Top analytics trends 2017 – An INFOGRAPHIC

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Top analytics trends 2017

Top analytics trends 2017 – An INFOGRAPHIC

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Here are the top analytics trends 2017 for businesses based on what industry and our clients are saying.

These trends reveal a pattern similar to the one observed last year. Embedded BI facilitates the analytics of everything on demand. Moreover, application of IoT devices continues to increase rapidly. Gartner estimates that 20.8 billion connected things will be in use worldwide by 2020.

While analytics, IoT and their applications in business continue to permeate deeper, artificial intelligence (AI) and machine learning (ML) is gaining further attention.

Artificial Intelligence and Machine learning is also the number 1 among the 2017 strategic technology trends report published by Gartner.

Until a few years back, mid-size organizations hardly considered AI as a possible solution to any of their problems. However, the pressure on margins due to increasing competitiveness fueled by online players is making it imperative for all businesses, big and small, to be more efficient.

Besides analytics, IOT, and AI, there is one interesting trend that silently continues to grow and intensify because of how human beings are evolving – the urgent need for clear, relevant, and crisp visualization of data.

According to a research by scientists, human attention span is shrinking so much that even a goldfish can hold a thought for longer. The study by Microsoft says that average human attention span has fallen from 12 seconds in 2000, (or around the time the mobile revolution began), to 8.25 seconds in 2015.

While the comparison with the attention span of goldfish is debatable, the underlying insight – that humans are less attentive than ever before – hardly is. Powerful visualization of information remains the key.

Another trend catching the attention of businesses is the use of predictive analytics. In today’s uncertain business environment, companies want the ability to forecast future business performance based on the past. Predictive analytics tries to answer questions such as: What is likely to happen tomorrow? How can we make the business improve? Consequently, predictive and prescriptive analytics are among the most discussed analytics trends among the professionals.

In summary, smart businesses are recognizing the contribution of analytics (and the associated technologies) in their ongoing success. The top analytics trends 2017 continue to reflect this new reality. Unfortunately, Business analytics talent is scarce. Companies are struggling to hire (and afford) the right people that will help them realize the true benefits of analytics. This makes it ever-more critical to engage with partners that will bring on-board the right combination of computing know-how, analytical and visualization skills, and business acumen.

So, here are the top analytics trends 2017 at a glance. Do read-on, review and respond.

Download PDF of Analytics top trends 2017

 

Top analytics trends 2017

Top analytics trends 2017

 

 

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Analytics in Healthcare - A Case study. Image of a medical professional using analytics in complex medical procedures

Analytics in Healthcare: A Veravizion Case Study

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This article isn’t just about the application of analytics in healthcare. This is more about how analytics is being harnessed to evaluate the latest innovations in healthcare technology in order to help leaders in healthcare make policy decisions about embracing the new technology.

 

Techno-medical innovations

Over the last couple of decades, there have been quite a few noteworthy technological advancements in healthcare industry. Electronic health records (EHRs), HAART for HIV combined drug therapy, minimally invasive surgery, needle-free injection technology, MRI, genomics, and non-invasive diagnostics are just to name a few. These innovations are extraordinary because they are transforming the way patients are being diagnosed and treated in a better, faster, and safer way.

One such technological advancement was endoscopic surgery or minimally invasive surgery. This innovation revolutionized the way surgeries are performed. Knowledge@Wharton once ranked it tenth among the “Top 30 Innovations of the Last 30 Years” list. The conventional surgical procedures were highly invasive, riskier, painful, and time-consuming. They required long post-operative hospital stays and longer recovery times. Thanks to technological innovations, today, patients have an option of choosing either robotic surgery or endoscopic (non-robotic) surgery, which result in much shorter recovery times, less pain and dramatically reduced scarring.  This augurs well for patients who are looking to return back to work quickly.

Minimally Invasive Cardiac Surgery

Many hospitals and cardiac care centers worldwide are evaluating the efficacy of the newer – minimal invasive approach – for specific cardiac surgical procedures. The conventional cardiac valve repair/replacement surgery involved opening up a patient with an 18 -20 cm vertical incision at the sternum. The newer minimally invasive procedure involves approaching the heart through a much smaller (horizontal up to 7cm or a key-hole) incision under the right breast. The new cardiac procedures are more complex for surgeons to perform as the area of access (to heart) narrows down drastically compared to the wider access that the conventional surgery allows. Nevertheless, the new procedure is believed to involve less bleeding, lower risk of infection, faster recovery times, and lesser expenses for patients.

MinInv-mod

 

Analytics in Healthcare – A Case Study

Our client# had been studying the effectiveness of the new method of cardiac surgery compared to the conventional way of performing the same procedure. The study allowed them to enroll patients for one of the two types of procedures depending upon a number of physiological and health factors of each patient. They performed the study over a period of three years and recorded the observations. The observed data included the type of procedure performed along with a number of associated output parameters such as hospital stay duration and pain levels experienced, among many others. A power of 80% was chosen; the data was collated and randomized for detailed analysis. Our analysts collaborated closely with the client to understand the nature and significance of each output parameter. We identified the right statistical tools and techniques to be applied based on the nature and type of the data to be analysed. The analyses were performed with a statistical significance level of 5%. The results were examined in detail both for statistical and clinical significance. The statistical test results were cross-checked quantitatively as well as qualitatively with subject matter experts for completeness and correctness in order to arrive at unambiguous conclusions. Each conclusion shared with the client was solidly backed with data. The results would help them make a fact-based policy decision to embrace the newer procedure for their center. Going beyond the statistical analysis, a predictive model was developed based on the results of the initial study. The predictive model would assist the client in determining the right approach to adopt for a future patient depending upon a number of factors. This is expected to improve the cardiac procedural outcome at the healthcare center.

 

Application of analytics in healthcare

Predictive modeling using machine learning is a powerful technique that helps in forecasting a probable outcome based on empirical data. Predictive modeling and analytics has tremendous potential in healthcare to improve the overall quality of patient care services. Analytics has shown promise to all the constituents involved in the healthcare sector viz. patients, physicians/surgeons, hospitals, pharmaceutical companies, insurance companies, and public health professionals.

  • Patients – be more aware of self-health

Some of the uses of predictive analytics include increased accuracy of diagnosis, early detection of a disease condition in at-risk patients using genomics, and evidence based medicine. In general, with the proliferation of wearables, patients can be more aware and assured of their own physical conditions.

  • Physicians/Surgeons – increase diagnostic accuracy

When a patient is visiting a physician complaining chest pain, it is often difficult for the physician to know whether the person needs hospitalization. If the doctor is using a well-tested predictive diagnostic system, in which he can accurately input the patient’s physical and clinical condition, then the system can assist the physician make an informed judgement.

On the treatment side, a physician can follow a patient’s data (or EHRs) for many years and can prescribe a treatment regime tailored to the patient’s specific condition. This fact-based treatment reduces the probability of causing any major side effects.

  • Hospitals – improve patient care with low mortality rates

Like the case study narrated above, predictive analytics can help hospitals and research centers in evaluating the efficacy of various procedures and treatments in order to improve the mortality and morbidity rates during the post-op period.

