Where Most Businesses Go Wrong with Machine Learning


Over the past few years machine learning has continued to prove its worth to enterprise.

Over 70% of CIOs are pushing digital transformation efforts, with the majority of those focusing specifically on machine learning.

Almost the same number (69%) believe decisions powered by data are more accurate and reliable than those made by humans.

Still, some companies struggle to get value from their machine learning processes. They have trouble finding talent, and their projects are slow to reach ROI.

The problem isn’t with machine learning – it’s with the company’s approach.

The Pitfalls of Reinventing the Wheel

Sometimes companies get so caught up in new technology that they forget what business they’re in.

They don’t need to build complex data science systems or experiment with new types of algorithms or push machine learning as a science forward.

What they need is to extract actionable insights from their data. Companies should be aware of and maintain their data infrastructure, but that isn’t their primary focus. Their focus is running their core business.

However, the majority of companies approach machine learning with a misguided idea of what makes it work.

They assume their specific business needs mean they have to start from scratch, to build a machine learning solution from the ground up.

These companies get bogged down by mechanics without enough thought for how the output will be put to use.

As a result, they wind up building the wrong kind of infrastructure for their machine learning project. One common place this flawed infrastructure shows is in the type of talent chosen.

Companies go straight for high level data engineers who build machine learning software.

That’s a large – and often costly – mistake. In an enterprise context, data engineers aren’t as useful as applied machine learning experts with experience in turning data into decisions.

Imagine a business traveler looking for the fastest route to a meeting in a new town. Would they have better luck getting directions from a civil engineer or a taxi driver?

The civil engineer knows how to build functional roads, but they don’t necessarily know a specific city’s streets or layout.

The taxi driver knows how to use the streets to get results: arriving at the meeting in time despite traffic, construction, and seasonal issues.

This might sound like a silly example, but it’s exactly what businesses do when setting up machine learning programs.

They focus too much on the “how” (building data systems) and not enough on the why (what business goals the system needs to fulfill).

In other words, they think they need civil engineers when what they really need is a good seasoned taxi driver.

The result is wasted resources and higher program failure rates. A big enough failure can also risk future projects when leaders blame the technology rather than the flawed execution.

Why Companies Get Stuck in A Rut

There’s a very good reason why otherwise smart people make mistakes with machine learning: it’s complicated.

Artificial intelligence and machine learning are incredibly complex topics with thousands of subdisciplines and applications.

There is no “catch all” job description for someone who can do all kinds of machine learning.

Those few people with experience in several phases of the data-to-decisions pipeline are high-level, in-demand experts who very probably won’t take an average enterprise position.

On top of this, executives aren’t always sure what type of talent they need because they aren’t clear on what their data science needs are.

They hire data engineers, give them vague directions to “increase efficiency”, then get frustrated when they don’t see results.

Even the best machine learning system can’t create value without working towards a goal.

Getting More by Doing Less

Laying the groundwork for successful machine learning is a case of “less is more”.

Don’t get caught up in high-level, experimental machine learning which seeks to advance the science unless there’s a good business reason (and for enterprise purposes, there almost never is).

A PhD in artificial intelligence and experimental mathematics is not necessary to run a productive enterprise machine learning program.

Instead, find the right experts: statisticians, data intelligence experts, applied machine learning engineers, and software developers with experience in machine learning software.

The truth is, most businesses won’t need to build a machine learning program from scratch. There are many tried and tested solutions available that can be customized to fit a specific company’s needs.

Better yet, they’ve been tested by others at their expense. These tools remove the need for those high-level machine learning construction experts.

Practical talent choices and existing machine learning tools can make the difference between project success and failure.

Using them helps companies get to data quality assurance and usable results faster, meaning the project reaches ROI sooner. The project is more likely to succeed, and future projects will have an easier time winning support within the company.

In short, don’t hire the civil engineer to build roads when there are several existing routes to get where the company is going. The taxi driver is usually the better choice for the job.

Staying on Target

Most importantly, remember the core business and focus on tools that support that instead of distracting from it.

