Is Your Startup Idea Validated?

Globally, more than 100 million businesses launch in a year. Sadly, more than 90% of them fail or shut down within a couple of years. And funny enough, the number one reason why startups fail is that they’re unable to match their startup idea/product/service with market needs. In other words, 42 million startups launch every year with the idea the market doesn’t need.

It is critical to an entrepreneur’s success that his/ her idea is valid and well-researched. So before you start investing and scaling your business, ask yourself if the world needs you. Let’s dive deeper into the best ways to search your business idea:


The best time to startup is when you can identify a problem that exists in the market. Not only does the problem exist, but there are no other reasonable alternatives to solving the problem. In this scenario, you have a massive advantage. The only question left to answer is – Can you solve the problem? If yes, startup!

To find out if the problem is substantial or not, you can search online on forums, discussions, social groups, etc. to measure hype around the topic. For instance,  I browsed Amazon to check if there are enough books on starting up for students to validate my idea for a masterclass on “Starting Up for students.” I also checked for reviews on those books to analyze readers’ interests. I found plenty of evidence to support and further shape my startup idea.

Also, ask the target customers (individuals/ companies). Instead of guessing, it’s always better to find out from your future customers. Make a list and start surveying. With online tools for building surveys and reaching out to target audience organically or through paid ads, it is so much easier today to get feedback for your startup idea. Oh and don’t worry about someone stealing your idea (remember your concept is worth “zero” right now).


To explain in a phrase – Are you the next Uber or Netflix? Both these companies did not reinvent the wheel. They only changed the way their industry worked using technology at hand. There are so many examples of startups that used disruptive ideas to succeed and equally enough examples of ones who still failed. Ask me why disruptive startup ideas are not fail-proof? Because not all disruptive ideas are a problem that the market needs solving. Yes, finding a problem and solving it is still at the core of success. Doing this with the latest tech is your unique value proposition.


Investing in emerging technologies by analyzing megatrend development patterns of the future is a win in the long run. For instance, Elon Musk invested in SpaceX in 2002 with a vision to send a common man into space and the human race to Mars. Back in the days people thought this was crazy, but today, SpaceX has shown the world that a more significant vision can lead to unimaginable possibilities. Investing in the future requires funds, so this method works well for those who already have access to funds.


Evaluating the business potential of your startup idea will give you truthful facts and figures (not just an imagined story) to make your business. Key points to focus on while assessing business value:

  • Market size – Investors want to know how big your market is. It’s best to figure out demographics according to regions, age groups, ethnicity, and more if you can. It will further aid you in forecasting financials as accurately as possible.
  • Shrinking market vs. growing market – You don’t want to startup in a market that is likely to die in a few years. For instance, Nokia continued to invest keypad phones even though the market was shrinking, while Apple and Samsung understood the growing market needs for touchscreen phones and capitalized on it.
  • Market push vs. market pull – It’s safe to say that business ideas that aim at markets that exist are easier to implement than for those that don’t. For example, Samsung and Huawei pushing foldable screens, for which currently, no market exists. Such companies, with massive investments and brand value, can afford even a market push strategy. A market push strategy can be high-risk for a startup, on the other hand.

You are on the path to success with the above four strategies all applied together to validate a business idea before investing capital. Also, you minimize risk wherever possible and are in a position to draw accurate business models.

Remember, a failed idea is still better than the invalidated one!

by Nitesh Marwaha

CategoriesDigital Transformation

9 Elements of a Successful Digital Transformation in 2019

written by Victoria Zagorsky

As companies around the world seek competitive advantage in the digital economy, they make significant investments in the technologies that enable the digital transformation. According to IDC, worldwide spending on digital transformation is forecast to increase at a five-year compound annual growth rate of 16.7% to reach nearly USD 2 trillion in 2022.

According to the latest McKinsey Global Survey on digital transformations, more than eight in ten respondents say their organizations have undertaken digital transformation efforts in the past five years.


Only 16 percent of respondents polled by McKinsey say their organizations’ digital transformations have successfully improved performance and also equipped them to sustain changes in the long term. An additional 7 percent say that while performance improved as a result of digital transformation, these improvements were not sustained.

