Definitions of Big Data

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Definitions of Big Data

Q: Can you please provide me with a definition of Big Data?

A: The definition of Big Data is a moving target.

Opentracker big data definition

In order to make it possible to follow the discussion, as it evolves, we see have started a list of definitions, as we read them on the internet.

Author names: Andrew Brust (ZDNet), Bill Franks (FCW article), PCmag encyclopedia,  John Rauser (Networkworld), John Weathington (TechRepublic Blog), Cory Janssen (, Mike Gualtieri (Forrester),  John Ebbert (Adexchanger), Edd Dumbill  (O’Reilly Strata), Boyd & Crawford (cited by Leslie Johnston on the Library of Congress), Tim Gasper (cited on TechCrunch), Margaret Rouse (TechTarget), Mike Loukides (O’Reilly Radar), Jimmy Guterman (O’Reilly), Wikibon,  Steven Burke (CRN), Urbandictionary .com, Slashdot (SAP survey), George Dyson (personal correspondence Tim O’Reilly), Doug Laney (Stackexchange), Brian Hopkins and Boris Evelson (Forrester), Bob Gourley (Smartdatacollective),  SAS,  Stephane Hamel.

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  1. “Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization.” Cited from Wikipedia
  2. “Big data is the term increasingly used to describe the process of applying serious computing power – the latest in machine learning and artificial intelligence – to seriously massive and often highly complex sets of information.” Cited from 4/2013 the Microsoft Enterprise Insight Blog
  3. “We can safely say that Big Data is about the technologies and practice of handling data sets so large that conventional database management systems cannot handle them efficiently, and sometimes cannot handle them at all.” Cited from 1/2012 ZDNet Blog by Andrew Brust.
  4. “An easily scalable system of unstructured data with accompanying tools that can efficiently pull structured datasets.” Cited from a 4/2013 post on the FCW Blog.
  5. “The definition of big data? “Who cares? It’s what you’re doing with it,”” Cited from 3/2013 FCW article, quoting Bill Franks.
  6. “The definition of big data refers to groups of data that are so large and unwieldy that regular database management tools have difficulty capturing, storing, sharing and managing the information.” Cited from
  7. “Big Data refers to the massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. Big Data includes business transactions, e-mail messages, photos, surveillance videos and activity logs (see machine-generated data). Scientific data from sensors can reach mammoth proportions over time, and Big Data also includes unstructured text posted on the Web, such as blogs and social media.” Cited from the pcmag encyclopedia
  8. “Any amount of data that’s too big to be handled by one computer.” John Rauser cited 5/2012 at
  9. “To define big data in competitive terms, you must think about what it takes to compete in the business world. Big data is traditionally characterized as a rushing river: large amounts of data flowing at a rapid pace. To be competitive with customers, big data creates products which are valuable and unique. To be competitive with suppliers, big data is freely available with no obligations or constraints. To be competitive with new entrants, big data is difficult for newcomers to try. To be competitive with substitutes, big data creates products which preclude other products from satisfying the same need.”
    Cited from 9/2012 John Weathington on the TechRepublic Blog
  10. “Big data refers to a process that is used when traditional data mining and handling techniques cannot uncover the insights and meaning of the underlying data. Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware.” Cited from a Cory Janssen post on
  11. “Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone. This data comes from everywhere: sensors used to gather climate information, posts to social media sites, digital pictures and videos, purchase transaction records, and cell phone GPS signals to name a few. This data is big data.” Cited from
  12. “A more pragmatic definition of big data must acknowledge that:
    Exponential data growth makes it continuously difficult to manage — store, process, and access. Data contains nonobvious information that firms can discover to improve business outcomes. Measures of data are relative; one firm’s big data is another firm’s peanut. A pragmatic definition of big data must be actionable for both IT and business professionals.The Definition Of Big Data: Big Data is the frontier of a firm’s ability to store, process, and access (SPA) all the data it needs to operate effectively, make decisions, reduce risks, and serve customers.”
    Cited from a 5/2012 Mike Gualtieri Forrester Blog post
  13. “The world has always had ‘big’ data.  What makes ‘big data’ the catch phrase of 2012 is not simply about the size of the data.  ‘Big data’ also refers to the size of available data for analysis, as well as the access methods and manipulation technologies to make sense of the data.” Cited from 12/2012 article by John Ebbert