Always build machine learning systems around business objectives. Have specific issues or opportunities to address with each tool, and be sure everyone on the team understands the goal.

When machine learning is treated as a tool rather than a goal, companies are much more likely to see value from their investment.

There’s a wealth of machine learning tools out there to use- but sometimes it’s hard to manage incoming data from different software. Concepta can help design a solution to put your data in one place. Schedule a free consultation to find out how!

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Mobile Enterprise Series: Native Mobile Apps


Hybrid mobile apps have come a long way, but native mobile apps are often seen as the “gold standard” of mobile apps. As actual downloaded software they offer more functionality than the current breed of hybrids.

There are even times when they’re the only practical option for an app. User experience is growing in importance, and native apps definitely deliver in that area. Still, there are limitations to be aware of before investing in native.

What It Means to Be Native

A “native mobile app” is built for a specific platform (Android, iOS, or Blackberry) using that device’s specific programming language. iOS relies on Objective-C or Swift, for example, while Android favors Java and Blackberry uses C++. These apps are downloaded from an app store or another hosting location and live on individual devices.

The Power of Native

Native apps have a lot of advantages over other formats.


Because they’re written for a specific device, native apps have none of the compromises developers have to make when building hybrid apps. They open faster, handle data-intensive or complex functions well, and generally have superior performance.

Access to device functions

Native apps can potentially access all a device’s functions, whether they’re hardware or other apps. This includes the camera, microphone, flash, compass, accelerometer, gyroscope, calendar, alarms, phone book, and any other feature the user allows. They also offer push notifications. With up to a 65% open rate, push notifications are incredibly useful for keeping users engaged with an app. Hybrid apps can reach more device functions than ever, but they have structural limitations when it comes to full access.


Offline access is in demand, especially in emerging markets and for business travelers. Since they live on a device, native apps have excellent offline potential. Users can access selected functions outside of a coverage area with the assurance that the app will update once the connection is restored.


Device specific development means better compatibility, so native apps are less prone to failing. They have a relatively high rate of availability when compared to hybrid or web apps.

User experience

Reliability, speed, and availability combine to create a high quality user experience. Plus, native apps use familiar device conventions that make navigation and trouble-shooting intuitive for fans of the platform.

Found in App Stores

The first hurdle in enticing users to download an app is helping them find it in the first place. When it’s in the app store, it turns up in searches by customers looking for similar apps. Potential users can view ratings and reviews from current users, which has been shown to increase consumer confidence. There’s also the peace of mind inspired by an app’s presence in the App Store since there are quality guidelines imposed by the App Store itself.

Better vendor support

Building an app is a significant investment for companies. Native apps have more assurance of long-term vendor support. They offer platform-specific Software Development Kits (SDK) that make development easier and increase the final quality of the app. Stability like this can be a major draw during a hectic digital transformation process.

Limitations of Native Mobile Apps

If there are so many advantages to native apps, why aren’t all apps native? There are some unavoidable drawbacks to native apps.


Native apps are more expensive to develop and maintain. They have a longer development cycle that needs a team of platform specialists. Because they only work on a single device, companies that choose native must build a different app for each platform they plan to support. This is potentially a serious problem for creators of enterprise apps.

App Store approval

Being in the App Store reassures users for a reason. The approval process can be complicated, and there’s no guarantee that an app will be accepted at all. While it’s not an everyday problem, changing guidelines can result in last-minute changes to what was a finished app.

Download barrier

Users need to download the app to use it. The average American downloads one or two apps a month, so competition for device space is fierce.

Support issues

When users are working on different devices, app support and customer service become complicated.

When Native is the Best Choice

Despite the higher cost, there are times when only a native app can handle the project at hand.

If connectivity expected to be an issue, few hybrid models can match native offline performance.

Games and other processing-intensive apps need the better performance of native to provide the kind of user experience that keeps retention rates high.

Also, when an app needs to use a lot of specialty hardware features native is the logical choice.

That applies to cross-app interactions (when the app needs to access other apps like calendars, alarms, and contacts) as well.

