Is There A Place for AI in Small to Medium Businesses?

Many small to medium business owners view artificial intelligence as something only huge corporations need.

In reality, it can help position them to compete with those corporations on a whole new level.

It seems like everyone in the business world is launching artificial intelligence programs.

That’s partly because nearly everyone is. 61% of businesses have already begun using some form of artificial intelligence, many of those focusing on predictive analytics and machine learning.

71% report they plan to expand their use of predictive analytics and other AI applications over the next year.

For most companies the decision to adopt AI is an easy one.

For small to medium businesses (SMBs), though, there are tough questions to answer.

Even successful SMBs don’t have the same depth of financial resources as a multinational corporation.

They need to invest cautiously, and artificial intelligence can sound like a science fiction daydream.

That’s unfortunate, because artificial intelligence is fast becoming the kind of tool that can help small to medium businesses keep up with their larger competitors.

Read on to explore the things keeping SMBs from investing in artificial intelligence. then find out how to get past them and what technologies are best suited for small to medium businesses.

Practical Artificial Intelligence

“Artificial Intelligence” brings to mind futuristic robots and complex movie plots, but the reality is much simpler.

The term refers to teaching machines to “think” and interpret information like humans do. Humans have very flexible minds.

They can handle a variety of rapidly-changing topics and navigate difficult conditions that confuse computers (although computers have a greater ability to process repetitive data quickly and accurately).

Modern artificial intelligence has come a long way.

It can’t quite mimic human thought yet, but there have been some exciting advances using AI techniques like machine learning and deep learning that show potential for nuanced processing.

The technology is proving its value as an enterprise tool, too.

There are a few common applications that some people don’t realize are based on artificial intelligence:

  • Predictive analytics, especially embedded features in enterprise software
  • Chatbots on websites or social media pages
  • Intelligent assistants in office and productivity software
  • Recommendation engines used for suggesting Netflix titles and upselling in ecommerce

What Holds SMBs Back

Even as larger companies move to wider integrations of artificial technologies, small to medium businesses are slow to adopt.

Their hesitation is understandable – after all, a failed technology project could threaten the future of their company – but it also holds them back.

The truth is, many of their concerns aren’t as serious as they think.

The issues have practical workarounds or can otherwise be mitigated through proper planning.

Here’s why the leading reasons SMBs aren’t adopting artificial intelligence don’t have to be unmovable roadblocks to progress and how they can be overcome.

AI is too expensive

Industry news reports tend to cover high-end artificial intelligence ventures done by major international corporations, with price tags in the millions (or occasionally billions).

That kind of investment is an intimidating prospect for an SMB who just needs a better way to utilize their data.

The thing is, those programs usually involve the most difficult and expensive forms of AI.

Experimental programming, complex interactions, sensitive health information, government-regulated data, huge amounts of simultaneous users, and other complicating factors raise the costs above the average for enterprise AI projects.

SMBs don’t need the same amount of scale or infrastructure. Their modest needs can be met at a much more reasonable price point.

There is no “usual” price for AI. The costs associated with artificial intelligence are based on many factors, including safety and regulatory protocols and the complexity of necessary interactions.

To build an estimate, developers will ask questions such as:

  • Does the program need access to sensitive information?
  • Is it designed to address a specific set of circumstances or is it more a broad-spectrum tool?
  • What level of interaction with humans is desired?
  • What’s the scale involved?
  • Will the AI need to perform complex actions?

Even when a full artificial intelligence program is out of reach, there are ways to integrate AI on a limited budget.

For one thing, AI is included in many enterprise software packages. Most companies already have access to some AI tools, even if they don’t realize it.

Targeting tools in email marketing software and personal assistants on smartphones are both driven by artificial intelligence.

More in-depth AI toolsets are often available with a reasonably-priced software upgrade to enterprise level from free or lower-tier accounts.

It’s work checking with vendors to see what’s within reach.

The rise of reusable code and powerful development frameworks has put small-scale custom solutions within reach, as well.

Developers have platform options for creating analytics dashboards and chatbots that makes the costs approachable for SMBs.

AI isn’t ready for enterprise because the projects fail too often

Project failure is a daunting prospect for SMBs, who usually have a longer list of desired business improvements than they have capital to spend.

They need to prioritize projects because they can’t do everything they’d like.

Investing in AI means putting another project on hold, something they aren’t willing to do when it seems like all they hear about is failed artificial intelligence projects.