  14. “Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it.” Cited from 1/2012 post by Edd Dumbill on O’Reilly Strata
  15. “We define Big Data as a cultural, technological, and scholarly phenomenon that rests on the interplay of:
    (1) Technology: maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets.
    (2) Analysis: drawing on large data sets to identify patterns in order to make economic, social, technical, and legal claims.
    (3) Mythology: the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy.” From Critical Questions for Big Data Boyd & Crawford (2012) as cited by Leslie Johnston on the Library of Congress website
  16. “The definition of Big Data is very fluid, as it is a moving target — what can be easily manipulated with common tools — and specific to the organization: what can be managed and stewarded by any one institution in its infrastructure.  One researcher or organization’s concept of a large data set is small to another.” Cited from 10/2011 Leslie Johnston Library of Congress
  17. “Big Data is presently synonymous with technologies like Hadoop, and the “NoSQL” class of databases including Mongo (document stores) and Cassandra (key-values).” Tim Gasper cited 10/2012 on TechCrunch
  18. “Big data (also spelled Big Data) is a general term used to describe the voluminous amount of unstructured and semi-structured data a company creates — data that would take too much time and cost too much money to load into a relational database for analysis. Although Big data doesn’t refer to any specific quantity, the term is often used when speaking about petabytes and exabytes of data.” Cited from a 3/2011 post by Margaret Rouse on TechTarget
  19. “But I do like Roger Magoulas’ definition of “big data”: big data is when the size of the data becomes part of the problem.” Cited post by Mike Loukides 2/2013 post on O’Reilly Radar
  20. “Big Data: when the size and performance requirements for data management become significant design and decision factors for implementing a data management and analysis system. For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.” Cited from 6/2009 Jimmy Guterman O’Reilly
  21. Big data has the following characteristics;
    1. Very large, distributed aggregations of loosely structured data – often incomplete and inaccessible:
    2. Petabytes/exabytes of data
    3. Millions/billions of people
    4. Billions/trillions of records
    5. Loosely-structured and often distributed data
    6. Flat schemas with few complex interrelationships
    7. Often involving time-stamped events
    8. Often made up of incomplete data
    9. Often including connections between data elements that must be probabilistically inferred,
    10. Applications that involved Big-data can be:
    11. Transactional (e.g., Facebook, PhotoBox), or,
    12. Analytic (e.g., ClickFox, Merced Applications). Cited from
  22. “We think at the end of the day, big data is not just about analytics, it is about data-centric applications. It is about driving some experience to a customer and causing them to do things in realtime.” Paul Mauritz quoted in a 4/2013 Steven Burke article on CRN
  23. “Modern day version of Big Brother. Online searches, store purchases, Facebook posts, Tweets or Foursquare check-ins, cell phone usage, etc. is creating a flood of data that, when organized and categorized and analyzed, reveals trends and habits about ourselves and society at large.”
  24. “A new survey by SAP suggests that nearly 76 percent of executives see “Big Data” as an opportunity. However, respondents’ definition of “Big Data” varied to a considerable degree. Nearly a quarter of the 154 C-suite executives felt that “Big Data” was the technologies designed to handle the massive amounts of data swamping organizations. Another 28 percent defined “Big Data” as that flood of data itself. Still another group (19 percent) equated “Big Data” with storing data for regulatory compliance. Around 18 percent viewed “Big Data” as the increase in data sources, including social networks and mobile devices.” slashdot 6/2012 citing an SAP survey
  25. “Big data is what happened when the cost of storing information became less than the cost of making the decision to throw it away.” Tim O’Reilly quoting personal correspondence via email from George Dyson 20 March 2013, regarding Dyson’s talk at the Long Now Foundation 19 March 2013.
  26. The Big Data Landscape:
    1. Apps
      • Vertical Apps
      • Ad / Media Apps
      • Business Intelligence
      • Analytics and Visualization
      • Operational Intelligence
      • Data as a service
    2. Infrastructure
      • Analytics Infrastructure
      • Operational Infrastructure
      • Infrastructure As A Service
      • Structured Databases
    3. Technologies

