Analytics, Visualization, Reporting 2018-03-13T16:28:47+00:00
Quantilus Analytics and Visualization

Analytics, Visualization and Reporting – Data Warehousing, Business Intelligence and Big Data

Turn Your Data into Opportunities

Your data is your business. But it’s a challenge to make use of all the unstructured information that’s available to your company—whether it’s internal data such as emails and click streams, or data that originates outside your organization, such as photos and social media content. Quantilus can help you collect, organize, and analyze this trove of information so you can make better business decisions. With a plan in place, you can discover connections among disparate streams of information. From there, you’ll be in a position to:

  • Increase your competitiveness
  • Gain new insights into your customers and industry
  • Adapt more quickly to changing business environments

We’re experts at sifting through large volumes of data to find the hidden connections that you can turn into opportunities. We use our deep industry experience to create models to help you spot trends, identify hidden relationships, and find new insights to create more value for your business.

FEATURED WORK

SA Global - Social Media Popularity Scoring

SA Global – Celebrity Social Media Popularity Scoring

SA Global - Social Media Popularity Scoring
OSU Student Analytics

Ohio State University – Student Performance Analytics

OSU - Student Performance Analytics Dashboard
Market Research Data Visualization

OSG – Market Research Data Visualizations

Market Research Data Visualization
Bed Bath Beyond - Centralized Customer Database and Reporting

Bed Bath and Beyond – Centralized Customer Database and Reporting

Bed Bath Beyond - Centralized Customer Database and Reporting
NYMAGIC - Claims Analytics for Marine Insurance

NYMAGIC – Claims Analytics for Marine Insurance

NYMAGIC - Claims Analytics for Marine Insurance

TECHNOLOGY STACK

Some of the frameworks and tools that our development teams have used recently. A list that grows by the day.

RELATED RESEARCH

Relevant, interesting and current curated research content in the field.

What is the Internet of Behaviors?

January 15th, 2021|Categories: Data Science and Analytics, Emerging Tech - AR, Cloud, Blockchain|

The Internet of Behaviors is one of the top tech trends of 2021. This is partly due to the pandemic shifting how consumers interact with brands, consequently requiring companies to adjust how they engage with consumers. However, before we dive into what the Internet of Behaviors (IoB) is, one must first understand the Internet of Things (IoT). IoT refers to any device that is connected to the internet such as smartphones, virtual assistants like Alexa, fitness wearables, appliances, TVs, and more. To learn more about the Internet of Things, check out our article explaining the technology and how it’s used.

Defining the Internet of Behaviors

In short, the Internet of Behaviors utilizes collected user data to influence behaviors. So, what does that really mean?

Essentially, companies analyze the data collected from a variety of Internet of Things devices. Businesses then use this data to change consumer behavior. Some of the data companies may record include a person’s geographical location, time spent on a particular app, and what time a person wakes up among many other pieces of information.  More often than not, the goal of changing consumer behavior is to get them to buy or engage a particular product or service. However, this technology can also be used to change the behaviors of other stakeholders, including employees to ensure they are following correct procedures.

 

How is the Data Collected?

Data from consumers can be amassed from numerous sources, including a company’s website, social media profiles, sensors, telematics, beacons, health monitors (ex. Fitbit), and a wide variety of other devices and places.

Each of these sources collects different pieces of data. For example, a website might track the number of times a particular user views a specific webpage or how long they stay on a page. Furthermore, telematics might monitor how hard the driver of a vehicle brakes or the driver’s average speed.

 

What Happens to the Collected Data?

Businesses collect and analyze the data for a multitude of reasons. These reasons include helping companies make informed business decisions, personalizing marketing tactics, product and service development, driving user experience design, and more.

To help analyze this data, companies set benchmarks in place. Meaning, when a particular action(s) is performed by a user, the company then begins to persuade the user to change their behavior. For example, if the user returns to a company’s page selling men’s skinny jeans more than three times, the digital retailer might serve the user a pop-up ad offering them 25% off a pair of jeans. The digital retailer is aiming to get the consumer to buy the pair of jeans.

Another example is that if a driver regularly brakes too abruptly, telematics can alert the driver to make them aware of a recurring issue. In this example, behavior telematics attempts to change the driver’s braking habits.

 

Combining Data from Various Sources

An additional aspect of the Internet of Behaviors is combining data from different sources and analyzing it to make a decision. Pulling data from various sources provides companies with the ability to create in-depth, user profiles for each user. These profiles can then be examined to determine the best course of action to take regarding the user.

For example, a consumer named Ted comments on a picture of a new sneaker on brand’s Instagram page. A few days later, Ted then heads to brand’s website and looks at the same shoe. A week passes, and Ted is on YouTube watching a commercial featuring the shoe. Meanwhile, the brand is tracking all the touchpoints Ted makes along the way with the digital content. The brand can then synthesize this data and develop a course of action on how to convert Ted into a customer, since the brand identified that Ted has a high-interest in its shoe. Actions the brand may take include remarketing display ads or emailing Ted a discount code. 