Native apps translate into performance. Hybrid apps have many useful applications and there are many use cases where the difference in speed is negligible, but it’s important to know when there’s no substitution for native.

Orlando Mobile App Development Company

The final call should be based on recommendations from a reputable mobile developer. A loyal, varied customer base is a strong sign of a good developer.

Concepta, a leader in the Orlando mobile development market, has experience with international lending companies like Service Finance and regional tourism businesses like Kingdom Strollers.

Having a diverse group of satisfied clients demonstrates a sound understanding of technology as well as a commitment to building long-term relationships over making maximum profit on a single project.

At the end of the day each app needs to be taken on a case by case basis.

Consult with a developer, discuss options with stakeholders, and make the decision based on long-term business needs even if that does call for a larger up-front investment.

After all, the cost of building an app that doesn’t work is always higher than investing in a native app.

Trying to decide between a native and hybrid app? Bring your questions to Concepta’s development team. We can provide tailored advice with your specific business goals in mind. Consultations are free, so schedule yours today and take the first step towards a successful release!

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What Does the GDPR Mean for Enterprise?


Something big has changed in the way enterprise handles data, and even customers are noticing. Privacy policy updates have been flooding email inboxes all over the world.

Users log into favorite sites to find pop-ups urging them to review terms and conditions. There are even memes floating around making fun of the phenomenon. For enterprise, however, this is no laughing matter. The General Data Protection Regulation (GDPR) is now live, and companies need to take steps to protect themselves.

This legal framework for managing and protecting consumer data in the European Union went into full effect at the end of May. Companies- even those based outside the EU – who violate the GDPR are subject to steep revenue-based fines. That raises some critical questions for enterprise. What is the GDPR? Who is bound by it? More importantly, how can businesses protect themselves from costly penalties?

Note: This article is meant to be an overview, not legal guidance. A business attorney is the best person to offer specific advice on how the GDPR affects your company.

GDPR Overview

The GDPR was created in response to the rising threats to consumer safety posed by Big Data and evolving IoT technology. It defines data privacy as a fundamental right and puts protections in place for the personal data of European Union residents. That includes things like names, addresses, phone numbers, email addresses, photos, identification numbers, IP addresses, human resource records, and biometric data.

Essentially, the GDPR provides more consumer control over data for EU residents. It applies to people and activity within the EU’s sphere of legal influence, specifically:

  • Residents of the EU
  • Businesses and other organizations based in the EU
  • Entities operating in the EU
  • Entities who collect or process data in the EU or data otherwise covered under the GDPR
  • Monitoring of behavior within EU

There are two categories of entities that are bound by the GDPR. Controllers own and maintain data while processors analyze or process data on the controller’s behalf. Both of these groups are responsible for protecting consumer data, removing the excuse that a company wasn’t responsible for a processor’s actions.

Although this is an EU regulation it has global repercussions. Any company that doesn’t want to implement location-based blocks on data collection from their website or cut off operations in the EU must ensure that data is being protected. The penalties for breaches are potentially high, too. Many international companies are finding it more practical to implement compliance procedures in general to prevent accidental mishandling of EU-related data.

What Changes for Enterprise

GDPR guidelines are simple but wide-reaching, all aimed at putting improving individual data control and peace of mind.

Here’s what changes:


The GDPR affects both any handling data of EU residents anywhere in the world and anyone within EU processing any data. The applicability of data protection laws used to be ambiguous; companies could simply process data outside EU to avoid legal protections.

Consumer control and consent

Individuals have much more control over what happens with their data. They must be told specifically whatis being collected, why it’s being collected, how it’s being used, and what protective measures are in place. There are also exceptions for legal allowances like public safety, a controller’s legal obligations, and the legal data interests of another person. Controllers can’t refuse service for denial of data usage unless data is necessary to provide the service. This set of rights comes with specific additional rights:

  • Access to Data: Consumers can access their data as well as what it’s being used for on request.
  • Data portability: Controllers must provide a data subject with their data in a commonly used format and transfer that data to another controller on data subject’s request.
  • Right to be forgotten (RTBF): Consumers can have their data erased on request both by controller and by any entity who was given the data.