It’s easy to become discouraged by high-profile AI failures or assume tools are overhyped, because some projects do fail and some tools are overhyped.

Artificial intelligence is at a point in the Hype Cycle where its applications are being rigorously tested, and some won’t make it through to becoming everyday technologies.

However, project failure is more often an organizational issue instead of a technological one.

Projects fail for a variety of reasons, most commonly:

  • A weak discovery process results in a weak final product.
  • Internal adoption rates are too low to realize the project’s potential.
  • Misaligned business goals lead to the company creating a product that no longer fits within their workflows.
  • The company experiences an outsourcing failure or developer issues.

Avoiding these issues is somewhere small to medium businesses may have an edge over larger corporations. Why?

  • Pushing internal adoption on a small team is more effective because the company leadership can personally talk to everyone (or at least every team leader) to convince them of a project’s value.
  • There is less opportunity for confusion over business needs and goals.
  • The development process has fewer moving parts, so it’s easier to make needs clear during discovery.

What SMBs need to watch out for is the tendency to default to the lowest bidder, especially when outsourcing overseas.

If they focus on quality as much as price, they’re more likely to get a quality return on their investment.

Choosing Agile development methods is another way to ensure a positive outcome.

Developers who use Agile and conduct a thorough discovery are actually seeing a rise in project success rates, and have been for a couple of years.

AI isn’t practical for a small to medium business; it only works for massive corporations.

Many SMB owners see AI as something that can’t help their business.

They assume they don’t have enough data to process or that the impact of AI won’t be noticeable at a smaller scale.

A lot of those same owners would be surprised to realize how much data they already have – data which is going untapped.

Putting that data to work might result in smaller gains, but proportionately those gains matter more.

One interesting thing about AI is that is has opposite benefits for SMBs and larger companies.

It helps giant companies operate with the personalization of an SMB while allowing SMBs to function with the efficiency of a massive corporation.

That is, it gives small to medium businesses the edge they need to “punch outside their weight class” when it comes to competing for market share.

While there are some AI applications that won’t help smaller-scale businesses, there are many more that will.

A small bookshop with five employees wouldn’t get value from predictive scheduling software, but they could see an impressive return on predictive ordering and email marketing programs.

AI doesn’t apply to this industry

There’s a perception that artificial intelligence is only for high-tech fields like software development or banking.

That couldn’t be farther from the truth. AI can be applied anywhere where data is generated – that is, everywhere – to improve efficiency, guide decision making, and maximize the impact of marketing and sales campaigns.

Some examples:

  • A cleaning company uses AI to intelligently manage their leads and upsell current clients.
  • A stroller rental company builds an AI-powered solution to manage their inventory and give customers more options for customizing deliveries.
  • A vacation rental agency uses price optimization to get the best possible pricing on rentals for owners.
  • A landscaping company decides where to expand based on data gathered from predictive analytics tools.

These are all small but important decisions, and they’re made easier using insights gathered by artificial intelligence.

AI is too hard to learn

SMBs tend to have long-time employees in leadership positions with lower turnover in mid-level roles.

They often hesitate to push something that seems high-tech or confusing due to established relationships with employees.

These fears are large unfounded. Building enterprise AI tools is complicated.

Using them is less so, especially with custom tools created specifically for non-technicians.

Most enterprise AI software is designed to be user-friendly at an operator level, so the on-boarding process would likely be much less complicated than SMBs might expect.

Where there are problems, there are well-established training solutions.

The most popular AI tools have online classes at a variety of price points, from free YouTube tutorials to subscription-based professional development platforms.

Developers generally offer training and support packages for their software at reasonable rates.

With so many options even the most technophobic staffer can find a way to get on board with new tools, especially once they realize how much easier AI makes their job.

Staying In The Game With AI

Larger companies are already investing in artificial intelligence.

As they do, they’re gaining a lot of advantages traditionally enjoyed by SMBs, like personalized service and shorter response times to changing local market conditions.

Small to medium businesses have a choice. They can make the AI investment that will help them stay competitive or risk losing their customer base to larger, better-informed companies.

At the end of the day, that isn’t much of a choice at all.

Artificial intelligence doesn’t have to be a headache. Concepta can help you build an intelligent business intelligence solution that fits your needs- and your budget. Schedule your complimentary appointment today!