  • Pharmaceutical Companies – bring new more effective drugs to market faster

Researching a new drug and conducting a clinical trial for the new drug are two very lengthy, costly and resource intensive processes for pharma companies. The R&D process for pharma companies can become more productive by leveraging the power of machine learning to systematically test the mixtures of existing proven molecular components. This may help in identifying new drugs with higher probability for success. Moreover, predictive modeling can be implemented to test the effectiveness of new drugs in a faster and less expensive manner. This will not only help them bring the drug to the market more quickly, but will also reduce the overall healthcare costs per patient significantly.

  • Insurance Companies – reduce cost of insurance

Healthcare insurance service providers can implement predictive analytics models to better forecast insurance cost for individuals. Presently, the insurance cost is more a function of a person’s age, current medical condition, and the ‘plan’ they are opting for. Now, advancements in medical technology have made it possible to make genetic information and other healthcare related data easily available. Insurance providers can make use of this information to arrive at future medical expenses for a person, and make more informed decisions about the insurance costs associated with that individual. This will be a more realistic assessment of insurance needs for a person and will be beneficial to both sides in terms of provisions to be made.

  • Public Health (Professionals)

The World Health Organization defines public health as all privately and publicly sponsored measures to prevent disease, promote health, and prolong life among the population as a whole. Its activities aim to provide conditions in which people can be healthy and focus on entire populations, not on individual patients or diseases. Here, analytics can be implemented in predicting early detection of pandemics and flu outbreaks. GoogleFlu was a project which estimated Flu and Dengue fever based on search patterns. While the project is not publishing anymore, empirical data is available for research purposes.

 

Conclusion

While application of analytics in healthcare is possible in all spheres of patient care, it is more about leveraging the power of analytics in rapidly evaluating the true value of techno-medical innovations for human benefits. Analytics makes it possible to make fact-based decisions about adopting and internalizing these latest technological advancements that promises to help us lead a quality life for a few years more.

 

 

References:

http://knowledge.wharton.upenn.edu: a-world-transformed-what-are-the-top-30-innovations-of-the-last-30-years/

http://pharmajet.com: significant medical innovations of the past 20 years

 

 

Cover image credit: ©Pharmajet

# Client names and other details are confidential


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Top Analytics Trends 2016 for SMBs

Top Analytics Trends 2016 for SMBs

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Like last year, we bring to you top analytics trends 2016 for SMBs based on what industry stalwarts and our clients are saying.

A quick comparison with last year trends reveals that some trends continue to evolve. Topics like Deep learning, Self-service-BI, or Cognitive computing are some latest ones being discussed. Nonetheless, others are rapidly gravitating towards some common theme. One such theme is ‘Big Data Analytics’. More and more small and medium-sized businesses (SMBs in short) are going digital, embracing analytics, and leveraging their data to turn insights into higher revenues, reduced costs, and overall business growth. According to analysts, the big data analytics market is expected to reach nearly $50B by 2019.

Our Trends focus on applicability of these technologies to small and medium business (SMB) organizations. Although, technology indeed plays a vital role in running a successful business, some of these emerging technologies are not immediately relevant to SMBs. It can be helpful to develop an awareness of these technologies but very few SMBs are actually going to use say, 3D printing or AI in 2016 – and nor should they if they have no real business benefits.

This year, we have identified nine top analytics trends that are most relevant to SMBs.

 

1. More SMBs use analytics for business benefits.

Until a few years back, big data (analytics) was more hype than a reality. Google was awash with searches involving keywords centered on ‘Big Data’. However, over the last couple of years, analytics has left the hype-curve to provide real value. Today, (big data) analytics is everywhere.

Earlier, SMBs were not too savvy about maintaining the data of their customers, product-orders, and suppliers. This was largely because of the costs involved in data storage without the apparent benefit of maintaining the data. However with the data explosion through various media over the last couple of years and with the availability of custom-analytics providers, they woke to the possibility of utilizing their data for getting answers to some key questions around their businesses. As the benefits started becoming visible – in terms of exponential business growth in a few cases – SMBs started focusing on analytics and become more data-driven to improve their business results.

However, making this data meaningful and easy to understand is still a challenge for many. We think that 2016 will be the year that small-scale analytics will really take off for SMBs, as it allows them to leverage their data from disparate data sources for their business benefits.

 

2. Internet of Things (IoT) enters our daily lives.

This is what Nikola Tesla said in a 1926 interview with Colliers magazine:

Top Analytics Trends 2016 - Internet of Things globe pic

IoT world

When wireless is perfectly applied the whole earth will be converted into a huge brain, which in fact it is, all things being particles of a real and rhythmic whole… and the instruments through which we shall be able to do this will be amazingly simple compared with our present telephone. A man will be able to carry one in his vest pocket.

How it has become a reality less than a century later!

 

The Internet of Things (IoT) in its current form proliferated with the surge in low cost sensors embedded with Bluetooth wireless capability onto a small chip. And it is rapidly evolving from the realm of fascinating gizmos to real-world utility gadgets. Many leading companies such as Google, Amazon, Cisco, Dell, and TI have developed their versions of IoT products. There are already some cool IoT devices like Nest, Fitbit, and Belkin, to name just a few, that are vying for consumers’ attention in the market.

So what’s in it for SMBs?

In terms of Google trends shown in Exhibit-A, IoT today is where big data analytics was around 4-years back. Gartner forecasts that there will be 6.4 billion internet-connected things in 2016. Although the potential of IoT is huge, few SMBs consider it their ‘critical’ priority for investment at the moment. For them, it is still a nice buzzword. There is still time before every visible thing will have a sensor attached to it that will communicate with your servers in real-time. Meanwhile, SMBs are willing to watch and ride the hype-cycle.

Top Analytics Trends 2016 - IoT interest over time (Google graph)

 

3. Predictive analytics to address cybersecurity concerns.

As SMBs expand their technology footprint to run their business operations, the need to secure and protect data grows. Data security and privacy concerns continue to exist among small and large business organizations. However, many SMBs feel challenged and intimidated to deal with the rising complexity of cybersecurity breaches. Companies are normally content with the conventional approach of putting defensive mechanisms to ward off security risks. However, with technology advancements, the security breaches have also become more sophisticated and more risky wherever consumer data is involved.

While large organizations invest heavily into advanced (read: expensive) security mechanisms, SMBs do not have the luxury to do so. Nevertheless, they are now custom-developing predictive analytic models to proactively monitor log files and other user data sources to detect any threat perception or breach alerts. Clustering algorithms can help them identify anomalies in user login or other events which can be recorded on an ongoing basis. 2016 is likely to see an increase in the application of predictive analytics to deal with cybersecurity concerns.

 

4. Machine Learning algorithms foster man-machine collaboration.

We are entering the ‘smart’ era – smart people working alongside smart machines in smart cities. IDC¹ predicts that companies will spend more than $60 billion on cognitive solutions by 2025. Theoretically, machine learning algorithms based on neural network and AI have existed for a long time. However, their widespread application in everyday life is getting acceptance only now. This is made possible due to the tremendous increase in processing power that enables real-time split-second decision making.

Machine learning algorithms are currently being employed primarily in retail industry. With more people shopping across multiple channels looking for lowest prices, machine learning algorithms will become very popular in implementing dynamic pricing and devising on-the-spot offers in retail stores to retain the buyer.

