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Delivery Hero SE
Enable Customers to Order Food Whenever & Wherever They Need
SAP Innovation Awards 2021 Entry Pitch Deck
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Company Information
Headquarters
Industry
Web site
Berlin, Germany
IT
www.deliveryhero.com
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Challenge
Solution
Outcome
Thanks to support from SAP
Services and the Intelligent
Enterprise Institute, we better
understand the dependency of
customer experience on our
delivery processes.
Leveraging SAP® S/4HANA,
Experience Management
solutions from SAP and
Qualtrics, and machine
learning models, we now know
how to improve data flows and
best use our data to meet
customers’ needs.
Sebastian McClintock, Global Director,
Customer Experience, Delivery Hero SE
Delivery Hero SE
Combine Customer eXperience data from Qualtrics and Operational & Financial data from SAP S/4HANA so we
can understand dependencies. Financialize NPS so management can weigh impact of operational decisions on
customer experience.
• Understand impact of stacked orders on delivery time, customer experience and business efficiency.
• Change in delivery time has immediate impact on customer loyalty.
• Deriving an acceptable delivery delay range to find the right balance between positive customer experience
and business financials.
~2%
We know a lot about our customers from analyzing experience data collected from surveys. However, we know
little about how shifts in loyalty metrics impact operational data, and much less about financial data. For
example: what are the costs and benefits associated with driving down delivery time on customer loyalty (NPS)?
…stacked deliveries have no significant impact on customer loyalty but positive impact on rider utilization rate.
up to
3
… delay is
tolerated by our
customers.up to
10 min
Ordering Food Whenever & Wherever Customers Need
… increase in
NPS for every
minute decrease
in delay.
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Business Challenges and Objectives
Key Challenges:
• Breaking down silos by bringing together the right mix of individuals from within the company to dedicate time and resources to investigate the project needs and requirements thoroughly.
• Exploring the automation and analytical capabilities between SAP and Qualtrics to pioneer solutions with respect to combining FOX (Financial/Operational/eXperience) data from various sources and building machine learning models.
• Understanding customer loyalty trends with regards to profitability, since we not only wish to look at the cost, but also revenue elements incurred on an order level.
Key Objectives/Questions to answer:
• What is the right balance between logistic expenses/operational KPIs and customer loyalty (NPS), e.g. how many riders do we need on the ground to deliver superb delivery experience for our customers?
• Understand impact of each journey stage regarding operational, experience and financial data. Which interaction points drive the customer experience and how do we need to change our current SLAs to meet or exceed customer’s expectations?
• What are the financial implications on attribute level (e.g. 10% increase in Satisfied Customers with Delivery Time equals to 2% uplift in Average Spend within 100 days)?
5PUBLIC© 2020 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Project or Use Case Details
A maximum level of customer insights are derived from Experience Management (XM) solutions from SAP and Qualtrics, as this valuable data affects customer satisfaction, delivers customer loyalty, influences expectations, instils confidence, supports the brand, and creates emotional bonds with customers.
We know a lot about our customers thanks to these customer insights. Now is the time to understand the financial and operational impact of an increasing or decreasing loyalty. In order to match the experience data with operational financial data on a transactional level we teamed up with the Finance and Product/Tech department to leverage the respective data out of SAP S/4HANA.
Through a valuable design thinking-based workshop jointly with SAP Data Science team, we decided to start with the customer journey stage of “Delivery” and its impacts on customer NPS. At a later stage, this will enable us to understand financial implications on the attribute level and to be able to predict financial impacts even for process or product changes.
Using the analytical capabilities of SAP BW/4HANA and R Statistical Modeling Tool, SAP helped to analyze the data set, build the models, and supported with evaluating the results to an extent where we were able to operationalize the NPS. This helped us learn more about various elements affecting the delivery stage.
