data-driven-decision-making-in-digital-entrepreneurship

Abstract

Data-driven business models are more typical for established businesses than early-stage startups that strive to penetrate a market. This paper provided an extensive discussion on the principles of data analytics for early-stage digital entrepreneurial businesses. Here, we developed data-driven decision-making (DDDM) framework that applies to startups prone to multifaceted barriers in the form of poor data access, technical and financial constraints, to state some. The startup DDDM framework proposed in this paper is novel in its form encompassing startup data analytics enablers and metrics aligning with startups' business models ranging from customer-centric product development to servitization which is the future of modern digital entrepreneurship.

Full text

1 Abstract—Data-driven business models are more typical for
established businesses than early-stage startups that strive to penetrate
a market. This paper provided an extensive discussion on the principles
of data analytics for early-stage digital entrepreneurial businesses.
Here, we developed data-driven decision-making (DDDM) framework
that applies to startups prone to multifaceted barriers in the form of
poor data access, technical and financial constraints, to state some . The
startup DDDM framework proposed in this paper is novel in its form
encompassing startup data analytics enablers and metrics aligning with
startups' business models ranging from customer-centric product
development to servitization which is the future of modern digital
entrepreneurship.
Keywords—Startup data analytics, data-driven decision-making,
data acquisition, data generation, digital entrepreneurship.
I. INTRODUCTION
S a powertrain of the digital economy, data have proved to
be one of the competitive advantages that businesses
established on such resources excel. Accordingly, data science
and practices have evolved, incorporating the dynamic business
needs over time. With the increasing stock of big data from the
digital footprints of users, integrating and exploiting the
benefits of such information in decision making is proven to
facilitate decision making and unlock business potentials [4].
Moreover, open data initiatives by the data hoarding tech
companies, like Microsoft, can help businesses utilize the stock
of digital footprints they hoard in their data empire [10]. With
the fast pace of digitization, reinforced with COVID-19 and
multiple other factors, by 2024, global data created, captured,
copied, and consumed are predicted to reach 149 zettabytes
from as low as 12.5 zettabytes a decade ago in 2014 [11]. In a
world where such data can be used at a near-zero marginal cost,
entrepreneurial DDDM strategies and robust analytics are vital.
There is evidence that using data in decision-making has a
significant cost reduction effect, specifically in relation to
operational efficiency [1].
Startups face multilayer problems in today's dynamic
business environment. Accordingly, informed decisions of this
form in an entrepreneurial course of actions rather than mere
reliance on intuition can improve business performance.
However, in most cases, startups fail to incorporate DDDM in
their core business lines. Some of the factors identified to limit
startups from using data analytics include a mere focus on
product development, team expertise in data analytics, amount
of data at stock, a mere focus on core product delivery, attention
Abeba Nigussie Turi and Xiangming Samuel Li are with University Canada
West, Canada (e-mail: abebanigussie@ucanwest.ca, samuel.li@ucanwest.ca).
to market penetration, and seeking customer validation in the
short-run [3].
In this paper, by focusing on digital entrepreneurial practices,
we will flesh out strategies to monetize the primary or
secondary chain of data that businesses access in a way that
boosts their potential growth opportunities. By identifying the
value of data and examining the potential to exploit such values
through smart business models, the paper presents a framework
to unlock such potential in one of the digital economy's
underutilized resources. Besides, by taking use cases and
highlighting the respective on-demand business intelligence
tools, the paper will flesh out the standard practices and
strategies to accelerate growth through DDDM. Further, it will
provide an extensive discussion on the potentials which open
data holds for startup businesses and strategies to utilize such
an open resource to boost business growth at a scale. Digital
blueprints, data valuation, smart business models, on-demand
business intelligence tools (services and software for processing
digital footprints), DDDM, and data-centric value creation are
the main topics covered in this paper.
The rest of this paper is organized as follows. Section II
presents related works and highlights the research gap we
intend to fill in this work; Section III briefly presents some data
analytics tools, Section IV presents the practice of data-
powered decision making in the startups’ context, Section V
presents data source, challenges, and opportunities in relation
to startup data analytics, Section VI highlights the core data
analytics application areas for startups; and, Section VII
presents data analytics strategy and a conceptual framework to
DDDM. Lastly, Section VIII concludes our key findings with
direction for future works.