For example, during this year’s holiday shopping season, leading retailers such as Amazon and Walmart were relying heavily on algorithmic pricing. Both retailers re-priced 15% of 18,000 product SKUs being tracked by a pricing intelligence solution on November 14th alone. These algorithms will be the backbone of any and every e-commerce business striving to win and retain customers.

[Example credit: Forbes]

 

5. Rising smartphone and tablet penetration continues to increase consumer mobility.

According to a comScore – Morgan Stanley research, mobile users globally have surpassed desktop users at the beginning of 2014. Rising mobile adoption, among people of all ages, impacts consumer purchasing patterns in a big way. With the increasing mobility, SMBs view mobile apps as a way to reach and engage end-users. SMB Group’s 2014 SMB Mobile Solutions Study indicates 59% of SMBs view mobile solutions and services as ‘critical’ to their business.

 

6. Hybrid cloud options still complex for SMBs.

2015 saw cloud making deep inroads into data-centers, data warehouses, centralized storages, and servers. SMB group’s market study shows that the cloud is poised to overtake on-premises deployment in the next year in areas such as collaboration, file sharing and marketing automation.

However, SMBs are largely using public cloud and staying away from private (or hybrid) cloud options because of the lack of clarity. Microsoft, Dell, and IBM have their own cloud platforms as hybrid cloud options however they do not yet seem to provide a compelling proposition for SMBs to embrace.

 

7. Omni-Channel integration or cross-device challenge?

Omni-channel is not a buzzword anymore given the availability of multiple screens every customer has. People have indicated that they love to shop across channels. So, more and more brands are going omni-channel way in a bid to woo consumers and to help them buy in their preferred channels. Brands are applying strategies like location based analytics to make relevant offers when consumers are in the vicinity of their stores. Businesses (like Macy’s or Virgin) that offer a unified omni-channel experience to their customers appear to have a competitive edge over others that cannot.

However, in a March 2015 study by Signal, 51% of marketers worldwide reported that they did not have a single view of customers, and only 6% of marketers worldwide reported they had an adequate single view of customers or prospects across all devices and touchpoints.

From our perspective, this year businesses will make this decision of whether they will play the omni-channel game and how.

 

8. Real analytics talent is (still) scarce.

According to the 2015 MIT Sloan Management Review survey² of business executives, managers and analytics professionals, 49% of respondents, who believe analytics creates competitive advantage for their organization, say that their company lacks appropriate analytics talent. While there is no dearth of analytics CVs in the market, very few of those appear to have real data science skills. In reality, companies need data scientists who possess the rounded knowledge of computer science, algorithms, math-statistics, business, and analytical skills.

Organizations that hire the less than appropriately skilled analysts end up wasting more than just money without any real benefits accrued. This is the reason more than 50% of analytically challenges organizations have stated that they outsource analytical services to external consultants or organizations, according to the survey.

Smart companies are realizing that analytical talent is critical to their success and in short supply, but more than 40 percent struggle with finding the talent they need. There seems to be a growing belief among SMBs that it is best to focus on customers to grow business leaving the necessary analysis tech work to specialists.

 

9. Visualization will be vital to SMBs application of analytics.

Data and the insights from that data are no more relegated only to the analysts. Business owners and SMB executives want to visualize their data to understand ‘what’s really going on’ in their business. And they want to do it in minimum possible time to be able to focus on the more important aspect of applying those insights for the improvement of their business. Business analytics in the SMB space is likely to stop being just a set of bar- and pie-based charts, and will be more multi-variate and intuitive. SMBs will demand more from the analysts in terms of visualization techniques that makes it easier and faster to visualize, understand, and explore data and uncover real insights from it.

 

Conclusion

This is an extremely exciting time for SMBs who can now apply customized analytics as per their specific requirements to take their business to a new level in an economical way. We believe this was not an option they previously had. It will be interesting to see how SMBs embrace business analytics to leverage the opportunity and explore unlimited possibilities.

 

References:

1 International Data Corporation

2 Ransbotham, D. Kiron and P.K. Prentice, “The Talent Dividend: Analytics talent is driving competitive advantage at data-oriented companies,”MIT Sloan Management Review, April 2015.

3 IoT image credit: wikipedia

 

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Most Popular Perspectives from 2015

Most Popular Perspectives from 2015

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It’s New Year again – Happy New Year 2016!

Thanks for your overwhelming response to our insights shared with you over the last year.  We are excited to announce the most popular perspectives from 2015 published at Veravizion/Perspectives. These are our biggest stories of 2015 in case you missed them.

One of the wonderful aspects about sharing our insights is appreciating the incredible business acumen, diversity, and depth of thinking of our readers. Our articles, which we call our perspectives, are written after carrying out thorough research on every topic. Our belief is that these articles will push you into thinking about how the (business) world is transforming before our eyes, and how some long-standing business principles may not necessarily hold true today.

As the year is over, take a quick glance at how the world is getting used to being data-driven. Enjoy these stories and let us know about your top content in the comments. In the next one, we will see how the analytics world is likely to unfold in 2016.

Most Popular Perspectives from 2015

Story# 13 Lessons Every Executive Must Learn From Wimbledon Centre Court For Business Success

Most Popular perspectives from 2015 - Lessons from Wimbledon

Sports has always had many lessons to share for business success; and everyone and their grandpa knows this. Nevertheless, its relevance has never been as great as it is in today’s analytics age.

This article illustrates this phenomenon by drawing lessons for business success from 2015 Wimbledon final between Djokovich and Federer.

 

 

Story# 2Data Science: The Next Frontier For Business Competitiveness

Most Popular Perspectives from 2015

This article on Data Science by Veravizion was originally published as the cover story in the July-2015 edition of Computer Society of India – Communications magazine. You can also read this article at its source at http://www.csi-india.org (Link path: http://www.csi-india.org->PUBLICATIONS->CSI Communications->CSIC 2015->CSIC 2015(July)).“

 

 

Story# 3The Digital Transformation Imperative: Why Businesses Must Have Online Presence – An INFOGRAPHIC

Most Popular perspectives from 2015

INFOGRAPHIC: click to enlarge

The business world is fast going online, so what’s the big deal? The big deal is in grasping the fact that it may replace your business if you do not become a part of the change, soon.

The infographic in this article gives a glimpse of how fast the consumer purchasing trends are changing from physical to digital, and what you can do about it.

 

 

Story# 4How Do You Achieve Strategic Transformation For Enduring Growth Of Your Company? – Part-I

Most Popular perspectives from 2015

Historically, leaders of cities, communities, and organizations have been embracing strategic initiatives to ensure long term sustenance and growth of their respective ecosystems. Many a times, these initiatives were ‘intentionally’ directed at bringing about long term transformation of their systems. But do such initiatives specifically aimed at strategic transformation always result in the lasting growth of the entity? We discuss it in this article.

 

Story# 5What Does Digital Maturity Really Mean?

Most Popular perspectives from 2015

This is the last article in the Digital Business series in which we illustrate how small and medium businesses can transform themselves from mere-physical to also-digital, and be more competitive. We do this by taking a visual example of a fictitious light business of our lovable businessman Bobstick.

 

 

 

We hope you enjoy these stories!

 

 

Strategic transformation photo credit: businessinsider

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What Does Digital Maturity Really Mean

What Does Digital Maturity Really Mean?

