AIQRATE in 2020 ….A walk to remember
“Enabling clients reimagine their decision making & accentuate the business performance with AI strategy in a transformation, innovation and disruption driven world”
In today’s fast paced & volatile VUCA world, leaders face unprecedented challenges. They need to navigate through volatility while staying focused on strategy, business performance and culture. Artificial Intelligence is fast becoming a game changing catalyst and a strategic differentiator and almost a panacea to solve large, complex and unresolved problems. To be an AI powered organization, leaders not only need to have a broad understanding of AI strategy, they need to know how and where to use it. AIQRATE advisory services and consulting offerings are designed to enable leaders and decision makers from Enterprises, GCCs, Cloud Providers, Technology players, Startups, SMBs, VC/PE firms, Public Institutions and Academic Institutions to become AI ready and reduce the risk associated with curating, deploying AI strategy and ensuing interventions and increase the predictability of a durable leader’s success.
In the age of the bionic enterprises, AI continues to dominate the technology & business landscape. Under the aegis of transformation, disruption and innovation, AI has several applications and impact areas which usher a new change in how we make decisions in the enterprise and personal spheres. Traditionally, human decisions are to a large extent based on intuition, gut and historical data. In the age of AI, several of our decisions will be taken by algorithms. Leveraging AI, the ability to mimic the human brain and the ensuing ability to sense, comprehend and act will significantly go up and will result in emergence of augmented intelligence in decision making. Enterprises, GCCs, SMBs, Startups and Government Institutions are attempting to harness the power of AI to change the way they do business. All these industry segments are looking at AI becoming the secret sauce behind making them gain a competitive advantage. If you have not started yet, you are already behind the competition, however large or pedigreed you might be.
So, where are you placed on your AI journey? At AIQRATE, we can guide you on your journey of understanding what AI can do for you, embedding it within your business strategy, functional areas and augmenting the decision-making process.
At AIQRATE, we are here to help you with the art of the possible with AI. Through our bespoke AI strategy frameworks, methodologies, toolkits, playbooks and assessments, we will bring seamless Transformation, Innovation and Disruption to your businesses. Leveraging our proven repository of consulting templates and artifacts, we will curate your AI strategic approach roadmap. Our advisory offerings and consulting engagements are designed in alignment with your strategic growth, vision and competitive scenarios.
We are at an inflection point where AI will revolutionize the way we do business. The paradigms of customer, products, offerings, services and competition will change dramatically; and being AI-ready will become a true differentiator. AIQRATE will be your strategic partner to help you to prepare for what’s next in order to stay relevant.
Wish you a great 2021!
Chief Executive Officer
Bangalore , India
AI led Algorithms can decide on how we need to emote, behave, react, transact or interact with an individual – Sameer with SCIKEY
AI led Algorithms can decide on how we need to emote, behave, react, transact or interact with an individual – Sameer with SCIKEY
In an exclusive interaction with SCIKEY, Sameer Dhanrajani, CEO at AIQRATE Advisory & Consulting, speaks about how the future of work will look like enabled by AI, and it’s contribution in building productive teams and the emerging AI trends to watch out for in Post COVID scenario.
“AI led algorithms can decide on how we need to emote, behave, react, transact or interact with an individual,” Sameer Dhanranjani
Sameer is a globally recognized AI advisor, business builder, evangelist and thought leader known for his deep knowledge, strategic consulting approaches in AI space. Sameer has consulted with several Fortune 500 global enterprises, Indian corporations , GCCs, startups , SMBs, VC/PE firms, Academic Institutions in driving AI led strategic transformation and innovation strategies. Sameer is a renowned author, columnist, blogger and four times Tedx speaker. He is an author of bestselling book – AI and Analytics: accelerating business decisions.
In an exclusive interaction with SCIKEY, Sameer Dhanranjani, CEO at AIQRATE advisory consulting, speaks about how the future of work will look like enabled by AI, and it’s contribution in building productive teams and the emerging AI trends to watch out for in Post COVID scenario.