Security by design and default

Controllers must make secure settings the default in all scenarios and take active steps to ensure data security.

Breach handling

Breaches must be disclosed if they could result in any risk to the rights and freedoms of data subjects, including the right to data privacy. Public disclosure must happen with 72 hours of the organization becoming aware of the situation. Processors have an additional duty to inform controllers of breaches on their end without “undue delay” to expedite public disclosure.

Data Protection Officers

This is one of the least understood parts of the GDPR, but it doesn’t need to be complicated. Organizations only need a specific data protection officerin select cases, specifically:

  • When a public authority is processing personal data (except courts conducting official judicial business)
  • When there is regular, systematic monitoring of individuals on a large scale
  • When monitoring certain categories of data including biometric data, data about religious or political beliefs, trade associations, health information, and criminal or legal backgrounds; additionally, this data can only be processed in specific circumstances (ie, with explicit consent of data subject, when legally required)

If an organization does need a designated DPO, there are rules meant to avoid collusion and improve the quality of oversight. The DPO can be a contractor or internal employee so long as their contact information is made available to the relevant Data Protection Authority.

They must be trained on GDPR requirements and data protection best practices. There can’t be any conflicts of interest with other duties or associations. Finally, they have to have complete executive support in terms of training and resources with the ability to report to the highest level of management.


There are standard enforceable fines for violations of the GDPR. Fines are based on different factors (like how much damage was caused, how the issue was discovered, and what the Controller is doing to fix the situation). The basic types of fines call into different categories:

  • Accidents and oversights are punishable by up to the greaterof €10 million or 2% of the organization’s global annual turnover.
  • Carelessness or deliberate violations can cost up to the greaterof €20 million or 4% of global annual turnover.

Protecting Your Company

Here’s a quick checklist of steps that will help companies ensure GDPR compliance and avoid imposing fines.

  • Assess whether the GDPR could potentially apply, keeping in mind that online ordering systems that collect EU data are included.
  • Make GDPR compliance an executive priority. Incorporate the GDPR into onboarding and refresher training.
  • Determine whether a DPO is needed and, if so, make sure they have an unobstructed direct line to company leadership.
  • Identify all types of individual data collected by the company and how it’s used.
  • Minimize personally identifiable data used in general. Good analytics can be done with anonymized or pseudonymized data, so prioritize that.
  • Update privacy policies to spell out data usage, individual rights, and the mechanism for obtaining or deleting individual data records.
  • Review and improve data security measures to include breach handling policies.
  • Recommit to good data management policies(update software, soft target minimization, and so on).
  • Be vigilant for second-hand vulnerabilities like data transfers to non-compliant entities.

Compliance with the GDPR may seem like a hassle, but it’s significantly less expensive than paying for a violation. Plus, having these standards in place benefits companies in the long run by improving public trust and preventing costly breaches.

Deciding whether the GDPR will apply to your enterprise means figuring out where your data comes from and how it’s being protected. Concepta can help. Set up a free consultation with our knowledgeable staff to review your data intelligence process and protect your business from accidental GDPR violations.

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How Enterprise Can Take Advantage of AI: Putting AI to Work


Tension is building in the business world. On one side lies mounting evidence that implementing artificial intelligence can rocket a business past its competitors. On the other, executives worry their company isn’t ready for AI.

Preparation is the difference between success and a wasted investment, and seeing high profile losses deters leaders from pushing forward with their own AI initiatives. That puts them at a disadvantage versus data-ready competitors.

A low-stress way to resolve this tension is to pilot one of the more “entry level” AI technologies. There are several relatively simple tools which can be used to build confidence in artificial intelligence.

A Cautious Approach

72% of companies feel AI is a major competitive advantage. It’s mostly larger companies moving to adopt, though. 40% of organizations with more than 500 employees are launching chatbots or intelligent assistants this year compared to about a quarter of smaller companies.

Why is there such a gap when so many recognize the potential of AI?

