AI is here and here to stay. Dozens of AI-related use cases, such as Predictive Analytics, Machine Learning, NLP, RPA and others, have developed from early proof of concept and select success stories to almost mainstream adoption. But which role does Pricing play among the widespread use cases around AI and Advanced Analytics, and where is it in the hype cycle? I am exploring and discussing in this presentation where and how far we are with AI Pricing, what showcases are emerging in retail and what it takes for a successful implementation of AI Pricing.

1. How AI is transforming Pricing
Which role Pricing will play among the widespread use cases around AI
Felix Krohn,
Sr Advisor, Interim Executive & Pricing Coach
Monetized® 2019
B2Me Pricing Forum

2. Felix Krohn,
Sr Advisor & Pricing Coach
Overview
◼ AI is here – what is it? What is real, what is hype?
◼ What does AI Pricing really mean?
◼ How AI is taking Pricing to the next level
◼ Implementation challenges and how to overcome them
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3. Felix Krohn,
Sr Advisor & Pricing Coach 3
Source: Boston Consulting Group, IDC, Gartner, 2018
The world is changing; finally setting the right conditions
& momentum for Artificial Intelligence (AI) to thrive
Massive
investment in new
tools & techniques
driving faster
innovation cycles
Faster pace
of change
Over the last two
years alone, 90%
of all data in the
world was
generated
Data
explosion
Ready to engage
with brands
anytime &
anywhere
New
consumer
Computational
power & storage
ready for AI
Enabling
technologies
Advanced analytics
enable unprece-
dented visibility on
customers & busi-
ness performance
Analytical
tools
1700 TB
of data created every
50 secs.
2X
human and machine-
generated data every 2 years
3X
more connected devices than
global population by 2020

4. Felix Krohn,
Sr Advisor & Pricing Coach 4
Source: The Opex Analytics Blog, 2018
AI: A tech buzzword that takes on a new meaning every
so often and causes confusion for business leaders

5. Felix Krohn,
Sr Advisor & Pricing Coach 5
Prescriptive
What’s the next
best action?
(Optimization,
NLP, ML, RPA)
Predictive
What will happen?
(Prediction,
extrapolation)
Diagnostic
How and why did it
happen?
(Modeling,
experimental design)
Descriptive
What happened?
(Reporting,
dashboards)
Data Insight Foresight Decisions
BusinessValue
Complexity
The unstoppable transformation from “Analytics” to “AI”
Source: Own elaboration, based on Thomas H. Davenport
Traditional analytics
(manual, one-off)
AI
(automated, self-learning)

6. Felix Krohn,
Sr Advisor & Pricing Coach 6
Cutting through the noise:
What’s new about AI-enabled decision support?
Self-LearningAutomation
◼ Learn to understand context and adapt
◼ Natural language processing,
understanding and generation
◼ Supervised vs unsupervised learning
― Supervised: Data is labeled;
algorithms learn to predict the output
from input data
― Unsupervised: Data is unlabeled;
algorithms learn to inherent structure
from the input data (latent structure
discovery); clustering vs association
◼ Evidence-based learning and self
optimizing
◼ Replicate repetitive labor-intensive
human tasks
◼ Earlier RPA was based on screen scra-
ping, process mapping and rules engines
◼ Algorithms replace rule-based selections.
Focus is now on workflow/ business
process integration: to support and
automate decisions, and take action
◼ AI helps classify information and make
predictions faster and at higher volumes
than humans, while removing any
personal bias
◼ Automation vs augmentation

7. Felix Krohn,
Sr Advisor & Pricing Coach
AI in Gartner’s Hype Cycles. ML reaching productivity.
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Source: Gartner, 2018
Emerging Technology (2018) Digital Marketing & Advertising (2018)

8. Felix Krohn,
Sr Advisor & Pricing Coach
Pricing a winning application area and use case in AI?
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◼ AI expected to have
substantial impact in the
Marketing & Sales area
◼ Behavioral/customer analytics
are key application area,
helping to facilitate
commercial/OBPPC strategy
◼ E-commerce retail expected
to benefit most from AI
thanks to its steady customer
usage data collection
Source: McKinsey Global Institute, 2018
Dominant AI Use Cases, by Function

9. Felix Krohn,
Sr Advisor & Pricing Coach
Pricing a winning application area and use case in AI?
9
◼ Own AI Pricing Expert Panel initiated
and surveyed in May 2019 (practi-
tioners, consultants, SW vendors)
◼ Pricing still “early days” use case for
AI: Only half the surveyed Expert
Panel consider it a quite relevant use
case today
◼ However, vast majority expect
Pricing to become a top use case for
AI over the next 3-5 years
Source: AI Pricing Expert Survey (n=31), May 2019
Pricing: Expected Relevance for AI
Today
In 3-5 years
+11PP
2X

10. Felix Krohn,
Sr Advisor & Pricing Coach
OK, but what will AI Pricing exactly be then?
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??