II. RELATED WORKS
Several studies have been conducted to unlock
entrepreneurial business potential and identify key challenges
such businesses observe [3], [5]. With this is the potential of
digital footprints generating big data for predictive and visual
analytics [6], [13]. Yet, there is evidence that early-stage
startups face the challenges of skills, capital, market
uncertainty, technological uncertainty, time management, and
privacy issues to generate values that contribute to poor startup
success rates [3], [5], [12]. Hartmann et al. [6] provided an
extensive discussion on the taxonomy of data-driven business
models used by startups, encompassing seven key business
activities: free data collection and aggregation, analytics-as-a-
Data-Driven Decision-Making in Digital
Entrepreneurship
Abeba Nigussie Turi, Xiangming Samuel Li
A
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service, data generation and analysis, free data knowledge
discovery, data-aggregation as-a-service, and multi-source data
mash-up and analysis (see also [7]). Kandel et al. [7] identified
an enterprise's organizational feature as one of the key factors
defining enterprise data analytics' efficiency.
The literature in the field is limited in providing theories and
practices for early-stage startup data analytics. This paper will
flesh out the principles of DDDM in the startup context.
Besides, we extend the work on startup data analytics by
providing a general conceptual framework of DDDM that
applies to digital entrepreneurs.
III. THE PRACTICE OF ENTREPRENEURIAL DATA-POWERED
DECISION-MAKING
Data analytics can play a crucial role at different stages and
scales of business operation, starting from the inception stage
of a product life cycle. Before launching a new business or
introducing a new product, big data can be used to make
informed decisions. This will allow product testing with an
accurate picture before getting into the actual market. Relying
on the core bubbles or a small circle of people to test a product
might create some bias when bringing the product to general
users. The digital economy has brought the world together into
a virtual planet and this has made it easier for startups to have
insights and tests to their new products from the crowd. For
example, location analytics can help identify an appropriate
business location that fits your business lines. Another example
is property valuations of the real estate industry. A stock of data
enables the analysis of markets and identifies non-economic
and economic signals and trends of the real estate industry.
To help us look into the practice of data-powered businesses
by startups, we picked the clients from the data analytics
company, Mixpanel. The company tracks web and mobile
usage that allows targeted product development. Here, we will
pick and look at two sample companies that rely on its tool to
make informed decisions using data.
One of companies is the transaction management and
electronic Signature tech company, Docusign, which uses data-
analytic tools from Mixpanel to boost its global reach. The
system allows Docusign to enhance its basic and custom
product metrics like customer retention, the number of
documents customers successfully complete, and upgrades. For
example, Mixpanel behavioral analytics enhanced Docusign’s
paid upgrades by exposing some premium features that
incentivize users for the conversion. The analytical tool
increased upgrade conversions by 5%, bringing about 130,000
new users per day [14].
Kast is another company using Mixpanel's data analytics tool
in its service provision of a "virtual living room". The company
uses content sharing technology to allow the traditional
physical human interaction experience (e.g., watching movies
or playing games in a shared space). The startup uses Mixpanel
for its DDDM to understand its users. For example, the data
analytics took user feedback to identify "Power Users" (active
members in the last 15 days and a minimum of 30 time-
frequency of partying through the app). This feedback is used
for product and feature developments. The analytics allowed
the startup to trace users' behavioral patterns of the app usage
by tracking user data. Such an informed product decision
allowed the company to increase its customer retention by about
50%. See also [12] for the feedback loops in the data-driven
economy applied to the prosumers’ blueprint towards data
monetization in the supply chain.
IV. DATA SOURCE FOR STARTUPS: CHALLENGES AND
OPPORTUNITIES
Data analytics applies to the notion of enabling technologies
that capture relevant information for economic and non-
economic value generation purposes. Such information can be
revealed from massive volumes of data, big data, coming from
the information society's digital blueprints. The massive
blueprint of human-machine interaction comes from
transactional mobile and internet records, user-generated
content across social media, blogs, marketplaces, and various
digital platforms, ad-hoc content collected through sensor
networks, IoT, and other channels.
