11 BEST PRACTICES FOR PROGRAMMATIC...
Transcript of 11 BEST PRACTICES FOR PROGRAMMATIC...
11 BEST PRACTICESFOR PROGRAMMATIC ADVERTISING
PRINCIPAL AUTHORS
ROLAND SIEBELINK, HEAD OF CAMPAIGN QUALITY & PRODUCTIVITY, ROCKET FUEL INC.
JARVIS MAK, SENIOR VICE PRESIDENT, CUSTOMER SUCCESS, ROCKET FUEL INC.
…and the setup tips that marketers need
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11 Best Practices For Programmatic Advertising More questions? Contact us at 020-3651-1300 or [email protected]
This whitepaper is about
the practical ways to make
programmatic advertising more
effective. It focuses on how to
set up programmatic advertising
programmes for success, and how
to coax the best possible
performance out of them.
People who will benefit from
this whitepaper include media
planners, marketers, ad-operations
experts, and media analysts whose
primary focus is to actually run an
advertising programme, as opposed
to buying it.
HOW TO AVOID COMMON PROGRAMMATIC ERRORS
Clearly, marketers have a lot of
questions about programmatic
buying, and a need for
simple answers. The world of
programmatic buying is evolving
so quickly that what might have
been accepted as general
knowledge last month could be
completely obsolete next month.
Rocket Fuel‘s “always-on“
philosophy of programmatic
advertising lets
us test, and optimise advertising
programmes every minute of the
day, every day. As soon as a
programme can be improved, it is.
This "always-on" approach is a
vast improvement to what
marketers have long considered
best practices in traditional
advertising. As in every paradigm
shift, a little extra knowledge can
go a long way toward helping you
gain a sustainable advantage over
your competitors.
BEST PRACTICES FOR PROGRAMMATIC EXECUTION
This whitepaper is a checklist
of simple tactics that can help
you make your programmatic
advertising programmes more
successful. We’ve also included
examples of actual user cases
throughout to help illustrate the
best practices we describe.
PLEASE NOTE:
This whitepaper does not focus
on what programmatic advertising
is, what makes it different from
traditional media buying, or how
to select a good programmatic
buying partner. It assumes that
the buying decision has already
been made. If you are looking for
answers to those pre-buying-
decision questions, we strongly
recommend downloading the
whitepaper, “10 Questions on
Programmatic Buying... and the
Answers Marketers Need.“
INTRODUCTION
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Artificial Intelligence. Real Results.
11 BEST PRACTICES
ONE: INTEGRATE PROSPECTING AND RETARGETING ........................................... 04
TWO: COLLECT DATA ON EVERY PAGE .................................................................... 06
THREE: OPTIMISE TOWARD REAL BUSINESS GOALS ........................................... 08
FOUR: RELAX YOUR CONSTRAINTS ........................................................................ 10
FIVE: TEST DATA MODELS HEAD TO HEAD .............................................................12
SIX: TEST MULTIPLE CREATIVE VARIATIONS LIVE ..................................................14
SEVEN: ENTRUST DAILY OPTIMISATION TO THE ALGORITHMS ............................16
EIGHT: EMBRACE VIEW-THROUGH CONVERSIONS .................................................18
NINE: SHARE RESULTS IN REAL TIME ......................................................................20
TEN: RANK VENDORS OPENLY . ................................................................................22
ELEVEN: SELECT A PROGRAMMATIC PARTNER THAT EMPOWERS ALWAYS-ON MARKETING . ................................................ 24
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ONE:
INTEGRATE PROSPECTING AND RETARGETING Programmatic advertising works best
when it’s allowed to combine upper-
funnel and lower-funnel efforts
in the same programmes. The best
practice is either:
1. Do not delineate funnel stages at all,
and let programmatic vendors compete
fairly on full-funnel effectiveness, or
2. if the advertiser insists on delineating
funnel stages, let each programmatic
vendor choose which stages they want
to compete in.
SHARE VALUABLE MODEL LEARNINGS
Having access to all funnel stages multiplies the amount
of data generated, which greatly speeds up learning
cycles and increases effectiveness.