    big dataDerived from an informational diagram on The Big Data Landscape

  27.  The recently updated Gartner definition also recognizes the value aspect: “Big Data are information assets with volumes, velocities and/or variety requiring innovative forms of information processing for enhanced insight discovery, decision-making and process automation.” – Doug Laney posting at stackexchange  citing his original piece outlining the 3Vs of big data now republished.Here is the original 2001 paper entitled “3-D Data Management: Controlling Data Volume, Velocity and Variety” by Laney.
  28. “Big data: techniques and technologies that make handling data at extreme scale economical.” by Brian Hopkins and Boris Evelson at Forrester 8/2011. In diagram form. big data
  29. “To date, our key message has been that it is the enterprise CTO who is responsible for defining how the term should be used.”
    Bob Gourley (who originally posted the big data definition on wikipedia) posting on 12/2012.
  30. “A phenomenon defined by the rapid acceleration in the expanding volume of high velocity, complex, and diverse types of data. Big Data is often defined along three dimensions — volume, velocity, and variety.”  [Big Data requires] “advanced techniques and technologies to enable the capture, storage, distribution, management, and analysis of the information.”
    The TechAmerica Foundation’s Federal Big Data Commission Comprehensive Guide to Best Practices for Big Data, cited by Bob Gourley here 10/2012.
  31. “Big data is a popular term used to describe the exponential growth, availability and use of information, both structured and unstructured.
    Ultimately, regardless of the factors involved, we believe that the term big data is relative; it applies (per Gartner’s assessment)  whenever an organization’s ability to handle, store and analyze data exceeds its current capacity.”  SAS.
  32. The simplest definition of “Big Data” is “it doesn’t fit in Excel”
    from the full quote; “I have joked that the simplest definition of “Big Data” is “it doesn’t fit in Excel” – and when you think of it, it’s true for most people who wonder how to make the shift from a traditional approach to a Big Data one.”
    Stephane Hamel comment 8/2012 Big Data – What It Means For The Digital Analyst.
  33. More to follow…

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Power of Customer Analytics

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Power of Customer Analytics

Today’s businesses are swimming in data. But, as any swimmer knows, it’s not enough to just be surrounded by water – you have to know how to use it to your advantage. That’s where customer analytics come in. Customer analytics is the process of using data to generate insights that can improve your business, increase revenue, and drive customer retention. 

There are four main types of customer analytics: descriptive, diagnostic, predictive, and prescriptive. In this blog article, we’ll take a closer look at each one, and explore how you can use customer analytics to improve your business.

Descriptive Analytics

Descriptive analytics answer the question “What happened?”

They provide a snapshot of what has already transpired, such as how many customers made a purchase last month, what was the average order value, or how long it took for a customer to make a purchase. Descriptive analytics are important because they provide a foundation for understanding the way your customers behave. This type of data can be used to identify trends and patterns, which can be helpful in forecasting future behavior. To generate insights from descriptive analytics, businesses need to have access to data that is organized and structured in a way that makes it easy to slice and dice. This data can come from a variety of sources, such as customer surveys, website analytics, transaction records, and social media data. However, descriptive analytics cannot tell you why something happened, which is where diagnostic and predictive analytics become valuable.

Diagnostic Analytics

Diagnostic analytics answer the question “Why did it happen?”

They help you identify the root cause of an issue, such as why customers are leaving your site without making a purchase, or why orders are being delayed. Diagnostic analytics use data mining techniques to uncover relationships and identify causal factors. This type of analysis can be used to improve processes, optimize performances, and resolve problems. Diagnostic analytics can be obtained through surveys, customer interviews, focus groups, and other qualitative methods where customers can provide feedback about their experience. Alternatively, services like Opentracker can provide website data that can be used to diagnose problems so you can improve the customer experience and mitigate issues before they cause long-term damage.

Predictive Analytics

Predictive analytics answer the question “What could happen?”

Based off of historical data, predictive analytics uses statistical modeling to generate insights about future trends and behavior. This type of analysis can be used to identify opportunities and risks, make decisions about resource allocation, and develop marketing campaigns. For example, predictive analytics can be used to determine which customers are likely to churn so you can take steps to retain them, or to identify which products are likely to be popular so you can stock more of them. To generate predictive insights, businesses need data that is clean, accurate, and complete. Having a team of experts onboard who are skilled in statistical modeling is also critical for this type of analysis. In-house data scientists can be costly, which is why access to our team of professionals is included in an Opentracker subscription so we can assist you in generating predictive insights and interpreting the results as your business grows.

Prescriptive Analytics

Prescriptive analytics answer the question “What should we do?” 

It’s not enough to know what happened in the past or what could happen in the future. To make data-driven decisions, you need to know what actions you should take to achieve your desired outcome. This is where prescriptive analytics comes in. Prescriptive analytics uses optimization algorithms to identify the best course of action, given a set of constraints and objectives. This type of analysis takes into account a variety of factors, such as costs, risks, and benefits, to help businesses make decisions that are in their best interest. With the right data in your hands, you can start to develop prescriptive analytics solutions that will allow you to automate decision-making and improve your overall efficiency.

No matter what type of business you have, customer analytics can be a powerful tool for driving growth and success. By understanding the different types of customer analytics and how you can use them, you can make better decisions, improve your operations, and move your business forward.


Opentracker offers a suite of tools and services that are designed to be user-friendly and scalable so businesses of all sizes can benefit from data-driven decision-making. We help you automate your customer journey reporting, uncover actionable insights, and make better decisions that drive real results. With direct access to our data analytics team, a dedicated customer success agent, and a dedicated database engineer, you can be sure that you’re getting accurate, actionable data you need to grow your business. 