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Where Most Businesses Go Wrong with Machine Learning

machine-learning-business

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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The Transformative Power of Real-Time Analytics

Real-Time-Analytics

Real-time analytics capture data as it is collected, providing timely insights and immediately usable guidance for decision-makers at all levels.

Data is everywhere. Every day people create over 2.5 quintillion bytes of data, and that number keeps rising as the Internet of Things expands.

More importantly, data scientists are learning more and better ways to ethically collect data.

There’s enormous transformative potential hidden in that data – if businesses can find a way to analyze it in time.

Enter real-time analytics, a way to interpret data at its freshest point.

What are Real-Time Analytics?

Real-time analytics, also known as streaming analytics, involves analyzing data as it enters a system to provide a dynamic overview of data, its current state, and emerging trends.

It puts data to work as soon as it’s available.

Real-time analytics is done through the use of continuous queries.

The system connects to external data sources, pulling fresh data and enabling applications to integrate specific types of data into its operations or to update an external database with newly processed information.

The practice stands apart from descriptive, predictive, and prescriptive analytics.

All of those require a batch of historical data to be exported and analyzed. In real-time analytics, software intercepts and visualizes data as it’s collected.

Of course, data isn’t a single-use item. It can be funneled into other analytics methods as well.

The advantage is that by using real-time analytics owners can start putting their data to use while more in-depth processes run.

There’s an Expiration Date on Data

Batch analysis provides a host of useful insights, but it takes time. Waiting on results delays the availability of information. In some cases, the potential value of the insights gained is worth the wait.

After all, Artificial Intelligence exponentially reduces the amount of time needed for deep analysis.

Sometimes that short window matters, though. Data ages fast, and much of it is most useful within a short window after collection. Its value degrades as it ages.

For example:

  • Demand is surging for a specific service.
  • There’s too much inventory of a perishable item building up.
  • A customer is in a brick and mortar store.
  • A customer has been searching for a type of product in the app.
  • A marketing campaign is flagging unexpectedly.

All of these insights need to be acted on quickly.

If data owners wait for more thorough analysis, any actions taken have a weaker effect.

The client leaves the store, or sales don’t quite meet their potential.

Real-time analytics is the tool that provides timely insights to aid executives in ongoing management and rapid response.

It isn’t a replacement for other analytics. In fact, more through forms of analytics are usually where analysts find the best performance indicators to track using real-time analytics.

There’s a synergistic effect: predictive analytics suggests that a specific situation will lead to a major issue if left unchecked, then real-time analytics identifies the beginnings of that situation in time to act.

Where Real-time Analytics Shines

The most lucrative uses of real-time analytics fall under one of two categories: solving problems before they become major issues and spotting opportunities in time to take action.

Solving Problems

As mentioned earlier, descriptive and predictive analytics are incredibly useful for highlighting the best key performance indicators (KPIs) to track.

They aren’t always responsive enough to detect the changes that signal the earliest stages of a problem, when small corrections can have a large impact.

That’s where real-time comes into play. Streaming analytics tracks KPI as they’re recorded, flagging anything that might be a concern.

Use Cases:

Spotting Opportunities

The sooner a company can move on an opportunity, the greater their potential for profit.

Real-time analytics helps narrow the gap between receiving indicators of a time-sensitive opportunity and being able to act on that information.

Streaming analytics are usually displayed through dynamic visualizations which are easily understood by busy executives.

They’re a low-complexity tool for integrating integrate analytics usage into daily operations.

Use Cases:

  • Contextual marketing campaigns
  • Social media management
  • Suggestive selling
  • Mobile asset deployment

Changing the Game for Enterprise

Integrating real-time analytics into the decision-making process is a huge advantage.

Companies who use it are more responsive to actual conditions instead of playing catch-up using outdated data.

When potential windfall conditions form, they have the forewarning to maximize their profit. If there’s a problem brewing, they can take action to minimize the disruptions.

It’s also easier to judge the impact of new programs with a constant stream of data.

This helps to level the playing field between small to medium businesses (SMBs) and large companies.

SMBs can exploit their data to achieve higher efficiency while large companies gain the fine control and fast responsiveness of SMBs.

Real-time analytics don’t impose a perfect balance; multinational corporations tend to have better analytics programs while small businesses can be more flexible in response to changing customer needs.

They are, however, becoming necessary for companies that want to stay competitive.

Those who fully utilize their data consistently outperform their peers, enjoying:

  • More revenue
  • Less wastage
  • Higher efficiency
  • Improved customer and employee satisfaction
  • Greater ROI from marketing campaigns

In short, companies who aren’t maximizing their data usage are handing their rivals the competitive edge.

