Download Impact Radius - How to Explain Algorithmic Attribution to Your

HOW TO EXPLAIN THE VALUE OF
ALGORITHMIC
ATTRIBUTION
TO THE REST OF YOUR ORGANIZATION
Table of Contents
Page 1
Introduction
Page 2
What is Algorithmic Marketing Attribution?
Page 3
What are Some Limitations of Rules-Based Models?
Page 4
Critical Success Factors for a Speedy Adoption of
Algorithmic Attribution
Page 5
How to Overcome Common Challenges
Page 6-7
Be Sure Your Solution Can Do This
Page 8-9
Let the Data Pave the Way
Half the money I spend on advertising is wasted;
the trouble is I don't know which half.
John Wanamaker
John was a department store merchant back in the early 1900’s when
advertising was all print. Digital marketing brings the power of
measurement but also introduces the challenge of omnichannel
attribution. Today’s marketing organizations depend on marketing
analytics and decision support tools to optimize their investments.
F
or decades, various techniques have been used by marketing
analysts to measure the effectiveness of advertising.
Marketing mix models originated in the offline world as a way
to answer Wanamaker’s fundamental question. Some of the
most popular methods used to model marketing
effectiveness have been top down, bottom up and control/test.
Modern marketers are beginning to use fractional attribution to better
understand “which half” of their advertising investment is performing
best at the customer journey level. Marketing mix models are often
built at a much higher level of granularity and should include both
offline and online marketing channels. Rules-based attribution models
are used by most marketers but lack the learning intelligence of
algorithmic modeling.
More and more digital marketers are realizing that, in addition to
rules-based models, there are great benefits to using algorithmic
models to analyze the effects of your marketing. While more
advertisers are acknowledging the importance of evidence-based
algorithmic models, adopting one is no simple task. This article
demonstrates the importance of algorithmic attribution and what to
look for in a trustworthy solution.
Page
1
What is Algorithmic Marketing Attribution?
Algorithmic attribution is the process of assigning a portion of a credit for a conversion to each
touchpoint based on effectiveness. The key differentiator of algorithmic attribution is its use of
advanced statistical modeling and inferences to continually optimize and customize based on your
results - put more simply, human-assisted machine learning.
With algorithmic attribution you can:
Statistically measure the effectiveness of each marketing event and its
contribution to the conversion
Statistically associate channel performance to channel weighting
Account for external factors such as seasonality and market conditions in order
to accurately measure contribution of paid media
Forecast sales and conversions
Optimize your marketing spend
Provide decision support that reduces risks
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2
What are Some Limitations of Rules-Based Models?
Last Click is Limiting
Optimizing marketing channels based on a last click model means that the last
marketing event gets all the credit for the conversion. This model relies on
recency alone and ignores other channels that may have contributed to the
conversion. It also ignores channel frequency, relative channel effectiveness
and external factors.
Even Model is Inaccurate
Optimizing marketing channels based on an even model means that the
advertiser is rewarding frequency alone and not recency nor any external
factors such as seasonal or macro-economic factors. The biggest problem with
this model is that diminishing returns and relative channel effectiveness are
not accounted for as all channels and path positions are credited equally.
Path Position is Arbitrary
Path position does not take into consideration customer behavior observed
in the marketer’s data.
Time Decay Rewards Path
Time decay models reward path position regardless of the relative
effectiveness of each of the channels in the customer journey. Just like the
even model, they ignore frequency and external factors.
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3
Critical Success Factors for a Speedy Adoption
of Algorithmic Attribution
When it comes to spend in marketing, there typically aren’t a lot of extra funds laying around.
Algorithmic attribution is an investment that will make your dollars work much harder. Here are
several things to consider that can impact the cost of implementation and become a barrier to a
quick adoption:
The project team must be clearly defined and include the necessary parties on both sides.
Your solution provider should be able to identify the key players and estimate the time
required of each deliverable.
The attribution model should be well-documented and validated using best statistical
modeling practices. You want a vendor who can explain how their model works to you,
in turn helping you to explain to your stakeholders and get buy-in. Be wary of mysterious
black boxes.
There should be a comprehensive and flexible data model that accounts for the various
data sources and the logistics of real-time integration.
The reporting tool should be easy to use and understand to support fast decision-making.
Once you’ve tackled implementation, it’s time to focus on stakeholder adoption. The ease of
interpreting the algorithmic model results will greatly impact stakeholder adoption. For this reason,
marketers need to be able to explain how the algorithmic attribution model works and how it is
different from non-algorithmic models. At all times, the focus should be on how the algorithmic
attribution model can be used to provide actionable insights such as shift dollars from one media to
another, identify new target audiences based on behavioral insights, align creative to audience insights,
and inform your forecasts (spend, traffic, revenue).
The volume of data available to marketers is massive. According to Forbes, “Data volumes are
exploding. More data has been created in the past two years than in the entire previous history of the
human race.” This creates both an opportunity and a challenge. In order to benefit, marketers need to
use sophisticated technology that can capture the data and provide actionable insights. Algorithmic
attribution modeling is built to do just that; manage very large volumes of data and provide real-time
decision support metrics.