Small and medium companies hesitate for several reasons:

  • Feel the technology isn’t enterprise-ready yet
  • Security and privacy concerns
  • Think cost is too high
  • Not enough success stories
  • Too complicated to implement

These fears reveal a common misconception about artificial intelligence. Adopting AI doesn’t have to mean a complete overhaul of existing tools and workflows. Companies can start small and integrate gradually to grow into a process that works for them. Here are some entry-level artificial intelligence tools to get the ball rolling.

Virtual Assistants

Virtual assistants are probably the easiest form of AI to use. There’s a very low bar to entry since many companies already have them available. Virtual assistants often come bundled with popular enterprise software and productivity tools. Cortana, Siri, Google Assistant, Alexa, and similar assistants require little to no setup. They can be learned in a single training session or even by using the software’s tutorial. In fact, nearly half of American adults use virtual assistants for personal business.

Virtual assistants vary, but common applications include:

  • Voice to text dictation
  • Team collaboration
  • Calendar management
  • Email management
  • Travel planning
  • Small scale research
  • Data analysis

Right now, virtual assistants are most regularly used in the IT department. That’s an unfortunate waste of resources. Integrating these tools into daily operations cuts down on tedious administrative tasks and improves the efficiency of interdepartmental workflows. For example, when used to plan travel or meetings, the assistant updates all relevant calendars, so everyone is on the same schedule.

Since it’s likely a company already has virtual assistants available, putting them to work is mostly a matter of spreading awareness. Hold training to generate excitement and demonstrate the possibilities. Guide mid-level managers in integrating assistants into their existing workflows and make easy-to-navigate resources available for reference.


Conversational interfaces make a huge difference when they’re customer-facing, too. Chatbots can handle a flood of incoming customer inquiries without making customers wait on hold. They’re available all day, even after business hours, and are unfailingly polite no matter how frustrated a customer is. Chatbots typically transfer difficult issues to a live agent, but in practice they can handle 80% of routine questions unassisted.

About 45% of global internet users actually prefer a chatbot to a live representative as a first point of contact. They’re more willing to engage with bots than humans early in the purchase cycle, when they’re researching options. No-pressure information provided by a chatbot can inspire conversions down the road.

As an added benefit, chatbots power future artificial intelligence ventures. The data they provide on what customers want and need feeds the sales and marketing process.

There are a number of online bot builders, but those tend to be little more than toys. Security can also be an issue if the builder doesn’t understand the larger technical pictures. It’s safer- and surprisingly economical- to have a chatbot specifically built for the company website or social media page.

Marketing Email and Text Optimization

Marketers spend as much as 35 hours a week crafting and testing emails, and for good reason. Emails and texts have high ROI potential. They’re a major driver of business with low overhead. 61% customers like to get relevant emails from brands, and artificial intelligence helps create that relevance while reducing the time humans spend on the more tedious aspects of the process.

Intelligent email optimization software can generate personalized messages triggered by specific customer activities that indicate interest. They use tailored subject lines, internal content, and timing designed to catch each customer at the ideal spot in the purchase cycle. Artificial intelligence is the driving force behind the 3 year high on marketing email and text open rates.

While email optimization isn’t as simple to put in place as chatbots or virtual assistants, it’s an excellent choice for the early stages of AI adoption. It can demonstrate its value clearly and relatively quickly. Take Sprint as an example. Sprint began using artificial intelligence to guide their text interactions with customers. With targeted, relevant messages the company was able to reduce the number of texts sent to customers while improving base SMS marketing returns more than six times over.

Leading from The Front

Most importantly, have executives lead by example with these early tools. High adoption rates are a huge part of making any tech project work. If leaders show commitment to the artificial intelligence tools, their enthusiasm could make the difference between success and failure.

Ready to explore how artificial intelligence can benefit your enterprise? Set up a free appointment with one of Concepta’s developers to find out what your options are!

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How Data Science Can Help Your Enterprise Generate More Revenue


Data science is a dry term for a surprisingly cool field. When used right it acts like a team of digital detectives, sifting through a company’s data to ferret out inefficiencies and spot opportunities in time to act.