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When I went to medical school, the term 'digital' applied only to rectal exams.” - Dr. Eric Topol

Well, things have certainly changed since! The previous article – an infographic – discussed about the urgent need for businesses to achieve digital maturity in order to survive and thrive. But exactly what does digital maturity really mean for a small and medium business organization that thus far has focused primarily on serving local customers?

You might be thinking whether this question – what does digital maturity really mean – is really relevant in today’s digital age. Our implicit hypothesis is that most businesses, especially those in the developed countries, are already digital by now, and so they must know what digital maturity really means.

Well, evidence shows that the above assumption is far from true.

Consider these stats from the UK Business Digital Index 2015 report which states that almost a quarter of UK businesses still lack basic digital skills:

  • Almost 35% of around 5.2 million organisations in the UK have a very low level of digital understanding and capability – they do not make use of the internet for their business and do not have any web or social media presence
  • Barely 53% of the businesses have their own website
  • Only 46% organizations have a medium level of digital maturity – i.e. they use basic e-commerce tools, perform some banking transactions online, and have basic social media presence.

Therefore our question is extremely relevant even today!

 

So what does digital maturity really mean?

To understand this, let us quickly recapitulate why businesses want (or need) to go online in the first place.

Businesses go online for a variety of reasons (read: benefits) such as expanding markets to grow business, deepening engagement with target customers, broadening product and service offerings, leveraging multi-channel capabilities, and in general staying competitive amidst the changing global business landscape.

All these reasons can be summed-up in one simple line: Business organizations, like yours, are going digital because your customers are increasingly seeing, hearing, feeling, searching, interacting, sharing, and buying stuff online.

The above sentence encapsulates the entire online activity happening today in the business world. [tweet this]

In short, your market has gone online and it would serve you better if you do, too.

So what takes you there?

Here are the 5-stages on your journey to achieving digital maturity for your business:

  1. Digital Apathy
  2. Digital Literacy
  3. Digital Transactions
  4. Digital Engagement
  5. Digital Maturity

 

Let us look at each one a bit more closely with an illustrative pictorial example for each:

 

  1. Digital Apathy:

This is the initial (or default) stage of any organization typically born before internet. This company mostly sells their products mainly to customers in its neighbourhood through its physical stores. There is a passive resistance (or indifference at best) in accepting digital strategy due to inertia mixed with scepticism towards going digital. There is absolutely no online or any beyond-the-shop interaction with the customers. The business owner is unmindful about going out of business in this increasingly digital world, and apparently suffers from ‘it won’t happen to me’ syndrome.

 

What Does Digital Maturity Really Mean?

 

  1. Digital Literacy:

There is (almost) a reluctant acceptance to the changing business scenario. The business has a (mostly passive) website that displays the products and services on offer but hardly anything beyond that. On the positive side, customers now have a gateway to your offerings and can find information about your products and services. There is a new one-way channel to update customers about new product and service offerings – a good beginning to say the least.

What Does Digital Maturity Really Mean?

 

  1. Digital Transactions:

The business finally wakes up to enormous possibilities the e-commerce world offers and introduces online transactions to sell its products online. There is a conscious effort to implement basic customer analytics to understand buying customer profile to grow revenues. The business also tends to apply e-commerce intelligence to provide leads reports to sales teams to grow further. There is an emphasis on generating and distributing user-oriented content in order to draw target customers to purchase online. Businesses may introduce their own inventory management and service fulfilment back offices to excel in their customer service to build customer loyalty. The business starts learning about rule based prioritization as they explore the benefits of implementing analytics for revenue and profitability growth.

What Does Digital Maturity Really Mean?

 

  1. Digital Engagement:

When a business establishes itself on the various social media platforms, there is a step change in the way it perceives customer interaction, customer engagement, and marketing. Old channels and methods of one-way communication are renounced in favour of digital channels which enable listening to customers’ feedback first-hand and responding in their preferred channel to facilitate effective customer engagement. One important aspect of increasing engagement is to create product touch-points across all channels vis. physical, desktop, mobile, kiosks, catalogue, direct mail, and social media. The order in these cross-channel chaos is set by the use of marketing analytics which helps to mine hidden consumer insights, understand customer purchasing journeys, optimize advertising spend, and engage with prospective customers at early st(age) to nurture them into loyal followers.

 

What Does Digital Maturity Really Mean?

 

 

  1. Digital Maturity

The focus at this stage is on innovating the existing business model and on integrating the overall strategy. Personalization is the key here! Customers have 24x7x365 access to the products and services across different digital channels but still have an Omni-channel experience. For example, a customer becomes aware of your product in one channel, say Pinterest, actively searches for it online on his office desktop, physically touches and considers buying it in-store, ends-up purchasing the stuff on their mobile, and shares his new purchase with Facebook friends. Matured businesses (like P&G and Amazon) have institutionalized integrated use of analytics services to study individual consumer behaviour through comprehensive understanding of customer interests, affinities, and actions. They are drawing intelligence trends to predict customers’ future wants and needs before customers themselves realize it. Considering the enormity of data getting generated every day, matured businesses are implementing advanced algorithms to auto-analyse data at its source for more real-time application.

 

What Does Digital Maturity Really Mean?

 

 

Achieving digital maturity is not the end; rather a beginning of the implementation of a truly personalized digital strategy for each consumer. Businesses embracing digital strategy will eventually lead the way.

We are in the throes of a transition where every publication has to think of their digital strategy” - Bill Gates

 

 

Cover photo credit: yourgenome.org

 

If you liked this article then you may also like to read The Digital Transformation Imperative: Why Businesses Must Have Online Presence – AN INFOGRAPHIC.

You can also subscribe to our blog – Our Perspectives – to receive interesting articles and tips in email. We would love to read your perspectives and comments on that.

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The Digital Transformation Imperative

The Digital Transformation Imperative: Why Businesses Must Have Online Presence – An INFOGRAPHIC

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“81% of US consumers turn to search engines to find information on products, services, and businesses before making a purchase,” according to GE Capital Retail Bank’s second annual Major Purchase Shopper Study. The digital transformation imperative study performed by Veravizion indicates that this observation is not much different in other parts of the world.

Less than 20 years back, Google (or an online search engine in its current form) did not exist. Most commercial transactions took place at a brick-and-mortar store. People booked long distance train tickets standing in a long queue at the ticket counter, bought airline tickets from travel agents, rented DVDs in video stores, read about new fashion in a print magazine, and purchased music CDs in record stores.

Today, 70% of all the travel bookings and hotel reservations take place online.

Music CD shops selling LPs, Vinyl records, and CDs have ceased to exist.

DVD stores have virtually disappeared from the streets.

Paper magazines and print advertisements have given way to their online cousins.

 

‘In the present day, if your customer cannot find your business on Google, you probably don’t exist for them.’ In a relatively short span of time, this has come to be one of glaring truths that business leaders must accept. Today’s consumer seems to have too many things to do, and appears to have become impatient because of limited time at hand. She wants to get everything done at the snap of a finger.

The digital world allows them this convenience of having (almost) everything in just one-click or touch. In fact, customers are adapting to this technology-driven shopping so well that they are touching every screen – even ‘dumb terminals’ – looking for an interactive touch-screen experience. Recent research on e-commerce points to a growing trend of digitalization of businesses and even non-profit social organizations.