Many digital footprints left by users lay the basis for
structured and unstructured data, which businesses can harness
in their best interest. In most cases, specific data that apply to a
given business come from the data businesses capture from
their online store visitors across different sessions, platforms,
and domains. This treasure contains potential customers of the
product, which businesses pull for their marketing strategies.
However, several barriers limit entrepreneurs from using these
potentials [3].
One of the main challenges early-stage digital entrepreneurs
face is the limit in the amount of data they have in stock. With
this limit in place, it will be difficult for startups to rely on
internal data to make business decisions. In this regard, it is
ideal for such startups to use relevant external data and tools
that apply to their business needs. To mention one, leveraging
social data mining of third parties can help in targeted ads. For
example, data exchange platforms, vendors, and tools like
Ocean Protocol, Gnip, Immersive Labs, Data Sift, BDEX,
Quantcast, and iGrant.io facilitate and enable such real-time
data for customer-centric businesses. Similar to these are the
open-source big data analysis platforms and tools like Hadoop,
MapReduce, GridGain, HPCC Systems, and Storm that have a
significant innovation diffusion. Another way to excel on this
is to make proper use of open-source data available on the web.
An equally important aspect to discuss here is data optimization
practice. Optimization of the data businesses capture from
online store visitors across different sessions, platforms and
domains can allow optimal business performance through
improved business operations and accurate decision making.
V. DATA ANALYTICS APPLICATION AREAS FOR DIGITAL
ENTREPRENEURSHIP
Data analytics, including data visualization and machine
learning, can empower digital entrepreneurs to acquire new
customers, create new value chains, and improve operational
efficiency. Operational efficiency is one of the forefront value-
add coming to businesses with data analytics. The following
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attempts illustrate some of the application areas of data
analytics for startups.
A. New Product Design
Creating personalized new products for each customer can be
challenging. However, the digital footprint of users has enabled
this through aggregated data analytics. For example, Hellofresh
(a meal-kit company) consumer data streams are massive,
aggregated from social media, emails, and user impressions on
their websites, allowing customized fresh ingredients and
recipes for individual customers. Here, a special case of digital
entrepreneurship takes the form of prosumers in which
consumers of digitally enabled products and services are the
producers themselves, thus adding to new product development
and designs.
B. Online Platform Building
Data analytics tools can help digital entrepreneurs develop a
professional website without HTML, Web 3.0, CSS, and
JavaScript. For example, AI-powered website builders like
Wit.ai, Wix, and Dialogflow. Content generation tools such as
Articoolo, Wordsmith, and Quil can analyze and learn from
other web data and generate human-like content to help startups
engage online customers more. Besides, the Google Analytics
tool allows to monitor, analyze and improve startup web
business performances.
C. Reputation Management
Trust, as repeatedly coined, is the currency of the digital
economic system. There are diverse rating and reputation
schemes in the P2P business models. For example, Li [9]
applied text analytics and machine learning approaches,
including Naive Bayes, logistic regression, support vector
machine (SVM), and long short-term memory (LSTM-AI), to
more than three million online text reviews collected from the
Airbnb platform. A large knowledge-based label (CORPUS) is
built, and an innovative text review score (TRS) is constructed
as a new online trust measure. Digital entrepreneurs can adopt
a similar approach or take advantage of this sizeable online
review CORPUS to rapidly build up new online reputation
systems.
D. New Product Market Launch
Digital startups can use data analytics to find an optimal
marketing strategy to launch their new products successfully.
For example, various relevant data points from previous market
launches can be collected from open data sources for similar
products in a specific market; and data visualization and
analytics tools can be employed to find the effectiveness of each
marketing promotion and product pricing.
E. Digital Marketing and Customer Profiling
Data analytics can empower digital startups to know more
about their customers regarding product features and pricing.
Personalization is vital to delivering a satisfactory customer
experience. Data analytics enables an efficient, personalized
user experience enhancing customer conversion and retention.
For example, Netflix traces users' movie or TV show preference
or Amazon recommends related products based on transaction
records and other users' digital footprints.