11. Felix Krohn,
Sr Advisor & Pricing Coach 11
Source: AI Pricing Expert Survey (n=26), May 2019
Key technologies to power and enable AI Pricing
AI Technologies:
Expected Relevance for Pricing
◼ Practically every expert (96%) rated
Predictive Analytics as one of the
three key AI technologies for Pricing
◼ ML and Recommendation Engines
considered the other top-3 most
relevant AI technologies
◼ Incorporate more accurate demand
sensitivity into self-learning
algorithms
◼ Pricing teams will be more
productive as they receive real-time
price recommendations to predict
the effect of every pricing decision

12. Felix Krohn,
Sr Advisor & Pricing Coach
AI Pricing is likely to impact B2C businesses much more
than B2B, with tactical/dynamic pricing leading the field
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Source: AI Pricing Expert Survey (n=26), May 2019
B2C B2B
Product prices (price
model/structure) 50% 31%
Product prices (dynamic
pricing, revenue management) 96% 35%
Promotions
77% 31%
Service prices
23% 23%
Trade Terms/Discount
Management 35% 35%
CPQ (Configure, Price, Quote)
23% 46%
Contract Management
4% 27%
◼ B2C much more prone to
AI Pricing than B2B
◼ Dynamic pricing &
promotion optimization
winning application areas
for AI Pricing
◼ Most impacted industries:
― Travel/Hospitality/
Car Rental (92%)
― Retail/FMCG/
Electronics (73%)
― Mobility Services/
Parking/Fuel (38%)
◼ In B2B, CPQ expected to
see AI-driven advances

13. Felix Krohn,
Sr Advisor & Pricing Coach 13
* Source: Forrester Consulting, August 2018
Dynamic Pricing 1.0 Dynamic Pricing 2.0 AI Pricing
So, what is different to state-of-the-art Dynamic Pricing?
◼ Algorithms incorporate
market prices, supply/
demand, and other
external factors
◼ Origins in travel industry:
Rev Mgmt to optimize
utilization vs revenue,
using time-dependent
price differentiation
◼ Retail adjusts prices
according to competitors,
time, traffic, conversion
rates, sales goals
◼ Unpopular with consumers
◼ Low degree of automation
◼ UBER, AirBnB and Amazon
have pioneered nextgen
dynamic pricing
◼ Price setting based on
microsegments (categories,
offers, location) to better fit
price with demand
◼ Software vendors force
dynamic pricing
professionalization
◼ Examples include
—Demand forecasting
—Store-specific prices
—Markdown optimization
(stock, expiration, season)
◼ Big Data & ML:
Classification & prediction
◼ RPA enables high
frequency price changes
◼ 77% believe they will drive
results with personalized
prices and promotions*
◼ Context via location data,
shopping/browse history,
style preferences on social
media and even the look
on the consumer’s face
◼ Omni-channel personali-
zed customer experiences
◼ 1:1 pricing at scale

14. Felix Krohn,
Sr Advisor & Pricing Coach 14
Source: AI Pricing Expert Survey (n=26), May 2019
Prominent and promising use cases for AI Pricing
Price Change
Automation
◼ Price watching & matching
◼ Save humans from routine tasks and allow
them to make more high-level decisions
◼ Pitfall: Underestimate shoppers’ willingness to
pay a fair price
Real-time
Contextualization
◼ Capture critical information to help build more
intimate customer relationships over time
◼ Incorporate data like device, customer journeys
/browse history, seasonal/weather, social
profiling etc.
Price
Optimization
Modeling
◼ Move retailers beyond simple price matching
◼ Based on real-time (cross) price elasticity
◼ AI learns non-linear interrelations between
products and makes counterintuitive price
recommendations
Expert rating
Top-3 use
case: 58%
Top-3 use
case: 54%
Top-3 use
case: 54%
1
2
3