Another example is if a consumer records their workouts on their smartwatch. When the watch is in workout mode, it can track the user’s heart rate. Therefore, when the user’s heart rate reaches or exceeds a certain level, the watch can send a notification to the user reminding them to drink water. This is beneficial for the user because they are reminded to hydrate and cool down, while the company is provided with valuable insight of how the watch is used.

 

Ethical Implications

The Internet of Behaviors is an innovative technology for businesses; however, the tech does come with ethical concerns. The majority of worries stem from user and consumer privacy.

Controversy surrounds the intrusiveness of the data collected. This is because the data can be obtained from countless locations where consumers may not even realize they are being tracked. Furthermore, the sensitive nature of some of the collected data is also a concern. For example, if a consumer is wearing a smartwatch, the data collected by the wearable technology can be highly private information, such as the consumer’s heart rate. Therefore, users may not be aware of all the data the watch is collecting, nor how the data will be shared (or sold) or will be used to influence their behaviors.

There is also the concern about cybersecurity. Businesses are currently flocking to the cloud to use as their company data warehouse. Consequently, hackers and cybercriminals may attempt to gain access to this data and do with it as they please, including leaking or selling the sensitive data.

Currently, laws regarding the Internet of Behavior vary widely, but we should see more consistency in the coming years.

 

Conclusion

The Internet of Behaviors provides companies with cutting-edge ways of marketing products and services, along with influencing user and employee behaviors. This technology is extremely beneficial for businesses since they can optimize their relationship with the consumer based on the collected data. Behavioral data technology continues to evolve. However, with the proliferation of new IoT devices, the debate over what constitutes essential data and responsible use is just getting started.

Interested in learning ways to implement data science and analytics in your business? Contact us at info@quantilus.com for a consultation to learn how we can help.

Implementing Cybersecurity During The Coronavirus Pandemic

June 29th, 2020|Categories: AI, NLP, Machine Learning, Data Science and Analytics, Emerging Tech - AR, Cloud, Blockchain, News and Announcements|Tags: , , |

Quantilus Innovation continually monitors cybersecurity news and developments that could impact companies like ours—and yours. Our team has compiled useful information and resources below to grow awareness of potential threats and prevent any compromise to systems.

Most of the working world continues to be a participant in a massive experiment on distributed, remote work structures. However, the coronavirus pandemic combined with the unprecedented, massive work-from-home shift creates another kind of threat—a breeding ground for cyber criminals to capitalize on vulnerabilities. The public health crisis has sparked a rise in cyber incidents, from phishing scams and malware to VPN DDoS attacks and vulnerabilities with teleconferencing and cloud SaaS applications, so here’s the latest on what you need to know:

Contact us to discuss penetration testing and other assessment options.

The cybersecurity specialists at Quantilus can identify your company’s susceptibility to specific external and internal threats and collaborate to mitigate the short-term and long-term risks. Call 212-768-8900 or email info@quantilus.com.

Pricing Strategies for SaaS Products

August 18th, 2018|Categories: Data Science and Analytics, Web Development, Marketing, SEO|Tags: , , , |

The Pricing Decision

Your SaaS product is finally ready for market! Hooray! You’ve spent your nights and weekends coding, and have deployed what you think is the production ready version. And now comes one of the hardest decisions you have to make – how do you price the damn thing. Its obviously worth millions (billions?) to you, but how much are your potential customers willing to pay?

For most of your target customers, software is productivity not pleasure. There is no joy in software installation, integration or instruction. Companies undergo the trouble because they have to: the inertia against change is substantial. A lower introductory price might help to work down that resistance, and help keep you on the short list during a long sales cycle. Free beta installations are also part of minimizing the initial implementation resistance.

But once the customer is yours, the situation switches in your favor. The customer is still reluctant to switch to something new, only now they’re afraid to switch from you, and not to you. They would much prefer to add the functionality of your upgrades without having to learn how to use new software. From your perspective, price sensitivity is much lower as comfort and ease factors increase. So we might raise our upgrade price accordingly.

While acquiring new customers is great, you also need to realise that once a price expectation is set it is very difficult to move away from that. So if you price too low to penetrate the market you may end up losing money when you are unable to raise prices later. We will now discuss some of the common strategies adopted by companies in making the right decision.

Pricing Strategies

Pricing strategies fall into one of several categories. While most companies with multiple products will use multiple pricing strategies, each product has a dominant pricing strategy. The common strategies are described below:

Cost Based

Vendors price their products based on the variable cost of goods. They may use rules of thumb like $10 over cost or 3X manufacturing cost. Many hardware manufacturers used to do this. Software distributors often use this method. However, this strategy does not consider the value to the customer. Also, you must be a low cost producer to win.