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4
How to Overcome Common Challenges
Here are some ways to overcome challenges:
Challenge:
Tips:
Lack of full marketing data integration
Try to integrate the most significant marketing
channels in the study period. Work with an
integration partner.
Lack of visibility of some display
banners on the visited page
Partner with a vendor who tracks viewable
impressions to provide better impressions input
to the model.
Cookie deletion issues
Adopt a sampling method that minimizes the
negative effect of cookie deletion to avoid
negative impact on the model.
Cross-device jumping
Your vendor should have robust identity
resolution capabilities to ensure accuracy in
today’s multi-device world.
Difficulty due to identity mapping
Implement an identity mapping method that
helps achieve a higher matching rate.
Difficulty related to competitive
and personally-identifiable data
unavailability
Implement anonymous user identification by
stripping personally-identifiable information
and segmenting customers into homogeneous
groups.
Difficulty in integrating non-addressable
offline channels and macro-economic
factors in the user-level model
Implement a modeling approach that can easily
incorporate non-addressable and external
macroeconomic factors.
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5
Be Sure Your Solution Can Do This
These key features must be present for an algorithmic attribution
model to effectively aid decision-making:
An integrated marketing tracking platform must collect and expose relevant marketing channels
executed by the advertiser. The model should have the ability to implement an evidence-based
attribution model that learns from the advertiser’s own data and applies credit based on a
statistical algorithm. The algorithm should be designed to measure the effectiveness of each
channel in the customer journey.
The attribution model must account for external factors such as promotional activities,
seasonality, major holiday events, macroeconomic factors and competition.
The attribution model must account for frequency, recency, differentiated channel effectiveness,
diminishing returns and timing of advertising effects. The reality is that not all customers convert
because they are exposed to an advertising channel. Some customers may go to the advertiser’s
site and convert because of the time of year or non-advertising events.
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The attribution model should be able to generate intuitive performance metrics to help explain
marketing events based on changes in inputs to the model. Algorithmic attribution model
reporting should have the following:
Trend of channel weights: provides insight on how channels are performing overtime
and based on the existing optimization done by the marketer.
Trend of each channel’s execution side-by-side with their attributed conversions trend of
spend weights: gives insights on relative channel executions overtime and how the
marketer is allocating channel spend. Ideally, high-performing channels should get
relatively higher spend than low-performing channels up to the point of saturation
(diminishing returns).
Period-over-period channel conversion comparison helps compare how channels are
doing from one period to the next.
Period-over-period channel spend comparisons helps compare changes on how the
advertiser is allocating budget across channels.
Incremental vs. non-incremental reporting: compare the credit from marketing
contributions with the credit generated from non-marketing activities.
The model used for attribution should be predictive at a reasonable level of statistical confidence
so that the model can be used for decision-making.
The model used to generate attribution data should be validated with in-sample as well as
holdout sample (a sample of data not used in fitting a model). The holdout sample can be used
to assess the performance of the models.
The attribution model should produce outcomes that are consistent with economic theories of
demand behavior and marketing science – that is, an increase in advertising should result in an
increase in demand with a diminishing return effect. It should also account for the effect of
advertising’s timing. Consideration should be given to how channel position in the path, channel
recency and frequency can contribute to conversion. Timing of advertising does matter when we
are approaching high seasonal days like the holiday season with peak days such as Black Friday
and Cyber Monday.
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Let the Data Pave the Way
In today’s digital world, consumers interact
with as many as 4-5 devices in a day and
are often exposed to many types of media
before making a purchase. This customer
journey shows just how complex the
path to purchase can be:
Imagine the value of an algorithmic attribution model. Imagine if you had a continually-learning system
that can process every customer’s path and tell you the next optimal decision. American statistician
W. Edwards Deming said it best: “Without data you are just another person with an opinion.” Get ready
to open your eyes and challenge assumptions.
Start simple. Use gradual learning with the algorithmic attribution model. Initially use
recommendations directionally, and then progressively adopt the recommendations based
on evidence of improved effectiveness.
Have a clear understanding of how the model works, namely what inputs are included in the
model. An inability to explain the methodology can lead to doubt within the organization.
Use the data to optimize channels and be able to demonstrate the impact of the data by
comparing the benchmark to the modified investment and the resulting impact on revenue.
Don’t attempt to use non-algorithmic attribution model data to carry out channel optimization
because algorithmic attribution model data measures effectiveness while the non-algorithmic
attribution does not.
Advertisers who have all their channels integrated in their tracking system will benefit most
because algorithmic attribution attempts to incorporate all sources of demand generation -the more complete the integration the better.
The tracking data must be relatively clean with consistent dimension definitions and limited
collection errors. As the saying goes, “garbage in, garbage out.”
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The explosion of big data brings tremendous opportunity to marketers.
With the right technology they can accurately track conversion paths and
gain insights to more effectively reach their audience.
If you can’t measure it, you can’t manage it.
Attribution models, especially algorithmic models, provide a powerful
advantage to marketing teams in this dynamic economy. Those who neglect
to learn and implement these new technologies will be left behind.
Want to learn more? Let's chat.
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