Book a discovery call today to learn more about how Opentracker can help you turn data into insightful, actionable customer intelligence. 

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What Web Metrics Can Do For You

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What (exactly) web metrics can do for you. Fail to plan : plan to fail

Business is based on margins and profits.

In order to generate a specific profit X, there is a minimum amount of sales Y that need to take place.

Business forecast and managing a business rely on calculations. Whether or not costs are covered and sufficient revenue is generated should not be left to chance. Website and customer journey analytics are necessary to determine what is working, what is not working and how to ensure business health. Click to read about driving performance with customer journey.

In a nutshell: business and website managers need to stay on top of actual performance and make ongoing changes


This is where statistics come in. For a web-based business the success of the business depends on a combination of several metrics:

  1. The amount of traffic that comes in
  2. The amount of traffic that converts
  3. The average, or total, value of the conversions
  4. Additional consideration is whether or not the revenue is recurring

The take-home is that these metrics need to be reverse-engineered to ensure that the business stays healthy and grows. Projections can be made based on historical data. Current data is then used to calculate revenue projections. Action can be taken if the numbers are not high enough.

Conversion rates tend to stabilise and do not change again, unless something else change – click to read how this works. In other words, if you receive on average 20-30 visitors per day, this will remain more-or-less constant.

Unless you increase or decrease the budget or change your marketing content you will receive a constant amount of visitors.

In a great many cases, the amount of traffic that converts (conversion rate) is also consistent or stable.

The point is that you can use metrics to:

  1. Reverse engineer (predict) how much revenue will be generated based on current and historical traffic.
  2.  Perhaps more importantly – know when the numbers are too low, when your efforts are not succeeding; and when you need to add or change traffic sources and marketing strategies.
  3. Businesses rely on predictability. Metrics measure what is actually happening and are used to calculate:
    • Predictions
    • Signals that additional actions to generate revenue need to be taken
    • Validation of continued attempts to generate both traffic and ensure that the traffic is converting. Especially as there are multiple sources of traffic, which needs to be measured per channel (see below).

The last point in this train of thought is that there are multiple channels (traffic sources) through which your customers journey:

  • New traffic
  • Existing (returning) traffic
  • Nurturing (newsletters)
  • Direct cold outreach emails
  • Blog posts and articles
  • White papers and downloads,
  • SEO campaigns with different vendors; Google, Facebook, Instagram, Linkedin, etc. 

The take-home is that (conversion) metrics and customer journey analysis tells you which of these channels are working and which data is essential to creating and managing success.

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A sneak peak at increasing sales by 30% and much more

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A sneak peak at increasing sales by 30% and much more

From time to time, every CFO or HR personnel has had to answer a perplexing question: What is the ROI here? Am I getting the best bang for the buck?

There’s a lot Big Data can do: It can help organizations shore up their bottom lines by helping them review the performance of their departments, gauge the outcomes of their services, track loyalty of their customers and even help weed out non-performing human resources.

Here’s a look at hard numbers on what it means for manufacturers, retailers and pretty much anyone who has a company to run.

Cutting costs and keeping your organisation lean using Big Data

An early adopter of the technology, Germany’s Federal Labour Agency or ‘’BA’’ in short, is tasked with the finding jobs and providing support services for the unemployed.

Armed with a 54 Bn budget and 1,00,000+ employees, it has cut more than €10Bn in costs by employing big data strategies by identifying and eliminating inefficient programs, creating new targeted programs and pitching them to segmented audiences and helping disseminate and adopt best practices.  

Image Source:  Mckinsey Report

Surveys reveal that customers of BA highly appreciate the relevance and efficiency of their services. No matter the size or type of organisation, understanding where to direct your resources is crucial to profitability and the continued market-viability of any organisation.

Image source: Mckinsey Report

The above image illustrates the savings accrued in the form of increased tax collection, detection of fraud and increased operational efficiency by deploying Data Analytics. Such gains are achievable and relevant even for private businesses.

Government’s, NGO’s and businesses can all build capabilities that allow them to deploy Data Analytics to make better sense of how their resources are utilised and how well they connect with their target audience.

Data Analytics can successfully lead innovation in your company. 

Launching a new product or service always carries with it a high up-front cost in terms of time and money spent on recruitment, R&D, product development, marketing & publicity and other costs. Sometimes, with millions invested and the survival of the company at stake, here’s how some companies are using Big Data to get it right.

Pramad Jandhyala, co-founder of LatentView, had this to say,’’ Earlier, a food company would go to panel of chefs or focus groups to see what product to launch. In that model, people didn’t tell you anything until you asked a question. Now, you don’t ask any questions, only study trends.

Tapping the power of social media and data analytics, her firm helped a client decide to start a restaurant in favour of flavours from Peru, Thailand, Korea and Nepal.