F. Customer Relationship Management through Chatbots
One of the powerful data analytics applications is online
chatbots powered by artificial neural networks (ANN) as a deep
learning technique. The chatbot is a conventional software
agent to communicate with human users through a natural
language based on ANN. Chatbots have the advantage of being
self-service, including 24x7 full online care, global languages,
cost-saving for call centers, as well as interactive and intelligent
user experiences. Chatbots are expected to help enterprises to
save about $8 billion annually by 2022 in customer-support
costs, compared to only the $20 million estimated saving in
2017 [13]. One of the common chatbots is Amelia by IPSoft, as
the market-leading digital employee and conversational AI
application. Chatbots can offer high-volume and high-impact
customer services such as Frequently Asked Questions (FAQs),
service ticket management, account management, and customer
care.
G. Fraud Prevention
Data analytics has been extensively applied for business
fraud detection and prevention. Many fraud detections use UPL
classification models, such as decision tree, logistics regression,
SVM, and even ANN deep learning. In business practice,
various unsupervised learning methods like profiling,
clustering, anomaly detection, and co-occurrence association
are also used for different fraud detection and prevention.
H. Data Analytics for IoT Machine-to-Machine (M2M)
Applications
M2M-based IoT data differ from traditional big data in terms
of the growing "ocean" volume, heterogeneous formats,
embedded noises, and real-time attention needed like medical
care and animal surveillance. The wide-spreading 5G networks
and IoTs applications will trigger a new round of Industry 4.0
innovations, including smart homes, smart cities, smart
agriculture, and smart globe, or called Smart Xs (Smart
Everything). Meanwhile, this brings a huge potential for new
entrepreneurs to create various M2M-based IoTs applications
using advanced data analytics tools.
VI. DATA ANALYTICS STRATEGY AND CONCEPTUAL
FRAMEWORK FOR DIGITAL ENTREPRENEURSHIP
Based on the startup metrics of Kemell et al. [8] and the
Pirate Metrics, or “AARRR” of McClure [10] and Hartmann et
al. [6] taxonomy of data-driven business models for startups,
we developed a conceptual framework for the entrepreneurial
DDDM process.
Here, we aggregate the foundation for the entrepreneurial
DDDM under two main pillars: (1) The enablers which allow
the digital entrepreneur to make data-driven decisions, and (2)
the business metrics which need to be identified to fit the
decision-making process with the core business needs and
goals. Kemell et al. [8] provided key startup metrics for tech
entrepreneurs, including engagement metrics that measure how
often people engage with a product. For example, for an
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uberfication business model, Daily Active Users (DAU) and
Monthly Active Users (MAU) metrics measure customer
engagement with the app. Another metric is the churn rate
which measures customer stickiness to a product, like the rate
of cancelation or non-renewal in a subscription-based business
model. As is typical for startups to get customer endorsement
and validation, customer engagement, product reviews, and
customer feedback are essential metrics startups focus on for
product development and enhanced services. As startups
consider scaling their business, equally important are the
growth metrics.
Under the first pillar, by extending the key big data analytics
enablers indicated by Behl et al. [2] and Kandel et al. [5], we
identified four main enablers which we identified them as
people (encompassing the technical skills and entrepreneurial
leadership a startup owns), startup's organizational features and
process (referring to the startup's internal business process,
relationships and diversity of data sources), infrastructure and
platform (including analytical tools, cloud storage technology,
security and privacy-preserving solution) and data (acquisition-
external data-capture from data exchange platforms, data
analysis platforms, open-source like social data for targeted ads
and real-time data for customer-centric business, keyword
performance and ranking, search behavior, page visits, tweets,
likes, share comments, or generation- internal data capture from
tracking (potential) customer or user data, transaction records,
automated user tracking, customer feedback, customer
engagement, business operations).
The second pillar of business metrics is the performance
metrics under (i) Business and financial metrics, (ii) User/
customer metrics that capture behavioral patterns of (potential)
customers allowing for customer-centric business, (iii) Service
metrics that capture the process and product metrics, and (iv)
Social media metrics as a measure of business visibility for
demand-pull.
Fig. 1 Entrepreneurial DDDM Process: A Conceptual Framework
A data pool from diverse sources allows the startup to draw
business insights through analytics and visualization using
analytical tools in the decision process. This will feed into the
entrepreneurial business model based on the revenue models
defining the startups' potential and actual business lines. Here,
the analytics can support customer-centric value-add;
servitization to build new revenue streams through service
business lines (demand-pull), business reengineering; product
development; process-oriented value-add (tech-push and
automation); or an overall business transformation and
reengineering. Business analytics and visualization capture
value from data and integrate data-powered decisions into the
business strategy. Hence, we identified the major courses of
action in the DDDM process.