As majority of the initiatives do not reach their goals, it is not surprising that the digital transformation risk is the top concern for directors, CEOs and senior executives in 2019.


George Westerman, MIT principal research scientist and author of Leading Digital: Turning Technology into Business Transformation, defines digital transformation as “a radical rethinking of how an organization uses technology, people and processes to fundamentally change business performance”

To better understand the concept of digital transformation it helps to distinguish it from digitization and digitalization.


The journey begins with digitization, the process of converting information from analog to digital format. Once this task is accomplished, organizations can use digital technologies to enhance their operations. This the process is known as digitalization. And the final task of digital transformation involves leveraging digital technologies to re-invent the entire business model and strategy.

In other words, an organization digitizes information, digitalizes processes, and digitally transforms the business.

What can you do to ensure success of the digital transformation efforts in your organization in 2019 and beyond?

Organizations with winning digital transformations follow 9 common digital transformation principles.


Enterprises undertaking digital transformation integrate digital technologies such as cloud, containers, big data, machine learning, and Artificial Intelligence into all areas of their business. A technology-first approach to digital transformation, however, will not produce the desired outcome. While it is important to develop a strategy for how to effectively leverage digital technology, the starting point of the digital transformation journey is the customer needs. Successful digital transformations are driven by customer-centric end-goals, rather than a digital technology. As Marc Benioff, Chairman and Co-CEO at Salesforce put it, “Every digital transformation is going to begin and end with the customer.”

This translates into the need to invest into understanding the customer journey to uncover opportunities to deliver superior experience.


Enterprises cannot afford to wait to react to the moves of competitors or disruptors to respond. Instead, to ensure success of digital transformation program, it is vital to make preemptive changes. Martin Reeves of BCG’s Henderson Institute, says “The observation of biological systems teaches us that it is optimal for companies to begin searching well before they exhaust their current sources of profit, and that firms should use a mix of ‘big steps’ to move to uncharted terrain and ‘small steps’ to uncover adjacent options at low cost.”


In 2019, digital transformation programs need to better demonstrate tangible results, and the key areas of focus should be aligned to organization’s primary strategic goals. It is also critical to implement KPIs to assess the impact of digital business initiatives to showcase the measurable value, performance and impact on the bottom line.


Level of involvement and engagement of leadership in a digital transformation initiative can spell the difference between its success and failure. This applies to both senior leaders of the organization and those that lead specific aspects of a digital transformation.

According to the McKinsey Global Survey, a transformation’s success is more likely when an organization has digital-savvy leaders in place. For example, less than 30% of survey respondents said their organizations engaged a chief digital officer to support the digital transformation initiative. However, those that did turned out to be 1.6 times more likely to see the positive outcomes of the digital transformation.


To be successful, digital transformations require the right talent equipped with the 21st century skills, engaged in the new initiatives and empowered to work in new ways. Two specific roles that are critical to building stronger internal capabilities in the workforce are integrators and technology innovation managers. Integrators combine technical expertise with the business acumen which allows them to effectively connect the traditional and digital parts of the enterprises. Specialized technical skills enable technology-innovation managers lead work in the area of digital innovations.

Adopting a Silicon Valley startup mentality and culture can be helpful to break down silos and to foster collaboration among employees which has the positive impact on the digital transformation outcomes.


Embracing adaptive design is one of the most important factors for success in a digital transformation effort. While performance targets need to be developed up front, monthly, if not weekly adjustments, are essential to re-calibrate plans based on the progress of the digital transformation.


Enterprises with winning transformations are more likely to adopt agile execution. This requires the workforce to be innovative, take risks and work collaboratively. The process of digital transformation is inherently uncertain. Organizations should take decisions, iterate, fail and learn fast in order to increase speed, streamline processes and ultimately improve outcomes of the digital transformation.  


Andy Jassy, CEO of Amazon Web Services said at the AWS re:Invent conference, “I don’t know if it is five years from now or 10 years from now, but virtually every application will have machine learning and AI infused.”

In 2018, a number of barriers impeded the growth of AI. Among them, the key challenge was an outcome of the insufficient information architecture and data governance issues which made companies not data-ready for AI. Most of AI proofs of concept were too narrow in their focus having either singularly tested the technology or applied AI to the specific operations, missing out on an opportunity to deliver value and create efficiency gains.