Mr Dhanranjani, you have consulted with several Fortune 500 enterprises, GCCs also start-ups in driving AI-led strategic transformation strategies. What according to you, are the topmost strategic considerations to weigh for managing accelerating business in Post COVID world for a start-up?
The unprecedented times of COVID-19 have brought the aspect of decision making under consideration. This includes tactical, strategic, and operational decision making that is crucial to make the venture more sustainable. Today the use of artificial intelligence is quite high amongst organizations. It can be used by start-up ventures and other outfits to make decisions irrespective of the area that needs decision making.
Most decisions that need to be made strategically are being passed on to artificial intelligence-enabled interventions. The algorithm makes similar decisions based on the previous decisions taken. Algorithms can decide how we need to emote, behave, react, transact or interact with the opposite individual This advancement in AI brings the challenge for organizations to create products and services specific to each customer through hyper-personalization and micro-segmenting. However, it can also be considered as an opportunity for organizations to emerge from the pandemic with newer business models and experiences for customers. Start-ups, especially, can make use of such advancements to reinvent and rejuvenate the organizational ecosystem.
You are known for your passion for Artificial Intelligence and are an author to the bestselling book – AI and Analytics: Accelerating Business Decisions. Tell us where how can AI be strategically significant while building productive teams.
My experience has led me to deal with engagements in the entire value chain of HR, ranging from hiring to engagement to incentivization that has leveraged using AI. It is phenomenal to see how AI can help build, engage, and sustain productive teams. AI can help in hiring through the detection emotions, facial expressions, tone modulations of the interviewee through computer vision and image classification techniques.
In the creation of productive teams, AI can gauge the engagement levels of an employee. It tries to look at the various interventions made by an employee regarding their attendance, participation in virtual meetings, and propensity to ask and engage themselves in conversations. It also keeps in check the number of pauses, intervals, and breaks taken by an employee. Every aspect of the employee is being marked to see how productive, inclusive, as an individual and in teams.
What are the top 5 AI trends to watch out for in Post COVID the scenario of the next one year?
When it comes to AI, the first trend emerging is that AI is not a tool or a technology, but it is now being touted as a strategic imperative for any organization. This means that AI strategies will become an intrinsic part and feature of every organisation.
The second trend is the democratization of AI. There is a possibility of the emergence of an AI marketplace where virtual exchanges related to business problems, demo runs etc. can be conducted. One would actually be able to figure out which algorithm is best for them in customer experience, supply chain etc.
The third trend being the cloud will act as a catalyst for AI proliferation. The propensity for cloud providers to enable AI companies with possible aspects of microservice API’s, Product Solutions will be created on the go. This means that the cloud enablers will have options to see various possibilities specific to their organisation when it comes to AI-specific use cases.
The fourth trend is linked to skilling. AI today is a part of a lot of course curriculums. But what is missing is the whole aspect of how does it get applied? The new courseware will be focused on how is AI implemented, adopted in the organization.
The last fifth trend is decision-making enabled by AI, which means humans will have no option but to upskill and reskill themselves to take a more rational, pragmatic and sanguine approach. So new models, new emerging realities of decision making will emerge.
How is AI powering the Future of Work, what are critical considerations for business and tech leaders considering the rapidly changing business dynamics due to COVID?
The future of work will be about AI and what we call AI plus a set of exponential technologies. This means that every aspect of our performance interaction and our responses will be gauged very manually through these technologies. This indicates that the level of performances in terms of how we go up-to-date needs to be worked upon. The future of work is an ecosystem where one particular employer cannot do it all.
This means that if learning must occur through an external player, it must come through the ecosystem of co-employees and the employer. In the future, we will not be caged as mere professionals doing our job but will be encouraged to push our boundaries to explore more at work. At the same time, transformation, innovation, and disruption will be a part of the future’s performance metrics. They will become a major parameter for the organization to create a mediocre versus proficient employee or a professional. This is where the onus will fall on the employees to ensure that they are not just doing what is being called out, but are going beyond to create what we call a value creation for the organisation.