Assigning certain vendors only to specific parts
of the funnel, such as using separate vendors for
prospecting and retargeting, means each vendor
needs to build up its own data about consumers,
interests, actions, and behaviour.
The best practice here is to let vendors work on this
funnel end to end. This enables them to leverage
more data and capture more consumer actions in their
algorithms, thus deriving far better funnel models for
the advertiser‘s product.
SPLIT THE RETARGETING POOL
It used to be a best practice to prevent bidding wars
for retargeting impressions by assigning retargeting
exclusively to one vendor and prospecting to others.
This is no longer the case.
First, splitting the retargeting pool between different
vendors prevents bidding wars more efficiently than
exclusivity does, with far more day-to-day control.
Second, letting programmatic vendors compete full-
funnel provides more insights and a better use of the
most advanced technology. Ultimately, this provides a
better ROI than letting “specialised“ vendors cherry-pick
the easiest conversions without replenishing the funnel.
Finally, retargeting can be so expensive that it is crucial
to keep comparing the ROI—not just between this month
and last month, but also between competing vendors.
USE THE SMARTITIONER SCRIPT
Setting up a simple script on the advertiser‘s website
will help split the retargeting pool fairly across several
programmatic advertising partners.
1. Download the Smartitioner script, which
Rocket Fuel has contributed to the open-source
community.
http://www.github.com/rocketfuel/smartitioner
2. Pick the vendors that you want to split the
retargeting pool between and assign a percentage
of the allocation to each of them. For example,
you can give one partner (the incumbent) 75% of
the retargeting traffic, and another partner (the
challenger) 25%.
3. Implement the script on the advertiser‘s website,
or use a tag-management system that cleanly
splits the retargeting pool. The more flexibility
you have to adjust the allocation, the better.
4. Remove all other vendor pixels from the
advertiser’s website.
5. Your retargeting pool will now split cleanly
between the vendors defined, according to the
allocation you determined.
6. After an initial evaluation period, begin adjusting
your retargeting allocation in favour of the
better-performing partner. You‘ll want to confirm
sustained performance before switching over
completely to a single partner.
7. Don‘t rest on your laurels. Implement another A/B
test six months to one year after you begin so
you can ensure your partner of choice continues
to be the strongest competitor.
FOR EXAMPLE: DOUBLED PERFORMANCE FOR A CREDIT-CARD CLIENT
A major credit-card company wanted us to split their
campaigns between prospecting and retargeting goals,
with 50% of the budget devoted to each, and using a
separate creative for each stage.
When initial CPA results were disappointing, the
agency recommended running one end-to-end tactic
including both prospecting and retargeting. This let our
algorithms decide how to balance the stages, and which
creative to serve, at which moment.
Almost instantly, we saw the prospecting/retargeting
balance move away from 50% /50% to 85%/15%, and
the CPA for the overall programme drop by more than
half. Once clients try integrated prospecting/
retargeting tactics, they rarely return to separate
tactics again.
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TWO:
COLLECT DATA ON EVERY PAGE/URL
Programmatic advertising works
best when it knows as much
as possible about consumers
previously exposed to the
advertiser‘s owned or paid
media. This is why advanced
programmatic vendors ask an
advertiser to not just place a pixel
on a conversion page (or a few key
pages on their website), but on all
possible pages and other digital
media that users may access.
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Artificial Intelligence. Real Results.
LEARNING FROM NON-CONVERSION ACTIVITY
Marketers often believe that pixels only matter for
retargeting. The truth is that information about the
consumer is valuable at every stage of the funnel.
The earlier that programmatic advertising vendors can
build a profile of an anonymous consumer, the more data
they can gather about the conversion funnel, and the
easier it is to optimise the entire advertising programme
toward the funnels that have proven most ideal.
DON‘T LIMIT EXPOSURE OF A VENDOR PIXEL
The most common error is to limit pixels to the
conversion page. The few seconds of effort webmasters
save by not placing a universal pixel could well result in
an ROI that is hundreds of thousands of pounds lower
than it could have been.