Real-Time in Action

The biggest companies around the world are already using real-time analytics to drive profit. Take a look at how it’s being used today:

BuzzFeed

The digital media giant collects streaming data on when their content is viewed, where it’s shared, and how it’s being consumed by more than 400 million visitors a month. Employees can analyze, track and display these metrics to writers and editors in real-time to guide targeted content creation.

Shell

Royal Dutch Shell, better known simply as “Shell”, uses real-time analytics in their preventative maintenance process. The system collects and monitors data from running machines to spot issues before they break. This saves a huge amount of money from lost productivity and secondary equipment failures caused when something breaks.

UPS

Package delivery depends on a seemingly endless number of factors, and customers expect their packages within the delivery window regardless of outside circumstances. The UPS system tracks scores of data points to provide real-time “best route” guidance to drivers. It also updates depending on office hours (for commercial deliveries) and customer change requests.

Navigating Challenges

Putting real-time analytics to work comes with its own set of challenges.

Data integrity

Bad data leads to flawed insights. Companies need to have a system in place to monitor data quality to ensure it comes through the pipeline ready for analysis.

Internal adoption

A business intelligence tool can’t work if no one wants to use it. There’s no getting around the fact that pushing real-time analytics will cause workflow disruptions in the beginning. The trick is to sell the team on its value using actual success stories from other projects. When they understand what they have to gain, they’ll be more willing to work through the early disruptions.

Security

Data security is a serious concern with every business intelligence project. A major security leak puts both the company and its customers at risk. Know where data comes from, set up strong security protocols, and be sure it’s being collected legally and ethically.

Making the Most of Real-time analytics

Getting the most from real-time analytics requires planning and executive support. Here are some ways leaders can help ensure success:

Focus on relevant KPI

The point of real-time analytics is to gather time-sensitive insights for immediate use. Flooding the dashboard with irrelevant data or things unlikely to make an impact in the short term can hide those valuable insights. Identify KPI that have an immediate potential impact and prioritize them for streaming analytics. Always have a specific business reason for adding KPI to the tracked list.

Promote data-driven decision making on an institutional level

Encourage management (and decision makers at all levels) to refer to data early and often. If a new course of action is suggested, ask what the data says. Provide resources for learning how to access company data intelligence products. Lay out company guidelines for collecting, vetting, and using data. This kind of cultural shift starts at the top, so be sure data is king in the C-suite as well.

Modify rules and decisions based on data- but allow time for changes to affect metrics first

There’s a fine line between watching a problem grow without stopping it and abandoning a good plan before it’s had a chance to work.

For example, a restaurant location accidentally orders more fruit than they’re likely to need.

A regional manager spots the problem and launches a digital ad campaign along with tableside upsells to use as much as possible.

It takes time for customers to find and respond to ads, so the manager should wait to see if the promotions work before searching for another solution.

Make real-time analytics part of a larger analytics program

Data intelligence has the greatest impact when several techniques are used in combination with each other. Small changes noticed during real-time analytics might not seem relevant on their own, but they could take on new weight when measured against historical data.

Sell key internal users on real time analytics

Internal adoption can make or break a project. Choose stakeholders wisely during discovery, and make an effort to win support from the entire team before launching a new analytics program.

Invest in quality tools

Many real-time analytics tools are built into enterprise software. When a company moves beyond those entry-level options, it’s critical to make quality as important as cost. Substandard tools are often worse than nothing. They cause frustration among the team and lower the project’s chances of success. Stay within budget, but be sure it’s a practical budget that puts core requirements in realistic reach. That’s easier than it sounds. Modern real-time analytics is surprisingly affordable between off the shelf software and modular custom software. Consider consulting a developer before making a purchase to be sure it’s worth the investment.

Don’t forget about upkeep

Real-time analytics is a tool that needs to be maintained. Stay on top of software updates and maintenance. Enforce good data management policies, and use common sense. If results seem strange, find out why instead of acting anyway.

Final Thoughts

Have realistic expectations about real-time analytics. They’re a tool, and a powerful one, but they’re only as good as the data that feeds them and the people that use them. Keep practical considerations in mind and the benefits of real-time analytics can be transformative.

Where should you start with real-time analytics? Our experienced developers can help you put together the right analytics program for your company. Set up a free consultation today!