She added,’’We listened in on social media conversations around which restaurants people ate at, their recommendations etc. Now companies want to see what they can learn from what people are saying rather than merely understand if they are saying good or bad things about a product’’. 

Understanding and capitalizing on trends is important to stay ahead of the game. Introducing the right product at a wrong time or marketing incorrectly to the wrong customer-set is a sure-shot way to burn cash.  

Lesson to learn? Simple: Your customers are leaving digital clues. Learn to read them.

What do the experts have to say on this?

Image Source: Mckinsey Report


Every single figure in the above image stands as a business opportunity for avenues of growth.

A  Mckinsey Report explained, ‘’Big data can help manufacturers reduce product development time by 20 to 50 percent and eliminate defects prior to production through simulation and testing. Using real-time data, companies can also manage demand planning across extended enterprises and global supply chains, while reducing defects and rework within production plants’’.


Notice, (in the above image), how costs can be slashed and revenues shoot up, regardless of where Big data is deployed in the value-chain.

In sectors like retail, some retailers like Wal-Mart have developed the capacity to mine petabytes of data on customer preferences and buying behavior, giving them the leverage to win important pricing and distribution concessions from consumer product goods companies.

The report went on to add that, ‘’Retailers across the industry are becoming more sophisticated in slicing and dicing big data they collect from multiple sales channels, catalogs, stores, and online interactions. The widespread use of increasingly granular customer data can enable retailers to improve the effectiveness of their marketing and merchandising’’.


Did you know? Here are 3 real-life examples of increasing revenue using data mining.


1. employs Data Analytics to generate “you might also want” prompts for each product bought or visited. At one point, Amazon reported that 30 percent of sales were due to its recommendation engine. Another example of this lever is using big data analyses to optimize in-store promotions that link complementary items and bundled products


2. Location-based marketing targets consumers who are close to stores or already in them. For instance, as a consumer approaches an apparel store, that store may send a

special offer on a sweater to the customer’s smartphone.


The startup PlaceCast claims that more than 50 percent of its users have made a purchase as a result of such location-based ads. Nearly 50 percent of smartphone owners use or plan to use their phones for mobile shopping.


3. Retailers can use big data to integrate promotions and pricing for

shoppers seamlessly, whether those consumers are online, in-store, or perusing a



Example, Williams-Sonoma has integrated customer databases with

information on some 60 million households, tracking such things as their income,

housing values, and number of children.


Targeted e-mails based on this information obtain ten to 18 times the response rate of e-mails that are not targeted, and the company is able to create different versions of its catalogs attuned to the behavior and preferences of different groups of customers.

Source: Mckinsey Report




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Improving Customer Experience

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How does deeply understanding your customer’s journey unlock revenue?

You’re in the business of satisfying your customer with a great experience. But how do you know if you’re delivering?
customer experience

Businesses are spending more on promotions than ever before, but most struggle to know what actions to take and where to focus their efforts.

When customers don’t buy, do you know why? Can you improve the buying experience? The marketing? Differentiate the product? Without an idea of what your customers are feeling or how they are engaging, it’s hard to make informed decisions.

Privacy laws are making it clear that it is important to collect your own data related to your own customers. Trusting third parties like Facebook or Google to give you these insights is now a significant risk. More and more businesses are choosing first party solutions.

Getting sales is great, but how many sales are you losing because you are not seeing bottlenecks in the customer journey? Knowing what delights and discourages your customers is next to impossible without controlling your own data.

Customers have 5 touchpoints, sometimes spanning days or weeks, before making a purchase and you following up. Are you measuring these touchpoints to increase conversions?

Moving The Needle And Driving Revenue

Not tracking your customer’s journey is like tossing money out the front door.
For any business, tracking the customer journey is essential. As Peter Drucker said “If you don’t measure it, you will not improve it”. All too often, you’re forced to guess how your customers will buy. You don’t know what’s working and what’s not. Consequently, you have no idea what to optimize. You’re wasting money.

You should guide customers through their journey. Provide support when they get stuck. Learn what makes them leave and why you can’t close the deal. Don’t rely on just Google’s or Meta’s report.

Track The Customer Journey. Own Your E-Commerce Dashboard.

Customer Journey by Opentracker makes it easy to control the customer’s journey. We help you get a clear picture of how you can improve what you’re doing – and where you need to make adjustments. The result? You get more sales.

Our platform helps track customer behavior and delivers essential insights. Whether it’s tracking new leads and acquisition or understanding retention and the factors that drive repeat purchases. Customer Journey provides clarity and actionable insights that boosts your ROI.