The next most common error is to place the pixels of
competing vendors inconsistently. Fair competition
requires a fair exchange of consumer data, which can
only be achieved if each competitor has the same
number of pixels in the same places.
The third most common error is reusing the same pixel
for vastly different marketing activities, without passing
a parameter where the pixel was placed. This can be
confusing to the programmatic partners‘ algorithms.
PLACE A UNIVERSAL PIXEL AND A CONVERSION PIXEL
A good programmatic advertising vendor will propose
at least two pixels: One specific conversion pixel for the
conversion page and one universal landing pixel for all
other pages.
It is a best practice to implement those pixels as per the
recommendations of the vendor:
• The universal landing pixel in the footer that gets
attached to each page
• The conversion pixel on the conversion page
In addition, another best practice is to have separate
pixels for all the different marketing channels.
FOR EXAMPLE: QUADRUPLED PERFORMANCE OVER DIRECT COMPETITOR
Placing a pixel on every page can drive up performance
2–4x, a much bigger factor than many marketers might
guess.
We can illustrate this with an interesting case of two
telecom advertisers—let‘s call them ComTel and TelCom.
ComTel and TelCom offer similar products and services
at similar prices. They focus on similar segments, and
run similar advertising programmes. The only difference
is ComTel had a pixel on its conversion page only,
whereas TelCom had a universal pixel that gathered
data on every page.
The gap in their performance was striking. ComTel‘s CPA
was invariably less than half of TelCom‘s, and—more
typically—closer to a quarter.
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Programmatic advertising drives the best results for advertisers when
agencies and marketers are able to move beyond the short-term
advertising goal. If they can identify the underlying business goal, and
link that goal directly to the programmatic efforts, marketing results will
suddenly become much more relevant for the advertiser, making the
contribution to the advertiser‘s bottom line rise exponentially.
THREE:
OPTIMISE TOWARD REAL BUSINESS GOALS
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ROI IS HIGHER WHEN DRIVING BIGGER-PICTURE RESULTS
An example is moving from a CPA focus to a customer
lifetime value (CLV) focus. Suddenly, the programme
will ignore the same old repeat customers and drive
new consumers to the advertiser—people who are
highly likely to buy multiple times and at a high value.
The perceived ROI of the advertising programme can go
up by several factors, and will rise higher, the closer it is
tied to the advertiser’s strategy and bottom line.
For example, an advertiser growing its business in
new markets, given the difference in awareness levels
alone, will have little chance of success if it expects
the same CPA it enjoys in an existing market. But focus
the same programme on the discounted cash flows of
future customers, and the new markets suddenly look
far more attractive.
DON‘T REDUCE BUSINESS GOALS TO LOWEST COMMON DENOMINATORS
Being fully aware of the business goal for the client,
but “translating“ that into a pure marketing goal for the
programmatic advertising vendors is a common error.
This is partially based on habit, and partially on
insecurity about programmatic advertising vendors not
being able to measure something outside of the core
advertising metric toolbox.
DO SHARE THE REAL BUSINESS RESULT AND UPDATE IT REGULARLY
A best practice is to share the real business result, or
even to ask for it if the advertiser did not include it
in the original briefing. Allowing the most advanced
programmatic advertising companies to optimise
directly toward this business metric will generate
enormous success. It will also show a much clearer
differentiation between the kinds of optimisations
that different programmatic advertising platforms are
capable of.
FOR EXAMPLE: MAXIMISED CASH INFLOWS FOR NEW CHECKING ACCOUNTS
After the financial crisis hit, banks were scrambling for
consumer pounds and advertising their savings
accounts aggressively. A leading online bank found that
its programmes persuaded many consumers to open
bank accounts, but that those consumers did not
necessarily fund those accounts with much cash. We
worked with the agency to shift the focus from
accounts opened to aggregate pounds flowing in.
This optimised programme generated as much as a 20%
lift in bank accounts that are profitable for the bank,
thus helping the bank to drive much higher ROAS.
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Programmatic buying works best
when it can make full use of all
the consumer data available, then
amplify successful conversions to
new audiences that exhibit many
of the same data points.