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Predicting Profit: Growing Revenue by Scoring Opportunities with Data Intelligence

artificial-intelligence-applications

Of all the artificial intelligence applications making their way into enterprise, one of the most effective is predictive analytics.

It has the potential to transform the decision-making process from something based on “gut instincts” and historical trends to a data-driven system that reflects actual current events.

Those who have adopted it are outperforming their competitors at every turn.

Some organizations still hesitate to launch their own predictive initiatives. Artificial intelligence has been a buzzword for decades, and many times in the past it failed to deliver on its promises.

Executives with an eye on the bottom line might avoid any kind of AI technology out of understandable skepticism.

That hesitation could be holding these companies back. Predictive analytics has matured into a viable enterprise tool. It’s being used all over the world to find opportunities for growth.

The impact is so striking that 71% of businesses are increasing their use of enterprise analytics over the next three years.

Read on to learn more about predictive analytics, what it offers for enterprise, and how it can drive a measurable increase in revenue.

What Does “Predictive Analytics” Mean?

Predictive analytics is the practice of analyzing past and present data to make predictions about future events.

Technically it can describe any process that seeks to identify the most likely scenario by drawing parallels between historical and current conditions, then placing those conclusions in a modern context.

Analysts look at what happened in the past when conditions were similar and how certain events played out. They assign more weight to factors which have tended to be more influential or which have greater potential for an extreme outcome.

Most people assume predictive analytics is a recent invention, something that arose after computers established their role in enterprise.

In reality, businesses have been relying on it since the late 1600s, when Lloyd’s of London began applying the practice to their shipping insurance estimates.

In pre-artificial intelligence days, people used statistical modelling and complex mathematical equations to perform predictive analytics.

Many updated versions of those models are still in use for industrial shipping and route planning.

Non-intelligent methods have limitations, though. They only consider factors users decide to include, so there’s a heavy likelihood of human error and bias.

It also takes time to perform the calculations. By the time they’re done and put into use the data is already becoming outdated.

The modern style of predictive analytics incorporates artificial intelligence and machine learning. It allows users to include many more variables for a broader, more comprehensive analysis.

The process highlights unexpected connections between data and weighs factors with greater accuracy. All of this can be done with a short enough timespan to create timely, reliable insights.

The Intersection of Science and Enterprise

AI and machine learning have exciting potential, but like all emerging technology they require investment. Global brands like Google might have the resources to shrug off a failed project.

For the majority of organizations, though, there has to be a reasonable expectation of profit to consider launching a technology initiative.

Successful enterprise leaders aren’t reckless with their IT budgets. They focus on actual business goals and source technology that addresses those instead of trying out popular new tools.

A smart executive’s first question about a new tool should be, “How does this benefit the company?”

Predictive analytics has an answer for that question: growing revenue through refined opportunity scoring.

The Value of Opportunity Scoring

Opportunity scoring involves assigning a value to a sales lead, project, goal, or other potential course of action in order to determine how much relative effort to spend in pursuit of that opportunity.

Scoring opportunities allows a company to get a greater return on their time and money. No company can put the same investment into every customer or chance that crosses their path.

They shouldn’t even try. 80% of revenue comes from 20% of clients, so it makes sense to prioritize that 20%.

It’s something every business does, even if there isn’t a standardized process in place. High value sales leads are called frequently, given more flexibility with concessions, and assigned better sales representatives.

High value projects get bigger teams, more access to resources, larger budgets, and scheduling priority.

The trick is deciding what opportunities have the most potential- and that’s where predictive analytics come into play.

Manual Versus Machine Learning

Two main types of opportunity scoring methods are in widespread use today: those that are done manually and those that take advantage of machine learning.

Manual scoring is where people assign scores, either personally or using a statistical model, based on their own set of influential characteristics.

There are a number of problems with this method that can slow businesses down or leave them with unreliable scores.

Inaccurate

Opportunity scoring is incredibly valuable- when it’s reliable. Manual scores have a wide margin of error. They depend on direct user input with no ability to suggest other relevant factors. Unlike intelligent scoring, manual methods can’t easily be used to find unexpected commonalities among high-return accounts.

The problem with this approach is that executives can’t realistically imagine or keep track of everything that might influence their company.

There’s too much data to consider, and it changes constantly. All manual opportunity scoring is therefore based on aging, less useful data. On top of that, it’s easy to make mathematical mistakes even with a computer program’s assistance.