According to Forrester, in 2019, AI will help organizations improve their data governance. AI and RPA technology innovations will also combine to enable enterprises to broaden the scope of proofs of concept in order to create greater business value.


Developing web applications was a key priority for IT twenty years ago; ten years ago, focus shifted to mobile and cloud native application, and today automation is gaining traction as the primary source of business value. Enterprises increasingly explore Robotic Process Automation (RPA) tools to radically increase efficiency and speed of processes.

RPA is the technology that allows to configure computer software to emulate the actions of a human interacting within digital systems to execute a business process. Any high volume, repeatable and business-rules driven process qualifies for automation. The impact of automation extends far beyond cost savings. Enterprises that implemented robotic process automation also cite reliability and service level improvements among their key gains. 


2018 can be characterized as the year when companies dreamed big pursuing opportunities to leverage digital technologies to re-invent their businesses, enter new markets and uncover new revenue streams. Forrester predicts that in 2019 focus will shift from enterprise-wide efforts to pragmatic approach to developing a portfolio of focused digital investments aiming to make incremental yet tangible changes to the processes to deliver greater value to customers. Innovation will focus on the key business priorities such as migrating customers to lower-cost digital channels, launching digital products, monetizing data, and automating process to reduce costs and improve efficiency.

CategoriesBig Data Analytics

Unlocking The Potential of Big Data in the Banking Sector

Written by Victoria Zagorsky

To sustain an advantage in a consumer-empowered economy, financial institutions must develop an in-depth understanding of customers they service and markets they operate in. Many marketing and product teams continue to rely on consumer surveys to obtain important information that guides decision making process in developing new products and launching advertising campaigns. However, banks have access to more customer information than businesses in any other sector, and it is vital to effectively leverage information assets.

Currently, transactional data remains one of the keys areas of focus for financial institutions. Analyzing transactions can uncover powerful insights into customer needs, preferences and behaviors. However, transactional data represent only one type of information assets that banks possess. Other important types of information that reside within an organization include both structured data (demographic profiles, website browsing activity) and unstructured data (call center logs, correspondence). In addition to these rich internal data sources, banks can take advantage of the external data.

Social media represents an important source of the data that banks can integrate with their existing information to develop a holistic view of their customers. Social media also offers many opportunities for developing personalized campaigns and offers. One of the areas of interest to the leading banks is utilizing social media data to target customers and leads with personalized offers and campaigns based on the recent life events, such as birthday, graduation or marriage. As the volume, velocity and variety of the internal and external data continues to increase, banks need to equip their employees with tools and skills to glean powerful insights from the data to drive business forward.

Research reveals that over 60% of financial services institutions in North America consider big data analytics to be a source of a significant competitive advantage. Over 90% believe that “successful big data initiatives will determine the winners of the future.” [1]

While the value of big data initiatives is universally recognized, the key area of focus for many banks continues to be the risk management to comply with the regulatory requirements, while customer analytics, receives less attention. As fraud has significant cost implications to the banking industry globally, it is understandable that banks increasingly use big data analytics to address fraud threats. This typically includes monitoring various types of transactions in real time across multiple channels to identify and take action against suspicious activity. When checking customers’ names against a sanctions blacklist, it is important to avoid the possibility of identifying a false positive, which may have a significant negative impact on the strong existing relationship with the legitimate customer. Using big data techniques enables banks to manage such reputational risks.

Importance of big data analytics in risk management cannot be overestimated; however, customer analytics can potentially become a key source of a competitive advantage for financial institutions. Research reveals that banks applying analytics to understand customer data and to gain insights into attrition have a lead in market share over banks that don’t. [2]

To maximize profitability, banks focus on driving top-line growth while managing costs and ensuring efficiency of operations. Top-line growth can be achieved via customer acquisition (attracting new customers) and customer life-time value maximization (cross-selling and up-selling to the existing customers). To ensure profitable business, it is also critical to prevent customer attrition. All of these initiatives need to deliver positive ROI, and work best when personalized to individual customer needs. This is the area where big data analytics comes into play.