Many industries like flowers and footwear, where customers’ need to touch and feel the product was considered important, now have above average online penetration. The grocery and general merchandise retailer Tesco is a case in point. It was one of the chains that saw an increasing role of technology in day-to-day household shopping and launched their online operations; it is now world’s second-largest retailer by revenues. A few industries like online grocery and pet foods (remember Webvan.com and Pets.com?) had a false start because of issues with their online business models, but are now being resurrected by the likes of Amazon and FreshDirect. Slowly but surely, every industry is joining the digital bandwagon.

Consumers on their part are enjoying the omni-channel shopping experience. Omni-channel purchase means a customer buying across multiple channels – online through mobile or desktop, call centre, catalogue, direct mail, kiosks, physical stores, and social media – and having a seamless shopping experience. So a customer may discover a great product offer while browsing Facebook during breakfast, search more information about it online via desktop after reaching office, ring a few call-centres to compare prices during lunch, check the product out at a nearby physical shop on the way back home, and finally purchase it online from their home using a smartphone. Once the product arrives, they may update their friends on social media posting pictures of their new purchase. The entire shopping experience becomes conveniently embedded in their routine and is fun.

Thus, internet is playing a key role in how businesses are run today. Nevertheless, it still has some way to go. An e-commerce foundation report shows that a disproportionately high percentage of businesses, even in developed countries with high internet penetration, are yet to go digital. For example, almost one in four businesses in UK has none to low digital maturity, while the ratio is reversed in some of the developing countries in Asia-Pacific, where the rate of digital transformation is much higher.

The attached infographic presents a quick glimpse of how business landscape is rapidly changing. It implores, with substantial evidence, why business (and social) organizations must have an online presence to survive and thrive in this third millennium.

What has been your experience of going digital? We would love to hear.

The Digital Transformation Imperative

The digital transformation imperative

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Data Science

Data Science: The Next Frontier for Business Competitiveness

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This article reposted here was originally published as the cover story in the July-2015 edition of Computer Society of India - Communications magazine. You can also read this article at its source at http://www.csi-india.org (Link path: http://www.csi-india.org->PUBLICATIONS->CSI Communications->CSIC 2015->CSIC 2015(July))."

Data Science means extraction of knowledge from data. The key word in data science is not data; it is science[1]. Science of something means study of that thing to extract knowledge about it. In most generic sense, the purpose of every data science project is to answer a question (or a set of questions) backed by hard-facts. Academicians and researchers apply scientific principles to get specific answers about a research subject. Similarly, businesses employ data science principles to improve customer engagement, devise growth strategies, optimize operations, and build competitive advantage. This article shares a perspective on what data science really is, how it impacts various industries, what benefits does it offer to organizations – both for-profit and not-for-profit, and what are the key data science trends prevalent today.

 

DATA SCIENCE: WHAT IT IS (AND ISN’T)

Apparently Peter Naur and John W. Tukey seem to be among the first ones to have treated data analysis within the precincts of science[2]. John W. Tukey, who coined the term ‘bit’, has mentioned it in his 1962 paper ‘The Future of Data Analysis’. In my view, while the term ‘data science’ is relatively young, its application is not. There is an early evidence[3] of 1854, of Dr. John Snow applying scientific principles of data analysis to detect the root cause of The Cholera Epidemic in London. So data science has been around for a while albeit in different forms.

While we tend to associate data science with several other terms such as artificial intelligence, machine learning, data mining, analytics, statistics, computer science, and operations research, each has its own specific meaning that is different from another. Artificial intelligence is intelligence exhibited by machines and it pertains to the creation of a software system that simulates human intelligence. Machine learning is a science that involves development of self-learning algorithms which can be used to make data-driven predictions in a similar but unfamiliar environment. Popular examples include self-driving cars and web searches. Statistics is a study of collection, organization, analysis, and interpretation of numerical information from data. Data mining is the practice of analyzing data using (machine-learning) algorithms and statistical techniques in order to solve a problem. Computer science covers computational complexity, distributed architectures such as Hadoop, data compression, optimization of data flows, and not to mention computer programming languages (such as R, Python, and Perl). Advanced analytics or Analytics is just a marketing driven terminology that applies many of the data science principles to solve complex problems faced by businesses and society. So while the differences are subtle, each one has its own application in industry and academia. Nevertheless, data science overlaps with computer science, statistics, operations research, and business intelligence in many ways and almost completely encompasses data mining and machine learning.

The subtle differences notwithstanding, data science is an independent discipline which amalgamates statistics, computing skills, and domain knowledge. At the core, data science helps in deriving valuable insights from data. The data science process involves data collection, data pre-processing and cleaning, data modelling and analysis, and insights generation which are applied within a given functional domain to make decisions. Although the process is similar to knowledge discovery and data mining (KDD), a data scientist requires computing skills and domain knowhow to arrive at context-specific decisions. The person working in data science needs to exhibit three distinct skills applied in the different phases of a data science project. As shown in EXHIBIT-A[4], an individual with data science expertise possesses (or needs to possess) a combination of mathematics and statistics knowledge, hacking skills, and substantial domain understanding. The hacking skills include familiarity (but not necessarily proficiency) with software programming but more importantly, a propensity at being able to manipulate any type of data. This is because real-world data hardly exists in a nice tabular format. It[5] is scattered in thousands of text files or on hundreds of web sites or in numerous unstructured excel sheets at best. True data scientists that possess all the three skills are not abundant; because the role entails making sense of amorphous data, deriving bespoke models, and developing algorithms to analyse a complex problem specific within a domain.

Data Science Venn Diagram

Unfortunately, simply churning out numbers or fiddling with inefficient models rarely solves a problem. This is the reason data scientist is one of the most coveted roles in industry today.

Data science is being applied in many industries. Some of the uses in various industries include weather forecasting, intuitive search in online search technology, customer engagement in retail and consumer products and services, fraud detection in banking and credit cards, prediction of sources of energy in Oil and Gas, evidence based medicines in healthcare, and sentiment analysis from social network feeds. Some fields that are routinely implementing analytics services are eCommerce, retail, consumer products and services, financial services, insurance, pharmaceuticals, manufacturing, telecommunications, and high-tech.

 

HUNTING PEARLY INSIGHTS IN THE OCEAN OF DATA WITH DATA SCIENCE

More and more businesses are embracing data science and analytics in multiple organizational functions. There are mainly three most common ways in which data science is deployed depending on the size of an organization. Large corporations usually deploy their own in-house analytics departments by recruiting data analysts. Business leaders in large corporations typically have humongous quantities of data to sift through in order to make decisions that are important for their business growth. While having an in-house analytics team may not always be an ideal way for institutionalizing data science, even for large corporations, they seem to be driven by large amount of resources at their disposal. Secondly, some companies prefer to buy a COTS (Commercial-Off-The-Shelf) product to cater to some standard requirement. Thirdly, many mid-to-large sized companies prefer to employ customized data science or analytics services to solve their specific data analysis and business operational requirement. This option seems ideal for businesses looking for the flexibility to hire precise services for their bespoke needs.