FOUR:
RELAX YOUR CONSTRAINTS
The best DR marketers allow the programmatic
models to discover these new data points and
generate conversions even in unexpected corners of
the market, rather than constraining advertising to
traditional segments.
IMPROVES LEARNING AND REDUCES WASTE
Traditional marketing tends to avoid waste by limiting
spending to closely prescribed target segments, then
selecting media that claim an acceptable degree of
reach into that segment.
Advertising programmes, however, buy every single
impression individually and have millions of data points
to predict whether an impression will ultimately lead to
a conversion. They minimise waste by the very act of
optimising toward the programme goal, and no longer
need the traditional trinity of segments, content, and
reach.
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Artificial Intelligence. Real Results.
BEST PRACTICE FOR BRAND MARKETERSTrying to discover new segments does not fit with brand programmes.
Brand marketers have audience
composition and reach as core
performance measurements, so the
approach must be different for those programmes.
The good news is that programmatic advertising programmes are excellent
at driving a high reach within a very
specific audience composition. The key to success is to optimise the programme
for that measure and not for an
inaccurate proxy measure such as clicks.
Advanced programmatic advertising
companies have permanent
partnerships with brand-measurement
companies like Nielsen and comScore to
integrate the programmatic advertising programme with tried-and-true brand
metrics, and even to optimise toward
these brand metrics in-flight.
DON‘T FORCE A TRADITIONAL WAY OF SEGMENTATIONLimiting programmatic DR programmes to only
impressions within a certain segment or on certain
publications will often be counterproductive because
this will limit both:
•Conversions that happen to fall just outside of
the segment
•Learnings for more advanced modeling of the
consumers most likely to respond to the
programme
We have seen many examples of programmatic
programmes with “hard constraints“ on segments or site
lists under-performing because the algorithms could
simply not learn enough about this limited set of
consumers to discover the programme‘s additional
market potential.
DO SPECIFY DESIRED SEGMENTS AS STARTING POINTS
The most effective way to use audience segments in a
programmatic DR programme is to specify them as
“soft constraints“:
1. Set a clear goal and a numerical constraint for the
programme performance
2. Limit hard constraints to geographical
constraints only
3. Specify all demographic, psychographic, and other
targeting criteria as soft constraints
4. Let the campaign perform and optimise within the
constraints and build on the starting points given
FOR EXAMPLE: HALVED ACQUISITION COST FOR A POWER COMPANY
Relaxing audience characteristics can double or triple
the impact of an advertising programme.
A major automotive marketer translated its intent to
reach more Spanish-speakers into “only advertise on
Spanish-language websites.“ The resulting acquisition
costs were typically 2-3x higher than the goal
originally intended.
A major power company started out with a troubled
advertising programme too, defining micro budgets for
each DMA. Once we removed this restriction and
focused on the same geography at a state level, the CPA
dropped by more than half.
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On the other hand, buying third-party data models and
investing in the quality of first-party data segments is
expensive. A simplistic programmatic buying
programme may shift a large chunk of the efficiencies
gained in media buying toward data buying, unless it
tests aggressively which data actually works.
The best approach for marketers is to handle data the same
way they test vendors: Try out as much as possible, but
never commit large budgets until performance is proven.
DON‘T FOCUS ON SPECIFIC DATA MODELS DIRECTLY
Simplistic programmatic advertising programmes buy
multiple third-party data models, maximise reach and
frequency among the consumers in these segments,
and then try to find more consumers that “look” like the
consumers that ultimately converted in these segments.
But because nobody can reliably predict the validity of
data, this approach is generally inefficient. It takes the
validity of the third-party data models for granted, and
pays for their use no matter what their influence on the
marketing results is.
DO INVEST IN DATA THAT IS PROVING ITS WORTH
The best practice is to suggest all of the possible data
sources to the programmatic advertising vendor, but to
consider them just a starting point.
Advanced programmatic advertising algorithms will run
massive tests of many combinations of data sources,
Marketers gaining experience with programmatic buying start to
notice how their advertising becomes less dependent on the sites
and apps where ads are served, and more on the right consumer data
underpinning the selection of impressions. This is why it makes sense
to add more data.
