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ROB VOGELBACHER: Hi, everyone.
My name is Rob Vogelbacher.
I'm a Machine Learning Customer Engineer at Google
where I help customers build and deploy machine
learning applications on Google Cloud.
In this video, we'll be discussing
how to use machine learning services in Google Cloud
to assist you in your customer retention efforts.
For startups and enterprises alike,
acquiring customers is frequently a top priority
for the organization.
For startups in particular, demonstrating growth
through customer acquisitions is a critical requirement
for gaining additional investment
and demonstrating confidence in the product, platform,
or solution you are bringing to market.
In addition to acquiring customers,
retaining customers by understanding
who is at risk of churning and knowing
when and how to engage with them is
equally important for building a sustainable and profitable
business.
So how can you use machine learning
to help understand which customers
are at risk of churning and help identify
your most loyal customers who could further evangelize
your services and products?
With Google Cloud, we provide several different ways
of building machine learning models
that help you retain your customers
and predict who is most at risk of churning.
One of the most popular and easily accessible
ways of building machine learning models
is with BigQuery ML, where you train models on your customer
data using native SQL.
With Vertex AI AutoML capabilities,
you can train and deploy a highly-performing ML model
using a point-and-click interface
in Google Console as well as through BigQuery and then
programmatically through easy-to-use SDKs and APIs.
And then finally, for established
and advanced data science teams, we
offer a rich set of capabilities for developing your own custom
ML training applications on Google Cloud with Vertex AI.
For today's conversation, we'll be
focused on BigQuery ML and Google Cloud's
AutoML capabilities.
BigQuery is Google's internet scale data warehouse
that offers some unique advantages for startups.
It is fully serverless, easy to load data into,
and has built-in machine learning algorithms as well
as a very generous free-to-use tier
where you're allocated 10 gigabytes of data storage,
a terabyte of query processing, and 10 gigs of data process
when creating ML models each month.
It also supports direct integration
with Google Analytics and lets you build reports
off your customer data with a few clicks of Data Studio.
Building and deploying ML models with BigQuery
can be as simple as writing a few lines of SQL code.
The integrated ML engine has smart defaults
in place for traditional model types,
like linear and logistic regression,
as well as the more advanced methods, like neural networks
and boosted trees.
Using the integrated query scheduler,
you can automate the building and deploying of your ML models
without the need for moving your data out of BigQuery.
Once you have your customer and engagement data in BigQuery,
you can easily build a classification model which
predicts the probability of a customer churning
and help you identify those users
on the fence, who would be good targets for an intervention.
In order to build that model, you
should collect a wide variety of customer-related data, how
your customers are interacting with your product
and platforms, their purchase history, in-app behavior,
engagement metrics, in addition to some descriptive demographic
data about your customers.
Define "churn" based on a lapse in engagement
over a specified period of time or by identifying
those customers who haven't renewed a subscription.
Ultimately, how you define "churn"
is going to be up to you based on how
you manage your business.
To build your machine learning model in BigQuery,
choose a classification model type, like logistic regression
or boosted trees.
Identify the column with your churn flag,
and then select the table or view holding your customer
demographic and behavioral data.
That's it.
After the model has been built, you
can inspect the results directly in the BigQuery console.
Or again, use SQL to understand how well the model performed
and its predictive value across different probability
thresholds.
For your churn classification model,
explore the confusion matrix, the F1 score,
and the different error metrics reported back
by the evaluation.
Be sure you iterate and test.
Once you are happy with the performance of the model,
generating predictions is as simple, again,
as writing a few lines of SQL.
Referencing your churn model as part of the ML
predict function to output those churn probabilities
for your customers so that you can
take the appropriate action.
You can also easily export out your prediction data
as a CSV file or a JSON file out to Cloud storage
to support integration with your marketing
systems and other downstream CRM systems.
As you can see here, with a few simple steps,
you can easily use BigQuery machine learning
to build a useful churn prediction
model to help ensure that you focus your intervention
efforts on the right customers, making best
use of your limited resources as you scale your company.
Now in addition to BigQuery ML, Google's AutoML technologies
are another great way to build highly-performing models
to help retain and meaningfully engage with your customers.
AutoML is Google's automated machine
learning service that handles much
of the heavy lifting for you when
building models to help engage and retain your customers.
Building a model can be as easy as selecting your model label
or target in your data set, specifying the training
budget and the number of hours to train, and then clicking
"train."
Simple as that.
Behind the scenes, the AutoML service
is inspecting and transforming your data
to improve the chances that your ML model is
able to learn the meaningful signals from your customer
data set.
It additionally includes guardrails
to help ensure you build models that don't perform poorly
due to some issues with your data,
such as imbalanced classes or missing values in your data
set.
AutoML is built on the latest research
and techniques in AI machine learning
developed in Google Research and automatically searches
through a wide range of model types
and approaches to find the best candidate
model for your specific customer data set.
In addition to being available via the Google Cloud
Console and programmatically through our SDK and APIs,
AutoML is also available to use directly from within BigQuery
simply by specifying the model type as an AutoML model type
when building your classification or regression
model.
We see AutoML and BigQuery ML as complementary technologies
that open up avenues for building machine
learning models without heavy custom development.
And for those organizations who are building custom models,
both AutoML and BigQuery ML are additional tools
in your toolbox that can help you to acquire and retain
customers by easing the burden to produce high-quality
and well-performing ML models without extensive code.
We have a wide variety of additional resources
out there to help you effectively use
machine learning as part of your customer-retention strategy.
Instant BQML is an online resource
you can use to build a series of standardized BQML models off
of your Google Analytics data in just a few clicks
as well as helping you to automate that process using
integration with open-source CRM.
We also have a full-blown example
created by our Developer Advocacy Team
which walks you through the process of developing
a churn prediction model.
In this case, an example targeted
at game developers using Google Analytics and BQML.
In addition to these churn and propensity models,
we also have a multi-series blog post and accompanying code
which demonstrates how to use AutoML to predict customer
lifetime value, so you can know which of your customers
are driving the most revenue to help ensure your efforts are
focused on retaining the right customers versus those that
may be negatively impacting your bottom line.
And with that, I want to thank you for your time today,
and I wish you happy model building.
I look forward to engaging with you live in the CBC community
and the Q&A. Special thanks to our BQML developer advocates,
Polong and Minhaz, for creating much
of the content around churn prediction.
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