Dan Wilson, Fetch, and Aurelie Genet, trainline: Driving the value of your assets in mobile campaigns @ iMedia Data-Fuelled Marketing Summit, Feb 2016.
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Dan Wilson, Global Head of Mobile at Fetch, and Aurelie Genet, Mobile Marketing Manager at trainline, discuss driving the value of your assets in mobile campaigns at the iMedia Data-fuelled Marketing Summit, Feb 2016. http://www.imediadatasummit.co.uk/
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Dan Wilson, Fetch, and Aurelie Genet, trainline: Driving the value of your assets in mobile campaigns @ iMedia Data-Fuelled Marketing Summit, Feb 2016.
- 2. “The unique opportunities with mobile data”
- 3. “The unique opportunities with mobile data”
- 4. “The unique opportunities with mobile data”
- 5. “The unique opportunities with mobile data” Longevity Location Lifestyle
- 6. “The unique opportunities with mobile data” Longevity Location Lifestyle
- 7. 6.3x more effective at start of user journey 4.8x more valuable users with video priming distribute these for you “The unique opportunities with mobile data”
- 9. “The unique opportunities with mobile data” Longevity Location Lifestyle
- 10. 17.6% 85 8.4% 128 4.2% 149 3.7% 185 3.1% 67 3.0% 133 The mobile consumer
- 13. “The unique opportunities with mobile data” Longevity Location Lifestyle
- 14. The fickle, mobile consumer
- 15. The fickle, mobile consumer
- 16. The responsive, fickle, mobile consumer
- 17. The realm of the mobile consumer
- 18. “The unique opportunities with mobile data” Longevity Location Lifestyle
Editor's Notes
- Longevity – as mobile records everything on device id rather than cookie id we see users for longer And the device id is transferable and matchable between sources without any cookie syncing or multiple data collection Cookies are struggling, between 30% and 60% (depending on research source) don’t last a month, and a high proportion of users block third party cookies altogether. IDFAs don’t suffer from this, they were specifically designed for this purpose so provide consistent and long serving device identifiers
- Location – we get a latitude and longitude associated to all the user actions that we collect. As your phone knows where it is when actions are performed this gets transferred over to the tracking data. This allows us to match to both physical location (London vs Newcastle) and location context (airport vs at home)
- Lifestyle – having this great data is greatly enhanced by the fact that the device stays with people throughout their life. 72% of users are always within 5 feet of their smartphone 9% admitted to using their smartphone during sex The only device that allows a brand to always be there when needed, and the only device that allows us to see every brand interaction a user has throughout their life.
- So summed up into 3 key data themes, which we will pick up in some following examples
- Starting with Longevity…
- As we see the user all the way through their brand interactions, we can see the advertising interactions which drive a user to research as well as the interactions which drive a user to react. And we see the interaction between them This is commonly done using multi-touch attribution, which is typically cookie based. And with the restrictions we know on cookies, these chains often appear shorter and simpler than they actually are. We see massive effects of this in mobile video, we know that video is 6.3x better at driving research behaviour than at driving instant user action. This makes sense as we know it is harder to get a user away from the environment they have chosen to go to, rather than helping them to have a preference when they choose the action in future We also know that a user who goes on to act with this video priming effect are more than 4 times more valuable in future. They act through a different channel at a different time, but the video helps preference and that influences their future behaviour All matched on real users with real device ids, not samples of data. So we can model the longer term impacts on these users.
- A download is not a user yet. A user opens the app regularly and eventually makes a purchase. In order to optimise our media investment and deliver genuine users as opposed to dead downloads, we built a download quality score. Factors we consider are: How engaged people are with the app? How quickly do they make a purchase? How much is their AOV?
- Going onto Location… As mentioned earlier, location falls into two parts. Places and context.
- If we first look at context. Those latitude and longitude files allow us to match to various third party data sources (this is a proof of concept we ran with a company called adsquare using some Fetch install logs) So we can see where all our users are when they engage with us, through paid or owned content. And how valuable those users go on to be, using those same consistent identifiers. So here we can see 17% of users first engage with our brand within supermarkets, and the users who do have a value index of 85. So they are slightly lower value than the average customer. We see similar for others…
- This is a placeholder, Max is finalising a slide which shows geographical differences of trainline media performance
- This is a placeholder, Max is finalising a slide which shows geographical differences of trainline media performance
- Onto lifestyle…
- Having that always on device allows us to see how a users behaviour changes throughout the day, again using real consumption and behaviour data compared to traditional survey methods of getting this insight In general Fetch knows that mobile media consumption is pretty flat throughout the day, the white block. With some key peaks in commuting, and workdays. But much flatter than any other media channel. But we can see the value of each of these users depending on when they first engage with us. Here we see a much different relationships. Users who come to us in the morning are dramatically more valuable, we keep fairly high value users throughout the middle of the day, and those late night users are not very valuable. This gives the vital context of not just when people are responding, but when the right people are responding
- We can take this a stage further, if we look at what should trainline be saying at each part of the day. This shows message response throughout the day. So how receptive our users are to what we are saying to them as we go through the day. We know that savings message is strong all the time. Everyone loves a bargain. But early in the day we also get strong reception for our act now message (download) and functional information messages (things like live times and convenience) This drops off during the middle of the day as users only care about saving cash Then these messages become receptive again later in the day when travel increases again and users want to know things as well as save money. This gives us vital info to optimise our mobile activity, and is often done automatically through creative optimisation technology. But taking out this insight also gives us great live user views which are helpful to our other non-mobile, and non-media communications
- We can go beyond this type of context to also match our first party data to those non-mobile channels Matching to TV exposure we know that exposure to TV advertising can make mobile owned and paid channels perform significantly stronger Pushing this data into both media streams allows the two channels to plan better, and gives instant response mechanisms back to TV planning Trainline cannot share data but yes TV does help across the board. Some company like TV squared that allows to track TV conversions.
- New ecosystems Protecting screen real estate – own a piece of users world. Retention rates massively increase. Retention costs drop, and you no longer need to pay google to get your own customers back.
- So summed up into 3 key data themes, which we will pick up in some following examples