Value Based

This strategy is based on the performance/price ratio of a product. Vendors using this strategy offer a performance premium at a given price point (e.g. optimized workflows and document storage at lower prices). They can also offer a choice of performance options at different prices (e.g. good, better, best products at three different price points). They do not cut price to raise the value ratio.

Meet the Competition

This is often a promotional ploy and not a long-term strategy. However, many companies will have products that are directly comparable to a competitor’s offering. Competitors or customers will force these companies to play this game. Competitive upgrade and “suite” prices are two examples.

Market Skimming

Market skimming (as in “skimming the cream off the top”) strategy involves a new product in an emerging market setting a high price point to maximize revenues before the competition catches up.This was a common strategy for some engineering software companies and super-minicomputer vendors. Companies that are “first with the most” may be able to do this until a competitor catches up – or catches on.

Market Penetration

This strategy is the opposite of a skim strategy. In this case a vendor offers unheard-of-value at a price point. In the early 80’s, workstation vendors offered $50,000 products that outperformed $250,000 minicomputers. At a time when IBM’s Rational Suite was being sold for thousands of dollars a seat, Atlassian began offering JIRA and related products at tens of dollars or lower. At launch Atlassian’s functionality and performance was significantly lower than the competitions’, but their penetration strategy enabled them to overcome that hurdle. This strategy expands a market by opening new, price sensitive segments. If the price is so low another entrant cannot make money, this is also called pre-emptive pricing.

Follow the Leader

Many companies have a commanding lead in their product category like Oracle in databases, Microsoft in PC operating systems. In any product segment, any direct competitor will be compared with the leader. Many times, direct competitors have to follow what the leader does in pricing. IBM used to be a price leader in mainframes. Other mainframe companies followed their pricing lead and would price somewhat below IBM’s price points. When IBM lost their leadership position in the PC industry, prices collapsed.

What is the Best Pricing Strategy for Your SaaS Product?

In the case of your specific SaaS products, ask yourself the following questions:

  • Is there a dominant player in your market, and therefore does the Follow the Leader strategy apply?
  • Is the product or the concept new or radical enough to justify a market skimming strategy?
  • Are customers price sensitive and therefore is pricing a key factor in market penetration?
  • Can you quantify the value that your SaaS product is adding to the business processes of your customers?
  • Has the established competition already set the price range? This is especially true for relatively mature markets where you have little price flexibility and have to compete primarily on functionality.
  • Are your costs directly correlated to your revenues? Will increasing your base costs (and therefore quality/throughput) increase penetration?

The best pricing strategy for the product will usually to be a combination of diferent strategies based on answers to the questions above. For most products surveyed as part of this study, the strategy was a combination ot Cost Based, Value Based, and Competitive Pricing.

Costs and Value – Considerations for Pricing

Relevant Costs

Strategic pricing is a means of making a profit today, not of recovering costs spent a year ago. Therefore you should not use the cost of developing Version 1 as the basis of the price of Version 1. Instead, you will have to use the cost of developing Version 2 as the basis of the price of Version 1. In other words, the price of your SaaS product should be based on the cost of developing the improvements and enhancements that you can foresee for the next significant upgrade.

The other costs that need to be covered are the manpower costs incurred for actual implementation (customization, development of templates, etc.), training of users and administrators; and the marketing and administrative costs incurred.

Elements of Value

The initial step in establishing a value for the product is to determine whether a software product should be considered as a “tool” or as an “asset” that should be managed according to a dynamic usage metric.

Software products that that fit the “tool” category are usually relatively low cost and/or the product’s value is driven by the ability to solve potential problems at any time rather than actual use. Typical metrics are either node based or named user based.

As value increases, products are viewed more as assets. In order to manage these assets well, dynamic licensing metrics need to be chosen that closely approximate use or “work accomplished”. These software products can be segmented into categories according to functional characteristics and usage characteristics; different licensing metrics may fit each of these segments. The dynamic license metric most commonly chosen is the number of transactions, as this precisely measures the “work accomplished”.

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For more interesting strategic discussions about SaaS product development and marketing, contact us at Quantilus.

Using Longitudinal Data Systems to Reexamine Timeless Problems

January 12th, 2018|Categories: Content Mgmt and Publishing Tech, Data Science and Analytics|Tags: , , , |

This is a proposed research approach that attempts to shed light on the cyclical nature of education problems and our inability to adequately address these problems. We continually examine bits and pieces of the education process to understand the whole. This paper suggests that the use of longitudinal data systems be utilized as a holistic approach to reexamine issues regarding the degree of efficiency of our schools. Specifically, addressing the dropout problem through intervention strategies that are implemented at the wrong time will never be successful. Using longitudinal data systems with complementary analysis techniques, such as survival analysis, may help resolve some of the questions that have plagued the American education system for the past century.

Click Here to Download Paper

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