Measurement is important in considering the value of marketing. Meet Paulina Leperi, who is an analyst who thinks about this very topic of measurement. My name is Paulina Leperi. I'm a manager of delivery and analytics at Nielsen Catalina Solutions. We get data from both Nielsen and Catalina. So, from Nielsen, we get shopper data from their homescan panel, which is a pretty holistic panel, where about 60,000 households record every item that they buy. Then, from Catalina, we receive frequent shopper data. So, that is restricted to certain retail partners, but it also includes a much larger sample size. So, by combining those two sources of purchase data, we're able to gain a lot of insights into what households are buying, and we use that data to see whether households buy more of a product after they've seen an ad for it. It's a random sample, but also Nielsen has quite a bit of research around how to project those households into a nationally representative group. So, certain groups, for example younger households, tend to be a bit under-represented by the panel. So, in addition to recording what those households buy, there's also a way attached to give more importance to households that are under-represented, to make sure that the final projection is a really, really good strong reflection of the US as a whole. In sports, every team needs, they have various things they want to monetize, of course they want to get fans into this stands for every game, but then they also have merchandise that they want to sell, and TV ratings that they need to look out for. So, every piece of communication that comes from a team can impact the fan base, and performance on the team really matters, but so do advertising dollars. So, the more that a team can understand how to keep fans engaged, the more successful they'll be financially, hopefully on the field too. It helps them to understand whether their spending on media was worthwhile, and whether the sales lift was enough to justify continuing to spend on those sources of media. We can also break down by group to see whether households that were exposed to one version of the creative versus another were more responsive, or whether there were other characteristics that make households more likely to respond to an ad. So, the more that a company can get detailed insights into what aspects of their advertising are working, what aspects could use improvement, the more they can optimize their spend in the future. We have a very robust source of, well, we call it single source data, where we know exactly which households are exposed to a campaign. So, using purchase data for the year prior to the launch of the campaign for each household that we've exposed, we actually find a control, a household that's very similar in as far as demos, as far as purchase behavior, except that they were not exposed to the ad. So, then by aggregating the test and control groups we can actually see how much more the test group purchase compared to the baseline of the control. Something that people buy every week, or even every month, it's a lot easier to observe changes and purchase behavior that are coming from viewing an ad, as opposed to something like a car that people are only bind once every few years, it's a lot harder to figure out what are all the variables that lead into that decision, and that is rarely just one ad that makes up a person's mind. One major thing that we're working on now is just, programmatic advertising is growing really really quickly especially in the digital space, and that's something that's been a major priority, is being able to get ahead of what clients might want to do in that space, and making sure that our capabilities, and our partnerships will enable clients to activate programmatically their advertising on as many platforms as possible. I guess a couple of things, one is being very, very adaptable. One example of that is going to school at Northwestern, I was focused a lot on coding and learning SAS, and that was something that served me really, really well in the first couple of years my job, but the marketplace changes quite quickly, and so now, the company has decided that they want to move away from SAS and focus on R, which is open source and free. So, things like that that come up where you have a really strong set of skills, but then they just stop being in demand for various reasons, so you have to be able to adapt and learn quickly, and figure out how to quickly train yourself on the things that you're going to need to do your job moving forward. The other thing, and this is by no means specific to Nielsen, but just taking the initiative, especially early on being a younger newer employee. It's always a lot better if you ask a lot of questions, and you start to hear about things that people are working on, where you might be able to volunteer to help. So, even if you're not assigned a lot of projects from the get-go, the more questions you ask, the more people will start to give you more work to do, and you just, you learn a lot faster, and you start to be able to contribute a lot more if you do that.