
How to Lose Money on Paid Marketing - jamiequint
http://jamiequint.com/how-to-lose-money-on-paid-marketing/
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DLarsen
I'll also add that the attribution window is totally relevant as well.

In the multi-touch example given, the 5-day reporting window does not account
for the fact that tomorrow's revenue will be sprinkled into today. That means
that your ROI for today's spend isn't final until you're beyond the
attribution window.

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shostack
Yep. And when looking at data you have to be very clear on whether the
conversion data you are seeing reflects "date of click" vs. "date of
conversion." "Date of Click" data will continue to change throughout the
duration of your reporting window whereas "date of conversion" does not.

This can get sticky in things like AdWords where the main UI reports on "date
of click" and keeps changing, whereas Search Funnel reports report by "date of
conversion."

Fortunately, most bid management platforms like Marin let you make that
distinction in reporting.

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jamiequint
Date of click is fine as long as you are understanding your reporting over
fixed timeframes. e.g. What is my Day 7 ROAS on spend from 2 weeks ago, that
will never change once you get past 7 days from the window of spend that you
are looking at.

Date-of-conversion has the different (and in my opinion worse) issue of being
100% accurate in terms of _current_ cost-per-conversion but under-values all
marketing channels. This is because it attributes the cost of visits only to
conversions that have already happened, while more conversions may later be
attributed to those visits. This means that a conversion based model is
actually worse for channel analysis.

~~~
shostack
Agreed on both points. Ultimately, everyone should try to gain a better
understanding of the duration and number of touch points in their sales cycle.
This directly impacts the lookback window you set. It is also important to
know what these are when comparing any data across platforms.

For example, right now I'm going from a 90 day window for certain goals in
AdWords because I know that while the majority convert on that goal in ~30
days, there's a long tail. Unfortunately, Marin restricts their window to 30
days, so any deeper digging will need to occur within Google Analytics or data
from our data warehouse.

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jamiequint
Would be great to learn more about solutions you've seen in the wild since it
seems like you have a lot of experience on this topic. If you have time for a
chat my email is in my profile (tried to find you but shostack is ambiguous :)

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cassieramen
This is a great argument for having a data scientist or other statistician run
your marketing department. Tech is really changing the way people think about
marketing expertise. What counted 10 or even 5 years ago is quickly growing
irrelevant.

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dfragnito
Revenue per visit really? If a visit does not convert how does it have any
value (yes I understand the math)? Why not convert Cost-Per-Visit to Cost-Per-
Conversion (total spend / # conversions). I am going through this now with a
client. The CPC gurus need to justify the CPC budget and surprise surprise the
more cpc dollars spent the more they make. What perverse incentive system they
have set up for themselves.

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jamiequint
If a visit doesn't convert it has $0 value. You can convert cost-per-visit to
cost-per-conversion instead (the #s will come out the same) but its more
complicated to do technically.

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dfragnito
(total spend / # conversions) is pretty easy to do

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jamiequint
You can't do it that easily.

If you just take total_spend/conversions the question that leads to is what
spend do I measure over what timeframe and what conversions do I measure over
what timeframe? For example, if I want to know cost-per-conversion for my
Facebook traffic then which conversions should I consider in your equation
above?

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dfragnito
a time frame is assumed in my equation. Calculating conversions and ad spend
over a time frame is easy if you have your analytics tracking set up properly.

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jamiequint
You can't just add up marketing spend and conversions over the same period of
time and divide them. That's not an accurate method of measurement.

The conversions and the visits they lead to them are not guaranteed to happen
over similar timeframes. If you calculate this the way you're suggesting you
are making all sorts of implicit assumptions about variance in visit volume,
marketing spend, conversion rates, etc.

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dfragnito
For the sake of sanity keep your budgets the same/channel over a time period.
The variance in traffic attribution will thus even out so you can add up
marketing spend and conversions over the same period of time. I subscribe to
KISS

~~~
jamiequint
That works if you have short conversion timeframes, but if you're running
e-commerce, B2B, or anything with longer than same-day conversion timeframes
you're counting conversions that were driven by visits outside your analysis
time window that way, it may be simple but its not very accurate.

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dfragnito
I disagree, ceteris paribus the conversion associated with each channel will
be about the same once you past the longest conversion window. We are not
attempting to land on the moon, a high degree of precision is not required.
This is cookie based analysis so there goes any degree of precision, there is
more precise "user finger print" type of analysis but that gets creepy.

~~~
jamiequint
All other things aren't held equal in the real world. You may add a new
campaign, or stop one that is currently running. You might change up the
audiences or add to them. Basically any activity you would do to optimize a
campaign is going to potentially change a channel's behavior. Either you limit
yourself to not making those changes, or you severely slow down your decision
cycles by constraining them to an amount of time based on the longest
conversion window.

That may be an acceptable way to run marketing with small budgets (say under
$20k/mo) since you don't have the spend volume to come to many new
statistically significant insights about what is working and what is not in
short timeframes, but once you get to larger budgets the method of hoping that
all other variables are equal-ish is a great way to lose money on marketing.
If you run substantial marketing accounts it trivial to see the differences in
practice between the model you suggest and a more accurate model, you
shouldn't just hand-wave away the differences.

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dfragnito
Not factoring in Cost of Sales is also a good way to lose money on Paid
Marketing.

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swehner
I couldn't quite follow what the problem is.

I would define the revenue in the ROAS formula as revenue per time period, and
the marketing spend also per time period

Then I would qualify the revenue, as that part of the revenue that can be tied
to the marketing activity (cookies, refer URL, etc)

And then the ROAS ratio becomes an ordanary division, because both values are
known; no need to infer them as in the rest of the article.

