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How India's new currency was born amid drama
Before November of last year, no one ever spared a thought for the currency that was such an integral part of their lives. We paid for things, and expected the correct change to be returned. But did we ever think about the design of the notes? Or the script on it? Or saw them in burnt piles by the highway? Or pined for Rs 100 notes? Not really. The most popular rumour about the new currency notes, and probably the most far-fetched, was that the new notes came with embedded GPS tracking chips. Forget the fact that many rural areas in India still didn’t have access to proper water, electricity and sanitation, the government had somehow invented a hi-tech way to make the new notes ‘black money proof’! Demonetisation scared people and they thought the government can go to any extent to detect black money. ‘Changing’ the currency Even after spending hours standing in queues at banks and ATMs, many faced the problem of breaking the Rs 2000 note into smaller denominations. Suddenly, the Rs 50 and Rs 100 notes became more important than the biggest note. The highest-denomination currency note found no takers. Shopkeepers gladly accepted Rs 100 and Rs 50 notes but didn't want to even touch the new note
Pankaj Adhikari
BIG DATA
A Definition of Big Data Big Data is everywhere. But, do you really know what it is and how it can help your business? SAS perfectly captures Big Data as “a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis.” But, as SAS points out, the amount of data is not as important as what organizations do with it: analyzing Big Data results in the insights you need to make better business decisions and strategic moves. Lisa Arthur, Teradata Applications CMO and Forbes contributor, explains that Big Data “is a collection of data from traditional and digital sources inside and outside your company that represents a source for ongoing discovery and analysis.” She asserts that traditional data must be included in Big Data because it is an important piece of the Big Data picture. Indeed, incorporating data from all sources is key to optimizing the insights gained with Big Data. In recent years, there has been a boom in Big Data because of the growth of social, mobile, cloud, and multi-media computing. We now have unprecedented amounts of data, and it is up to organizations to harness the data in order to extract useful, actionable insights. But, because traditional systems cannot store, process, and analyze massive amounts of unstructured data, organizations are turning to Big Data management solutions to turn unstructured data into the actionable data needed for gaining key insights into their business and customers. Benefits of Using Big Data Analytics As the volume of data continues to grow, its potential for business seems to be growing exponentially as Big Data management solutions evolve allowing companies to turn raw data into relevant trends, predictions, and projections with unprecedented accuracy. Companies that use comprehensive Big Data analytics solutions reap the benefits, gaining even more insights that drive intelligent decision-making. Some of the benefits of Big Data analytics include… Identifying the root causes of failures and issues in real time Fully understanding the potential of data-driven marketing Generating customer offers based on their buying habits Improving customer engagement and increasing customer loyalty Reevaluating risk portfolios quickly Personalizing the customer experience Adding value to online and offline customer interactions Advantages of Big Data Management Solutions The best Big Data management solutions give companies the ability to aggregate a variety of data from hundreds of sources in real time. This results in better customer engagement through more effective inbound interactions and marketing programs, which ultimately leads to greater customer lifetime value. Big Data analytics powered by advanced Big Data management solutions gives organizations comprehensive customer profiles, enabling the delivery of more personalized customer experiences at every touchpoint throughout the buyer’s journey. And, these top Big Data management solutions eliminate data silos so that organizations get a single, 360-degree customer view that includes countless descriptive, calculated, and industry-specific metrics for building detailed records of individual customer’s behavior. These profiles, or what NGDATA calls “customer DNA,” give organizations a comprehensive understanding of their customers through deep customer insights and operationalized analytics, which allow for omni-channel impact. Big Data may seem daunting, but with the right Big Data management solution, your organization can tackle the data you need to get actionable insights and increase your customer lifetime value.