“Used right” is the key phrase here. Data science is a complex field, and finding a path to revenue presents a challenge for companies trying to modernize their digital strategy. Sometimes it’s hard to see past the hype to the actual business value of investing in data science.

To help cut through the noise, here’s a clear, results-focused look at exactly how data science generates revenue for enterprise.

Laser-focused marketing campaigns

When it comes to marketing campaigns, there’s no such thing as too much data. Over 80% of senior executives want detailed analysis of every campaign, but they often lack the time or data to gain real insight into campaign performance.

Source: Concepta, Inc

Data science addresses both those concerns. Artificial intelligence and machine learning methods cut down on the time necessary to process data while better data management provides the fuel to feel analytics.

The right combination of data science techniques can help track how campaigns are doing by market and by demographic within that market. This includes information as general as click-through rates to sorting the time spent on a company’s page by the originating site.

Armed with this information, marketers can refine the ads they push to each market based on what works, not what should work based on broad demographics. They can even identify customers who failed to convert late in the process. About 70% of these customers will convert after being retargeted.

The results are impressive. Using data to guide marketing campaigns leads to a 6% increase in profitability over companies that are reluctant to adopt data science.

Better e-mail follow-through

E-mail optimization is probably the most direct example of data science driving revenue.

E-mail is a major source of revenue for enterprise, especially for B2B companies and those that focus on e-commerce. A full 86% of professionals prefer to use e-mail for business correspondence.

The same percentage are happy to receive e-mail from their favorite businesses (providing it doesn’t get excessive).

More than half of CMOs say increasing engagement is their main concern about e-mail marketing this year, but three quarters of them don’t track what happens after e-mails are sent.

Only 23% use data science tools to track e-mail activity. A mere 4% use layered targeting, and 42% use no targeting at all. (Four out of five do perform at least some customer segmentation, though.)

This oversight has a serious effect on the bottom line. 51% of marketers say a lack of quality data is holding their e-mail campaigns back. Without data to guide them, they struggle to evaluate customer satisfaction with the frequency and quality of the company’s e-mails.

Increasing e-mail quality using data science has measurable benefits. When customers make a purchase through links in an e-mail they spend about 38% more than other customers.

80% of retail leaders list e-mail newsletters as the most effective tool in keeping customer retention rates high.

On a smaller scale, personalizing e-mail subject lines increases the open rate by 5%. Triggered messages such as abandoned cart e-mails have an astounding 41% open rate (and remember that 70% retargeting conversion rate from earlier).

Lead management

Sales staff only have so much time, and analog lead assessment methods yield questionable results. Artificial intelligence-powered data science tools can analyze a company’s past sales and customer data to effectively score leads, letting sales staff make the most of their business days. These tools consider factors like:

  • Actual interest in product as demonstrated by events like site visits and social media discussion
  • Position in purchase cycle based on time spent on specific areas of a website
  • Demonstrated potential purchasing power and authority to enter contracts

Using AI in lead management results in 50% more appointments than other methods. Those appointment are shorter and more productive, too, since businesses can target customers who are ready to buy.

The overall reduction in call time averages around 60% without damaging customer satisfaction rates. That’s why 79% of the top sales teams use data science to power their lead management.

Intelligent customer profiling

Knowing who the customer is and what they want is key to both marketing and customer service. Data science removes the potential for human biases about customers. Specifically, it looks for what customers have in common and groups them by that instead of imposing arbitrary demographic boundaries.

Profiling software analyzes all available data on a company and its customers to find previously unnoticed similarities. These hidden connections can be then used to drive revenue in different ways.

They’re particularly good at identifying customers with the highest potential lifetime value or highlighting potential extra services current customers might enjoy.

A great intelligent customer profiling success story in this arena comes from video distributor Giant Media. After using data science to build data-driven customer profiles they found 10,000 new leads across the United States.

500 brands were in their desired New York City market. The software even isolated 118 businesses that matched Giant Media’s idea profile and provided contact information fast enough to enable effective sales calls.