~~~
DLarsen
The whole issue is how you tie revenue to marketing activity. In a case where
you had multiple touch points before a conversion, it's a question of which
channel gets credit and in what time frame.

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shostack
Respectfully, this is a submarine PR piece. From the last paragraph:

> _" It’s a lot of work to build out accurate multi-touch attribution, but
> there are third-party solutions out there that can simplify the process. My
> company Interstate makes a pretty good one in my opinion!"_

That said, the importance of the topic, cross-channel attribution, cannot be
emphasized enough for any company doing any sort of serious digital marketing
these days.

A fantastic piece that goes much deeper would be Avinash Kaushik's article[1]
on various modeling approaches. It is a great primer for both people new to
the concept and approaches, as well as people with familiarity who are looking
for new ideas.

The broader challenges these days are shifting towards the cross-device
dilemma as well as reliance on 3rd party cookies and browsers' and users'
increasingly aggressive stance against those.

-Cross-Device Dilemma-

I visit initially on mobile and then convert on desktop. Even Google has
challenges with cross-device accuracy here, and depending on your mobile
usage, it could dramatically impact how you view your performance. There are a
variety of technology solutions out there that attempt to solve this, but
ultimately there is no great solution that doesn't make me truly question the
privacy aspects of what it takes to get to accurate data here.

If someone signs up for a trial and gives you a unique user ID, great, you can
join that to the data from usage on other devices and understand that user's
path to conversion. If they don't take an action that requires logging in, it
is much murkier (not counting cookie onboarding services like Liveramp etc.).

-3rd Party Cookies-

Google and Facebook are excellently positioned because most browsers recognize
their tags as 1st party. Many other networks and adtech platforms are boned
here unless they can have their tags served as 1st party. I've seen variances
of around 15-18% or higher in some cases between 1st and 3rd party tracking
setups. While it is REALLY hard to control for all of the unknowns (no two web
analytics platforms will ever give you the exact same data), my hunch is that
3rd party vs. 1st party cookies are playing a significant role here along with
cookie deletion. Would love any other data folks can share on this.

Beyond that, the author talks about static models, but does not address their
real limitations. At the end of the day, these look at aggregate data, when
the reality is attribution occurs at the user level. Every touch point has
some value, and that value is different for each user. For example, seeing a
display ad on one site does not necessarily have the same value as seeing it
on a different placement within that same site. Static models are very helpful
directionally, but dangerous when taken as religion.

The future is in dynamic attribution models that constantly shift based on the
data and whatever contributing factors you can feed in as supplementary data
to the model. I'm super excited to see what Google starts doing with Adometry
(check out the TV release they just had), and the other big players like
VisualIQ and Convertro will likely have their technology trickle out through
other platforms over time. FB's Atlas acquisition is also a big statement that
they take this seriously.

Net net, attribution is the hardest challenge in the marketing/advertising
world today. Period. End of discussion. It is plagued with data that is messy
as hell and incredibly nuanced based on the infinite variables of everyone's
unique setups. Look through a variety of attribution models as different
lenses to help inform directional decisions, but be cautious on saying "this
is the model we will use across everything." It might help initially, but
without constantly checking it against other models, you can miss shifts in
your efforts.

[1][http://www.kaushik.net/avinash/multi-channel-attribution-
mod...](http://www.kaushik.net/avinash/multi-channel-attribution-modeling-
good-bad-ugly-models/)

~~~
dfragnito
Or stop all media buys for a month and see how much sales are effected. Then
turn them on one at a time. Do this every now and then but change the order in
which you turn each channel on.

~~~
shostack
On/off tests like that can give you a macro view of things, but in general are
not an ideal way to go about this in many scenarios.

First, you risk the hit to your acquisition efforts. For many companies in
growth mode, that's the LAST thing you want to do, even if you could gain some
valuable data. If anything, you do a plus-up test to increase budget in a
measurable way (narrow geo-targeting for example) and look for some lift
there. But that's just one approach.

Second, attribution isn't just at the channel level. To REALLY be useful, you
need to understand it at low enough levels to make more tactical optimizations
to your efforts. A simple channel-level on/off test will never get you that,
hence why attribution models are really useful.

~~~
dfragnito
It's always increase your ad send, the solution is never to decrease the
budget. A macro view seems like the best place to start. Also the anlysis is
never based on profit it seems to be a ratio of gross rev and medis cost.
There are other variable costs like cost of goods sold that must be
considered. If cost of conversion is $15 and it generated $60 in rev wow we
have a winner but the COG is $45 oops we made no money.