DATA ANALYSIS
A Definition of Data Analysis Data analysis is a primary component of data mining and Business Intelligence (BI) and is key to gaining the insight that drives business decisions. Organizations and enterprises analyze data from a multitude of sources using Big Data management solutions and customer experience management solutions that utilize data analysis to transform data into actionable insights. Dennis Junk, a HubSpot certified inbound marketer with Aptera, aptly explains data analysis in his blog post: data analysis is “all the ways you can break down the data, assess trends over time, and compare one sector or measurement to another. It can also include the various ways the data is visualized to make the trends and relationships intuitive at a glance.” Data analysis involves asking questions about what happened, what is happening, and what will happen (predictive analytics). As Junk puts it, “analytics is generally the data crunching, question-answering phase leading up to the decision-making phase in the overall Business Intelligence process.” Data Analysis Model Gwen Shapira, a solutions architect at Cloudera and an Oracle ACE Director, outlines seven key steps of data analysisfor Oracle’s Profit magazine. Shapira explains that while each company has its own data requirements and goals, there are seven steps that remain consistent across organizations and their data analysis processes: Decide on the objectives – Determine objectives for data science teams to develop a quantifiable way to determine whether the business is progressing toward its goals; identify metrics or performance indicators early Identify business levers – Identify goals, metrics, and levers early in data analysis projects to give scope and focus to data analysis; this means the business should be willing to make changes to improve its key metrics and reach its goals as well Data collection – Gather as much data from diverse sources as possible in order to build better models and gain more actionable insights Data cleaning – Improve data quality to generate the right results and avoid making incorrect conclusions; automate the process but involve employees to oversee the data cleaning and ensure accuracy Grow a data science team – Include on your science team individuals with advanced degrees in statistics who will focus on data modeling and predictions, as well as infrastructure engineers, software developers, and ETL experts; then, give the team the large-scale data analysis platforms they need to automate data collection and analysis Optimize and repeat – Perfect your data analysis model so you can repeat the process to generate accurate predictions, reach goals, and monitor and report consistently Benefits and Challenges of Data Analysis Data analysis is a proven way for organizations and enterprises to gain the information they need to make better decisions, serve their customers, and increase productivity and revenue. The benefits of data analysis are almost too numerous to count, and some of the most rewarding benefits include getting the right information for your business, getting more value out of IT departments, creating more effective marketing campaigns, gaining a better understanding of customers, and so on. But, there is so much data available today that data analysis is a challenge. Namely, handling and presenting all of the data are two of the most challenging aspects of data analysis. Traditional architectures and infrastructures are not able to handle the sheer amount of data that is being generated today, and decision makers find it takes longer than anticipated to get actionable insight from the data. Fortunately, data management solutions and customer experience management solutions give enterprises the ability to listen to customer interactions, learn from behavior and contextual information, create more effective actionable insights, and execute more intelligently on insights in order to optimize and engage targets and improve business practices.
DATA-DRIVEN MARKETING
Definition of Data-Driven Marketing Data-driven marketing is the process by which marketers glean insights and trends by analyzing company-generated or market data, then translating these insights into actionable decisions informed by the numbers. The goal of data-driven marketing is to optimize marketing processes and strategies to cater to changing trends and the unique demands of audiences and consumers by leveraging data to gain deeper insight into what customers want. When brands fully understand the who, what, where, when and why of how consumers are engaging with their marketing efforts, they are able to make better decisions surrounding everything from the timing of advertising in a given medium to the customization of marketing copy to cater to specific audience segments. How Data-Driven Marketing Works The process of data-driven marketing relies on the use of information (in the form of data) in order to drive marketing efforts. Data is collected on every aspect of a user’s engagement from demographics to market-wide metrics and individual interactions, and is then analyzed in order to determine markers of success. These insights are then used to help decide where and how to focus marketing resources, the types of creative that are most effective at maximizing ROI, which customers are most likely to churn, and many other crucial details that can aid marketers in shaping brand growth. Data-driven marketing is gaining in popularity in large part due to its proven ability to maximize ROI. It also helps to maximize the efficiency of marketing efforts by reducing wasteful spend and improving resource allocation, and ultimately empowers brands to deliver a more customer-centric approach to marketing. Gaining a better understanding of a brand’s prospects and their preferences is one of the most effective methods for increasing conversion rates. Examples of Data-Driven Marketing There are thousands of examples of data-driven marketing in use every day if you know where to look. In fact, the majority of the marketing messages we’re exposed to on any given day are driven by data-derived insights. Data-driven marketing has become the new norm thanks to the widespread accessibility of data and analytics tools. One company that has used data-driven marketing to its advantage is Hootsuite. When they noticed both free and paid signups were dropping off, they looked deep into their data pool to determine the features different segments of their user base were relying on most. They then utilized this data to create different usage tiers for the service based on what their customers were actually using the product for. The result? An increase in both free and paid service signups. Another example of successful data-driven marketing in action is Status Page. This company used data to increase conversion rates by 311%. After identifying which stage in the process prospects were being lost, and why, they implemented site changes to make the signup process more appealing and saw tremendous success as a result. Of course, these are just two of the many examples of businesses successfully leveraging data-driven marketing to influence results. Benefits of Data-Driven Marketing Data-driven marketing offers