FIVE:
TEST DATA MODELS HEAD TO HEAD
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messages, and context, and will quickly figure out which
combinations of data points are most successful and
rise to the top.
Many other data sources will remain below the surface
and not show significant lift for a given programme
objective even though they may have performed well for
a different programme or client, or may do so in the
future.
FOR EXAMPLE: MODELED AUDIENCES SEEDED WITH DATA MODELS OUTPERFORMED BY 2.5X
A major CPG advertiser noticed that the third-party
data models that they had based their programme
on showed large differences in brand lift. At the
recommendation of the agency, it decided to test all
data models against an algorithmic-synthesised model
of their intended audience.
While two out of eight third-party data models
performed better than the synthesised model, six
out of eight did not, dragging overall brand lift
down 45%. Naturally, the client decided to move the
budget into the performing data models and the
synthesised model.
A major automotive manufacturer saw an equally
impactful difference in a head-to-head test for
behavioural targeting: the dynamic model did almost
2.5x better than the data models, with a 59% lower
CPA.
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Artificial Intelligence. Real Results.
While programmatic ads need to
look like a small print ad to be
effective, the big difference with
digital is that there is no need
for “final“ creative. If the goal is
to learn and optimise as much as
possible during the programme,
then it is actually
counterproductive to have “final“
creative at any point.
SIX:
TEST MULTIPLE CREATIVE VARIATIONS LIVE
MAXIMISE LEARNING AND EFFECTIVENESS
Programmatic advertising is most effective when it
can test and revise (or “learn“) across many cycles. This
learning should not be limited to tactics and settings;
it should also include the messaging. The reality of
programmatic advertising is that very subtle differences
in ads can result in strikingly different response rates.
Let the data from the response rates be your guide.
DON‘T TRY TO PICK THE SINGLE BEST CREATIVE UPFRONT
One of the worst errors in programmatic advertising
occurs when advertisers try to develop a single
“perfect“ ad rather than keeping a few variations on the
table. Programmatic advertising programmes do better
when they start with a few flavours of an ad and then
allow the market to decide which one(s) works best with
which consumers and in which context.
DO TEST VARIATIONS AND ADD POLISH LATER
The goal during the first week (or maybe even the first
day) is simply to learn as much as you can about which
creative elements work best. The algorithms will do
massive testing that yields a massive amount of data
about the best message, the best image, the best
promotion, and the best call to action.
FOR EXAMPLE: INCREASED PERFORMANCE AND LEARNING
A well-known financial services niche publication
started running just one creative. While the client was
happy with the initial performance, tests with additional
creatives kept increasing results. As a result, more
creative variations were added.
The client is now at a total of 47 creative variations,
across all creative types, sizes, and funnel stages.
Average performance is now 82% better than when the
programme first started.
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Many marketers, especially when working directly for the advertiser
rather than at an agency, are very hands-on when it comes to
managing their budgets. They are used to making constant changes
to the budgets based on the performance of the most recent
few days. While we applaud this hands-on management in
traditional digital media or in search advertising, it can be
counterproductive in programmatic buying, especially in a
fully algorithmic environment.
SEVEN:
ENTRUST DAILY OPTIMISATION TO THE ALGORITHMS
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MANUAL TWEAKING HURTS PERFORMANCE
The programmatic system already takes the variability
of the environment into account and adapts to it
constantly. Changing the goals resets the learnings and
can make previous learnings less valid.
DON‘T OVERIMPOSE OR OVERCORRECT
• The first common error is to correct manually and
reset the learnings before the algorithms have had
a chance to correct themselves.
• The second common error is to correct for naturally
occurring variability of the kind that an algorithm
can recognise and manage, but stressed-out human
marketers might overreact to.
• The third common error is to overcorrect. Marketers
may set a lower CPA than they can really afford,
just to “have the result reach the middle.“ In such a
constrained system, the outcome may be that the
partner‘s algorithm is only able to deliver a fraction
of the volume at the more aggressive goal.