Improving customer experience

One theme keeps popping up in sales and marketing discussions: customer experience is king. It’s predicted to be the primary brand differentiator by 2020. 86% of customers value a good buying experience over cost and will pay more for better service. Once they’ve had that positive experience they’re 15 times more likely to purchase from the same vendor again.

Source: Concepta, Inc

What is considered “good” customer service? Besides obvious factors like reliable customer service and solid quality, personalized service seems to be the key to winning over customers.

Data science provides insights that allow for that personalized service on a large scale. It can offer tailored interactions such as:

  • Suggesting products based on past purchases
  • Retargeting customers at appropriate intervals (for instance, reorder reminders for pet food or garage coupons as a customer’s vehicle hits certain milestone)
  • Reminders around holidays (like Mother’s Day or family birthdays)

In short, creating an outstanding customer experience requires knowing what the customer values and being able to offer it on demand. Data science is invaluable here. Chatbots in particular are useful for providing assistance that customers need, when they need it, and in an accessible format.

Timely sales forecasting

Sales forecasting without modern data science methods takes far too much time. Reports are huge, hard to get through, and don’t arrive in time to help sales staff. As a practical compromise sales staff often rely on wider-scope numbers which are more readily available instead of targeted data on local customers.

Data science – specifically predictive analytics – can provide near-real time information on what’s selling, where it’s selling, and who’s buying it. This prepares companies on a structure level to spot opportunities and make the most of them.

It increases overall enterprise flexibility. Plus, sales staff can use the information to build better pitches, improve relationships with their customers, and generally make better use of their time.

Supply chain management

Managing the supply chain feeds directly into revenue. After all, companies can’t sell what they don’t have. Data science provides insights that enable more efficient internal operations, which leads to better margins. To get specific, insights gained from data science can be used to:

  • Keep enough inventory on hand to meet demand, regardless of season
  • Make deliveries on time despite potential delays
  • Schedule services more accurately so customers can plan their day

Pitt Ohio Freight Company saw a major boost in sales after applying data science to their supply chain problems. They trained algorithms to consider factors like freight weight, driving distance, and historical traffic to estimate the time a driver will arrive at their delivery destination with a 99 percent accuracy rate.

Their customers were highly impressed. Pitt Ohio now enjoys $50,000 more in repeat orders annually, and they’ve reduced the risk of lost customers as well.

Price optimization

Pricing is tricky. The goal is to find a profitable price that the customer is happy to pay so as to ensure repeat business. An enormous number of factors affect pricing, and it’s hard for humans to tell what’s important and what isn’t.

Data science has no such handicap. It can be applied to customer, sales, inventory, and other market data to uncover what actually influences a customer’s willingness to buy at a specific price. Based on that, companies can find the ideal price to make everyone feel satisfied with the purchase.

Airbnb uses a dynamic system based on this concept. The company tracks local events, hotel trends, and other factors to suggest the best price to its hosts.

This is a major part of their business strategy since hosts aren’t usually professional hoteliers; that guidance is necessary to keep hosts happy and listing with Airbnb.

Some hotels have a more complex system for setting prices. Rates used to be uniform across the board. Changes were only triggered by season or maybe rewards club status. Data science opened the doors to a more egalitarian pricing strategy.

Now each user can be shown a customized price based on a number of objective factors.

  • Is the trip for business or pleasure?
  • What rates did the customer receive for past stays?
  • How valuable is the customer as a client?
  • Does the customer have a booking at a competitor which they might be willing to change?
  • Will the customer be using cash or points?
  • Does the customer have past incidents of bad behavior at the family of hotels?

Interestingly, customers who caused expensive trouble during previous stays may be shown higher rates to discourage a booking.

Marriott, who were early adopters of data science in the hospitality industry, is an interesting case. The hotel chain was generating $150-200 million per year in the 1990s by intelligently managing their Revenue Per Available Room, or RevPAR. It’s still growing at a rate of 3% a year.

As a general trend, applying data science to price optimization increases revenue by 5-10%. The most benefit is seen in season-dependent industries such as hospitality.

