Processing information is a huge task if you are dealing with big data. Fortunately, besides the increasing amount of information created every day, there are also tools and software that helps in such titanic task. But first of all, what is Big Data Analytics? […]
What Big Data means to organizations The amount of data uploaded, downloaded and stored on a global level has changed dramatically, especially in this millennium. In spite of the distances, people get closer with the “World Wide Web”. We can experience that closeness, for example, when people around the world express their opinions about certain topics with just a click. […]
Great analysts tell great stories . Stories, after all, make results user friendly, more conducive to decision making, and more persuasive. Time and time again in our experience, stories have been more than an afterthought; stories allow the analysts to construct a set of hypotheses and provide a map for investigating the data. The analyses will in turn enrich the initial stories and lead to deeper insights. What is important here is that the storyline, told before the analyses, enables an authentic human element to surface that would be more difficult to extract from the data alone. We can look at the stories to see how a customer journeys takes place and the decisions that are made on route. The more people that follow the same route the more we can look at the in market behaviour of others that potentially travel the same journey. The end is not the journey and many journeys can lead to the same end so how reasonable is it to assume that people travelling the same journey will arrive at the same destination? What path did our customer take to become our customer? In other words, we were interested not just in what customers were buying, but in the mechanics of how they make their purchases and how this may make them loyal. After the analysis, the true story of a customer’s path to loyalty is in fact revealed. Where do these stories come from? They may come either from the experience of an expert in the sector or brand or from qualitative research using observation or in-depth interviews. In order for a story to truly enable analytics, the story development process needs to be rigorous. We use the framework of grounded theory to ensure that the data and overarching storyline inform each other and are coherent with each other. The idea is for the analyst to navigate back and forth between the data and the developing story to ensure a good balance between the creative narrative and the analytics that reveal the facts and details of the story. The enabling storyline should not be too restrictive; it needs to support the development of the plot and characters as they emerge from the analysis, but without bias. Conversely, the storyline can suggest specific questions to be asked of the data for a more in depth analysis. Data can be manipulated to make any suggestion and interpretation. In a world that’s flooded with data, it becomes harder to use the data; there’s too much of it to make sense of unless you come to the data with an insight or hypothesis to test. Building stories provides a good framework in which to do that. Data Scientists and Data Analysts are the forensic scientists of Big Data and having a hypothesis can help them piece together the information either confirming or disproving the hypothesis and moving forward to a new story or a more complete story. The story of the customer journey is hidden within the noise of data but by understanding your existing customers journeys this information can be used to create a better understanding of the journey of potential customers and how you can influence their story. What is your customer journey and what stories will be revealed? […]
Back when there were a handful of channels, interactions between customers and brands were relatively simple. Today, by contrast, more than half of all customers move through three or more channels to complete a single task. “We have a treasure trove of data—it’s highly valuable.” To open a bank account today, for instance, a typical customer embarks on a multichannel journey: researching online, downloading an application speaking to a call center agent, linking brokerage accounts, visiting a branch, and installing the bank’s mobile app. Those steps leave a long and complex digital trail. That multi channel complexity, combined with the scale of the data, U.S. companies store at least 150 terabytes of it, makes divining insights into customer behaviors a serious challenge. Parsing this data, however, is critical to improving the customer experience and growing your business. In our view, the most productive way to get there is not by fixing individual touch points but by improving the entire customer journey. So not just marketing to potential customers but using the data to improve the customer experience. Analysing the drop off points in the customer experience can lead to better retention and loyalty. When a customer can not find and answer or has too many blocks in the road these need to be removed, but first they have to be identified. To put big data to work in improving the customer journeys, companies should keep three things in mind: 1. Focus on the top journeys. Companies may feel they need to study all the bits and bytes available to them. Our analysis across industries shows, however, that three to five journeys matter most to customers and the bottom line. They generally include some combination of sales and onboarding; narrowing the focus to those journeys allows companies to cut through the data clutter and prioritize. For instance, a cable television player used advanced data analysis of multichannel customer behaviors to focus on where drop offs in the journey occurred in two journeys—onboarding and problem resolution—to address nagging customer retention and loyalty issues. The data team helped them identify key service troublespots and ways to improve the onboarding process. Those insights Companies often hesitate to take action for fear their data is missing or a mess. In our experience, however, successful organizations tend not to overthink all the details and instead just roll up sleeves and get to work. Most companies, in fact, already have the data they need. The challenge is pulling the data together. “If we can unlock the value of all our data, we will have a wholly new revenue stream.” 