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How Startups can leverage AI to gain competitive advantage
Despite nationwide venture funding hitting a multiyear low, venture capital deployed to artificial intelligence (AI) startups has reached a record high.
Last year, VCs struck 658 deals with AI companies, nearly five times the number that signed on the dotted line four years before. To date, the market contains 2,045 AI startups and more than 17,000 market followers, with more joining by the day.
AI’s rapid rise has swept up startups and enterprises alike, including U.S. automaker Ford, which recently bought AI startup Argo for $1 billion. The acquisition cements experts’ suspicions of Ford’s coming foray into self-driving technology. Other startups — so many, in fact, that entrepreneurs need a “best of” guide — are betting heavily on bot platforms.
So while we’ve just glimpsed the tip of this innovation iceberg, it’s clear AI is no longer some nebulous technology of the future. Sixty-eight percent of marketing executives, report using AI in their operations. For a technology that only went mainstream in 2016 and barely existed four years ago, that’s a remarkable adoption rate. How, regardless of the platform you choose, can you join forward-thinking entrepreneurs and build your business with AI? Over the last few years , I have worked closely with multiple start ups across genres and ,So far, four ways stand out:
1. Get to know your next customer.
A politician wouldn’t dream of delivering a small-town stump speech to her urban constituents. Why? Because you’ve got to know your audience. The same is true for entrepreneurs. Before you broadcast your message, you need to know who you’re trying to reach.
Node, an account-based intelligence startup, uses natural language processing — a fancy term for teaching a computer to understand how we humans speak and write — to develop customer profiles. Node is crunching vast swaths of data to connect the dots between marketers and the companies they’re trying to reach.
Once you have ample customer data — Node uses data crawlers to scrape information from social media, news sites and more — pair machine learning and natural language processing models to extract sentiments from unstructured data. Then, just as senators segment constituents into demographic groups, Node uses cluster analysis to sort clients’ customers into like cohorts.
2. See how people truly use your product.
If, heaven forbid, you forgot to tag your neighbor at last week’s house party, Facebook was no doubt there to remind you of your error. How does Facebook know which of your friends you left untagged? It has gone all-in on an AI technique called convolutional neural networks.
Convolutional neural networks, which loosely model how the brain’s visual cortex interacts with the eyes, work by separating an image into tiny portions before running each of those specks through a multilayered filter. It then “sees” where each speck overlaps with other parts of the image, and through automated iterations, it puts together a full image.
Many different ways exist to apply this technology, but retail businesses can start with image classification. Try using a convolutional neural network to break down photos of your products posted online. The model can identify customer segments that frequently use your product, where they’re using it and whether they commonly pair other products with yours. Essentially, this automated image analysis can show you how your products fit into customers’ lives, allowing you to tailor your marketing materials to fit.
3. Get inside the user’s head.
Success on social media requires careful listening and quick action. When a social campaign isn’t working, it’s best to put it out of its misery quickly. On the other hand, when one strikes a chord with customers, doubling down pays dividends.
But to do so, you need real-time insights about customers’ reactions to your content. Fortunately, AI can take the emotional temperature of thousands of customers at once. Dumbstruck, a video-testing and analytics startup that I advise, has added natural language processing to its emotional analytics stack. This allows it to provide moment-by-moment insights into viewers’ reactions to media. Dumbstruck’s model grows stronger with each reaction analyzed, producing a program that perceives human emotions even better than some people can.
4. Provide affordable, always-on support.
Customer service is — or should be, according to consumers — the department that never sleeps. More than half of people, 50.6 percent to be precise, believe a business should be available 24/7 to answer their every question and concern. When asked whether businesses should be available via a messaging app, the “yes” votes jump to nearly two in three.