widespread benefits, including not only effectiveness but also ease of implementation thanks to the availability of user-friendly tools that do much of the heavy analytics lifting. Many marketers turn to data-driven marketing to improve audience targeting. With the right data, brands can know exactly who is engaging with their marketing efforts through which channels, and even at what time of day they are active. This can aid brands in laser-focusing their marketing efforts to the right mediums at the most effective times. Data-driven marketing also allows marketers to analyze the types of messaging and offers that consumers are most responsive to, often analyzed through A/B testing. This lets marketers focus their efforts into projects that will offer superior ROI and are of the highest value to the consumer. Conversely, it also allows marketing teams to see where they may be going wrong and how to correct the issue — as with the above example from Status Page. Data-driven marketing can also be used to optimize customer experience. If a marketer sees a bounce rate at a certain point of their campaign, they can evaluate the why and adjust as necessary so that the customer experience is optimized. This is the kind of initiative that has a powerful impact on growth and retention. Challenges of Data-Driven Marketing One of the biggest cons of data-driven marketing is also its biggest advantage — the intense focus on using insights in the decision-making process. This hyperfocus on using data to make marketing decisions can come at the expense of creativity, depending on how marketers choose to leverage data-derived insights. When metrics tunnel vision takes over, creativity is no longer driving the message and the ‘magic’ of connecting with consumers no longer happens. Savvy marketers implement the right blend of creativity and numbers-driven factors into every marketing campaign and tactic. Additionally, data can also indicate a course of action that goes against a brand’s values. While this is rare, marketers should maintain a sense of brand integrity and use data to inform, but not dictate, decision-making when it comes to sacrificing values. Brand guidelines are helpful to ensure that brand identity standards and values are maintained. Best Practices for Data-Driven Marketing The key to obtaining success with data-driven marketing efforts is to plan, test, analyze, iterate, and then redeploy and scale accordingly once you’ve gleaned enough insights to inform your course of action. Decide what you’re measuring and how you’ll be successful. Utilize A/B testing and other measures in order to see what works best and then compare results to your original KPIs. Then, implement and invest accordingly, taking care to analyze new data and adjusting your marketing efforts continuously. Data drives the majority of marketing decisions in today’s highly competitive world. If you’re not yet using data to derive marketing insights and inform decision-making, you’re already behind the curve.
CUSTOMER RETENTION
Customer retention refers to the activities and actions companies and organizations take to reduce the number of customer defections. The goal of customer retention programs is to help companies retain as many customers as possible, often through customer loyalty and brand loyalty initiatives. It is important to remember that customer retention begins with the first contact a customer has with a company and continues throughout the entire lifetime of the relationship. While most companies traditionally spend more money on customer acquisition because they view it as a quick and effective way of increasing revenue, customer retention often is faster and, on average, costs up to seven times less than customer acquisition. Selling to customers with whom you already have a relationship is often a more effective way of growing revenue because companies don’t need to attract, educate, and convert new ones. Companies that shift their focus to customer retention often find it to be a more efficient process because they are marketing to customers who already have expressed an interest in the products and are engaged with the brand, making it easier to capitalize on their experiences with the company. In fact, retention is a more sustainable business model that is a key to sustainable growth. The proof is in the numbers: according to studies done by Bain & Company, increasing customer retention by 5% can lead to an increase in profits of 25% – 95%, and the likelihood of converting an existing customer into a repeat customer is 60% – 70%, while the probability of converting a new lead is 5% – 20%, at best. Set customer expectations – Set customer expectations early and a little lower than you can provide to eliminate uncertainty about the level of your service and ensure you always deliver on your promises. Become the customers’ trusted advisor – You need to be the expert in your particular field, so that you can gain customers’ trust and build customer loyalty. Use relationships to build trust – Build relationships with customers in a way that fosters trust. Do this through shared values and fostering customer relationships. Take a proactive approach to customer service – Implement anticipatory service so that you can eliminate problems before they occur. Use social media to build relationships – Use LinkedIn, Twitter, and Facebook to connect and communicate with customers and give them a space for sharing experiences with your company, so they can become brand ambassadors. Go the extra mile – Going above and beyond will build strong relationships with customers and build long-term loyalty by paying attention to their needs and issues. Make it personal – Personalized service improves customer experience and is something customers are expecting and demanding. Make their experience personal to strengthen the bond with your brand. Rather than try to manage customer retention with a mishmash of customer retention strategies, many companies use customer retention software systems and targeted customer retention plans to improve customer retention. Some companies offer customer experience management solutions that enhance customer retention rates. Measuring Customer Retention and Key Metrics Attrition rate compliments retention rate. For example, if a company has a 20% attrition rate, it has an 80% retention rate. Companies’ attrition rates can be defined by the percentage of customers the company has lost over a given period. Specifically, companies can determine retention rate by using a simple customer retention rate formula: Retention rate = ((CE-CN)/CS))100. CE = number of customers at end of period, CN = number of new customers acquired during period, and CS = number of customers at start of period. At first glance, the formula may look complicated, but it’s not too difficult once you start using it. For example, if you start the given period with 200 customers and lose 20 customers but gained 40 customers, at the end of the period you have 220 customers. 220-40 = 180. 180/200 = 0.9, and 0.9 x 100 = 90. The retention rate for the given period was 90%. It is beneficial to track retention rates so companies can put their customer retention metrics into perspective and measure results over time.
