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Exacaster is a Big Data Analytics technology company
founded in 2011 by ex-telco marketers.
Customer Analytics
Predictive Modeling
Campaign
Management
Focus on the right
customers,
right offer, right time.
Contextual Marketing
About Us
Our mission is to automate middle management decisions with advanced
machine-learning tools, focusing on key loyalty and marketing challenges
including segmentation, churn, up-sell, loyalty programs, product
recommendations and pricing optimization.
Our expertise
1. Exacaster was the first company which started
using Big Data infrastructure Hadoop in a
production environment in Baltics region.
Our expertise
1. Exacaster was the first company which started using
Big Data infrastructure Hadoop in a production
environment in Baltics region.
2. In the last 2.5 years Exacaster invested more than
600k EUR to RnD activities in the areas of:
a. Advanced analytics;
b. Machine learning;
c. Recommendations and targeting algorithms;
d. Real time dynamic pricing;
Our expertise
1. Exacaster was the first company which started using
Big Data infrastructure Hadoop in a production
environment in Baltics region.
2. In the last 2.5 years Exacaster invested more than
600k EUR to RnD activities in the areas of:
a. Advanced analytics;
b. Machine learning;
c. Recommendations and targeting algorithms;
d. Real time dynamic pricing;
3. We did enormous educational work in Telco & Retail
industries, and we are establishing a “Deep Learning
Academy” – an open data science school for
professional education.
Our expertise
1. Exacaster was the first company which started using
Big Data infrastructure Hadoop in a production
environment in Baltics region.
2. In the last 2.5 years Exacaster invested more than
600k EUR to RnD activities in the areas of:
a. Advanced analytics;
b. Machine learning;
c. Recommendations and targeting algorithms;
d. Real time dynamic pricing;
3. We did enormous educational work in Telco & Retail
industries, and we are establishing a “Deep Learning
Academy” – an open data science school for
professional education.
4. Exacaster was recognized as the most advanced
high-tech services business in the Lithuania, winning
the Knowledge Economy Company 2014 award.
Exacaster - Knowledge Economy Company 2014
Our customers
Large Scandinavian grocery
retail chain
International online
advertizing company
Medium size daily
deals portal
Large Latin American
Mobile Operator
8 most common pricing
mistakes
8 most common pricing
mistakes
Basing prices
on costs, not
customers’
perceptions of
value
8 most common pricing
mistakes
Basing prices
on costs, not
customers’
perceptions of
value
Basing prices
on “the
marketplace”
8 most common pricing
mistakes
Basing prices
on costs, not
customers’
perceptions of
value
Basing prices
on “the
marketplace”
Same profit
margin across
different product
lines.
8 most common pricing
mistakes
Basing prices
on costs, not
customers’
perceptions of
value
Basing prices
on “the
marketplace”
Same profit
margin across
different product
lines.
Companies fail
to segment their
customers.
8 most common pricing
mistakes
Basing prices
on costs, not
customers’
perceptions of
value
Basing prices
on “the
marketplace”
Same profit
margin across
different product
lines.
Companies fail
to segment their
customers.
Companies
hold prices at
the same level
for too long.
8 most common pricing
mistakes
Basing prices
on costs, not
customers’
perceptions of
value
Basing prices
on “the
marketplace”
Same profit
margin across
different product
lines.
Companies fail
to segment their
customers.
Companies
hold prices at
the same level
for too long.
Companies
spend
insufficient
resources
managing their
pricing
practices.
8 most common pricing
mistakes
Basing prices
on costs, not
customers’
perceptions of
value
Basing prices
on “the
marketplace”
Same profit
margin across
different product
lines.
Companies fail
to segment their
customers.
Companies
hold prices at
the same level
for too long.
Companies
spend
insufficient
resources
managing their
pricing
practices.
Companies fail
to establish
internal
procedures to
optimize prices.
8 most common pricing
mistakes
Basing prices
on costs, not
customers’
perceptions of
value
Basing prices
on “the
marketplace”
Same profit
margin across
different product
lines.
Companies fail
to segment their
customers.
Companies
hold prices at
the same level
for too long.
Companies
spend
insufficient
resources
managing their
pricing
practices.
Companies fail
to establish
internal
procedures to
optimize prices.
Companies rely
on salespeople
and other
customer-facing
staff for pricing
intelligence.
Fixing mistakes
Chuck Norris/Big Data
way
Dynamic Pricing Based on
Market price
Dynamic Pricing Based on
Market price
5 strategies
I don’t care about market price
Price leader
Dynamic Pricing Based on
Market price
5 strategies
I don’t care about market price
Price leader
Dynamic Pricing Based on
Market price
5 strategies
I don’t care about market price
Price leader
Dynamic Pricing Based on
Market price
Price follower
5 strategies
I don’t care about market price
Price leader
Dynamic Pricing Based on
Market price
Price follower
Price looser
5 strategies
I don’t care about market price
Price leader
Dynamic Pricing Based on
Market price
Price follower
Price looser
Intelligent mix
of leader,
looser &
follower
5 strategies
I don’t care about market price
Price leader
Dynamic Pricing Based on
Market price
Price follower
Price looser
Intelligent mix
of leader,
looser &
follower
5 strategies
I don’t care about market price
Amazon
changes its
prices more
than 2.5
million times a
day
Market price leader - Amazon
Amazon
changes its
prices more
than 2.5
million times a
day
Market price leader - Amazon
“Gravity Fuels Gravity”, Jeff Bezos.