Fortunately, bots don’t sleep, eat or go off-script. A well-built bot can offer cost-effective, constant customer service. Of course, grooming your bot to serve customers requires front-end data — ideally hundreds of thousands of example conversations — but you can get started with a human-chatbot hybrid. With this approach, the bot answers run-of-the-mill questions, while a human takes over for the more complex ones. Then, as the data builds and the model matures, you can phase in full automation.
AI’s Impact on small businesses and startups
Small enterprises will begin to use the tried and tested platforms in innovative ways. While startups will gain a competitive edge in capturing the AI market, the larger enterprises will provide the infrastructure to startups for building innovative services. It is somewhat similar to the business model followed when the cable technology was introduced.
Startups leveraging AI technology for industry verticals, like agriculture, manufacturing or insurance are bound to be successful.
Startups can empower established insurance companies like State Farm, Allstate and Farmers with technology enabling them to become more proactive in policy planning. For instance, a new AI insurance underwriter will help to forecast natural disasters and accidents, and adjust premiums.
The predictive decision-making capabilities are more than just a novel technology. You can manage food supply chains with the help of AI. Startups could develop end-to-end farming solutions with AI analytics for reducing food waste. It will have a huge impact in tackling global issues of hunger and famine.
Whether serving as a research assistant in a large corporation, acting as a voice-activated resource in difficult medical procedures, AI is fast becoming a reality. The AI revolution will benefit new players who learn quickly to use it to their advantage. AI will be a fundamental predictive enabler helping us solve large-scale problems, and startups are poised to gain a competitive edge.
So what’s the ground level AI sentiment of Startups? – Mix of Hope & Fear
Regardless of which industry you operate, be careful that AI will affect your world in some way. Look into what is present now and how you can utilize it to gain a competitive edge.
The possibilities with AI are endless; enterprises will become efficient, intelligent and cost-effective.
Undoubtedly, the digital revolution and AI will advance to a point where it will offer real-world benefits to every business- large and small.
Mark Zuckerberg says, “We’re working on AI because we think more intelligent services will be much more useful for you to use.”
AI is relevant because of its immense power to deliver useful solutions; its other building blocks including cloud computing and superfast connectivity. But, if you want to take advantage of this novel technology you will need a reliable, secure, and continuously evolving infrastructure.
AI & FINTECH – TWO GAME CHANGING REVOLUTIONS IN THE DIGITAL ERA
More investors are setting their sights on the financial technology (Fintech) arena. According to consulting firm Accenture, investment in Fintech firms rose by 10 percent worldwide to the tune of $23.2 billion in 2016.
China is leading the charge after securing $10 billion in investments in 55 deals which account for 90 percent of investments in Asia-Pacific. The US came second taking in $6.2 billion in funding. Europe, also saw an 11 percent increase in deals despite Britain seeing a decrease in funding due to the uncertainty from the Brexit vote.
The excitement stems from the disruption of traditional financial institutions (FIs) such as banks, insurance, and credit companies by technology. The next unicorn might be among the hundreds of tech startups that are giving Fintech a go.
What exactly is going to be the next big thing has yet to be determined, but artificial intelligence (AI) will play a huge part.
The growing reality is that, while opportunities are abound, competition is also heating up.
Take, for example, the number of Fintech startups that aim to digitize routine financial tasks like payments. In the US, the digital wallet and payments segment is fiercely competitive. Pioneers like PayPal see themselves being taken on by other tech giants like Google and Apple, by niche-oriented ventures like Venmo, and even by traditional FIs.
Most recently, the California-based robo-advisor, Wealthfront, has added artificial intelligence capabilities to track account activity on its own product and other integrated services such as Venmo, to analyze and understand how account holders are spending, investing and making their financial decisions, in an effort to provide more customized advice to their customers. Sentient Technologies, which has offices in both California and Hong Kong, is using artificial intelligence to continually analyze data and improve investment strategies. The company has several other AI initiatives in addition to its own equity fund. AI is even being used for banking customer service. RBS has developed Luvo, a technology which assists it service agents in finding answers to customer queries. The AI technology can search through a database, but also has a human personality and is built to learn continually and improve over time.
