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Machine Learning:How Magento Helps Merchants

Turn Buzz Words into Strategy


Anita Garimella AndrewsDirector, Analytics Services

@agarimella

A confusing and challenging landscape.

The World of Machine Learning


Machine Learning has the Attention of your Peers

79%

75%

of executives say they will implement AI

within next 3 years.

of executives believe AI will make their

jobs easier and more efficient.

Because…

72% of businesses say that improving the

customer experience is their top priority.


of sales from personal recommendations

made by ML algorithms.

growth in revenue by using predictive

modeling.

decrease in stock surplus and 2mm returns

reduced each year.

improvement in customer support contact

& product offerings fit.

Machine Learning’s Big Impact in Commerce

76%

55%

30%

20%


There’s no single button to push, which leads businesses to choose

So many use cases, so many products


Unrealistic “point-based solutioning” expectations… …typical results:


When instead you want this…

Magento works with our merchants to drive

success, together.

A Holistic Approach


• Designing a house by starting with paint colors and bathroom fixtures can

lead to a house that is not up to code.

• Similarly, when seeking to integrate ML into your commerce strategy &

business operations, it’s vital to structure your solution design in an

ordered way:

– Asking the right questions

– Considering current technologies and predictions about future tech

– Evaluating budget and opportunity

– Assessing human talent needs and availability

Your Approach is Key


Machine Learning Integration Components

Technology

Utilization in Commerce

People

The Learning MachineThe Data

For Implementation For Evaluation & Analysis For Contextual Optimization


Data Technology

Dirty Data Clean Data

Capabilities:

• Cleansing & auditing

• Storage

• Advance Data Analysis

Questions:

• What are the right sources?

• What is the source of truth if a conflict arises?

• What historical nuances exist?


The “Learning Machine”

Flexibility in customizing AI

rules

Learned ML vs. Configured ML

Variety of output

mechanisms

Implementation complexity &

utility

Utilization of industry-wide

data sets

Not all machines are

created the same


Commerce Utilization

Examples:Multiple Outputs

• Shipping

• Product Reco’s

• Pricing

• Customer

Segmentation

Web / Mobile X

• Inventory

Management

• Shipping/Operations

• Fraud Detection

• Forecasting

Back Office


People bring diverse skillsets

• Identifying opportunities for using ML

✓ KPI and goal definition

• Evaluating results

✓ Cluster evaluation

✓ Logical groupings

✓ Performance

• Business expertise

Machine Learning Driven by People

Marketers Merchandisers

Data Scientists & Analysts

80% of marketers believe

personalized content is

more effective than generic

content.

52% of consumers are likely to

switch brands if they don’t

have personalized company

communication

From theory and approach to application.

Let’s See it in Action


Product Affinity: Related Products

CONTINUOUS

LEARNING


Product Affinity: Order relationships


Inventory = Cost.

Smart Inventory Management


Segment-specific Free Shipping Thresholds


Customer Segmentation & Expected CLV


1. ML is becoming more and more of a reality.

2. It is already having an impact on commerce.

3. Efficient and profit boosting ML & AI implementations require a holistic

approach (good data, good tech and good people).

4. Magento is working with our merchants to accomplish their goals around

ML.

• Additional questions? @agarimella or [email protected]

Key Takeaways & QA

machine learning - magento...© 2018 magento, inc. page | 2 machine learning: how magento helps...

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