The document discusses an AI company's approach to building empathy with customer data. It explains that the company uses a "Data Empathy Mapping" framework involving goals, data, and rules to understand how customer data is used and what systems support it. The framework involves defining problems and goals, evaluating available data, and incorporating business rules and constraints. The company applies this approach through experimentation and prototyping to develop minimum viable products, and aids deployment by helping customers operationalize new AI systems. Examples discussed include optimizing vending machines and predicting product demand.

1. Data Empathy
Design Thinking Approach to AI

2. Who is...

3. ● With small margins and retailers continually expecting more from their suppliers, the CPG/FMCG sector has never
been more competitive.
● The race for optimal shelf space is the most challenging problem in global consumer retail industry, costing
suppliers and retailers $400 billion* in lost sales per year.
● Outlets are grouped by location, size or other arbitrary measures using “human constructs”, typically at “cluster”
level.
● We have developed unique IP in AI & optimisation to provide “fingerprint recipes” at a per product SKU, per outlet
level. Providing “machine constructs” view.
Commercial In Confidence | Page 3*Source AI-powered, big data manages critical retail efficiency in real-time
The problem is simple...

4. ....there are many companies who have this problem
We help these companies... ….get the right mix in these outlets
Commercial In Confidence | Page 4

5. We build tools that augment our customers thinking in new ways never
possible before
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Just as in pathology, we see AI+pathologist together make superior prediction. We are doing the
same in retail with our product, space, price and promotion recommendations for our CGP/FMCG
customers.
Source: https://blogs.nvidia.com/blog/2016/09/19/deep-learning-breast-cancer-
diagnosis/

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Recap on the Design Thinking approach
STEP MISSING!
Design principle to Ideate
using Affinity Diagram/
Process Diagram

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Things like this...
Process DiagramAffinity Diagram

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Design Thinking approach is actually used in HIVERY’s product
development methodology. It’s part of our DNA
VS

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Discovery is all about building empathy and defining the
problem
VS

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While in Experiment, it’s about developing the model; training,
refining and testing it
VS

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And in Deployment; it’s about getting the enterprise ready for new way
of operating...

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But, I want to focus on this one first...

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There are tools to help us build user empathy...like this Persona
Empathy Mapping
Source: https://www.cooper.com/journal/2014/05/persona-empathy-mapping

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These tools help us gain empathy towards the segment we are trying
to solve. Empathy through…
Source: https://www.cooper.com/journal/2014/05/persona-empathy-mapping

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At HIVERY, we empathize with data not humans (at least initially).
If we can't build the engine, no point building the car.

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In essence, we use a “Data” Empathy Mapping framework
Persona Empathy Mapping:
Think, Feel, and Do
Data Empathy Mapping:
Goals, Data, and Rules
Rules:

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Data Empathy is about...
DATA:
RULES:
GOALS:
… gaining an
understanding
of how the data
travels
throughout the
organisation...

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DATA:
RULES:
GOALS:
...how it is used,
what system
and processes
support it; what
actions are
derived from it.
Data Empathy is about...

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Let's go deeper...
...break
down...

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What happens in “Goals”
● Define the problem and AI goals (eg Japan water)
● This provide team focus
● Need to distinguish “automation problems” (i.e.
human intensive 7000 planograms or 1 hr to optimize 1
vending machine) and “learning problems” (i.e. make
actionable recommendations at outlet/shelf/store/SKU
level).
● Examples of good machine learning problems include
predicting the likelihood that a certain type of user will
click on a certain kind of ad or in our case, what
predicting the likelihood that a certain type of product
will sell in a specific outlet (vending machine/store)
● Need to be clear if we are (or both)
○ creating machines that can automate work
○ obtaining insights into similarities & differences

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What happens in “Data”
● Once we verify our customer’s problem and goals for
machine learning application;
● The next step is to evaluate whether we have the right
data to train and solve it
● Understanding data means:
○ Determine system sources (ie legacy systems)
○ How good is the data quality (ie integrity)
○ How good is the data quantity (ie at least 12
months?)
○ How good is the ongoing data streams/flows?

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What happens in “Rules”
● Rules are important but often not considered business
constraints but need to be design into the algorithmic
model(s).
○ “Google Maps, A to B and avoid tolls”
○ “No coke in vending machines at schools”
● Business constraints allow enterprises to adopt and
operations AI recommendations
● In the future, a properly designed goal achieving AI
model allows humans to challenge assumptions via
"What if" scenario as ML predicts impact of your
assumption inclusion or exclusion.

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What happens in “Situation”
● This gives clarity over:
○ the problem
○ the goals of what needs to be achieved
○ the opportunity/challenges (ie data is poor (eg
China vending machines)
○ possible direction
○ Develop formalities:
● stakeholder engagement,
● project team,
● sponsor,
● communication plan,
● work plans,
● SoW etc

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...Ideate, Prototype and Test in the Experiment Phase while informing
Deployment Phase

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Experiment is about...
● Forming and agreeing on a hypothesis
● Formulating bulletproof experiment designs
● Visualizing data insights/opportunities (eg Japan)
● Refining the algorithmic models parameters as it learns
(training test)
● Validating (ie validating set) and iterating the model’s
predictions/ recommendations both from a business
value and operationalisation perspectives
● Start thinking about possible MVP - the “car design”

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And lastly Deployment is about getting the enterprise ready for new
way of operating...

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Deployment is about operationalising & project management
● From Design Thinking methodology to Project
Management methodology
● Ensuring enterprise adoption of AI
● Transition from MVP to Beta in an agile manner
● Formulating the plans around change management
and operationalization strategies
● Agreeing on the ongoing commercial model

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Let's chat about two real examples
...break
down...

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Example: Vending Analytics journey
Does it work?
Can we optimise a vending
machine better? 60 machines
experiment in Newcastle
Make it smarter?
Data scientists and client
validation of model and
understanding constraints
Remove the pain?
Understand what is critical
to build to interact with
model
Build MVP
Build MVP (else they still
see a spreadsheet)
Remove
pain point

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Example: Promotional Effectiveness
Data scientists and client
validation of model and
understanding constraints
Make it smarter?
Understand what model
interacts are required by
user for operationalising
Remove the pain?
Build MVP to learn, refine
and build features based
on validated learning
Build MVP
Can we use ML to predict future
demand of a product? If so, how
accurate? Better than human?
Does it work?

31. The approach applied to our category management toolg
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Does it work? Validate results Develop MVP
Can we optimize 60 feet of
shelf space and make it look
pretty at the same time?
Understand data, workflows,
and operations. Validate
assumptions.
Make it smarter
Co-design with strategic
partner and build MVP.
Conduct experiment to validate
results and proof business
case.

32. DATA HAS A BETTER IDEA
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