With Customer Journey, you will get the data insights you need faster, more reliably, and at a fraction of the cost. Our team is dedicated and knows how to work with e-commerce businesses. Our consultants will be there to make sense of your metrics so that you can make data-driven decisions.

Delight your customer. Happy customers pay more, more often.

Learn how Customer Journey by Opentracker can help boost your sales!

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The Customer Is King – Busting The Myth

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The Customer is King – Busting the Myth

Bad Clients

Clients are the lifeline of any business as demonstrated by the adage “The Customer is King”, — a corporate mantra that places the client at the center of business success. 

customer is king

Prominent gurus encourage businesses to  shower their customers with attention and pampering. As a result, most businesses find it hard to say “no”. Bad clients can wreak havoc on your business. Metrics such as the cost-per-acquisition (cpa) or the average time spent will suffer.

The best defence? Ensure that your business is not attracting bad clients to begin with! 

How do we do this? By understanding your online customers’ behavior, making informed business decisions backed by reliable data, and tailoring your marketing and buyer journey to ensure the right message is conveyed to the right audience at the right time.

The first step is to build a selection of ‘bad client avatars’ and avoid them in future. The following are examples of the types of bad clients to avoid.

The “Unreasonable”

Unrealistic expectations or demands. Never satisfied. Very challenging. “Unreasonables” put pressure on your resources and drain morale.
Tip: it’s important to set realistic expectations in the buyer journey so the potential buyer knows what to expect. Buyer journey insights will help accomplish this.

The “Low-Baller”

Low-Ballers are looking for the lowest price possible. They are not interested in quality or value and are frustrating to work with as they are only interested in the cheapest options. Identify your target market’s income bracket and ensure that your ads and content target your market.

The “Flake”

When you’re marketing your business, cost-per-click is everything. Avoid attracting clicks from people who are not interested in your product or service. Flakes are time-wasters who click on your ads without any intention of buying anything. They might be curious, or they might just like clicking on things. Flakes increase your cost-per-click without bringing any value to your business. The best way to avoid attracting Flakes is to create ads that are relevant and targeted to your ideal buyer persona. If you’re attracting a lot of clicks but not getting many conversions, it might be time to reassess your targeting strategy.

Opentracker allows you to identify the types of clients who are visiting your website and understand their behavior in real time. This insight empowers you to optimize your strategy and buyer journey to ensure that you are attracting the right kind of attention from the right people. Bad clients can have serious effects on your business, so it’s important to be able to identify them early on. By using Opentracker to understand your online customers’ behavior, you can make sure that you’re doing everything you can to weed out bad customers, improve buyer experience, and increase your conversion rates.

Click to book a discovery call today!

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Everything You Need to Know About Customer Journey Map


Everything You Need to Know About Creating a Customer Journey Map

Customer journey maps are one of the most important tools that businesses can use to understand their customers. A customer journey map is a visual representation of the customer’s experience with your business, from the moment they become aware of your brand to the point where they become a repeat customer. In this article, we will discuss what customer journey maps are, why they’re important, how to create your own customer journey map, and how to use a customer journey map to improve your marketing strategy.

customer journey map


What is a customer journey map and what are its benefits?

As we mentioned, a customer journey map is a visual representation of the steps that a buyer takes to become a loyal customer. By mapping out the customer’s journey, businesses can identify areas where they can improve the customer experience and make necessary changes to their marketing strategy. Additionally, customer journey maps can help businesses understand what motivates their customers and what barriers they face when trying to purchase a product or service. It allows a business to gain insight into customer perceptions and experiences at every stage of the customer journey.

There are several reasons why customer journey maps are so important for businesses. First and foremost, they help businesses to understand their target audience better. By understanding the customer’s needs, wants, and motivations, businesses can create a marketing strategy that is tailored to the customer’s unique perspective.

Additionally, customer journey maps can help businesses to identify potential areas of improvement in their customer service. By unpacking and understanding the customer’s experience, businesses can make changes to their processes in order to improve the overall customer experience and build lasting relationships.

Finally, customer journey maps can help businesses to create more targeted marketing campaigns. By doing the work to understand the customer’s thoughts, feelings, and motivations at every stage of the journey, businesses can create marketing campaigns that are more likely to speak directly to their target audience and convert leads or once-off visitors into lifelong customers.

How to create a customer journey map for your business

The first step is to identify your business’ goals. What do you want to achieve with your customer journey map? Do you want to improve your customer service? Do you want to create more targeted marketing campaigns? Knowing what your goals are upfront will allow you to create a customer journey map that is filled with the information you need to take actionable steps towards improving your outcomes.

Once you have identified your goals, you need to gather data. This data can come from a variety of sources such as customer surveys, interviews, focus groups, and customer service logs. Using an intuitive software like Opentracker to track your website traffic will give you valuable insight into how customers interact with your site so that you can identify exactly where you need to make changes to improve their experience.