Subjective

Because users choose and weigh the contributing factors, manual scoring is highly susceptible to human bias.

Preconceptions about social categories, personal characteristics, industrial domains, and other identifying factors can be given too much (or too little) weight. It allows for unhelpful biases to be introduced into the sales cycle.

Most of the time the result is simply less helpful scores, but it does occasionally create a public relations issue if the scoring system leads to operational discrimination.

For example, some real estate brokers have run into a problem where one racial group wasn’t shown houses in a certain area. The company’s scoring suggested those groups were less likely to buy there.

The realtors thought they were following good business practices by relying on their internal customer scoring, but those scores were skewed by biases about economic stability rather than actual data.

When the situation came to light it created a public impression that the realtors had the same bias. Suddenly they had bigger worries than opportunity scoring.

Inefficient

Creating and maintaining a manual scoring process takes a significant chunk of time. It’s not a system that responds well or quickly to change.

That’s a problem in today’s hyper-connected world, where an event in the morning could start influencing sales across the country that afternoon. Opportunities wind up passing before they’re even recognized.

Not everyone remembers to consider “hidden costs” of ineffective processes, like higher labor.

Tedious data entry takes time and focus away from more productive sales activities without a corresponding return on value due to the lower accuracy.

There’s human nature to consider, as well. Sales teams can tell these scoring systems aren’t effective.

It’s not uncommon for teams to resist wasting time they could be working leads. They don’t like putting their commissions at risk with bad information, but still need some kind of guidance to help manage their lead interactions.

More often than not, they operate with outdated scores or “gut instincts”. That in turn frustrates managers who have invested in the inefficient manual scoring process.

The conflicting pressures create an uncomfortable working environment that drives unwanted turnover among the most valuable sales agents.

Rigid

Even the most sophisticated manual scoring program can only account for things that have been specifically input into the equations.

They require humans to think of and assign value to every possible factor. This tends to enforce a “business as usual” mindset over more profitable responsive operations.

On the flip side, predictive opportunity scoring is among the leading AI-based drivers of revenue in an enterprise context. It has the edge over other methods in several areas.

Reliable

There are two central reasons behind the higher reliability of intelligent scoring. First and foremost, it reduces the impact of human error.

Machine learning algorithms perform calculations the same way every time. Even as they adjust themselves in response to new data, the underlying math is more reliable than calculations done by humans.

It’s also important that predictive analytics is purely data-driven rather than focusing on “traditional knowledge” about what makes an opportunity valuable.

Artificial intelligence expands a computer’s capacity to judge the relevance of seemingly unconnected events.

Predictive analytics leverages that capacity to identify characteristics shared by highly productive courses of action. Those commonalities are then used to weigh future opportunities with a greater degree of accuracy.

Responsive

Predictive analytics fed by a constant stream of new information allow predictive opportunity scoring tools to update scores in real time (or at least near-real time).

They highlight opportunities in time for companies to take action. Advance warning also leads to better advance planning when it comes to inventory, staffing, and marketing.

Objective

Intelligent scoring tools react to actual circumstances based on data instead of presumptions. They consider all data available for a situation from an impartial standpoint (providing, of course, that any developer bias is accounted for during design).

Efficient

The majority of enterprise analytics software is designed to be user-friendly and easy to operate. It connects to a company’s data sources, meaning there’s usually very little data entry required. This frees the sales team to focus on their accounts and other high value activities.

Optimized Opportunity Scoring = More Revenue

Predictive opportunity scoring is backed by sound theory. The practical benefits are just as solid, with several applications proving their enterprise value today:

Lead Scoring

There’s a saying that 80% of revenue comes from 20% of clients. Lead scoring helps identify that 20% so sales teams can focus on the most valuable customers.

Agents close more contract- and higher value ones- when they know where to profitably spend their time rather than chasing a weak prospect for a low bid.

Intelligent lead scoring is a dynamic process, meaning it regularly reevaluates leads based on changing circumstances.

A client marked as a low priority lead moves up as indicators show rising interest. Agents then step in at the right point in the purchase cycle with a targeted incentive to encourage a sale (or even an upsell).

Customized attention has the added benefit of increasing repeat business, because former clients aren’t bombarded with high-pressure closing tactics when they aren’t ready to buy again.

Enterprise benefit: Better closing ratios, greater PLV, higher average order value

Targeted marketing campaigns

When executives know where advertising has the most potential impact, they have more options for strategic spending.