Such courses of action include operational efficiency,
augmentation of value proposition and differentiation for
competitive advantages, and ultimately attaining business
sustainability through robust data-supported measures
prescriptive analytics. Fig. 1 depicts a conceptual framework
for entrepreneurial DDDM. Summing up, based on the
literature and best practices in data analytics, the main steps for
entrepreneurial data analytics are discussed as follows:
A. Business Understanding
Digital entrepreneurs should start with compelling business
problems in mind and look for robust data analytics tools to
solve.
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B. Data Collection
At this stage, we identify internal or external data sources that
will give an insight into the business and fuel growth for startup.
In line with the business rules, another important thing to
consider is to plan what you intend to acquire from the data. It
should be noted that new goals can arise as one observes the
trends and patterns in the data.
C. Data Understanding
It is crucial to understand the strengths and weaknesses of the
data; since there is no exact match between the data and targeted
problems to be solved. Because a historical dataset is often
composed of unnecessary features which do not serve the
purpose, it is vital that the analyst goes through the data and
filter the required information in order to draw insight out of it.
For example, a client database, product marketing response
data, and business transactional records cover different
intersectional populations with varying degrees of reliability.
D. Data Organization
This phase often is carried out with data understanding,
where the data are manipulated and processed into proper forms
for data visualization and data analytics tools to yield valuable
results. At this stage, the data are organized to form a
description and/or identify themes and trends in user behavior.
Examples include converting raw data to a tabular format with
rows and columns, dealing with missing data, changing data
types, removing excess data, data value normalization, etc.
Besides, data organization includes cleaning the data and
getting an insight with further considerations for data
compatibility issues in the data organization process. One of the
principal reasons for this is that as the startup grows, the data
analytics will be replicated at a scale as the business and data
analytics systems evolve.
E. Modeling
It is the primary phase where data analytics techniques are
applied to the data, including visualization, clustering,
classification, and deep learning (see Section IV). The model
has to specify key instruments and metrics on which the data
analytics relies in order to address the core business problems,
see Pirate Metrics, or "AARRR" [10].
F. Evaluation
Digital entrepreneurs can employ different evaluation
metrics such as the confusion matrix and ROC/AUC curves to
evaluate the model performance in terms of accuracy, precision,
recall, cross-validation, and overfitting capacity. Model
evaluation is a science rather than an "art". It includes a rigorous
assessment of various models to select a robust model for a
successful digital business.
G. Deployment
Deployment is the final phase of putting data analytics to real
use to achieve business results. It involves implementing the
best-evaluated model from the previous step, such as predicting
the likelihood of customer churn and sending a special offer to
highly possible "churn" customers to retain them in business to
maximize the return on per customer. A new fraud detection
model can be trained and integrated into a customer relationship
management (CRM) system to monitor accounts and identify
potential transactional fraud to minimize business loss.
VII. CONCLUSION
Digital entrepreneurs often fail to incorporate DDDM in their
businesses during their startup incubation, contributing to a
very high startup business failure rate. This work fleshed out
the core state-of-the-art data analytics tools and application
areas for early-stage startups, including new product design,
online platform building, digital marketing, customer care,
operational efficiency, and IoT-based machine-to-machine
automation. Further, we offer practical strategies for
entrepreneurial data-powered decision-making, such as data
acquisition in the face of limited internal data generation. The
conceptual framework for the entrepreneurial DDDM process
developed in this work relies on two key pillars of enablers that
allow the digital entrepreneur to make data-driven decisions
and the entrepreneurial business metrics that fit the decision-
making process with core business needs and goals. In this
regard, future work direction will be to look into the dynamics
of data analytics techniques and startup business performance
under the agile digital entrepreneurship environment. Another
avenue of research related to this is the empirical evidence and
quantitative analysis of data-powered decision-making in the
startup business context, including the sectoral analysis.
ACKNOWLEDGMENT
Funding by the University Canada West is gratefully
acknowledged.
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