DO LEARN FROM THE MASSIVE TEST RESULTS
Instead, a best practice is to set a clearly measurable
programme objective and let the algorithms do their job.
• Be honest about the real programme objective and
avoid “padding“ that number for better performance.
• Let the algorithms run, and provide the vendors
with honest feedback about performance (see
“Rank Vendors Openly“ on p. 22).
• Avoid making manual changes to content, segments,
objectives, and budgets until the algorithms have
run at least a week with stable settings.
• Delve into the insights that the algorithms’ massive
tests provide, and adapt other parts of your
programme to the new learnings gained.
FOR EXAMPLE: 45% IMPROVED PERFORMANCE AT 68% HIGHER BUDGET
We worked directly with the marketing team of a well-
known women‘s-apparel retailer. The marketing team
was extremely hands-on, prescribing exact daily and
weekly budgets and swapping out creative every single
day. This threw off the automatic calibration of the
models so much that the client missed its KPIs by as
much as 50%.
A major eyewear e-tailer had a marketing team that
was very hands-on, resetting budgets on a daily basis.
Looking for better performance at a higher scale, it
allowed our algorithms more freedom to move budget
between channels and between days of the month.
This flexibility made such a difference that CPA dropped
45%, even with a 68% budget increase.
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Consumers typically see ads numerous times: during a Google search, on
Facebook, as a display ad, in an email, or on an affiliate site—sometimes
all of the above. Therefore, it makes little sense to give full credit to
the last time a person clicked on the ad before converting. Effective
advertising programmes draw in customers every step of the way.
EIGHT:
EMBRACE VIEW-THROUGH CONVERSIONS
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STRONGER FUNNEL GROWTH
Apportioning credit to multiple touch points before a
person converts is not that difficult, and it pays huge
dividends. Embracing the effectiveness of view-through
conversions is a key step to capturing the full benefits
of programmatic advertising.
Experienced marketers understand the importance of
“top-of-the-funnel“ efforts designed to plant a seed in a
customer‘s mind. For most consumer products, only 14%
of consumers are said to be open to alternatives at the
actual purchase stage. This means that at least 86% of
the purchase decision is made before the last click.
DON‘T OVERSIMPLIFY YOUR ATTRIBUTION
Multiple analyses also show that people who click on ads
represent a tiny, and generally unattractive, demographic
segment of Internet users. One programme seeking
18-24 year-old city students of wealthier backgrounds,
for example, found that a majority of clickers were
retirees in poor households and rural environments.
Marketers that insist on click attribution will ultimately
find their marketing returns collapse. This is simply
because they are focusing on the skewed audience that
clicked on an ad in order to convert.
DO CHAMPION STATE-OF-THE-ART ATTRIBUTION
We recommend implementing a robust multi-touch
attribution framework that credits every stage in the
funnel appropriately, and updates programmes with
data in-flight (see “Share Results in Real Time“ on p.
20).You can build a robust attribution framework by
experimenting iteratively with the mix of partners,
channels, and attribution allocations across a few
programmes. Most likely, you can reach the same
bottom-line results much more efficiently with fewer
partners and fewer touch points.
FOR EXAMPLE: 99% OF ORDERS RESULTED FROM VIEW-THROUGHWe work with one of the biggest fast-food delivery chains,
driving both in-store sales and increasing the effectiveness
of TV advertising with programmatic support.
Initially, both the advertiser and the agency were
convinced that they should give credit only for
purchases attributable to a click. However, Nielsen
research proved clearly that there is “virtually no
relationship between clicks and brand metrics or offline
sales.“ ComScore supported this finding, saying that
only 16% of consumers ever click on ads, and that 8% of
users account for 85% of all clicks.
In a programme we ran for a major restaurant business,
clickers were also very different from converters.
Converters tended to be 18–44 years old, whereas
clickers skewed toward 65+/retired. Converters tended
to be interested in movies and video games, whereas
clickers preferred golf and ladies’ leisure.