Once you have gathered your data, it’s time to start mapping out the customer journey. Begin by creating a list of all of the potential touchpoints that a customer might have with your business. These touchpoints can include everything from becoming aware of your business and first interactions to purchasing a product or service and becoming a returning customer. Break down each touchpoint into smaller steps, so you can really see the detail in the journey start to emerge.

Once you have listed out all of the potential touchpoints, you need to create a timeline of the customer’s journey so that it is a logical progression. Include both the positive and negative experiences that the customer might have along the way. Additionally, be sure to include the customer’s emotions and reactions at each stage of the journey.

After you have created your timeline, you need to start adding detail to your map. Include information such as the customer’s needs and wants at each stage, the steps they might take to progress in the journey, and the channels they use to interact with your business at every stage.

Finally, you need to analyze your customer journey map. What does it tell you about your business? Are there any areas where you can improve the customer experience? Are there any areas where you need to make changes to your marketing strategy? By analyzing your customer journey map, you can gain valuable insights into your business and make the necessary changes to improve your customer’s experience and target your advertising and campaigns.

What are some common mistakes businesses make when creating customer journey maps?

One of the most common mistakes businesses make when creating customer journey maps is failing to include all of the potential touchpoints. It’s important to remember that customers can interact with your business through a variety of channels, so be sure to include all potential touchpoints in your map.

Additionally, businesses often make the mistake of failing to include the customer’s emotions in their customer journey maps. Customers have emotional reactions to every stage of their journey, and your map should reflect each one of them so you can have a complete picture of the customer’s experience and be ready to respond accordingly.

Obtaining data can also be a major challenge for businesses. While there are a variety of data sources available, it can be difficult to gather all of the necessary data and analyze it correctly. As a result, businesses often make decisions based on incomplete data, which can lead to sub-optimal results. To avoid this mistake, it’s important to work with a team of experts who have experience in obtaining valuable, real-time data that you can rely on to make informed decisions.

Opentracker allows you to track your website traffic in real-time so that you can obtain the data you need to create an accurate and actionable customer journey map. Our software empowers you to automate your customer journey reporting so that you can improve efficiency and maximize your resources. With direct access to our data analytics team, you can be confident that the data you’re using to create your customer journey map is realiable and will form the foundation of one of the most useful assets your business can own – a full picture of your customer’s journey that is ready for you to act on.

We hope you enjoyed reading this blog post

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Increase the bottom line with Offline Attribution Metrics

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Increase the bottom line with offline attribution metrics

Offline attribution has always been a measurement challenge. We use the example of a company we work with; StudyPoint, founded in Boston in 1999, deliver consistent and reliable academic and test prep results to families alongside individualized needs assessments and online homework tools.

By ‘offline attribution’ we mean the measurement of an important event that takes place offline.  For clarity: this as opposed to online events which are relatively simple to measure: ad clicks and campaign clicks are easy to collect through utm tagging Other examples of online touchpoints which can be measured are: contact forms and sign-up or login events, downloads, webinars, podcasts, and app visits. 

The most common examples of outreach which are difficult to measure are telephone calls, billboards, radio magazine and newspaper advertisements, and direct conversations at conferences and events. 

It is important to know how these types of outreach perform because knowing what is effective determines budget spend. Without insights, there is guesswork involved.

The problem is that, if at any point in the workflow, an offline activity takes place, this disrupts the entire measurement flow. Specifically the ability to attribute success (conversion) to a channel (source and medium). Another example is knowing which materials are converting visitors prospects or leads. Read about conversion and ROI here.

The goal in attribution is to measure which marketing efforts are having the best effect. This information tells you how to allocate your Marketing spend.  

For this article, we have chosen an example of a company for whom we designed a workflow that takes offline attribution into account and delivers a KPI-metric that can be measured.  Please get in touch with us if you would like to learn how we can solve this problem for you. 

Our case study involves a company that provides tutoring for children. StudyPoint is in the business of helping kids achieve their academic goals through personalized, one-on-one tutoring programs.

In this case one type of client (the parents) make arrangements for children (end-users). So there is immediately some complexity as there are a minimum of four parties involved (StudyPoint team, parents, students, tutors).

Because it’s a high ticket offer and the parent/ caregivers want to know who is tutoring their child, one or more phone calls take place. 

In terms of conversion, during the phone call the payment details are arranged. Once the offline event, i.e. the phone call, has taken place, the measurement of touchpoints can resume. 

Solution: we designed and deployed a seamless workflow to capture all activities and generate automated reporting. 

We hope you enjoyed reading this blog post

Book a call today to learn how Opentracker automatically measures and improves your (partner's) customer metrics!

