Microsoft has showcased how it solved the Fake leads problem as a Leader in Digital Transformation
“Fake leads” is the
problem to tackle
sign up via online forms, they sometimes give a fake name, company name, email,
or phone number. They may submit randomly typed characters (keyboard gibberish)
or use profanity. Or, they may accidentally make a small typographical error,
but otherwise the name is real—so we don’t want to classify the lead as junk.
abundance of fake lead names across Microsoft subsidiaries results in:
productivity for our global marketers and sellers. Fake names waste an enormous
amount of time since sellers rely on accurate information to follow up with
revenue opportunities. Among thousands of fake lead names, there could be one legitimate
thousands of people sign up using thousands of web forms. But, in any month,
many of the lead names—whether a company or a person—are fake.
The solution to tackle “Fake
data quality is critical. To do that, and to determine if names are real or
fake, Microsoft built a machine learning solution that uses:
Machine Learning Server (previously Microsoft R Server).
data quality service that integrates machine learning models. When a company
name enters the marketing system, the system calls their data quality service,
which immediately checks if it’s a fake name.
machine learning has reduced the number of fake company names that enter Microsoft’s
marketing system, at scale. Their solution has prevented thousands of names
from being routed to marketers and sellers. Filtering out junk leads has made their
marketing and sales teams more efficient, allowing them to focus on real leads
and help customers better.
Microsoft Machine Learning
a scalable way to eliminate fake names across millions of records and to build and
operationalize their machine learning model—in other words, they wanted a
systematic, automated approach with measurable gains. They chose Machine
Learning Server, in part, because:
can handle their large datasets—which enables them to train and score their model.
has the computing power that they need.
can control how they scale their model and operationalize for high-volume
is based on user name and password, which are securely stored in Azure Key
helps expose the model as a secure API that can be integrated with other
systems and improved separately.
The difference between
rule-based model to Machine Learning
create static rules to cover common scenarios. As new scenarios occur, new
rules are written. A static, rules-based model can make it hard to
capture varying types of keyboard gibberish (like akljfalkdjg). With static
rules, Microsoft’s marketers must waste time sorting through the fake leads and
deciphering misleading or confusing information.
are used to train the model and make intelligent predictions. Algorithms
help build and train the model by labeling and classifying data at the
beginning of the process. Then, as data enters the model, the algorithm
categorizes the data correctly—saving valuable time. Microsoft used the Naive
Bayes classiﬁer algorithm to categorize names as real/fake. This algorithm is
influenced by how LinkedIn detects spam names in their social networks.
Scenarios where the model is used
business team identified their subsidiaries worldwide that are most affected by
fake names. Now, they are weeding out fake names so that marketers and sellers
don’t have to. Going forward, they plan to:
a lead data quality metric with more lead-related signals and other machine
learning models that allow them to stack-rank their leads. The goal is to give
a list to their sellers and marketers that suggests which leads to call first
and which to call next.
contact information visible to their sellers and marketers when they’re talking
on the phone with leads. For example, if the phone number that someone gave in
an online form is real, but the company name isn’t, their seller can ask the
lead to confirm the company name.
Choosing the technology
the following technologies into their solution:
programming language R and the Naive Bayes classifier algorithm for training
and building the model are based, in part, on the approach that LinkedIn uses.
Learning Server with machine learning, R, and artificial intelligence (AI)
capabilities help them build and operationalize their model.
data quality service, which integrates with the machine learning models to
determine if a name is fake – person or company.
Designing the approach
designed their overall architecture and process to work as follows:
1. Marketing leads enter their data quality
and enrichment service, where their team does fake-name detection, data
matching, validation, and enrichment. They combine these data activities using
a 590-megabyte model. Their training data consists of about 1.5 million real
company names and about 208,312 fake (profanity and gibberish) company names.
Before they train the model, they remove commonly used company suffixes such as
Private, Ltd., and Inc.
2. They generate n-grams—combinations
of contiguous letters—of three to seven characters and calculate probabilities
that each n-gram belongs to the real/fake name dataset in the model. For
example, an n-gram that shows three sequenced letters of the name “Microsoft”
would look like “Mic,” “icr” “cro” and so on. The training process computes how
often the n-grams occur in real/fake company names and stores the computation
in the model.
3. They have four virtual machines that
run Machine Learning Server. One serves as a web node and three serve as
compute nodes. They have more compute nodes so that they can scale to handle
the volume of requests that they have. The architecture gives them the ability
to scale up or down by adding/removing compute nodes as needed based on the
volume of requests. The provider calls a web API hosted on the web node, with
company name as input.
4. The web API calls the scoring
function on the compute node. This scoring function generates n-grams from the
input company name and calculates the frequencies of these n-grams in the
real/fake training dataset.