Further research with the agency showed that repeat
customers (those with high lifetime value) were 8.8x
more likely to be view-through converters than click-
through converters. After we showed that click-based
revenue represented less than 1% of total revenue
generated, the advertiser quickly embraced view-
through conversions.
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Some of the low-hanging
fruit in programmatic
advertising is to share
performance data
with vendors on a
programmatic basis. With
just one login to your ad
server or analytics system,
you can create massive
performance improvement
solely on the basis of how much
the programmatic system can learn
from granular results.
NINE:
SHARE RESULTS IN REAL TIME
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Artificial Intelligence. Real Results.
SPEEDING UP THE LEARNING CYCLEProgrammatic buying is designed to respond to changes
in supply and demand in microseconds. To do this well,
it needs up-to-date feedback on how well it is doing for
the advertiser. Direct programmatic access ensures that
the programmatic advertising algorithms can respond to
these fluctuations instantly.
SHARING A PDF ONCE A MONTH IS AN OPPORTUNITY LOST
This means that it is not sufficient to tell your
programmatic buying provider about your results on
a weekly or monthly basis. You also shouldn‘t do it
in a format that is hard to import. And sticking with
manual processes causes frequent errors and delays in
importing the performance data back into the system,
which results in inconsistent calibration and reporting.
data from your systemBy giving a programmatic vendor real-time access to
the same data that you use to track their advertising
results, the vendor can optimise their programme in
real time rather than weekly or monthly.
The most common performance tracking
systems are the third-party ad servers. But,
if your “source of truth“ for marketing performance is
a web-analytics system like Omniture, then it‘s critical
to provide that feedback to your partners as often as
possible. If the web-analytics data conflicts with the
third-party data, but it‘s the web-analytics numbers that
matter to senior executives, then that‘s what all partners
need to be using. Otherwise, their systems are optimising
off the wrong data.
FOR EXAMPLE: 2X PERFORMANCE OVERNIGHT
We should mention that the first benefit of real-time
data sharing is less work for the agency or marketing
team. Once clients move to real-time data integration
with their ad server or analytics system, they notice
that campaigns start to auto-optimise without any
manual intervention. This productivity gain alone is a
massive benefit for busy marketers.
This optimisation, of course, also provides a
performance boost. After a premium automotive brand
decided to provide us with a login to its ad server, the
automatic optimisations kicked in and performance
doubled overnight.
Allow your vendor to pull
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A second aspect of sharing
performance is easy to accomplish
and highly effective too: Simply tell
each partner how they perform
against their competitors.
TEN:
RANK VENDORS OPENLY
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Artificial Intelligence. Real Results.
ENABLES CALIBRATION OF PERFORMANCE METRICS
The very nature of programmatic advertising is that it
has advertising programmes running on automatic pilot.
The software to run these programmes works best
when it can regularly calibrate itself to the performance
that the client sees.
DON‘T CANCEL A VENDOR IMMEDIATELY
This is why it is better to share relative performance
openly before taking a vendor off the plan. Whereas
performance with traditional media vendors is typically
fixed for any given programme, programmatic buyers
have at least a few levers they can pull to push
performance.
Cancel them too early, and you may miss out on a part of
the programme that could have performed better.
Smart programmatic vendors strive to be the optimal long-
term performer for the advertiser. Their goal is to drive
performance at a level that is better than competitors’,
but to also be scalable to a high volume at any given point
during the programme and over time. Calibration can help
facilitate a switch to a shorter-term perspective, when that
is what the advertiser really wants.
DO TELL EACH VENDOR THEIR RANK WEEKLY
We recommend sharing relative performance with all
partners on (at least) a weekly basis.
1. Rank all partners on the plan by the one metric
most important to the advertiser.
2. Tell each partner what place they reached in the
ranking, and indicate if the gaps between them
and their rivals are large or small.
3. Ask the best-performing vendors how much
more budget they could take at similar levels of
performance. The best programmatic vendors can
predict this with a high degree of accuracy.
FOR EXAMPLE: ALWAYS SHARE INFORMATION TO HELP ENSURE HIGHEST ROI
In one particular case, our algorithms were beating the
internal client goal, but the client did not inform us and
we could not see the same performance in our numbers.