How one company lost 42 million Pounds and what this means to be GDPR compliant.

How one company lost 42 million Pounds and what this means to be GDPR compliant.

With the constant stream of sound bites surrounding GDPR, one could be forgiven to assume that most companies would have taken care to update their privacy policies and inform their customers about this transition.

Shockingly, according to a ISACA survey, not only are most companies unprepared, but only around half of the companies surveyed (52 percent) expect to be compliant by end-of-year 2018, and 31 percent do not know when they will be fully compliant!

Let that sink in…52% of companies, as of this very moment, do not comply with the (GDPR) General Data Protection Regulation.

Some of the biggest Tech and social media giants like Google, Facebook, Instagram, and WhatsApp have already been slapped with lawsuits for violating the GDPR law that went into effect on May 25, 2018.

If found guilty, EU regulators can impose fines upto 4% of global annual revenues; numbers that could easily run into the billions.

In 2015, TalkTalk, a British telecom company failed to securely store customer data and in the aftermath of the loss of data due to a cyber attack, not only was the company fined around £400,000 by British regulators, but it also lost more than 1,00,000 customers and 42 million pounds.

Such instances of data breach or data mishandling tell us the devastating impact of under -preparedness – lost revenues, dwindled customer base, negative publicity and heavy regulatory fines – enough to bring any company down to its knees.

India with an active customer base of 240 million was the largest audience country for Facebook. In the wake of the scandalous Facebook-Cambridge Analytica affair, Facebook revealed that personal data of 5,62,455 Indian users was improperly shared.

What was the effect of this revelation?

Velocity MR, a market research company, released a survey that

that found that after the Facebook security breach, 24% of users started sharing ‘’lot less’’ data, while 7% stopped sharing data altogether.

Let’s take a moment here and do some quick back-of-the-envelope-math and what this might have cost Facebook.

7% of 240 million works out to 16.8 million people avoiding Facebook. Losing

17 million customers roughly translates to Facebook shutting down operations in both Sweden and Austria!

That’s a lot of advertisement money to go down the drain.

Not only this, CEO Mark Zuckerberg had to endure negative publicity and a televised Q&A grilling session with legislators on both sides of the Atlantic.

With the latest lawsuit over GDPR non-compliance, Facebook with its deep pockets could survive another round of missed opportunities in advertising revenues and regulatory fines.

But honestly, how many businesses can afford incidents like this?

A study by Ensighten revealed that one of the reasons firms seem unprepared for GDPR, could be the lack of consensus over who is responsible for data protection within a business and how to go about it. What should be the first step?

Ryan Wain, chief marketing officer at Unlimited Group, advised decision makers to undertake a full audit on data held by a business.

He added: “Possibly the most important consideration is to avoid viewing GDPR compliance as a process with a hard and fast endpoint. Rather, it will be an on-going journey as you gather and process new data moving forward.”

It’s time to be GDPR compliant

For more than 15 years, we have been helping companies take smart decisions using data analytics. Now, we are also helping small & medium sized businesses stay compliant with the GDPR law.

The GDPR law runs to 11 chapters and 173 recitals and let’s face it, who has the time to sit down and pour through the contents with a magnifying glass?

But the good news is that we have you covered. Here are 3 things that you should absolutely know.

  • Geographical location: Businesses in the EU are subject to GDPR—even if the data they’re accessing is processed outside of the EU. The reverse is also true. If you’re a company processing the data of EU citizens (either to offer goods and services, or to monitor behavior taking place in the EU)—it doesn’t matter where you’re based, or where you’re processing the data. You still have to comply with GDPR.
  • Greater Penalties for noncompliance: The maximum fine for noncompliance with GDPR is up to 4% of annual global turnover, or 20 million euros—depending on which is greater.
  • Explicit Consent required: Consent has to be given in an easy, accessible way before processing a person’s data. You also have to disclose the purpose for that data processing and make it as easy to withdraw consent as to give it.

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Here’s a quick 3-min guide of how GDPR affects your business.

At a one time fee of just €395, OpenTracker’s Quick-scan Analysis can do a thorough Data audit and help you identify non-compliant features and help you keep avoid the dangers of expensive regulatory fines.

With an expert team at your disposal, we can help you identify the loopholes and shortcomings in the present data handling regime in your company, the state of preparedness of your business to deal with the GDPR provisions and also chart out a custom plan to help your business become and stay GDPR compliant.

We have already helped hundreds of companies with GDPR-compliance. Have any questions? Why not ask? We would love to hear from you.

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Other titles for your consideration:

-83% of companies are in trouble due to GDPR non-compliance. Where do you stand?

-Facebook and Google are facing GDPR lawsuits. Is your company GDPR compliant?

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