While the data science projects in most for-profit organizations are getting more and more complex, the fundamental purpose underlying these projects remain the same – to achieve sustainable growth and improve profitability for their businesses. To that effect, the companies put data science into action to gain meaningful insights into their customers, operational processes, supply chain and logistics, product and/or service usage, financial aspects, and future business performance. Conventionally, data science has mostly been applied for market research and market segmentation. However, businesses have a lot more at stake with every business decision as competition has become more and more intense. Gone are the days when business decisions used to be taken on gut-feeling. In today’s globalized world, every major business decision needs to be data-driven. Data science assists organizations and individuals in making fact-based decisions that they can take and defend confidently. That is why it has become essential for organizations, business or otherwise, to deploy data science projects in every division responsible for making any kind of decisions. Some of the types of data science and analytics projects include customer focused analytics through clustering, recommendation engines, root cause analysis, automated rule engines, conjoint analysis to quantify perceived value of features offered, process simulations for operational analysis, predictive modeling for business forecasting, and clustering analysis to identify anomalies, just to name a few.

 

BENEFITS FROM IMPLEMENTING DATA SCIENCE INITIATIVES

There are some fantastic examples of business organizations gaining huge benefits by systematically and strategically deploying analytics initiatives that involve data science and ethnographic research. Procter & Gamble has institutionalized the data and design thinking approach to such as extent that it is now ingrained into their DNA. The result is that P&G boasts of more than 20 billion-dollar brands in their product kitty. Amazon, a technology company and not just an eRetailer, is really surviving and thriving by understanding customer preferences through the implementation of numerous algorithms. It has helped them to grow quickly from selling just books online in 1996 to target-selling twenty million products in countless other categories. There are many examples of smaller companies that streamlined their processes and implemented analytics based strategies to grow and enter into the big league. Data science initiatives within companies have rendered meaningful insights to drive their firm’s customer experience. These companies have utilized the insights to define their business growth strategies and pursue a culture of data-driven decision making. The benefits include getting pointers to new growth areas, generating ideas to introduce innovative new products, decreasing cost bases and improving productivity to boost profitability, identifying risks of obsolete technologies in their processes, detecting bottlenecks in supply chain, and streamlining inefficient operations.

Even as data science is rapidly changing the business world, it is also spreading its influence on other sectors such as academic research, governments, and social organizations. While the data deluge has increased the complexity for these sectors to analyze the data in a timely manner, it has also opened a plethora of opportunities for them.

Academic institutions in regions such as US, UK, and some countries in Asia are facing sustainability issues due to severe cuts in funding and grants. They are able to apply data science within their own institutional spheres to identify their respective competitive advantage and attract the right students to strengthen their reputation further. Similarly, medical research institutions are now able to work on projects like genome research, DNA sequencing, and stem-cell research for treatment of fatal diseases such as cancer and AIDS. Economists are able to analyze the publicly available data to determine relationships between income levels, education, health, and quality of life.

Governments and public sector organizations are concerned about issues such as monitoring and prevention of terrorist activities, early-detection and control of pandemics, and uniform aid distribution among the poorer countries, which they are able to tackle by sponsoring appropriate data science initiatives.

 

TACKLING CHALLENGES ALONG THE WAY

Data privacy and security concern has been one of the main reasons keeping some businesses from adopting data science. Moreover companies are facing real challenges in terms of bad quality of data, data inconsistencies, unreliable third party data, and information security. Nonetheless, all roads to meaningful business insights lead through data, whether it is organizational or public. Businesses need to put in place appropriate mechanisms to share data in a controlled manner with analysts and service providers in order to generate hidden insights that can be utilized for business benefits. Data breaches and data thefts remain a valid concern too. Past incidents, albeit few and sporadic, of customer confidential information getting stolen have deterred some from initiating analytics projects. However, business organizations are coming around to the fact that they are fast losing their competitive advantage to rivals due to staying away from analytics. Increasing number of organizations is taking up analytics to secure and grow their businesses as they do not want to be left behind any more. Organizations will increasingly recognize that it is not possible to operate in a 100 percent secured environment. Once organizations acknowledge that, they can begin to apply more-sophisticated risk assessment and mitigation tools. They will look to embed security at multiple levels viz. application-level, execution-level, storage-level, and even contract level. Interestingly, analytics itself is proving to be a great mechanism for security breach prevention.

 

KEY TRENDS AND THE ROAD AHEAD

In some of the western countries, data science has been thoroughly internalized within large corporations. Even the smaller businesses there employ analytics services to achieve specific business objectives. In India, while the (few) big corporations seem to be deploying such initiatives, most other organizations are still in the nascent stage. One survey of SME business owners cited that most common reasons for the slow pace of embracing [data science] are lack of awareness about the value offered by analytics, dearth of skilled resources, apprehension about technological complexity, cost and ROI concerns, and data security risks.

Notwithstanding the current adoption level, businesses are realizing that they may be taking a big risk not considering data science and analytics as a potent competitive strategy. There is a tremendous rise of personal data originating from social-media, sensor-originated data from wearables, and the Internet of Things (IoT) with the recent surge in the use of smartphones. More and more human actions are generating Exabytes of data today. To get a sense of the amount of data being generated, let’s just say that we will need around 50 billion trees made into paper to print 1 Exabyte of data. That’s roughly 9 huge stacks of papers, each touching Mars from Earth. This enormous amount of data will be of no use if not analyzed and utilized appropriately.

These trends are pushing businesses to re-think their business and growth strategies. There is an increased focus on teaching data science based courses by colleges and universities worldwide. Companies are realizing that the business environment has become uncertain with the fast pace of technological and demographical changes. As a result, many organizations are allocating higher budgets for deploying customized analytics for their businesses to deepen customer understanding, engage customers through multiple channels, identify new sources of revenue, improve productivity and profitability, streamline business processes, and build competitive advantage. Going forward, use of customized analytics will become pervasive. More and more organizations will develop their unique value propositions around the valuable insights they gain about their existing and prospective customers.

Implementing data science initiatives to build competitive advantage is a matter of leading and not following the pack. In an industry competing for the finite market share, early-adopters of data science best practices will be the eventual winners.

 

References:

[1] http://simplystatistics.org/

[2] Forbes: ‘A Very Short History Of Data Science

[3] Edward Tufte: ‘Visual Explanations

[4] Source: http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram

[5] ‘It’ refers to ‘the data’. In the modern world, the term ‘data’ is used in both singular and plural sense as per the context. Technically speaking, singular of data is datum.


Website link: http://www.veravizion.com/data-science-the-next-frontier-for-business-competitiveness/

 

 

“This article reposted here was originally published as the cover story in the July-2015 edition of Computer Society of India – Communications magazine. You can also read this article at its source at http://www.csi-india.org (Link path: http://www.csi-india.org->PUBLICATIONS->CSI Communications->CSIC 2015->CSIC 2015(July)).”

 


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Wimbledon Centre Court

3 Lessons Every Executive Must Learn from Wimbledon Centre Court for Business Success

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Last Sunday, the men’s singles final at Wimbledon between Djokovic and Federer was an enthralling match. Federer had hoped to become the first man to win Wimbledon a record eighth time; and Djokovic seemed to have something to prove after his recent defeat at the French Open.

The mixed doubles final, also played later that afternoon, was relatively a one-sided affair. The Paes-Hingis pair staged a clinical performance to clinch the title by decimating their opponents within 40 minutes.

