Datasong Case Study

8/17/2019 Datasong Case Study

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1Copyright 2012 Revolution Analytics

CASE STUDY

DataSong’s Big Data Analytics Platform forMarketing Optimization Helps Clients

Understand Buying Behavior and Improve

Customer Targeting

Company: DataSong, San Francisco, CA

www.datasong.com

Industry: Software Development (Marketing Attribution and Optimization)

Challenge: Economically develop a scalable, high-performing R-powered Big Data

Analytics platform on which to provide services to clients

Solution: Revolution R Enterprise, leveraging RevoScaleR for Big Data Analytics, and

Hadoop for data management

Results: Company has achieved performance and scalability required to support its

growth. DataSong’s platform processes 50 million scores per day per client.

DataSong saved a client $270,000 on one campaign; generated 14% lift for

another client.

Background

DataSong’s marketing optimization and attribution engine uses historical data about

individual customers to predict and influence future buying behavior. With many

companies vying for consumer spending, DataSong, a San Francisco-based software

organization is increasing the precision with which marketing is analyzed. DataSong has

developed a unique way to understand attribution that takes into account sales from all

of a company’s order channels as they come into their call center, web site, mobile sites,

or retail stores as well as activity in all of the marketing channels (display/retargeting,

email, social media, direct mail, etc). It goes well beyond traditional methods and is

significantly more accurate than simpler methods (last click, averaged, etc).

Additionally, DataSong is leveraging the attribution platform to offer solutions that

impact the top and bottom line, such as marketing treatment recommendations.

Examples include who to include in next month’s catalog mailing, or whom to exclude


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from your retargeting display campaign. As a result, DataSong’s clients save money,

increase revenue per campaign and customize each customer’s relationship with the

company.

Figure 1: Without precise attribution modeling, many marketing departments simply

credit revenue to the “last touch” the customer had before a purchase. DataSong’s

analysis shows this approach can cost marketers hundreds of thousands of dollars in

unnecessary spending.

Challenge: Attributing Marketing Spend to Customer Revenue

“Once you know what is driving your marketing, you can better direct what

treatments you are using for specific people.” – Brandon Mason, CTO, DataSong.

DataSong understands its clients’ challenges very well. Without quantitative data to

guide them, marketers often attribute revenue incorrectly. When marketers are

unaware of the actual purchasing influencers, they spend too much on the wrong

things, afraid to reduce any part of the marketing spend because they don’t know how it

will affect sales.

This problem is especially acute in consumer marketing companies that spend a large

percentage of revenue on marketing. Further complicating matters, many of these

companies have separate groups for catalog, e-mail or other marketing. Each group

justifies its budget by attributing revenue to its efforts. DataSong’s analysis has shown

that many company marketing groups use flawed approaches such as the “last touch”before purchase. As a result, the aggregate claims of revenue attribution of all the

departments often sums to greater than 100% of sales, which is impossible.

Marketing departments need an analytics-driven approach to help deal with the

increasing amount of complexity that has emerged in the last 10 years. Here’s why:


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• It used to be about being able to quantify the results of what is now considered

basic and rudimentary information: POS data, direct mail (including catalog), email

distribution, and/or telemarketing. Through codes, marketers would be able toattribute POS data with the specific marketing campaign. This approach isn’t

effective today. Marketers have a much larger assortment to influence purchasing

(e.g. email, mobile messages, banner ads, search, catalog, in-store promotions,

affinity credit cards, etc.). These channels need to be considered alongside factors

that aren’t in their direct control (and which are often excluded from a company’s

own assessment of revenue attribution), such as seasonal habits or the length of

time the person has been a customer.

• Operational complexity makes it difficult to have one view of the customer’s

activity. Many marketing tactics are executed using disparate databases and

platforms.

• IT systems that customize offers for each customer have matured, making it easier

to feature a new product, a price promotion, or loyalty points. Unless marketers

know what offers drives which customers, they’ll end up programmatically using

the wrong tools for the job.

• The availability of reduced “cost per touch” methods tempts marketers to increase

the frequency of touches, (i.e. mailing catalogs is orders of magnitude more

expensive than an email or text message), potentially over-saturating or even

annoying customers.

• Customer mindshare has become increasingly difficult to win due to the constant

barrage of marketing being presented on television, in print, on websites and

mobile devices.

• Marketers don’t know how to utilize the terabytes of data collected about their

customers because of both its variety and range of formats. The amount of data

being stored in organizations (e.g. every mouse click, transcripts of conversations in

call centers, customer demographics and history, and even third-party-sourced data

such as credit scores), often called “Big Data,” has outgrown traditional analytical

tools. The value of this data is often untapped.

