Holly Chen (Growth advisor and the former Global Head of Digital Marketing at Slack) is interviewed by Shamanth Rao (CEO of RocketShip HQ - Mobile Growth Consultancy) talk about setting up cross-channel attribution in the context of B2B marketing and incrementality testing.
Mobile efforts program need to evolve over time based on the business goals. Example: from top-line conversions to quality conversions to optimizing impact for cross-device impact.
For multi-touch attribution start looking at it with your Google Analytics report: past conversions, multi-funnel report and attribution model comparison tool which is useful to validate some of the assumptions.
You should also look at your internal data on the log level to see the user journey of your converted users. This analysis can help you assess the impact of the different attribution models that you might apply.
To capture data you can use your ad servers (e.g. DoubleClick suite for desktop) and MPP for mobile touch points in order to send raw data points to your internal data warehouse. Then use users' email addresses to connect the dots for converted users and layer in the LTV model.
Try to combine multi-touch attribution with incrementality studies (ideally by channel) so you can truly understand the true value of your touch points...Ghost ads can be an option.
For incrementality testing, a DMA-level (Designated Market Area i.e geo split) incrementality test is preferred because pre-post (pausing campaigns) can be impacted by seasonality or other factors.
Mobile efforts program need to evolve over time based on the business goals. Example: from top-line conversions to quality conversions to optimizing impact for cross-device impact.
For multi-touch attribution start looking at it with your Google Analytics report: past conversions, multi-funnel report and attribution model comparison tool which is useful to validate some of the assumptions.
You should also look at your internal data on the log level to see the user journey of your converted users. This analysis can help you assess the impact of the different attribution models that you might apply.
To capture data you can use your ad servers (e.g. DoubleClick suite for desktop) and MPP for mobile touch points in order to send raw data points to your internal data warehouse. Then use users' email addresses to connect the dots for converted users and layer in the LTV model.
Try to combine multi-touch attribution with incrementality studies (ideally by channel) so you can truly understand the true value of your touch points...Ghost ads can be an option.
For incrementality testing, a DMA-level (Designated Market Area i.e geo split) incrementality test is preferred because pre-post (pausing campaigns) can be impacted by seasonality or other factors.
Mobile efforts program need to evolve over time based on the business goals. Example: from top-line conversions to quality conversions to optimizing impact for cross-device impact.
For multi-touch attribution start looking at it with your Google Analytics report: past conversions, multi-funnel report and attribution model comparison tool which is useful to validate some of the assumptions.
You should also look at your internal data on the log level to see the user journey of your converted users. This analysis can help you assess the impact of the different attribution models that you might apply.
To capture data you can use your ad servers (e.g. DoubleClick suite for desktop) and MPP for mobile touch points in order to send raw data points to your internal data warehouse. Then use users' email addresses to connect the dots for converted users and layer in the LTV model.
Try to combine multi-touch attribution with incrementality studies (ideally by channel) so you can truly understand the true value of your touch points...Ghost ads can be an option.
For incrementality testing, a DMA-level (Designated Market Area i.e geo split) incrementality test is preferred because pre-post (pausing campaigns) can be impacted by seasonality or other factors.
Notes for this resource are currently being transferred and will be available soon.
In a lot of B2B products, mobile is an adopted child.
Macro-level: more and more important for B2B user journey
2017: seen decrease in mobile and high growth for desktop which seemed like a disconnect.
Also happened while doing a lot of research on LTV so they knew it was very important. Example: if creator of a team also has mobile usage then LTV 5-10x more.
First step is to try and have a holistic perspective around the role of model and its exploration, which is still not entirely clear.
A lot of competitors were bidding on Slack keywords on mobile so at least they should start doing brand defense.
One of the difficulties: connect all the touch points across devices until the conversion/activation.
It's about understanding the different touch points in a conversion funnel and properly give credits to these touch points. Typically the context is last-touch attribution that gives 100% of credit to the last touch point.
For Slack a lot of mobile touch points are affecting the decision.
Especially important for a B2B product because people need a lot of information before they convert: pages, watching videos, even going to events, etc.
At Slack it is still shorter than traditional enterprise products (because bottom-up and freemium) but a large volume of users convert outside of 30-day window and there are about 10 touch points before someone converts.
High-level approach to the transition:
[💎@19:46] You should also look at your internal data on the log level to see the user journey of your converted users. This analysis can help you assess the impact of the different attribution models that you might apply.
Capturing the data:
Need raw data for all the different touch points. Initially they explored out-of-the-box attribution model vendors but ended up using their ad servers.
[💎@21:19] To capture data you can use your ad servers (e.g. DoubleClick suite for desktop) and MPP for mobile touch points in order to send raw data points to your internal data warehouse. Then use users' email addresses to connect the dots for converted users and layer in the LTV model.