Some ventures are seeing bluer oceans by focusing on local and regional markets where conditions are somewhat favorable.
The growth of China’s Fintech was largely made possible by the relative age of its current banking system. It was easier for people to use mobile and web-based financial services such as Alibaba’s Ant Financial and Tencent since phones were more pervasive and more convenient to access than traditional financial instruments.
In Europe, the new Payment Services Directive (PSD2) set to take effect in 2018 has busted the game wide open. Banks are obligated to open up their application program interfaces (APIs) enabling Fintech apps and services to tap into users’ bank accounts. The line between banks and fintech companies are set to blur so just about everyone in finance is set to compete with old and new players alike.
Convenience has become a fundamental selling point to many users that a number of Fintech ventures have zeroed in on delivering better user experiences for an assortment of financial tasks such as payments, budgeting, banking, and even loan applications.
There is a mad scramble among companies to leverage cutting-edge technologies for competitive advantage. Even established tech companies like e-commerce giant Amazon had to give due attention to mobile as users shift their computing habits towards phones and tablets. Enterprises are also working on transitioning to cloud computing for infrastructure.
But where do more advanced technologies such as AI come in?
The drive to eliminate human fallibility has also made artificial intelligence (AI) driven to the forefront of research and development. Its applications range from sorting what gets shown on your social media newsfeed to self-driving cars. It’s also expected to have a major impact in Fintech due to potential of game changing insights that can be derived from the sheer volume of data that humanity is generating. Enterprising ventures are banking on it to expose the gap in the market that has become increasingly small due to competition.
All about algorithms
AI and finance are no strangers to each other. Traditional banking and finance have relied heavily on algorithms for automation and analysis. However, these were exclusive only to large and established institutions. Fintech is being aimed at empowering smaller organizations and consumers, and AI is expected to make its benefits accessible to a wider audience.
AI has a wide variety of consumer-level applications for smarter and more error-free user experiences. Personal finance applications are now using AI to balance people’s budgets based specifically to a user’s behavior. AI now also serves as robo-advisors to casual traders to guide them in managing their stock portfolios.
For enterprises, AI is expected to continue serving functions such as business intelligence and predictive analytics. Merchant services such as payments and fraud detection are also relying on AI to seek out patterns in customer behavior in order to weed out bad transactions.
People may soon have very little excuse of not having a handle of their money because of these services
Concerns Going Forward
While artificial intelligence holds the promise of efficiency, better decision-making, stronger compliance and potentially even more profits for investors, the technology is young. Banks need to find ways to lower costs and technology is the most obvious answer. A logical response by banks is to automate as much decision-making as possible, hence the number of banks enthusiastically embracing AI and automation. But the unknown risks inherent in aspects of AI have not been eliminated. According to a Euromoney Survey and report commissioned by Baker & McKenzie, out of 424 financial professionals, 76% believe that financial regulators are not up to speed on AI and 47% are not confident that their own organizations understand the risks of using AI. Additionally an increasing reliance on artificial intelligence technologies comes with a reduction in jobs. Many argue that the human intuition plays a valuable role in risk assessment and that the black box nature of AI makes it difficult to understand certain unexpected outcomes or decisions produced by the technology.
Towards the future
With the stiff competition in Fintech, ventures have to deliver a truly valuable products and services in order to stand out. The venture that provides the best user experience often wins but finding this X factor has become increasingly challenging.
The developments in AI may provide that something extra especially if it could promise to eliminate the guess work and human error out of finance. It’s for these reasons that AI might just hold the key to what further Fintech innovations can be made.