2. Figure Out Where Data is Stored Companies need to figure out where that data is stored, and what it takes to extract and aggregate it so they can understand the customer journey across multiple touch points. Since data often lives in systems managed by various functions, bring the necessary operations, IT, in-store sales, and marketing people together to identify the touchpoints. Track performance from the outset, mistakes and all, since that experience helps teams test, refine, and learn and ultimately accelerate the benefits. “Hedge funds will love our data—they will buy practically any set of data that might give them a potential edge.” 3. Focus on analytics, not reporting. Companies tend to focus on generating reports from their data about what has happened. Much greater value, however, comes from analyzing data to pinpoint cause and effect and make predictions. Big data harbors big opportunities to improve customer journeys and value. What it requires is a commitment to focus on what really matters. […]
“The numbers don’t lie.” You hear that all the time. Even if it’s mostly true, the numbers can be slippery, cryptic, and, at times, two-faced. Whether they represent findings about your customers, products, or employees, they can be maddeningly open to interpretation or misinterpretation With big data getting ever bigger and infiltrating more and more parts of your business, the need for ways to understand the numbers is becoming more acute. “fundamentally rethink how the analysis of data can create value for themselves and their customers.” Tom Davenport Everyday billions of transactions are taking place and huge quantities of data are being collected, but how can you translate all this information, all these signals into knowledge to increase your bottom line? The way the data is interpreted can lead to one set of data telling you an idea is not worth pursuing, while another will tell you it is; so which interpretation is right? Is big data enough? Aggregate numbers can tell you a lot, but they say very little about how individual customers are thinking and talking about your products. So is it possible to find a way to interpret the data to identify customer behaviour? Cultivating a strong flow of intelligence can have a powerful impact on business strategy. Information on competitors is used by most businesses but few are using Big Data to look for the clues left by customers, on their path to purchase; what were the behaviours of prospects and existing clients prior to a sale? Using transactional information and identifying the ‘path to purchase’ of existing clients can help understand and identify potential clients even before they have made a conscious decision to buy! So with this in mind it becomes important for companies’ consumer-data teams to have someone who truly understands statistics. Data can appear disorganised, chaotic or random, so being able to interrogate, analyze and recognise patterns becomes an art in itself. And are there times when you should completely ignore the data and go with your gut? After all, companies often fail to analyse data in ways that enhance their understanding and neglect to make changes in response to new insights. While recognizing companies struggle to make sense of information, what matters more than the type and quantity of the data, is establishing a deep corporate culture of evidence-based decision making. That means establishing one undisputed source of performance data, giving all decision makers feedback in real time (or close to it), updating business rules in response to facts, and providing coaching for decision makers. It also means encouraging everyone in the organization to use data more effectively. When business-unit leaders invest in training for managers as well as end users, “pushing for the constant refinement of analytics tools, and tracking tool usage with new metrics,” companies do eventually transform themselves, bringing data-driven thinking into all aspects of the business and fulfilling the data’s vast potential. Having data can add a lot of value to a company, but having data and not interpreting it right, is worth nothing. With the right interpretation and the right visualization, you can introduce new insights, and take hold of the opportunities that your data presents. It is time for a fresh view on old data. “Companies spend a lot of time and money developing KPIs and generating reports. But is anyone actually reading these reports or is that time and money just going to waste? ” KPMG To reach the potential using Big Data, we have to ask the right questions and for each company these questions will differ; in being too focused on preconceived ideas can also blind us to the stories within the noise of data. One aspect of data that can help all businesses increase their ROI is to get a deeper understanding of the behaviour of their customers and who is ‘In Market’ to buy their product or service. This is particularly true for organisations that have high value products where the customer will have undertaken some kind of research prior to purchasing. This research is part of their ‘path to purchase’; websites they visited, what exhibitions they attended, purchases made etc., all contribute to the customer marketing behavior. Your website site visitors can become customers as understanding their behaviour will enable marketing departments to focus advertising dollars on target markets, getting adverts in front of purchasers. In considering how big data insights can help your business, do you need to have the mind of a data scientist? While this will help, focusing on a key area of the business such as behaviorial marketing can change the way companies view big data. Learn how your company can benefit from ‘In Market’ leads […]