15. Felix Krohn,
Sr Advisor & Pricing Coach 15
Source: AI Pricing Expert Survey (n=26), May 2019
Prominent and promising use cases for AI Pricing
New Customer
Acquisition
◼ Deliver personalized content & conversations,
including personalized product suggestions
◼ Anticipate consumer behaviors and customer
product propensity scores (predictive analytics)
◼ Improve and automate recommendations
Dynamic In-store
Pricing
◼ Initiate dynamic pricing in physical stores
◼ Leverage LCD price tags, personalized coupons
◼ Regional/seasonal/time/weather/stock-based
price adjustments
Quotation
process
automation
◼ B2B pricing use case for configurable product
offers, including discounts
◼ AI-enabled functionality enhancement for CPQ
workflows
◼ Make Sales Rep/Account Exec more productive
Expert rating
Top-3 use
case: 42%
Top-3 use
case: 42%
Top-3 use
case: 23%
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5
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16. Felix Krohn,
Sr Advisor & Pricing Coach 16
Source: Stitch Fix, Goodwater Capital
Showcase example: Stitch Fix4

17. Felix Krohn,
Sr Advisor & Pricing Coach 17
Real-time
Contextualization
Price Optimization
Modeling
Price Change
Automation
Location
Time
Season/
Weather
Social/
Roles
Relation
-ships
Senti-
ment
Intent
History
Most promising use case for AI Pricing1 2 3
◼ Change individual prices
◼ Self-learning algorithm
considers competitor
actions and profitability
◼ Price changes happening
thousand times a week
◼ Balance multiple factors
◼ Across categories and
channels
◼ Determine full bottom-
line impact
◼ Drive business goals

18. Felix Krohn,
Sr Advisor & Pricing Coach 18
Source: AI Pricing Expert Survey (n=26), May 2019
AI Pricing shall be mostly calibrated towards margin &
profitability, while maintaining customer engagement high
Margin
50%
Customer
Engagement
27%
Revenue
19%
Sales
4%
Margin Customer Engagement Revenue Sales
Commercial
Goals
◼ Omni-channel presence,
convenience, and providing a great
experience are key battlegrounds in
today’s B2Me world
◼ Hence, most CMOs driven by
customer engagement metrics!
◼ However, AI Pricing expert panel
foresees an increasing focus on
margin & profitability
◼ Gartner: By 2022, profitability will
replace customer experience as
CMO’s #1 strategic priority

19. Felix Krohn,
Sr Advisor & Pricing Coach 19
Source: AI Pricing Expert Survey (n=26), May 2019
What’s next – AI Pricing 2.0?
ExpectedAdoption
QuitelikelyRatherunlikely
Maturing Nascent
Technical Feasibility
Face
recognition Eye
tracking
Brain
scanning
Biometrics
Conversation
listening
◼ Listening & face
recognition well
developed
◼ Eye tracking &
biometrics in
research/pilot
stages. Use cases
rather in security
than retail
◼ Alibaba’s Pay
with a Smile (CN)
◼ Biometric age
verification for
self-check out
systems
◼ No pricing uses

20. Felix Krohn,
Sr Advisor & Pricing Coach
Concerns regarding a widespread introduction of AI Pricing
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◼ Legal & Compliance concerns top of
mind, e.g. regarding
― GDPR
― Pricing Bots collusion
◼ Ethical & Reputational concerns
widely discussed. Some address
consumers’ unfavorable opinion of
profiling (esp. in EU)
◼ Many companies struggle to excel at
analytical pricing transformations
― From idea to use case production
is 10% algorithms, 20% tech, 70%
about changing how people work
― Doubt effectiveness of pricing
engines: “black box”, counter-
intuitive recommendations
Source: AI Pricing Expert Survey (n=26), May 2019
AI Pricing:
Major barriers for widespread adoption

21. Felix Krohn,
Sr Advisor & Pricing Coach
Some thoughts & tips regarding implementation
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Data Tools
People Organization
◼ Existing/internal & new/external data
◼ Historical & competitive data (>1-3 years)
◼ Centralized data lake to store and ingest
structured & unstructured data
◼ Match (fuzzy & exact) and link big data
across sources and types
◼ Toolkit to build & reuse AI/ML algo-
rithms with scalable computing power
◼ Develop integration/interaction layer
◼ Make or buy: R/Python/Scala/Spark vs
cloud provider vs comm’l Pricing SW
◼ Pilot test, test, test!
◼ Develop or source data science
& advanced analytics capabilities
◼ Focus their efforts on high-value
activities, not data prep or
reporting
◼ Teach team to trust AI price
recommendations
◼ Ensure an appropriate budget
◼ Overcome local data silos and ensure AI
resources are located close to the actual
work (Pricing/Marketing/Sales, not IT)
◼ Lean Start-up: Build-Measure-Learn;
Fire bullets, then cannonballs.
◼ Show the money, over-communicate!

22. Felix Krohn,
Sr Advisor & Pricing Coach
Thank You!
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