Data Sciences @ Fintech Companies for Competitive Disruption & Advantage
Long considered an impenetrable fortress dominated by a few well-known names, the banking and financial services industry is currently riding a giant wave of entrepreneurial disruption, disinter-mediation, and digital innovation. Everywhere, things are in flux. New, venture-backed arrivals are challenging the old powerhouses. Banks and financial services companies are caught between increasingly strict and costly regulations, and the need to compete through continuous innovation.
How does an entire industry remain relevant, authoritative, and trustworthy while struggling to surmount inflexible legacy systems, outdated business models, and a tired culture? Is there a way for banks and other traditional financial services companies to stay on budget while managing the competitive threat of agile newcomers and startups that do business at lower costs and with better margins? The threat is real. Can established institutions evolve in time to avoid being replaced? What other strategies can protect their extensive infrastructures and win the battle for the customer’s mind, heart, and wallet?
Financial technology, or fintech, is on fire with innovation and investment. The movement is reshaping entrepreneurial businesses and shaking the financial industry, reimagining the methods and tools consumers use to manage, save, and spend money. Agile fintech companies and their technology-intensive offerings do not shy away from big data, analytics, cloud computing, and machine learning, and they insist on a data-driven culture.
Consider a recent Forbes article by Chance Barnett, which quantifies fintech startup investments at $12 billion in 2014, quadrupling the $3 billion level achieved a year earlier. Adding to the wonderment, crowdfunding is likely to surpass venture capital in 2016 as a primary funding source. And people are joining the conversation. Barnett writes, “According to iQ Media, the number of mentions for ‘fintech’ on social media grew four times between 2013 and 2014, and will probably double again in 2015.” All of this activity underscores how technology is rattling the financial status quo and changing the very nature of money.
Yesterday’s Bank: A Rigid Culture, Strapped for Funds
Established banking institutions are strapped. The financial meltdown in 2008 questioned their operations, eroded trust, and invited punitive regulation designed to command, control, and correct the infractions of the past. Regulatory requirements have drained budgets, time, and attention, locking the major firms into constant compliance reporting. To the chagrin of some, these same regulations have also opened the door for new market entrants, technologies, platforms, and modalities—all of which are transforming the industry.
For traditional banking institutions, focus and energy for innovation are simply not there, nor are the necessary IT budgets. Gartner’s Q3 2015 forecast for worldwide IT spending growth (including all devices, data center systems, enterprise software, IT and Telecom services) hints at the challenge banks face: global IT spending is now down to -4.9%, even further from the -1.3% originally forecast, evidence of the spending and investment restraint we see across the financial landscape.
With IT budgets limited, it is hard to imagine banking firms easily reinventing themselves. Yet some are doing just that. Efficient spending is a top strategic priority for banking institutions. Many banks are moving away from a heavy concentration on compliance spending to instead focus on digital transformation, innovation, or collaboration with fintech firms. There is a huge amount of activity on all fronts. To begin, let’s review the competitive landscape of prominent fintech startups.
Data Sciences Intervention
Digital data has snowballed, with the proliferation of the internet, smartphones and other devices. Companies and governments alike recognize the massive potential in using this information – also known as Big Data – to drive real value for customers, and improve efficiency.
Big Data could transform businesses and economies, but the real game changer is data science.
Data science goes beyond traditional statistics to extract actionable insights from information – not just the sort of information you might find in a spreadsheet, but everything from emails and phone calls to text, images, video, social media data streaming, internet searches, GPS locations and computer logs.
“Data sciences enables us to process data better, faster and cheaper than ever
With powerful new techniques, including complex machine-learning algorithms, data science enables us to process data better, faster and cheaper than ever before.
We’re already seeing significant benefits of this – in areas such as national security, business intelligence, law enforcement, financial analysis, health care and disaster preparedness. From location analytics to predictive marketing to cognitive computing, the array of possibilities is overwhelming, sometimes even life-saving. The New York City Fire Department, for example, was one of the earlier success stories of using data science to proactively identify buildings most at risk from fire.