For Slack, email is part of the registration process so it is the easiest way to connect the dots.
It's not perfect and just 1 of the approaches to measure effects of marketing.
[💎@27:45] For incrementality testing a DMA-level (Designated Market Area i.e geo split) incrementality test is preferred because pre-post (pausing campaigns) can be impacted by seasonality or other factors.
Mobile still complimentary to desktop.
Shifted a lot of efforts from top-line growth to effect across devices. Also started to think about a mobile-first design for advertising and messaging which made a big impact.
In a lot of B2B products, mobile is an adopted child.
Macro-level: more and more important for B2B user journey
2017: seen decrease in mobile and high growth for desktop which seemed like a disconnect.
Also happened while doing a lot of research on LTV so they knew it was very important. Example: if creator of a team also has mobile usage then LTV 5-10x more.
First step is to try and have a holistic perspective around the role of model and its exploration, which is still not entirely clear.
A lot of competitors were bidding on Slack keywords on mobile so at least they should start doing brand defense.
One of the difficulties: connect all the touch points across devices until the conversion/activation.
It's about understanding the different touch points in a conversion funnel and properly give credits to these touch points. Typically the context is last-touch attribution that gives 100% of credit to the last touch point.
For Slack a lot of mobile touch points are affecting the decision.
Especially important for a B2B product because people need a lot of information before they convert: pages, watching videos, even going to events, etc.
At Slack it is still shorter than traditional enterprise products (because bottom-up and freemium) but a large volume of users convert outside of 30-day window and there are about 10 touch points before someone converts.
High-level approach to the transition:
[💎@19:46] You should also look at your internal data on the log level to see the user journey of your converted users. This analysis can help you assess the impact of the different attribution models that you might apply.
Capturing the data:
Need raw data for all the different touch points. Initially they explored out-of-the-box attribution model vendors but ended up using their ad servers.
[💎@21:19] To capture data you can use your ad servers (e.g. DoubleClick suite for desktop) and MPP for mobile touch points in order to send raw data points to your internal data warehouse. Then use users' email addresses to connect the dots for converted users and layer in the LTV model.
For Slack, email is part of the registration process so it is the easiest way to connect the dots.
It's not perfect and just 1 of the approaches to measure effects of marketing.
[💎@27:45] For incrementality testing a DMA-level (Designated Market Area i.e geo split) incrementality test is preferred because pre-post (pausing campaigns) can be impacted by seasonality or other factors.
Mobile still complimentary to desktop.
Shifted a lot of efforts from top-line growth to effect across devices. Also started to think about a mobile-first design for advertising and messaging which made a big impact.
In a lot of B2B products, mobile is an adopted child.
Macro-level: more and more important for B2B user journey
2017: seen decrease in mobile and high growth for desktop which seemed like a disconnect.
Also happened while doing a lot of research on LTV so they knew it was very important. Example: if creator of a team also has mobile usage then LTV 5-10x more.
First step is to try and have a holistic perspective around the role of model and its exploration, which is still not entirely clear.
A lot of competitors were bidding on Slack keywords on mobile so at least they should start doing brand defense.
One of the difficulties: connect all the touch points across devices until the conversion/activation.
It's about understanding the different touch points in a conversion funnel and properly give credits to these touch points. Typically the context is last-touch attribution that gives 100% of credit to the last touch point.
For Slack a lot of mobile touch points are affecting the decision.
Especially important for a B2B product because people need a lot of information before they convert: pages, watching videos, even going to events, etc.
At Slack it is still shorter than traditional enterprise products (because bottom-up and freemium) but a large volume of users convert outside of 30-day window and there are about 10 touch points before someone converts.
High-level approach to the transition:
[💎@19:46] You should also look at your internal data on the log level to see the user journey of your converted users. This analysis can help you assess the impact of the different attribution models that you might apply.
Capturing the data:
Need raw data for all the different touch points. Initially they explored out-of-the-box attribution model vendors but ended up using their ad servers.
[💎@21:19] To capture data you can use your ad servers (e.g. DoubleClick suite for desktop) and MPP for mobile touch points in order to send raw data points to your internal data warehouse. Then use users' email addresses to connect the dots for converted users and layer in the LTV model.
For Slack, email is part of the registration process so it is the easiest way to connect the dots.
It's not perfect and just 1 of the approaches to measure effects of marketing.
[💎@27:45] For incrementality testing a DMA-level (Designated Market Area i.e geo split) incrementality test is preferred because pre-post (pausing campaigns) can be impacted by seasonality or other factors.
Mobile still complimentary to desktop.
Shifted a lot of efforts from top-line growth to effect across devices. Also started to think about a mobile-first design for advertising and messaging which made a big impact.