We therefore recommended that they cancel with us
in order to work with another partner (not knowing we
were the best performer on the plan). If our system
had been re-calibrated on the basis of the performance
that the client saw, we could have scaled the campaign
significantly, delivering a much higher return on
advertising spending than the client ultimately
obtained. It was an opportunity lost for both of us.
On the flipside, a leading online music-distribution
service initially declined to provide relative performance
feedback. Since our programme beat the official CPA
target, the team did not see the need to tweak
performance versus scale and sustainability.
After the client informed us that performance was more
important than volume and that another vendor was
driving better CPA results, albeit at a lower scale, some
tweaks quickly drove a 15-20% performance improvement.
More importantly, this discussion also generated more
insight into the client‘s real business goal: driving better
prospects with a higher potential lifetime value. After we
learned that, we quickly optimised the programme, to much
higher satisfaction of the client.
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11 Best Practices For Programmatic Advertising More questions? Contact us at 020-3651-1300 or [email protected]
ELEVEN:
SELECT A PROGRAMMATIC PARTNER THAT EMPOWERS ALWAYS-ON MARKETING
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Artificial Intelligence. Real Results.
We hope that this whitepaper has shown many actionable ways to make
your programmatic advertising run better.
While some of these tips can be applied liberally in
a self-service setting, the reality is that the vast
majority of programmatic advertising runs through a
full-service relationship with one or more specialised
vendors. Therefore, our final recommendation is to
be critical about vendors and to pick the ones that
ultimately deliver the best ROAS (return on ad spend)
to the advertiser.
Obviously, advertisers may want to take into account
buying criteria like transparency, data access, low
margin, and a beautiful user interface. But, in the end,
we find that the best practice is to focus on attaining a
sustainable ROAS first and foremost. If you do that, the
rest will then fall into place.
TWO VENDOR “MUST-HAVES”:
1. SPECIALISTS WITH PROVEN TRACK RECORDS
Choosing the right partner is important because
programmatic advertising is changing much faster,
and thus is much less user-friendly to operate
than, say, search advertising.
2. DON‘T HAVE MULTIPLE VENDORS ON THE PLAN
“FIGHT IT OUT“
Choose a number of partners for your plan, and
give each every opportunity to show you their
best stuff—letting them know how they are doing
against (anonymised) competitors.
Since programmatic depends so much on learning and
strategic data integration, the worst way to choose
programmatic advertising partners is to have a half
dozen or more seemingly comparable partners “duke it
out“ without utilising some recommended best
practices. Instead, choose one or two partners that
have long and proven track records, and the expertise,
the technology, and the roadmap to build a long-term
strategic relationship with your company.
Think of the vendor-selection process almost as a
technology decision—one that is more far-ranging than
running a media plan for one month. With that in mind,
it’s clear that it should take plenty of time to investigate
various vendors for the best long-term fit. The best
programmatic marketers think about adapting their
strategies for an always-on marketing world. Rather
than thinking about campaigns with fixed starts and
ends, they think about marketing as a 24/7 practice, and
advertising that learns and improves doesn’t need to
stop or sleep to improve ROAS.
That said, you should also run a head-to-head test at
scale to ensure that each partner produces outstanding
results time and time again.
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11 Best Practices For Programmatic Advertising More questions? Contact us at 020-3651-1300 or [email protected]
27
Artificial Intelligence. Real Results.
The best way to run traditional
advertising is often not the
best way to run programmatic
advertising. This whitepaper has
outlined eleven best practices
to get the most out of your
programmatic marketing efforts,
and we hope you will be able
to apply most if not all of these
practices across all of your
programmatic advertising
programmes.
Choosing the right partner is
crucial. As the most successful
programmatic advertising platform,
Rocket Fuel is laser-focused on
delivering optimal advertising
results, day after day. Our
“always-on marketing“ approach
to programmatic means your
programme results are tested,
reviewed, and optimised all day
and all night. With this strategy,
we can take your advertising
results and marketing ROAS to
new heights.
CONCLUSION