Unleashing the power of Advanced Data Mining using Data Sciences
For banks – in an era when banking is becoming commoditized – the data mining provides a massive opportunity to stand out from the competition. Every banking transaction is a nugget of data, so the industry sits on vast stores of information.
By using data science to collect and analyses Data, banks can improve, or reinvent, nearly every aspect of banking. Data science can enable hyper-targeted marketing, optimized transaction processing, personalized wealth management advice and more – the potential is endless.
A large proportion of the current Data Mining projects in banking revolve around customers – driving sales, boosting retention, improving service, and identifying needs, so the right offers can be served up at the right time.
“Data sciences can help strengthen risk management such as cards fraud detection
Banks can model their clients’ financial performance on multiple data sources and scenarios. Data science can also help strengthen risk management in areas such as cards fraud detection, financial crime compliance, credit scoring, stress-testing and cyber analytics.
The promise of Big Data is even greater than this, however, potentially opening up whole new frontiers in financial services.
Seamless experience for customers
Over 1.7 billion people with mobile phones are currently excluded from the formal financial system. This makes them invisible to credit bureaus, but they are increasingly becoming discoverable through their mobile footprint. Several innovative Fintech firms have already started building predictive models using this type of unconventional data to assess credit risk and provide new types of financing.
While banks have historically been good at running analytics at a product level, such as credit cards, or mortgages, very few have done so holistically, looking across inter-connected customer relationships that could offer a business opportunity – say when an individual customer works for, supplies or purchases from a company that is also a client of the bank. The evolving field of data science facilitates this seamless view.
Blockchain as the new database
Much more is yet to come. Blockchain, the underlying disruptive technology behind cryptocurrency Bitcoin, could spell huge change for financial services in the future. Saving information as ‘hash’, rather than in its original format, the blockchain ensures each data element is unique, time-stamped and tamper-resistant.
The semi-public nature of some types of blockchain paves the way for an enhanced level of security and privacy for sensitive data – a new kind of database where the information ‘header’ is public but the data inside is ‘private’.
As such, the blockchain has several potential applications in financial markets – think of trade finance, stock exchanges, central securities depositories, trade repositories or settlements systems.
Data analytics using blockchain, distributed ledger transactions and smart contracts will become critical in future, creating new challenges and opportunities in the world of data science.
How Startups can Improve Visibility in the Market Using Analytics
One might be tempted to think we are living in a startup bubble, with investors being largely optimistic about startups and investing millions of dollars in them, with many startups crossing the billion dollar valuation on a regular basis. But managing a startup is tough, with almost unreal targets set in between funding rounds. The founders need to, at all times, be focused on the direction in which they need to head, and be sure of the selective performance indicators that they need to keep watch of. Creating data has become easy at current times. Though acquiring data from multiple sources has its potential benefits, but for a company at its seminal stage, dealing with multiple KPIs is a huge risk. Startups can easily get side-tracked by following the wrong KPI. In an ideal scenario, startups should keep only one performance indicator and keep scaling up in that direction before achieving a milestone and involving others in the development plan. With a large number of startups around, existence of a red ocean, and ample amount of data giving number of insights and scope for strategies, implementing analytics can be a sure shot way to keep startups focused on the optimal way to scale up, and in extension create organic buzz and visibility to scale further. Analytics has the capacity to point out which should be the root nerve of a startup and how to scale further in that direction. The seminal stages of creating a high visibility in the market and expansion of a startup involves a repeated cycle of – Building and improving of the Core Competency, and measuring the effect in KPIs and increase in adoption by customers. Let’s see where and how analytics can be optimally implemented for these scenarios.
Building Core Competency
The starting point for understanding core competences is understanding that businesses need to have something that customers uniquely value if they’re to make good profits. “Me too” businesses (with nothing unique to distinguish them from their competition) are doomed to compete on price: the only thing they can do to make themselves the customer’s top choice is drop price. And as other “me too” businesses do the same, profit margins become thinner and thinner.
