Addressable Campaigns: From Data Quality to Optimization

We are hearing more conversations on Addressable that focus on data optimization and quality. The media industry has historically relied on third party data but as more companies can amass first party data from their own customer pools, we are seeing a shift in how all types of data are being integrated and analyzed.  

Kemal Bokhari, General Manager Data and Analytics, DISH Media Sales, and Allison Metcalfe, General Manager, LiveRamp TV, take a look at this complex data issue from a right brain (holistic thinking and imaginative approach, or in this case, data quality) and a left brain (analytical or methodical approach) perspective. How can data optimization provide a continuous and confident feedback loop for advertisers to utilize in strengthening their creative campaigns? 

Charlene Weisler: How are companies finding the right data to use and making sure it's used correctly and to the greatest benefit? 

Kemal Bokhari: Dish is data agnostic so we don’t necessarily recommend that a client use a specific dataset. We find that many advertisers and agencies who come to us for addressable campaigns come with a target audience in mind and / or their own dataset. I believe that there is really no such thing as “the right dataset”. It all depends upon the specific goals of the campaign. We help our clients strategize and accomplish those goals.

Weisler: Given all of the discussions about data and privacy today, how do you protect user privacy while using data to give them a better experience?

Allison Metcalfe: To insure privacy, you use privacy-conscious deterministic onboarding like LiveRamp and only the highest quality data providers. LiveRamp had the first Chief Privacy Officer ever and we take this very seriously, requiring all partners to go through an audit, and annual audits thereafter to ensure that consumer privacy is being handled correctly. 

Weisler: What is the difference between first party data vs third party data?

Bokhari: First party data is data that you own yourself; Where you have a direct relationship with that subscriber or customer. CRM data is considered to be first party data. Third party data is the aggregator of first party datasets. They get a list of subscribers to target and then sell that to interested advertisers. 

Weisler: If you had to explain the use of first party, third party and data privacy to your grandparents how would you explain it?

Metcalfe: I would tell her that first party data is like Bloomingdales mailing you catalogues. You've given Bloomingdales your info and they have it to market to you. Third party data is like Visa packaging up buying behavior into segments like "department store spenders". Data privacy is about making sure that both Bloomingdales and Visa are responsible with your data and don't let it get to bad actors. (And I want to add that my grandma is an avid shopper so she will understand this explanation!)

Weisler: How can these two types of datasets enhance each other?

Bokhari: We have advertisers who come to us with their own CRM data that they may want to match to demographic data and other targets. An example would be with a credit card company who would want to contact existing cardholders for a new type of card and who live in a specific geographic zip code or DMA and earn about a certain income level. That is where we could help them enhance their first party data with our third party data to help them reach and hyper target these consumers. 

Weisler: How can data best be used for continuous ad campaign optimization? We all know that advertisers are using data to target, but how are they using previous campaign measurement to influence the way they target, the type of data they use, etc. 

Bokhari: Everyone can use their CRM data to create a targeting file. They use third party data to enhance their audience. And they learn from previous experiences what has worked and what has not worked. What has worked can be used in the next iteration of studies and campaigns to get a bigger ROI. 

Metcalfe: Many of LiveRamp's brands are conducting closed-loop measurement and are able to quite easily see how data has performed for their campaigns. Our most advanced advertisers are using multi-touch attribution modeling and/or data lakes so they can really analyze the impact of data on the customer journey.

Weisler: What is the biggest pain point for addressable advertisers when using different data sets, and how do you suggest avoiding it?

Metcalfe: I would say that the biggest pain point is probably scaling up a precise target to make sense for a TV ad campaign. They can conquer that problem by using trusted partners to onboard and model their audiences.

Weisler: Where do you see the use of data in addressable going in the next three years?

Bokhari: The only way is up. Advertisers will continue to use audiences in targeting. We expect this to expand and to be able to seamlessly target across platforms –across TV and digital. We know from our experiences that when someone runs an addressable campaign, they come back and do another. There is no waste and it is highly targeted.

Metcalfe: I see both growth and adoption as there are more and more addressable households and more inventory on a national scale to buy.

Weisler: What do you see as the biggest reason for advertisers to start using data to target audiences through addressable?

Metcalfe: It is because addressable offers advertisers the best of precise ad targeting within a powerful brand advertising medium.

For more info on DISH Media Sales’ Addressable Solution and additional Addressable Insights such as Wins and Reports, please head to the DISH Media Sales website. 

This article first appeared in www.MediaVillage.com

Coming Full Circle in Research by Making Big Data Smaller. An Interview with Maru/Matchbox’s Bruce Friend

“Technology-enabled research is a trend in the marketplace,” noted Bruce Friend, President of Global Media and Entertainment, Maru/Matchbox. 

Harnessing the power of technology in the pursuit of research insights is something at which Friend excels thanks to his previous work at OTX and Vision Critical. Now, much of his effort focuses on “building capabilities that are platform-based [unlike] a traditional research company, which still tends to be very focused on one-off, ad hoc studies, employing different survey-based methodologies.” 