Market price wars leader – Diapers
Market price wars leader – Diapers
Amazon lost competition with Diapers.com and
bought them for $540 m. in 2010.
Market price wars leader – Diapers
Amazon lost competition with Diapers.com and
bought them for $540 m. in 2010.
Key success drivers:
• Fast moving items – margin ~0.
Market price wars leader – Diapers
Amazon lost competition with Diapers.com and
bought them for $540 m. in 2010.
Key success drivers:
• Fast moving items – margin ~0.
• Associated and slow items – margin as high as possible.
Market price wars leader – Diapers
Amazon lost competition with Diapers.com and
bought them for $540 m. in 2010.
Key success drivers:
• Fast moving items – margin ~0.
• Associated and slow items – margin as high as possible.
• Delivery next day 7 days a week.
• Awesome customer service
Technical implementation building
blocks used by Diapers
1. Competitor prices tracking for fast moving items;
2. Recommendations engine for long tail items;
3. Real time dynamic price optimization for all items;
Key takeaways
1. Don’t compete on the price, unless your are Amazon.
2. If must compete on price – do it only on the items that
drive key traffic.
3. Keep slow moving items margin as high as possible;
4. Figure out competitive advantages that are not related to
the price;
Dynamic Pricing Based on
Customers’ Perceptions of Value
How much should I
charge for products that
are incomparable in the
market?
Set a random
price?
Do a market
research?
Negotiate with
customers?
Apply machine
learning?
15.99 €
How much should an awesome
45 min. massage cost?Common answer:
Well, maybe…
Example from daily deals portal
15.99 € 19.99 €
How much should an awesome
45 min. massage cost?Moderate analysts’ answer:
Let’s do A/B testing
Example from daily deals portal
10.99 €
How much should an awesome
45 min. massage cost?Exacaster answer:
Automated Machine Learning Algorithms
17.89 €19.99 €
20.79 €22.99 €12.00 €
15.99 €
Example from daily deals portal
Results form our beta testers -
GROUPON a like daily deals site:
Machine learning algorithms
autmatically spotted prices that on
average brought
7% bigger total margin compared to manual price selection
Technical implementation building blocks
Real time dynamic price optimization for all items;
Key takeaways
If there is nobody to compare, set your own prices
based on EXTENSIVE TESTS, not on COSTS
Dynamic Pricing Based on
Product bundles
Dynamic Pricing Based on
Product bundles
Dynamic Pricing Based on
Product bundles
$99.99
Dynamic Pricing Based on
Product bundles
$99.99
Dynamic Pricing Based on
Product bundles
Make her an
offer she can’t
refuse.
$99.99
Dynamic Pricing Based on
Product bundles
Make her an
offer she can’t
refuse.
$99.99A special deal for
your basket only:
Best in class Apple
mouse just for
$69.99
1. Recommendations engine;
2. Real time dynamic price optimization;
Technical implementation building blocks
Never miss a chance to up-sell on the very right
moment with the offer customer can’t refuse.
Key takeaways
Dynamic Pricing Based on
Revenue Management
Good Revenue Management
is selling the right product to
the right customer at the
right time for the right
price and with the right
pack.
Revenue Management
inventors and leaders:
1. Airlines
2. Hotels
3. Car Rentals
4. Show tickets
5. Fashion industry
Revenue Management in Airlines
Time before departure
6 m. 4 m. 2 m. 1 m. 1 w.
Price per
seat in $
Revenue Management in Airlines
Time before departure
6 m. 4 m. 2 m. 1 m. 1 w.
Price per
seat in $
Price sensitive
customers segment
Revenue Management in Airlines
Time before departure
6 m. 4 m. 2 m. 1 m. 1 w.
Price per
seat in $
Price sensitive
customers segment
Regular customers
segment
Revenue Management in Airlines
Time before departure
6 m. 4 m. 2 m. 1 m. 1 w.
Price per
seat in $
Price sensitive
customers segment
Regular customers
segment
Business customers
segment
Revenue Management in Fashion
industry
Time after new collection introduction
1 w. 1 m. 2 m. 3 m.
Price per
item in $
Revenue Management in Fashion
industry
Time after new collection introduction
1 w. 1 m. 2 m. 3 m.
Price per
item in $
Fashion
fans
Revenue Management in Fashion
industry
Time after new collection introduction
1 w. 1 m. 2 m. 3 m.
Price per
item in $
Fashion
fans
Regular
customers
Revenue Management in Fashion
industry
Time after new collection introduction
1 w. 1 m. 2 m. 3 m.
Price per
item in $
Fashion
fans
Regular
customers
Price sensitive
customers
1. Historical data analysis;
2. Real time price optimization;
Technical implementation building blocks
1. CUSTOMERS ARE DIFFERENT;
2. Segment your customer base and adjust prices and
value proposition accordingly;
Key takeaways
Dynamic Pricing Based on
Category Price Elasticity
Price per product
Quantity
of pro
duct
bou
ght
We can accurately
predict the quantity of
products bought
knowing only 1 thing –
it’s price.
1. Historical data analysis;
Technical implementation building blocks
1. Evaluate each products’ price range, you might be
missing a huge revenue opportunity;
Key takeaways
We are searching for partners who would like to participate in our
Closed Beta testing program to share our expertise and to help you
run pricing strategies in a better way.
Egidijus Pilypas
Chief Data Scientist
Should you have any
questions, please
kindly get in touch:
exacaster.com