5. To determine whether the input
company name is real or fake, the predict function in R uses these calculated n
gram frequencies stored in the model, along with the Naive Bayes rule.
summarize, the scoring function that’s used during prediction generates the
n-grams. It uses the frequencies of each n-gram in the real/fake name dataset
that’s stored in the model to compute the probability of the company name
belonging to the real/fake name dataset. Then, it uses these computed
probabilities to determine if the company name is fake.
What Microsoft learned about
Business, technical, and design considerations
Ideally, the business problem should be solved within your organization
itself rather than outsourcing it. Your organization will have deeper historical knowledge of the
business domain, which helps to design the most relevant solution.
Having good training and test data is crucial. Most of the work Microsoft did was
labeling their test data, analyzing how Naive Bayes performed compared to
rxLogisticRegression and rxFastTrees algorithms, determining how accurate their
model was, and updating their model where needed.
When you design a machine learning model, it’s important to identify how
to effectively label the raw data. Unlabeled data has no information to explain or categorize it. Microsoft
labels the names as fake/real and apply the machine learning model. This model
takes new, unlabeled data and predicts a likely label for it.
Even in machine learning, you risk having false positives and negatives,
so you need to keep analyzing predictions and retraining the model. Crowdsourcing is an effective way
to analyze whether the predictions from the model are correct; otherwise, these
can be time-consuming tasks. In Microsoft’s case, due to certain constraints they
faced, they didn’t use crowdsourcing, but they plan to do so in the future.
Machine Learning Server vs. other Microsoft technologies
technical and design considerations included deciding which Microsoft
technologies to use for creating machine learning models. Microsoft offers
great options such as Machine Learning Server, SQL Server 2017 Machine
Learning Services (previously SQL Server 2016 R Services), and Azure
Machine Learning Studio. Here are some tips to help you decide which to use
for creating and operationalizing your model:
you don’t depend on SQL Server for your model, Machine Learning Server is a
great option. You can use the libraries in R and Python to build the model, and
you can easily operationalize R and Python models. This option allows you to
scale out as needed and lets you control the version of R packages that you
want to use for modeling.
you have training data in SQL Server and want to build a model that’s close to
your training data, SQL Server 2017 Machine Learning Services works well—but
there are dependencies on SQL Server and limits on model size.
your model is simple, you could build it in SQL Server as a stored procedure
without using libraries. This option works well for simpler models that aren’t
hard to code. You can get good accuracy and use fewer resources, which saves
you’re doing experiments and want quick learning, Azure Machine Learning Studio
is a great choice. As your training dataset grows and you want to scale your
models for high-volume requests, consider Machine Learning Server and SQL
Server 2017 Machine Learning Services.
Challenges and roadblocks
Having good training data. High-quality training data begins with a collection of company names
that are clearly classified as real or fake—ideally, from companies around the
world. Microsoft feeds that information into their model for it to start
learning the patterns of real or fake company names. It takes a while to build
and refine this data, and it’s an iterative process.
Identifying and manually labeling the training and test dataset. Microsoft manually labeled
thousands of records as real or fake, which takes a lot of time and effort.
Instead, one can take advantage of crowdsourcing services if possible, to avoid
manual labeling. With these services, one can submit company names through a
secure API and a human says if the company name is real or fake.
Deciding which product to use for operationalizing the model. Microsoft tried different
technologies, but found computing limitations and versioning dependencies
between the R Naive Bayes package they used and what was available in Azure
Machine Learning Studio at the time. Microsoft chose Machine Learning Server
because it addressed those issues, had the computing power they needed, and
helped them easily scale out their model.
Configuring load balance. If Microsoft’s Machine Learning Server web node gets lots of requests,
it randomly chooses which of the three compute nodes to send the request to.
This can result in one node that’s overutilized while another is underutilized.
They like to use a round-robin approach, where all nodes are used equally to
better distribute the load. This can be achieved by using an Azure load
balancer in between the web and compute node.
Measurable benefits Microsoft
has seen so far
The gains Microsoft
has made thus far are just the beginning. So far, Machine Learning Server has
helped them in the following ways:
the machine learning model, their system tags about 5 to 9 percent more fake
records than the static model. This means the system prevented 5 to 9 percent
more fake names from going to marketers and sellers. Over time, this represents
a vast number of fake names that their sellers do not have to sort through. As
a result, marketer and seller productivity is enhanced.
have captured more gibberish data and most profanities, with fewer fake
positives and fake negatives. They have a high degree of accuracy, with an
error rate of +/– 0.2 percent.
time to respond to requests has improved. With 10,000 data classifications of
real/fake in 16 minutes and 200,000 classifications in 3 hours 13 minutes, they
have ensured that their data quality service meets service level agreements for
performance and response time. They plan to improve response time by slightly
modifying the algorithm in Python.
is excited about how their digital transformation journey has already enabled them
to innovate and be more efficient. They will build on this momentum by learning
more about business needs and delivering other machine learning solutions. Their