During the matches, the English commentator attributed Djokovic’s win to his skill, determination, power, and accuracy, and alluded to Federer as the ’33 year old tired opponent’. Interestingly, the same commentator (or it may have been another one) ascribed Paes–Hingis’ comprehensive win to their experience, homework, and coordination.

But why did Federer really lose, despite his stellar experience of ten Wimbledon appearances and near-perfect game? And how did the ageing pair of Paes (at 42 years) and Hingis (almost at 35 years) register such a convincing victory against a much younger team? So, what does it take to succeed at the highest level in sports? And what lessons, if any, can businesses take from Wimbledon?

The Centre Court at Wimbledon provides three crucial lessons for businesses to succeed:

1. Do your homework thoroughly. Federer’s serve is the key to his game; he tends to serve deep and aggressively goes for the kill on the return of the serve. During the semi-final clash with Andy Murray, Federer’s winning points came off 5 rallies or less (on an average); whereas Murray’s winning shots came from at least 8 rallies. Federer outplayed Murray winning more than 80% of points off his first serves.

While Djokovic’s own serves were lethal, he must have studied his opponent well and had perfected his returns too. He forced Federer to play more rallies by returning his serves into areas where Federer could not attack back. The longer the rallies continued, the farther Federer had to run, and the more he became prone to making unforced errors thereby strengthening Djokovic’s chances at converting them into winners. Djokovic’s most winning points came off rallies that lasted 8 or more shots.

EXHIBIT-A shows Federer committed unusually higher number of errors and had lower serving percentages against Djokovic as compared to those against Murray.

Wimbledon 2015 Semi-Final and Final Match Summaries

 

Similarly, companies operating in the marketplace must thoroughly understand their competitors. Companies must first comprehend how their serves (like introduction of new product, feature, or category) are going to be hit back by their competitors, and then plan their strategies based on those insights. If the retaliation is weak, the market leader wins the market share; whereas if the competitor does tit for tat, then the market leader is forced to choose between carrying on the duel (with further investments) and conceding the share to the competitor. While this is true in all industries, it is most evident in oligopolistic industries like FMCG, where there usually are two dominant players. For example, similar duel happened between P&G, which introduced Crest fluoride toothpaste in 1955, and Colgate-Palmolive, which had launched the world’s first commercial toothpaste.

 

2. Execute well. Djokovic executed his plan [to play long precision shots] perfectly. Many of his winning shots were executed so accurately that they scraped the outsides of the baseline and the sidelines.

In the mixed doubles final, Peya and Babos showed lack of coordination early in the game by crossing each other’s paths and getting mixed-up in returning shots. On the contrary, Paes and Hingis displayed an absurdly good performance by hitting powerful returns and playing deep cross-volleys at the nets. EXHIBIT-B displays their high serving percentages (in the 80s) and zero errors, which reveals a clean and flawless execution.

Wimbledon Mixed Doubles Final Match Summary

In business context, perfect execution of strategies is a pre-requisite to achieving long term success. There are innumerable examples of brilliant businesses going dud due to botched executions. Kodak, despite inventing the core technology in the digital cameras, failed to execute the strategy and went bankrupt. Few other examples of companies that fell due to failed executions are Atari, Research in Motion, and Woolworths.

 

3. Play to Win. The two finals played at the Centre Court made this third lesson very evident. In men’s singles, Djokovic lost the second set in tie-breaker because he seemed content to passively return Federer’s serve playing from outside the baseline. He just didn’t appear to be playing to win and that cost him the set.

However, the brief rain gave him an opportunity to clear his mind and bring back his focus on winning. In the third set, there was almost a different – calmer and more focused – Djokovic playing within the baseline only to win.

Likewise in the mixed doubles, Hingis and Paes were so focused on winning that they were actually enjoying the game right from the word go. Every rally and every return was confidently played by them to win the point (and eventually the title).

This is how great businesses compete too – to win! They take bold steps and confident actions in planning and executing their strategies. They strive very hard to grasp the real needs of their customers. They go all out in devising solutions that they know will address the real needs of their customers. They leave no stone unturned to market their offerings. For example, Steve Jobs was so badly persistent on winning that he stretched himself and his team members to no measures.

Djokovic summarized this point well when he said in the post-match conference,

“I am gonna [sic] have to win, he’s not gonna lose.”

 

References:
www.wimbledon.com
John A. Quelch, Jacquie Labatt-Randle: Colgate Max Fresh – Global Brand Roll-Out.

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achieve strategic transformation for enduring growth

How do you achieve strategic transformation for enduring growth of your company? – Part-II

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Thanks for your overwhelming response to the ‘achieve strategic transformation for enduring growth – part-I‘ of this two-part series. In the part-I of this article, we agreed with Mark that strategic transformation is indeed the key to enduring growth. However, we differed on the way to achieve transformation.

So how do you achieve strategic transformation for enduring growth of your business?

In part-I, we asserted that the secret to achieving strategic transformation is to be fiercely customer-centric.

In this part, let us discuss how you can transform your business and grow it further keeping customer-centricity at the core of your business strategy.

 

When you become passionately customer-centric, you make your customers’ delight the overarching goal of your organization. ‘Customer delight’ becomes the Shinbashira – the central pillar – on which your organization stands. You listen to your customers and place your customers (and consumers) at the centre of everything you offer. You direct your resources toward satisfying (and exceeding) the needs and wants of your consumers. As their needs change, the solution you offer changes, and the business strategy behind delivering those solutions changes too. You become so agile in responding to your customer’s changing tastes that you barely notice the incremental changes taking place in your organization. This continuous change becomes second nature to your organization.

And when you look back over a period of time, you realize that you have moved so significantly from your original business situation that there is hardly anything common between the old face and the new face of your organization. It is like the organization undergoing morphing.

This is when you have achieved transformation, without consciously embarking on doing that.

You don’t TAKE disruptive actions for strategic transformation; you take well-conceived actions with specific goals that BRING ABOUT ‘disruptive transformation’. There is a difference!

But this is a very simplified view of an organization going through transformation.

What actually happens (or has to happen) on ground is far more complex and far too deliberate.

Let us see how.

 

Our analysis of the top-100 brands revealed that when organizations commit themselves to becoming customer-centric, they tend to take these five steps so as to focus on creating maximum value for their customers:

  • They meticulously track usage of their products and services to figure out their customer’s usage patterns. This may involve analysing usage data from customers and in some cases their customer’s customers, who are using products that incorporate your product.
  • They strive to understand their customers thoroughly to comprehend the real reasons behind their customers’ purchases and apply that knowhow to identify real profitable customers. They perform detailed analysis of the customers’ data to generate deep insights into their purchasing behaviour and to spot any changes in them.
  • They incorporate their customers’ feedback into R&D to continually improve their products and services in line with the changing consumer expectations. Once in a while, this may result in conceptualizing an innovative product not yet in the market.
  • They put in place mechanisms to refine their operational strategies and organizational processes to address the changing priorities.
  • Most importantly, they use the feedback to calibrate their future roadmap and formulate the long-term growth strategy for their business.

 

These actions, when deployed effectively, enable these companies to embrace one or more of the three growth strategies: Market Expansion, Product/Industry Expansion, and Operational Improvement.

Let us briefly look at each of them.

