This document provides an overview of the deep.bi analytics platform for ecommerce companies. It describes how deep.bi collects detailed ("deep") data on products, customers, and customer behaviors. This deep data is analyzed to provide real-time insights. Deep.bi helps ecommerce teams improve performance in areas like merchandising, marketing, customer service, and site experience. It does this by tracking custom metrics and providing customizable dashboards. Deep.bi can be used as a standalone tool or integrated with other systems through its API.

1. deep.bi
for ecommerce
© 2015 by deep.bi

2. Every business is different
So, there’s no “one-size-fits-all”
analytics solution

3. Deep.bi is a real-time,
deep analytics platform
It helps ecommerce teams
improve their performance by providing
current and precisely tailored insights.

4. Operational excellence and performance
Category Managers / Merchandisers
Find new revenue opportunities, improve
rotation, promote best sellers, eliminate
non-sellers, up-sell, cross-sell.
Marketers
Deeply understand your customers and
what brings them to purchase. Then
optimize your marketing spent across
markets and channels.
Customer service
Maintain more personalized customer
relationships. Fulfill customer needs based
on their value.
UX / Design Team
Improve your site constantly by micro-
updates and monitor their impact on
conversion in real-time.
Tech / IT
Monitor site performance. Quickly deliver
new reports and functionalities. Focus on
business value, outsource heavy
engineering stuff to experts.
Executives / Managers
Get the up-to-date big picture of your
store performance. Monitor key metrics
like conversion rate, revenue, retention in
real-time.

5. Custom defined, real-time metrics

6. Historic data
is excellent for
strategy and planning.
Real-time data
gives the right,
current context
and helps take
action intraday
to improve
daily profits.
Is real-time
data useful
in ecommerce?

7. People are complex.
We make decisions
based on many
factors.
deep.bi helps extract
product, customer
and behavior
detailed attributes
to find real
purchase patterns.
What’s
deep data
and why
it’s important?

8. Example of tracked product dimensions:
• Name
• Brand
• SKU
• ID
• Offer Price
• Regular Price
• Save Amount
• Availability
• Specs
• Weight
• Color
• Size
• Box Size
• categories
Deep data: Product
Deep.bi extracts detailed
product characteristics and
make it available for analysis
with all other data.
Example insights:
• Most viewed but unavailable products
by brand
• Worst selling shoes by color
• Worst selling smartphones by battery
life, weight and size
• Top selling at full price Nike men
running shoes in California by detailed
features (size, color, weight)

9. Deep data: Customer
Example of tracked user dimensions:
• User
• Cookie
• Email
• First Name
• Last Name
• Gender
• Location:
• ZIP
• City
• Region
• Country
• Population
• Device
• Type
• Brand
• Model
• Version
• Internet Provider
• Type
• Name
Deep.bi can enrich raw user
data (like IP) with rich,
business-useful information.
That allows to deeply
understand customers and
prospects. By combining this
data with behavior and product
data, store managers can
optimize marketing spend,
traffic sources and inventory.

10. Deep data: Behavior
Example of tracked event behavior dimensions:
• Event Type
• Timestamp
• URL
• Referrer Host
• utm_source
• utm_campaign
• utm_mediium
• Active Engagement Time
By knowing detailed
characteristics of users’
behaviors ecommerce
managers can build detailed
users profiles, discover patterns
and track key metrics like:
• RFM (Recency, Frequency, Monetary),
• LTV (Customer Lifetime Value)
• CAC (Cost of Acquiring Customer)
• AOV (Average Order Value)
• COS (Cost of Sale – Ad Spend /
Revenue)
• Shopping Cart Abandonment
Track any important user behaviors like:
• product views
• adding items to cart or wish list
• purchases
• delivery confirmations
• newsletter subscription
• return request and any other.
Measure detailed behavior characteristics,
like active engagement time while reading
product descriptions.

11. Find deep buying
patterns and
correlations
Example: what buy and how behave people
from small cities who use iPhone and came
from Facebook and what product features are
important for them, etc.
Adjust prices and
optimize stock
Track best-sellers in real-time, optimize price
to maximize profits. Find non-sellers and
features that are common for them (maybe
brand, color, size, etc.)
Predict
Having collected detailed, raw and enriched
data you can build customer predictive models
using machine and deep learning algorithms.
Combining
deep data with
real-time
insights
creates
unprecedented
possibilities

12. deep.bi can be used standalone or
as a part of your current solutions
deep.bi
dashboards
Use our predefined
dashboards and
metrics or customize
them to fit your needs.
deep.bi
API
Use API to build your
own, external
dashboards or embed
our analytics in your
current systems.
deep.bi
data platform
Use deep.bi as a data
collector and enricher
to build your
customized solutions,
e.g. prediction system

13. Quick and easy
to implement
Minutes/hours vs. weeks or months. Just
embed our script and start tracking.
Real-time
Data latency <1s
Super-fast
Less than a 1s on TB-size data store
Scalable
Petabytes and more
Highly flexible
No relational data modeling
(schema-flexible), no data pre-aggregation,
no defining reports up front. Just dig into data
and explore it. Add new dimensions (columns)
and metrics on the fly.
Why deep.bi
is unique?
(technical explanation in blue)

