0:00:06i i'm professor marketing american university change in the night or inference
0:00:13paul williams i recently categorical probably children's services marketing that builds on some research we
0:00:19conducted over the last are yours this data flow from the time to try and
0:00:25we have used for about eleven years prior to getting that in domains
0:00:29by biased in human a large size firms forty one for and we would want
0:00:37to studies with these four we present to secure exact in organisations you realise there's
0:00:43millions of dollars we made by a single by one or two or three percentage
0:00:47points
0:00:48and so that focus was their initial focus only in customers the pointing it performs
0:00:53organisations in two thousand five we will consider your marketing and she solicited articles from
0:01:00somebody scholars and you'll just real pretty webster don't limit
0:01:05and others ask them based on your what with the shortcomings what you're the consists
0:01:12of those articles in that by j was that we in the morning more
0:01:20may actually work we show a little bit of this morning actions and decisions on
0:01:25financial performance in for specifically we need to be able to document the financial performance
0:01:31metrics relating to read from increasing task force that for changes in product or service
0:01:37delivery
0:01:39talking back to the financial performance of the four original
0:01:43so what we came up with it will be the stores to justify was that
0:01:48what we try to previous years
0:01:51at that i don't decided using so these longitudinal databases for the research so that
0:01:59way well as i started analysing the data to see what happens changes over time
0:02:06that strategies that's the final performance
0:02:09we focus is that the improvement over most studies are done feel because most a
0:02:14cross sectional those of eight three hundred by either way time but it doesn't show
0:02:20that i am i change over time and l s played by the performance
0:02:25so what or restaurant we went back to looking at something spanish for metrics be
0:02:31variables look row rescore share a
0:02:36r y are redraw tested so you try to find estimates
0:02:42what we try to do it stays databases strategies specifically customer satisfaction representations attractive over
0:02:51a period of three four five years so that are inside to okay is that
0:02:56into the changes by four
0:03:00so that i like to earn representation over all and he saw that some of
0:03:04the specific financial metrics that we the results came out
0:03:12okay and you will a particular study we were dealing with a building services industry
0:03:17when we talk about some heating ventilation and air conditioning systems most exciting research are
0:03:23want to take relevant and we're dealing with what complex onsets financially as well
0:03:31there were three main results we found the data that the first one is actually
0:03:36very strong relationship between customer satisfaction and actual attention with the for the was a
0:03:41renewal right wherever the satisfaction models
0:03:46okay but most there is just going to be the case now we got ninety
0:03:50hours of data group
0:03:52the second interesting result
0:03:54was that was for you growth particularly it was a very strong relationship between customer
0:04:00satisfaction hundred and you wrote cable leading to high level of satisfaction lead to higher
0:04:06levels of running you wrote with respect to the customer
0:04:10another interesting statistic we found in our data was customer satisfaction is highly correlated with
0:04:16small performance is represented by stock price and we also confirm that tokens q six
0:04:23actually correlation between the model performance for low prices
0:04:29and what the customers are satisfied
0:04:32conclusion a i think we show longitudinal data has been extremely insightful interesting and we
0:04:39have confirmed favourites already
0:04:42shown in conceptual study we think it is i value to use a more accurate