 

1. MARKET EXPANSION – Business growth by entering into new market(s):

When you are customer-centric, you strive to know the face of your most profitable customers and the benefits for which they are buying from you. Your growth objective is to identify more of such customers globally who may benefit from your offering. This insight assists you to explore new customer bases in newer markets. The way this objective is achieved varies from company to company. Some companies merge with or acquire other businesses having presence in the target market, and leverage that presence to market their products. Others may form joint-ventures on the agreement to share technology and markets. Yet others may plan to grow organically by entering a new market on their own.

Coca-Cola, MTV, Starbucks, Nestlé, Heineken, and scores of other companies have successfully located their target customer segments across the world. They achieved international expansion over an extended period of time transforming their businesses from being local entities to becoming global market-leaders.

 

2. PRODUCT OR INDUSTRY EXPANSION – Business growth by introducing better products and services:

As your customers’ preferences change, the products or services they employ to do their job (need to) change too. When you are constantly tracking this change, you get innovative ideas for improving your existing product lines and introducing new ones. A good grasp on customers’ needs in new areas can propel you to launch new product categories that didn’t exist previously.

Procter & Gamble has developed this expertise in consumer research through ethnographic methods; they claim to co-create new products or new line of existing products by closely observing how their consumers use the existing products.

Amazon is an excellent example of growth through product or category expansion. Pursuing the vision of becoming ‘earth’s most customer-centric company’, Amazon is single-mindedly striving to make customer’s (online) shopping experience easy, comfortable, and pleasant. In fact, they are in the process of disrupting the entire book publishing industry by planning to publish and sell every future book online. The established book-publishers lost touch with the changing consumer need of ‘reading books anytime-anywhere without having to spend time buying it physically’, and now their survival itself is threatened by the digital publishing industry; whereas the likes of Amazon, with relentless focus on the consumers, are thriving.

 

3. OPERATIONAL IMPROVEMENT – Growth by developing unique competitive advantage:

This strategy involves creating differentiation in your business model and/or operations. Companies are leveraging their organizational data by applying the latest analytics techniques to mine insights. These insights are utilized to devise new distribution channels, optimize logistics and supply chain management, improve operations by deploying superior technology, and streamline existing processes. Relentlessly pursuing these strategies eventually renders them distinctive advantage over their competition, and helps them grow in the marketplace.

FedEx addressed the customer need of next-day courier deliveries and launched ‘FedEx Express’.

eBay provided easy B2C and C2C sales services to consumers via its (online) eCommerce platform.

Michael Dell grew his company by sensing an opportunity among PC-savvy consumers who enjoyed the convenience of customizing their PC and buying it online to be delivered in days. Thus Dell has developed an innovative business model of ordering PC online which enabled them to lower their PC prices to impossibly low levels.

 

These strategies have the potential to propel your organization into a bigger growth curve.

And the key to doing this is to understand your customers as thoroughly as you can!

In this world of disruptive innovations, business owners and CEOs must focus all their efforts in deeply understanding their customers. When you become ardently customer-centric, you set yourself up for strategic transformation that is key to enduring growth of your business.

 

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strategic transformation

How do you achieve strategic transformation for enduring growth of your company? – Part-I

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In the April edition of Stanford Business Insights, Mark Leslie states that successful enterprises have a ‘Cycle of Life’ which lives through five phases: Product or Service Development during Start-up, Market Entry, Growth, Maturity, and Decline. He says that during the decline phase, revenue slows down and flattens out, margins stabilize at lower levels, operational expenses rise to unsustainable heights, and company spirals into negative growth marked by layoffs, high burn rates, and eventual bankruptcy or liquidation. Mark says that the key to enduring growth, to manage this eventuality, is strategic transformation.

We agree!

When companies are growing for decades, deliberately or not, they tend to undergo some form of transformation, irrespective of how they are growing.

Mark further adds that there’s a moment along the corporate Arc of Life between growth and maturity – a point he calls ‘the sweet spot for optionality’ – when companies should take initiative to steer into uncharted waters (a new line of products, a new business category, a new industry?). In other words, opportunity-driven leaders should attempt to transform the business rather than risk growth by continuing along the well-marked path through operational excellence.

On these two points, we do not agree!

  1. We do not concur that continuing along the path of operational excellence does not fetch growth.
  2. We are not of the opinion that the way to strategic transformation is to just change your path to uncharted waters.

Let us explain why:

Regarding the first point, let us hypothesize for a moment that operational excellence does not fetch long term growth. Then how do we explain the growths of the likes of GM and Wal-Mart in the past? After-all Alfred P. Sloan, Jr. and Sam Walton are known to have led their companies on the back of operational improvements.

Moreover, prominent brands like Toyota, McDonald’s, Zara, and Subway have grown primarily through laser-sharp focus on operational efficiencies. Toyota has continually enhanced its assembly-line operations leveraging its legendary Toyota Production System. McDonald’s has continued serving its fast foods with the promise of ‘fast and convenient’ service. Zara has reduced its shopfloor-to-store cycle time of its fashion garments to just five weeks compared to industry average of six months.

We thought that studying the growth strategies of the top-100 brands, which have been growing for decades, would point to the key behind enduring growth of businesses. It will help us prove or disprove the hypothesis.

A quick analysis, as shown in EXHIBIT-A, revealed that more than two-third of the top-100 brands achieved enduring growth through strategic transformation. Interestingly, almost one-third of the top-brands have attained steady growth primarily through operational improvements.

Strategic Transformation vs Operational Excellence

EXHIBIT-B illustrates that there is no obvious correlation between a company’s lifespan and the revenues it earns, with respect to their growth strategies. However, it also establishes that transformation-driven companies seem to out-live and out-earn the operationally-driven companies, especially over the longer term.

Strategic Transformation - Revenue Lifespan Chart

So the above two analyses disprove the hypothesis that companies cannot grow with operational excellence; and it also buttresses the theory that strategic transformation is the key to enduring growth.

 

The second point in a way suggests that business leaders see the sweet spot of optionality as an opportunity to hurtle their company to the next level by changing their course into uncharted waters. But it sounds a bit ambiguous and fuzzy in terms of identifying the sweet spot of optionality almost to the point of being too simplistic.

This is because when you DO transformations for the SAKE of transformation, then you achieve just that – a transformation, without any real benefits from it.

You cannot just APPLY transformation in business as easily as you apply a Fourier or a Laplace transform in engineering mathematics. (Bad analogy I know, but you get the point).

Strategic transformation is a result, and not a set of random actions.

Transformation is an effect, and not a goal.

Transformation HAPPENS over time when you take well-conceived actions, persistently, with single-minded focus on a specific goal.

In our analysis, surprisingly (or unsurprisingly) there was one common theme (or goal) observed across all these 100 leading brands. That theme is Customer-Centricity. Amazon wants to be Earth’s most customer-centric company. Apple demonstrates its customer-centricity in the intuitive products it designs. Gillette develops a different type of razor for each customer segment that has a different shaving habit. Oracle invests strategically into technology that customers are likely to embrace in the future, as it did with its E-Business Suite. There are as many examples of customer centricity as the market-leaders around.

In sum, the point is this: The secret to achieving strategic transformation for enduring growth of your company is to be fiercely customer-centric. When you are passionately customer-focussed in your business, the customer will show you the way to achieving strategic transformation.

 

In part-II, we will discuss how exactly do you transform your business with customer-centricity as your main strategy.

(Cover photo credit: theatlantic)

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