Friend helps clients create single source panels that leverage a company’s own consumer assets, essentially connecting behavioral data from their DMP and other data assets with survey-based demographic and attitudinal data to track over time. “Furthermore, we build custom panels where our clients own the panel members and all of the data collected on those members. So, we are helping them create their own insights platform and program that has tangible asset value.  This value then continues to grow, as the panel size increases and we collect more data on the members,” he explained.

Charlene Weisler:  So how has the custom research business changed?

Bruce Friend: We are seeing that companies want to work with research partners in an ongoing relationship - always on, always delivering in terms of data and in terms of thinking. That is where we are heading. We are doing so not only by continuing to partner with Vision Critical, which spun off the research consulting part of thir business to become Maru/Matchbox 2 ½ years ago but by also acquiring new companies that complement the part of the business that we are concentrating on as well as enable us to do things on a standalone basis, as well as on private panels and communities. 

Charlene Weisler: And the media landscape overall is changing.

Bruce Friend: It’s been an interesting time in this “pending merger world” that the media industry is in now. We are seeing (content) companies building out full capabilities to support the process from script-to-screen. And beyond script-to-screen, really – being able to control the entire ecosystem from the standpoint of developing content, marketing it, distributing it, continuing to build franchises and monetize the businesses going forward. It is also about the platform – not just the content anymore. Its about how we get the content to the consumers in different ways. We see that even from companies such as Amazon (Prime Video), Twitter (TV, Video), Facebook (Watch) and others, who are adapting to video being a new and  increasingly dominant content form. It always seems that it all comes back to video and we are certainly seeing that more and more in the online space.

Charlene Weisler: Yes video is important. But more people are talking also about voice.

Bruce Friend: Yes, obviously voice activation is going to be the norm. In the not too distant future, I see companies conducting surveys through Alexa and Google Home. There are certainly some privacy issues around that sort of thing and I am sure there will be ways of working through that. Probably by creating panels of homes where people will opt in. In addition to voice, audio is also making a resurgence. We currently work with four or five companies that are very audio-focused.  Just like with video, audio is finding many new areas where it can exist and thrive. The emergence of podcasting is only going to continue. It is an indication of where our business is heading, where people want to listen to what they want when they want to listen to it – just like video.

Charlene Weisler: In creating panels from a clients’ own dataset, it sounds like you are able to fully leverage first-party data. Is this a trend? And what if a company doesn’t have a lot of first party data?

 

Bruce Friend: We are leveraging both first and third-party data. Certainly there are many resources for third-party data. Obviously the first-party data is better because it is essentially a 100% match rate as we recruit customers directly from the client’s database to become panel members. But in some cases some people don’t have first-party data. In those cases, we look for third-party data matching opportunities. We also run our own panel here in the U.S. and in Canada – both have around 250,000 members – so we can leverage them, as well as look to match more data sources into them. Our panels can also be used to look at communities outside of the company’s own panel. If you want to look at competitive viewers and competitive distribution services for example, you can then leverage our panel in addition to your own. Clients don’t always want to talk to just their own customers. 

Charlene Weisler: Do you see any evolution in how online communities are being built and used?

Bruce Friend: When communities started, they were about better, faster and cheaper. Communities were the start of agile research. The client could control and use the platform as a DYI tool. Most of these panels were 5,000 to 15,000 members. The trend is now not to have these smaller siloed communities within one company and across different brands, but to build a mega-community or an enterprise-wide community. We can now put all of these communities together with an organization with 50,000 to 150,000 members across the organization. Going bigger is better and when you then connect your DMP or other specific first-party data. You then have a very powerful asset with enough scale to do some very interesting things with the data and with surveys on top of it to give you more strategic insights. Communities used to be “light tactical” research – most people were not using communities for very strategic work. What we are finding now with some of our larger clients, who have made the effort to build out bigger panels, some as large at 175,00 members, is that they can now do more strategic work on them. As a result, we are seeing budgets move from more traditional research into platform-based panel offerings, such as ours, where clients can better leverage their own big data. 

Charlene Weisler:  So where do you see research going?

Bruce Friend: I see this model where companies tie into technology with an embedded community where you can talk to someone today, talk to them again a week from now, and on an ongoing basis. The company owns the panel asset, is building out that asset, that asset really has (data) currency to them while they can still conduct large survey studies within the panel. But it’s really about the creation of a resource that links behavioral data, attitudinal survey data, qualitative data, etc. into an ongoing relationship in an ongoing data stream. Automation will drive a lot of this, as well as will A.I. We are making big data smaller, more contextual and more understandable because we are looking at data that is in a panel and is more representative of the audience or subscriber base that the client has. 

Charlene Weisler: Sort of bringing the data science and ethnography elements of research back together.

Bruce Friend: Yes. I feel that we are coming full circle, back to where we were years ago when I first entered the industry. At that time research and big data lived harmoniously within the same insights departments, and that must happen again in my opinion. Otherwise, companies today that haven’t already moved to effectively consolidate their research and data science teams into one, and build business intelligence assets that support their entire organization, run the real risk of rapidly falling behind their competitors that have.

This article first appeared on www.MediaVillage.com