14. Schedule a demo
demo@deep.bi

15. APPENDIX
More analysis examples

16. More analysis examples
Merchandising insights:
• Product, SKU, Category, Brand Report:
views, added to cart, sold, CR, etc.
• Hot Products (most viewed, most
added to wish lists, most added to
carts)
• Non sellers, cold products
• Most discounted products
Lifecycle marketing:
• Who viewed but did not purchase
• 30/60 day repeat purchase rate
• Best products/categories/brands for
repeat purchase
• Products/categories purchased
together
• Customer Lifetime Value by channel
(day 1, 90, 180, 360)
Conversion & performance:
• Purchase funnel incl. abandoned cart
• Conversion & attribution by traffic
source/referral, campaign
Customer intelligence:
• Best customers by detailed
characteristics (demographics, RFM,
best full price customers, etc.)
• Cold customers: not buying, low AOV
• Customer Lifetime Value (Day 30, 90,
180, 360)
• How long it takes customers to
purchase
UX improvements
• Checkout improvement: find that most
customers are abandoning carts when
they reach a confusing section in your
checkout process that you were not
even aware of.

17. APPENDIX
Going beyond data silos

18. Going beyond silos
Website:
Google
Analytics
Campaigns:
Agency
reports
Apps:
Dedicated
monitoring
tools
Other
systems:
Call center
IVR, emails
Instead of integrating current reporting tools we gather all
the single events that our customers generate.
Data is stored in silos. Reporting tools provide aggregated
reports impossible to integrate around single customer.

19. APPENDIX
Data Enrichment

20. Enrich web data with business information
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IP, URL, cookie, user-agent, timestamp, text…
Raw, web browsing data
Real-time data enrichment
Structured data
Enriched data
see the next slide

21. * Coming soon
Get deeper insight - enrich raw user data
50+information
from one
interaction
Purchase intent
Device
Time
Location
ISP
Online context
Weather*Demographics

22. User location details
Example use:
• international travellers
• townspeople
• people in mountains
• rainy day
• Country
• Region
• City
• ZIP Code
• Population
• Latitude & Longitude
• Time zone
• IDD prefix to call the city from
another country
• Phone area code
• Mobile Country Code (MCC)
• Mobile Network Code (MNC)
• Elevation
• Weather at the moment of event

23. Business context based on internet provider
Example use:
• [telco] competitors’ users-
> acquisition
• [telco] our users ->
retention/up-selling/cross-
selling
• people from particular
company or company type
• ISP name or Organization name
• Organization type:
• Commercial
• Organization
• Government
• Military
• University/College/School
• Library
• Content Delivery Network
• Fixed Line ISP
• Mobile ISP
• Data Center/Web Hosting/Transit
• Search Engine Spider
• Reserved
• Mobile brand
• Net speed

24. Detailed information about user’s device
Example use:
• smartphone users
• Apple users
• Samsung Galaxy users
• Google browser users
• Device Type
• Device Brand
• Device Model
• Device Operating System
• Operating System Producer
• Browser
• Browser Producer

25. APPENDIX
Data tracking examples

26. • Deep.BI provides two ways of ingesting data: using direct
REST API or through JavaScript snippet.
• In both ways data is sent in JSON format.
• Deep.BI is event-based analytics what means that we collect
full scope of information about every single tracked event
that happens on your site.
• We provide full flexibility of defining data dimensions:
• You can define as many dimension as you want (e.g. 100+
dimensions for every event)
• You can build infinite hierarchy of dimensions, e.g.:
product.specification.size.width.unit, or category.category.category…
• On the following slides we provide some examples of data
dimensions for: product, user and user behavior.
Deep.bi Data Ingestion API

27. {
"type": "product",
"title": "Nike Men's Dual Fusion Run 3 Running Shoes",
"brand": "Nike"
"sku": "B00LHX3IWG",
"productId": "653596-012",
"offerPrice": "$44.99",
"regularPrice": "$199.99",
"saveAmount": "$155.00”,
"availability": true,
"specs": {
"weight": "2.5 lb”,
"object_size": "11.0 x 7.0 x 4.0 in”,
“color”: “Grey/Black/White”,
“size”: “7.5 US”,
"asin": "B00LHX3IWG",
},
"categories": {
"name": "Clothing, Shoes & Jewelry",
"name": "Men",
"name": "Shoes",
"name": "Athletic",
"name": "Running”
}}
Deep data: Product JSON

28. { ”user”: {
”cookie": ”43dad90aad9a88a",
”email": "seb@deep.bi"
”fist_name": "Sebastian",
"last_name": "Zontek",
"gender": "male”
},
"location": {
"zip": "94107",
"city": "San Francisco",
"region": "CA",
"country: "USA",
"population": "837442"
},
"device": {
"type": "smartphone",
"brand": "Apple”,
“model”: “iPhone”,
“version”: “5S”
},
"isp": {
"type": "mobile operator",
"name": "T-Mobile”
}}
Deep data: Customer JSON

29. {
"event": {
"type": ”product-view",
"timestamp": ”2015121008550145"
"url": "http://www.amazon.com/Nike-Fusion-Unvrsty-White-
Running/",
"referrer_host": ”facebook.com",
"utm_source": "male”,
"utm_campaign": "social_media_paid",
"utm_mediium": ”cpc”,
"active_engagement_time": “120s”}
}
Deep data: Behavior JSON