Looking to the future

Industry leaders are taking note of these benefits. As a result, data science is fast becoming the preferred way to fuel digital transformation efforts. Global revenues for big data and business analytics were up 12.4 percent last year, and commercial purchases of hardware, software and services to support data science and analytics exceeded $210 billion.

Source: Concepta, Inc

Companies who hesitate to adopt data science will soon be left in the dust by their better-prepared competitors. Now is the time to make the business case for integrating data science- before it’s too late.

Not sure where to start? Concepta can advise on and customize powerful data science systems to meet your specific needs. Schedule a free, no hassle consultation to find out how!

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Everything Executives Need to Know About NodeJS


NodeJS is a rising star in the enterprise software world. It’s being used by everyone from fledgeling chains to entertainment giants. For those tasked with leading software projects, though, popularity is the least important aspect of technology.

They’re more concerned with tangible benefits – what NodeJS is, why developers love it, and how it can boost their digital initiatives.

Read on for answers to the most common executive questions about NodeJS.

What is NodeJS?

NodeJS is an open source platform for developing server-side and networking applications. Written in JavaScript, it’s quick to build with and scales extremely well.

What do people actually use NodeJS for?

NodeJS may be best known as a tool for real-time applications with a large number of concurrent users, but it also sees use for:

  • Backends and servers
  • Frontends
  • Developing API
  • Microservices
  • Scripting and automation

Why do developers like NodeJS?

Being easy to work with makes a tool popular with developers, and NodeJS is both lightweight and efficient. The Javascript is written in a clear, easy to read format.

Because developers can use the same language throughout the project developers find working with teammates assigned to other areas of the stack less disruptive.

The Node Packet Manager is another major draw. With half a million NPM packages available for use, developers can find something to suit all but the most specific needs.

There’s also the fact that technical tasks that are usually difficult – for example, communicating between workers or  sharing cache state – are incredibly simple with NodeJS.

Finally, many developers just like using NodeJS. Creating performant apps can be fast, easy, and fun. There’s an active and engaged community full of peers to share ideas or coordinate with on a tough problem.

When a tool makes their job more enjoyable, developers are going to want to use it whenever possible.

How does NodeJS benefit enterprise?

When it comes to business value, NodeJS brings a lot to the table.

  • Faster development: NPM packages help reduce the amount of code that must be written from scratch. On top of that, using the same language end to end makes for a smoother, more productive development process. It’s faster than Ruby, Python, or Perl. Testing goes faster, as well.
  • Scalability: NodeJS uses non-blocking I/O. Processes aren’t held up by others that are taking too long. Instead, the system handles the next request in line while waiting for the return of the previous request. This lets apps handle thousands of concurrent connections.
  • High quality user experience: Applications built with NodeJS are fast and responsive, handling real-time updating and streaming data smoothly. They provide the kind of user experience that makes a positive impression on customers.
  • Less expensive development: Open source tools are a great way to lower development costs. The productivity offered by NodeJS pushes savings even farther; developers spend less time building the same quality app as they would with other tools. NodeJS can be hosted nearly anywhere, too.

How are companies using NodeJS now?

  • Netflix: The largest and best-known streaming media provider in the world reduced their startup time by 70% by integrating NodeJS.
  • Walmart: As their online store gained popularity, Walmart experienced problems handling the flood of requests. NodeJS’ non-blocking IO improved their ability to manage concurrent requests for a better user experience.
  • Paypal: Originally there were separate teams for browser-specific code and app layer-specific code, which caused a lot of miscommunications. Switching their backend to NodeJS meant everyone was now speaking the same language. More cohesive development allows the Paypal team to respond faster to issues.

Are there times when NodeJS should not be used?

Although it’s a powerful tool, there are times when NodeJS doesn’t fit. Using it for CPU-intensive operations basically cancels out all its benefits.

It doesn’t support multi-threaded programming, meaning it’s not the best choice for games and similar applications.

The best use cases for NodeJS are when there will be a high volume of concurrent requests and when real-time updating is key. Other benefits – low costs, smoother development – can also be found with other tools, but performance at scale is a serious advantage.

Is NodeJS the right tool for your next project? Talk through your options with one of Concepta’s development team to find out!

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