Predictive Analytics
Predictive analytics involves extracting data from existing data sets with the goal of identifying trends and patterns. These trends and patterns are then used to predict future outcomes and trends. While it’s not an absolute science, predictive analytics does provide companies with the ability to reliably forecast future trends and behaviors. Gartner offers a predictive analytics definition describing the concept as any approach to data mining that contains the following key elements: Emphasizing prediction, rather than description, classification, or clustering Rapid analysis, with measurements in hours or days, rather than the traditional approach to data mining Emphasizing business relevance of the resulting insights Ease of use, making data and tools easily accessible by business users Predictive analytics emerged from a desire to turn raw data into informative insights that can be used not merely to understand past patterns and trends, but provide a model for accurately predicting future outcomes. How Predictive Analytics Differs from Other Analytics Models Gartner visualizes the various types of analytics as being on a spectrum, with each more advanced method of analysis being more difficult, but offering increased value. Descriptive analytics are at the low end of the spectrum, with a primary focus on information. Diagnostic analytics is the next level of analysis, providing insights on the motivations and causes driving trends and behaviors. Diagnostic analytics is followed by predictive analytics, or the ability to forecast what is likely to happen. At the top of the spectrum is prescriptive analytics, providing foresight and the knowledge required to create desired outcomes. Predictive Analytics Methods Predictive analytics is primarily concerned with analyzing data and manipulating variables in order to glean forecasting capabilities from existing data. Predictive analytics techniques rely on measurable variables, manipulating metrics to predict future behavior or outcomes given various measurable approaches. Predictive analytics models combine multiple predictors, or measurable variables, into a predictive model. This approach allows for the collection of data and subsequent formulation of a statistical model, to which additional data can be added as it becomes available. The addition of higher volumes of data as it becomes available creates a smart predictive model, relying on larger and larger data sets which produces more reliable predictions based on the volume of data analyzed. Additionally, relying on real-time data to fuel predictive analytics models results in greater accuracy of forecasting.
CUSTOMER EXPERIENCE MANAGEMENT
A Definition of Customer Experience Management Customer experience management (CEM) describes how a company takes control of its interactions with customers. Gartner offers a spot-on CEM definition: “the practice of designing and reacting to customer interactions to meet or exceed customer expectations and, thus, increase customer satisfaction, loyalty and advocacy.” Organizations’ departments and groups often plan customer interactions from their own perspectives, or silos, rather than from a customer’s perspective. CEM is the concept of viewing and improving those interactions entirely from the customers’ perspective, and true CEM encompasses the customers’ entire journey with a business. The goal of customer experience management is to build customer loyalty and to reduce customer churn. Benefits of Customer Experience Management As businesses find themselves trying to remain competitive in today’s ultra-competitive and connected world, customer experience management has become critical to their success. In fact, customer experience management is incredibly valuable to companies of nearly any size because of the benefits of CEM, as outlined by SAS: Strengthen brand preference through differentiated experiences Increase revenue with incremental sales from existing customers and new sales by word of mouth Improve customer loyalty, and create brand advocates, through valued and memorable customer interactions Reduce customer churn to lower costs Methods of Customer Experience Management There are several methods of customer experience management, but most companies approach it using the same basic CEM model. First, companies get feedback from their customers to get a fundamental understanding of their experience. Companies may capture feedback through surveys and customer satisfaction polls and outreach. After companies have gathered feedback from customers, they share the insights across the organization. It is most helpful if the insights are shared in a timely fashion and in the most relevant way for each department. It’s critical that the right people get the right customer insights at the right time. Finally, companies need to ensure they are delivering the optimal customer experience based on that feedback. Companies must continually evaluate their customer experience in order to improve upon it. Challenges of Customer Experience Management Of course, designing a successful CEM strategy can be a daunting task. Businesses must know their customers, and know them well, in order to get customer experience management right. One of the toughest challenges is creating a consistent multi-channel brand experience. Don’t forget that customers are interacting with your brand online, through customer service, in person, and more. It is crucial that you ensure brand consistency at every point of interaction. Another challenge of customer experience management is getting a single view of the customer. Companies get customer data from a multitude of sources, and the trick is to consolidate all of that data into a single view to gain a 360-degree view of the customer. This becomes especially difficult if the data remains siloed. Another challenge of customer experience management is personalization. Customers have expectations and are aware that companies have more information about them today than ever before. That translates to customer demand for personalization, and businesses must remember to personalize every interaction. Businesses can succeed at personalization if they add context to their customer focus so they can offer what the customer wants, when the customer wants it. Customer Experience Management Systems Companies do not need to handle their customer experience management all on their own. Customer experience management systems are software solutions that help to improve time-to-market by delivering more personalized experiences to customers. Leading customer experience management solutions connect data sources in real time so that companies are better equipped to engage customers through personalized experiences, which leads to greater customer lifetime value. The key to customer experience management is getting a single view of customers by breaking down silos and getting the deep customer insights that help businesses create more personalized customer interactions.