Top-line growth

Personalization is critical to successfully converting leads to acquire new customers as well as deepening the relationship with each customer to maximize their life-time value. Big data analytics allows banks to target specific micro customer segments by combining various data points such as transactions, demographics and sentiment analysis from social media

The US bank, the fifth largest commercial bank in the US used big data analytics to analyze a wealth of data from online and offline channels to develop a unified view of the customer and to identify the most relevant leads for the call center to contact. Additionally, analysis revealed interesting insights into how the customer engagement on the bank’s website could be optimized. These initiatives ultimately resulted in a 100% lead conversion rate improvement. [3]

Similar big data solutions were deployed by the Commonwealth Bank of Australia. One of their objectives was to personalize offers on the website to visitors based on their browsing activities. Previously, standard banners were displayed to website visitors irrespective of their demographic profile or online behavior patterns. For instance, an offer for a money travel card could be displayed to all website visitors. As only a few prospects were likely to be interested in this offer, the conversion rate was less as compared to the more targeted offers. Now the big data solutions enable the website to analyze prospects’ web searches and generate tailored offers in real time. For instance, knowing that a customer has been looking at properties, the website displays relevant offers, such as home loans. [4]

Customer experience optimization & customer service

Big data solutions also play a critical role in delivering customer centricity, which enables banks to attract and retain customers. There are two primary areas, where big data solutions are used to optimize customer experience.

1) Customer experience journey. Designing an optimal customer journey requires an in-depth understanding of how customer interacts with an organization through a variety of channels. Big data analytics improves organization’s ability to garner deeper customer insights used to build its business around customer needs.

2) Customer service. Ability to resolve customer complaints or address queries is an important driver of customer satisfaction and loyalty. Every interaction with the call center creates or undermines customer loyalty, and needs to be carefully managed. Big data solutions integrate data from a number of channels and give call center staff access to the unified view of each customer that can help them address customer queries more effectively and service them better. Additionally, issue resolution can be enhanced through analysis of unstructured data (voice recordings) to study sentiment and to determine trends and patterns.

Churn prevention

According to Ernst & Young Global Consumer Survey, in 2012, 50% of customers, globally, either changed their banks or were planning to change. As acquiring new customers or re-acquiring deflected customers costs significantly more than keeping the existing ones, banks place a great deal of importance on preventing churn. Big data solutions prove to be highly effective as the improved understanding of customer needs that they create helps achieve two goals 1) predict churn by understanding its early signs and 2) design effective personalized offers to prevent customers from deflecting. Banks need to analyze data from a variety of channels including branch (bank visits), contact center calls, online and mobile banking, as well as social media interactions to develop a holistic view of their customers. Studying customer behaviors may reveal that declining account balance, reduced credit card spending or negative feedback received via any of the channels may indicate high-risk churn targets. Similarly, analyzing customer data can help uncover drivers of loyalty and determine what offers are to likely work best for each customer segment. Developing targeted and personalized retention offers produces significant reduction in churn.


Another area that is currently largely neglected is innovation. In order to sustain a competitive edge, banks need to introduce new products and develop innovative solutions to achieve greater customer centricity. Customer insights can become an important source of new ideas for how to drive value for the customer and to stay competitive. Data may reveal different patterns in a credit card usage of a particular segment, and if launching a credit card to cater to the needs of this group proves to be a viable option, insights into their preferences towards features, benefits and privileges can help develop a highly competitive product.

In summary, risk management is one of the high-priority areas for banks using big data analytics. It will continue to remain so, however, as big data analytics already provide powerful customer insights that help banks drive top-line growth, maximize marketing ROI through micro-segmentation and personalization, achieve greater customer centricity, improve loyalty and prevent churn, we will see an increasing number of financial institutions taking advantage of the big data solutions to grow their businesses and to gain a sustainable competitive advantage.


[1] Big Data Alchemy: How can Banks Maximize the Value of their Customer Data? Capgemini Consulting, 2014.

[2] World Retail Banking Report 2013 from Capgemini and Efma.

[3] Big Data: Profitability, Potential and Problems in Banking. 2014.

[4] More Personalized Banking Through Big Data and Analytics

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