and leaders in successful co success of this book is just one modern-day example of the power of qualitative measures

Quantitative Data

Quantitative data usually means numbers-objective es in your car They also include information from automated systems like automated-call tools, which tell you howit took for the the call lasted

The debate used to be that one forued that quantitative data was better because it avoided the natural inconsistencies of data based on eued that soh or low on a satisfaction scale for many reasons other than the products' quality Soo 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 sihting 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 nuht 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 custoht The number of product returns The proponents of quantitative inforue that this is ful data

I'uessed that neither ca a mix of both types of data

Quantitative and Qualitative Data

For the most part, the flaith 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 frooes 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 risksome 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 assue 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 wefull 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 custoain-that they didn't like it

Not only should you use both types of data (and the acco 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 e 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

Even in the case of automated-call software, the results are only indicators

Quantitative data, while objective, are still only indicators If you don't knohy the nu at the reasons behind the nu at the answer

Metrics (indicators) require interpretation to be used properly

I advocate using triangulation (see Chapter 7) for getting a better read on the full answer to any root question This would direct us not to take qualitative or quantitative data alone The great debate bethich is better is unnecessary You should use so are principles to remember: Metrics are only indicators

Metrics are not facts Even when you have a high level of confidence in their accuracy, don't elevate them to the status of truth

The only proper response to a ate

When you tell the story by adding prose, you are explaining what theso that better decisions can be ress deterories of indicators: Qualitative and Quantitative Qualitative is subjective in nature and usually an expression of opinion Quantitative is objective in nature and co automated, impartial tools

Metrics by theht questions and take the right actions

Metrics require interpretation to be useful

Even the interpretation is open to interpretation- insight

Conclusion

Metrics are only indicators This doesn't mean they aren't valid or accurate Even the most objective, accurate, and valid metrics should only be treated as indicators From my days in the Air Force, I learned that ”perception is reality” This is true foran explanation to accoraphs, and tables is to limit the variance in perceptions of your metrics The interpretation of your metrics should not be left up to the viewer You should do the work and due diligence, and investigate what theyou You should take the results of your investigation to forhtful conclusions based on data These should be provided in the explanation for the metric

You will then do your best to sell your interpretation of the metric to your audience Even with that, you have to accept that your interpretation is open to interpretation by those viewing your metrics You also have to accept that your well-defined and fully told story is, in the end, only an indicator It should be a well-explained indicator and one that your diagnostics have correctly interpreted; but it is an indicator nonetheless This requires healthy humility on your part

Remember, metrics are only a tool They are not meant to be more