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Business or Social IT (optional) Human Empowerment
Benefits and Outcomes
- Up to 3 stacked deliveries have no significant impact on customer loyalty but positive impact on rider utilization rate
- Around 2% increase in NPS for every minute decrease in delay, especially when order delay is greater than 10 min
- Customers tolerate up to a 10 minute delay in delivery
- Understand delivery times and customer expectations in several different countries/ cities
- Support families to manage work and family life by offering restaurant variety to order food during COVID-19 lockdown
− Improve income of riders due
to improved utilization rate
− Making NPS and eXperience
data more commonly used
metrics in the company
− Help restaurants to stay in
business despite hard times
(COVID-19, curfews)
− Our efficient and speedy
delivery ensure sales for
restaurants
− Expanding our services to
other lines of businesses,
e.g. provide essential goods/
products to our customers
− Database which combines
several data points from
different data sources
− Learning: need to combine
multiple data sources
effortlessly, e.g. financial,
operational and experience
data on order level
− Top-down realization of the
importance of being able to
enrich eXperience data with
operational and finance data
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Architecture
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SAP® technologies used:
Date Number of users
Deployment status
Deployment
If you have used one or more of the services or support offerings from SAP Services and Support during the implementation or
deployment phase, please indicate which one(s) below with an
SAP MaxAttention™
SAP Value Assurance
SAP ActiveAttention™
SAP Model Company
SAP Advanced Deployment
Others:
X
SAP Innovation Services SAP Innovative Business Solutions
July 24th 2020 10-15
Live
Qualtrics
SAP product
Deployment status
(live or proof of concept [POC]) Contribution to project
1Experience Management solutions
from SAP and QualtricsLive Data source for customer experience data
2 SAP S/4HANA Live Data source for operational lo logistics and financial data
3 SAP BW/4HANA Live Data consolidation layer
4
x
x
9PUBLIC© 2020 SAP SE or an SAP affiliate company. All rights reserved. ǀ
The following advanced technologies were part of the project.
Advanced Technologies (1 of 2)
Technology or use case Product used* Contribution to project and how product used integrates with SAP products
1 Machine learning or artificial
intelligenceRobotic process automation, conversational AI,
AI-based knowledge graph
2 Intelligent data managementMulti-cloud, data virtualization and governance, smart
data tiering, persistent memory, data privacy
3 Advanced and augmented
analytics• Real-time and streaming analytics, spatial analytics
• Natural language query and generation
• AutoML to identify trends, patterns, outliers
• Predictive analytics (time series analysis and
forecasting, regression, classification)
Experience Management
solutions from SAP and
Qualtrics
SAP S/4HANA
SAP BW/4HANA
R Statistical Modeling Tool
One machine learning model created, tested and trained in R (Statistical Modeling Tool)
based on the data set consolidated in BW4HANA
R scripts used in the data analysis:
• Histogram to understand data distribution
• Correlation Matrix to understand the correlation between the selected data fields
• Regression Analysis & Scatterplot to deep dive into the functional relations between
the selected data points
R visualizations used for data results
4 Data and analytics solutions in
the cloud• Unified data and analytics cloud platforms by SAP
• Modern/self-service data to analytics
*If this is not an SAP product, explain how it integrates with SAP products.
10PUBLIC© 2020 SAP SE or an SAP affiliate company. All rights reserved. ǀ
The following advanced technologies were part of the project.
Advanced Technologies (2 of 2)
Technology or use case Product used* Contribution to project and how product used integrates with SAP products
5 Advanced cloud integration• API economy (monetization and API marketplaces)
• AI-based or crowdsourced integration
• High throughput, low-latency digital integration hub
6 Industry cloud platform
7 Blockchain
8 Internet of Things
9 3D printing
*If this is not an SAP product, explain how it integrates with SAP products.
11PUBLIC© 2020 SAP SE or an SAP affiliate company. All rights reserved. ǀ
Additional Information
Link video from FOX workshop:
Next Steps:
− Closer involvement from upper management and data partners to drive forward the next iteration of the project (FOX 2.0)
− Understand financial implications on attribute level
− Be able to predict financial impact of process or product changes
− Liaise closely with our Logistics Counterparts to help derive specific metrics, necessary for the FOX model and to align on target KPIs based on analyses results.
− Bringing together fragmented data sets into a single data source automatically and easily
− Select Target Country based on financial data set maturity.
− Extend Data set to cover a broader time frame to subvert seasonality and regional dimensions
− Observe Financial implications, in a dashboard, as a direct/indirect result of change in Customer Experience metrics
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