DataSong’s principals, self -described “data geeks,” saw an opportunity to create aplatform – an analytics engine – that would utilize dozens of data types as inputs to

individual buying behavior models for each of its clients’ customers. Since they wanted

the ability to analyze data at the granular level for each customer, the DataSong

platform would quickly grow to rely on many terabytes of data. DataSong looked at the

current off-the-shelf applications and approaches and rejected them. Their platform

must allow for data exploration, allow them to customize the statistical methods used in

“We’ve combined

Revolution R

Enterprise and

Hadoop to build

and deploy

customized

exploratory data

analysis and GAM

survival models for

our marketing

performance

management and

attribution

platform. Given

that our data sets

are already in the

terabytes and are

growing rapidly,

we depend on

Revolution R

Enterprise’s

scalability and fast

performance – we

saw about a 4x

performance

improvement on

50 million records.

It works

brilliantly.”


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the models, execute very quickly on big data and would need to scale without sacrificing

speed.

In order to be a game changer, they would need to build their own.

Figure 2: Modern marketers must utilize a huge amount of internal and external data to

attribute revenue per customer to specific variables (aspects of the marketing mix that

are in their control and elements that are out of their control) in order to optimize the

marketing mix and associated spend.

Solution: Custom Analytics with Revolution R Enterprise and Hadoop

DataSong’s purpose-built application is a modern, high-performance big data analytics

engine, scoring 50 million records per day for each of DataSong’s customers. It marries

Revolution Analytics’ Big Data Analytics capabilities with Hadoop’s data management

and computational power. Since no two clients are exactly alike, the statistical methods

that underlie the analytical models can be customized to meet each client’s exact

requirements.

From an analytics perspective, the company borrowed approaches from forward-

thinking and more analytically mature industries. For example, DataSong adapted

models used in the bioscience sector, where GAM (Generalized Additive Model) survival

analysis techniques effectively measure differences in the outcomes in patients underdifferent treatment regimens. However, many of the methods that DataSong wanted to

use had not been designed for Big Data analytics. Using Revolution R Enterprise, which

is based on the power of the R statistical platform, DataSong built a “big data analytics

engine” utilizing multivariate statistics, time-to-event models and GAM survival analysis

techniques, which tells its clients about:

“We like the fact

that Revolution

Analytics is

bringing R to

Hadoop, and has a

strong Hadoop-

related roadmap.

That kind of

enterprise support

is important to

us.”

Brandon Mason


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• Purchasing triggers

• Who is most likely to buy

• Cross-channel triggers – i.e. looking through a catalog, but making an online

purchase

•

Recommendations – which marketing treatment to apply (and when) per user

• Strategic allocation – Reallocating marketing funds as a result of modeling

DataSong’s platform utilizes Revolution Analytics’ RevoScaleR, a set of big-data

statistical analysis capabilities offered with the Revolution R Enterprise software. With

RevoScaleR, the scalable, high-performance “XDF” data file format optimizes the

process of streaming data from disk to memory, dramatically reducing the time needed

for statistical analysis of large data sets. It runs on commodity hardware, which has

helped reduce the cost of the platform.

CEO John Wallace describes the results. “We’ve combined Revolution R Enterprise and

Hadoop to build and deploy customized exploratory data analysis and GAM survivalmodels for our marketing performance management and attribution platform. Given

that our data sets are already in the terabytes and are growing rapidly, we depend on

Revolution R Enterprise’s scalability and fast performance – we saw about a 4x

performance improvement on 50 million records. It works brilliantly.”

According to CTO Brandon Mason, “We like the fact that Revolution Analytics is bringing

R to Hadoop, and has a strong Hadoop-related roadmap. That kind of enterprise

support is important to us.”

Figure 3: DataSong has utilized Revolution Analytics’ Hadoop capabilities to leverage

RevoScaleR’s Big Data Analytics capabilities and Hadoop’s data management and model

execution capabilities. The engine scores 50 million records per day per client using

commodity hardware.


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Results: Reduced Marketing Costs and Revenue Uplift for DataSong Customers

With the help of Revolution R Enterprise, DataSong has created a big data analytics

solution for marketing optimization that has been credited with saving one client

$270,000 in just one campaign and delivering a 14% revenue uplift for another.

DataSongenables marketers to plan, measure, and optimize marketing campaigns across

all marketing channels using integrated software and customer-level, advanced revenue

attribution. Whether it’s three or a dozen multi-channel touches to the consumer,

marketers can reliably plan and allocate spend for each marketing channel based on

actual performance.


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Datasong Case Study

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