This is why there’s such an emphasis on building and selling USPs (Unique Selling Points) in business. If you’re able to offer something uniquely good, customers will want to choose your products and will be willing to pay more for them. Here are three ways to turn analytics insights into actions that make your company more competitive:
Gain control along with visibility of patterns
People often use analytics to understand what’s already happened, but don’t look beyond “what”, to ask “why.” By understanding why certain patterns emerge in your data, you gain greater visibility and control over what’s happening right now.
For example, when you understand why certain factors affect your margins, your sales team is better able to address underperforming products and customers, identify potential revenue opportunities and design more optimal coverage models for your reps.
Put analytics in the right functional areas to drive change
To get results, you need a way to deliver analytical information to sales reps at the product and account level. This empowers reps to negotiate from an informed position and use data to have strategic conversations with customers.
Also, when reps have good access to customer analytics, they’re better able to invest coverage resources in high-quality leads. It helps them to identify opportunities with large value and position sales offers in the context of a dynamic market. For example, if there’s a lot of variability in a commodity and price wars break out, you want to quickly reposition your offer in relation to that dynamic market.
Build an ecosystem
To get the best results from your analytics, you need the ability to monitor what’s happening and use that data to adapt. As you build this process into your company’s DNA, constantly evaluate the criteria you’re using to ensure they stay relevant: Are you looking at the right variables and assessing the marketplace effectively? By maintaining the quality of this information, you’re developing a competitive advantage through pricing and sales sophistication.
Measuring Business Traction
Analytics helps your business determine what is working well, and what needs to be improved. We can always go off of a hunch, but the real power comes when we know the hard data behind our marketing or business management efforts, and can make informed decisions that improve our business over and over. Seasoned entrepreneurs know just how important analytics are in growing your business. Without a serious analytics strategy, you are simply relying on hope and luck to grow your company.
In a startup you are constantly under pressure and have way too many distractions. Having a set of metrics that you watch & that you feel are the key drivers of your success helps keep clarity. And the more public you can make your goals for these key metrics the better. Make them widely available inside the company and share your most important goals with your board. Transparency of goals drives performance because it creates both a commitment and a sense of urgency.
If you don’t have a stability goal stated for the company and if you don’t regularly measure how you’re doing against this goal you won’t have your resources focused on the right priorities in the company.
Most companies have some measurements, but I would argue that people often measure the wrong stuff, measure with the wrong precision. The best way is to start by asking yourself at management team level: what are our company objectives and how do we best measure them? Because it can be hard to define or agree company objectives at an early stage I believe most people avoid them.
At the highest level you’ll obviously want to track how many customers you’re adding every month (and for some businesses that have hit scale this is measured on a daily basis). If you can break this down by channel that you’ve acquired them from this is obviously better.
How many additions came through organic SEO? How many through affiliate deals? How many through SEM? Do you have a customer referral program? If so, make sure you can track which leads come from this. Measuring viral adoption is obviously important.
Usually you have a catch-all bucket for “direct” or similar that often came through PR or word-of-mouth.
If you have multiple versions of your product, how many are web vs. mobile? How do the mobile customers break down by device type?
The next step after measuring the customers you’re adding is to add the “cost to acquire” by channel. This is important because it will later tell you whether you have a scalable business or not. In the early phases if you can’t acquire customers cost effectively enough you’ll need to diagnose why and how to fix it.
The Final Question of Scalability
The repeated cycles of Building and re-engineering and Core Competency and Measuring the Market environment effects takes the startups further and further into the final stages of having a scalable model. Like I mentioned at the beginning, there could be n number of directions a startup can head towards, as many as there are number of significant KPIs that need to be improved. But finding the right nerve and chasing the wrong performance indicator is the difference between ending up with a scalable business model and ending up with a marginally incremental model. In this current age of every changing topography of the market with disruptive ideas entering and washing off many hopeful businesses, only having an optimal analytics solution to track their locus can make sure startups sustain and succeed.