Specific demographics can be targeted with tailored campaigns to increase their lifetime value, keep them from falling out of the sales cycle, or meet another specific business goal.

Enterprise benefit: Higher ROI on advertising campaigns

Inventory management

Information can travel around the world near-instantly, but inventory still has to be physically moved.

That puts a natural drag on rearranging supply levels in different regions – and with it, a cap on how much companies can exploit regional fluctuations in demand.

This causes lower sales in general, but the effect is most striking during an unexpected surge in demand.

Predictive analytics can spot early indicators of a potential spike while excitement is still building.

Executives have the warning necessary to shift goods and team members where they’re needed most. Amazon uses this method to stage their merchandise for faster shipping of commonly ordered items.

Enterprise benefit: Higher overall sales revenue

Opportunities for growth

Opportunities aren’t always about marketing campaigns or scoring leads. Sometimes executives want guidance when choosing between growth strategies.

Predictive analytics and opportunity scoring are useful here as well, answering questions like:

    • Which stores will be most valuable over the next year?
    • Where should the company expand?
    • Should more franchises be sold in a specific area?
    • What ventures are most likely to succeed?
    • Is a specific project worth the investment?

There’s no guarantee that any course of action is the best, but incorporating data cuts out many of the risk factors that lead to failure (such as hype over new technology or personal bias).

Enterprise benefit: Faster, more sustainable growth

Putting Data to Work

At the end of the day, data is only valuable when it serves real-world business goals.

Opportunity scoring is one of the most proven ways to extract value from data. It’s also one of the most accessible, since embedded analytics are built into the majority of modern enterprise software.

With so much to gain at a relatively low investment point, those who haven’t adopted yet should be giving predictive analytics a closer look.

Are you frustrated by trying to navigate multiple streams of data? One of the most common pain points of data intelligence initiatives is reconciling data from the enterprise software used by different departments. Concepta can unite data from programs like Salesforce, MailChimp, and other analytics programs to put data where it’s needed most- in your hands!

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Top Technology Stacks for Enterprise Software Development

enterprise-software

When designing enterprise software, prioritize business goals and long-term viability by choosing these business-oriented stack options.

Enterprise software has its own special set of priorities. The architecture needs to be scalable but cost-effective, secure yet user-friendly, and above all should deliver the kind of high-quality user experience that gets results.

Trying to balance business pressures with technical realities can be a challenge for even experienced developers.

What gives those experienced developers the edge is knowing the best tools for the job.

Every technology has distinct advantages and limitations; using ones whose strengths play into those enterprise priorities leads to superior software.

Here are some of the best back- and front-end technologies around for building powerful enterprise software.

Back-End Technologies

NodeJS

NodeJS is at the top of a lot of technology lists for good reason. It lets developers build with JavaScript from front to back.

Having a single language across the stack breaks down communication barriers among the team and makes for more easily maintainable software.

Built on Chrome’s v8 engine, Node features non-blocking IO.

It can handle multiple requests simultaneously, meaning apps scale better, run faster, and take up less system RAM.

The Node Package Manager (NPM) is one of Node’s biggest draws. It houses an expansive repository of packages created and tested by other developers, that’s growing every day.

Incorporating reusable JavaScript like this shortens development timelines and lets teams focus on innovating instead of reinventing the wheel at every turn.

.NET

The continued value offered by Microsoft’s .NET is a compelling argument for using mature technology over tools which are “cooler” but less tested.

This open source cross-platform development platform is used to build, deploy, and run modern Windows applications across devices and environments.

Technically, .NET has a lot to offer. It’s easy to write and maintain, which both contribute to lower development costs.

Developers have plenty of tools for building in security from the start. Plus, .NET lends itself well to horizontal scaling.

That combination of value and enterprise features is what’s kept .NET popular even as newer tools are released.

PHP

Despite being developed back in 1994, PHP is still the most common language for server-side development. About 79% of all websites use at least some PHP.

PHP’s popularity is due in part to budget concerns. It’s open source, and all features and updates are free to use.

Since it was designed specifically for the web, developers need less time to create websites with dynamic features.

Open source tools and shorter development cycles translate into a smaller upfront investment.

The other half of PHP’s appeal is ease of use. One of its core strengths is powering database-enabled websites with intuitive content management systems (CMS) that can be managed by non-technical employees.

Employees are able to update and query their own system without having to do more than a short tutorial.

Python

As of 2018 Python is the fastest-growing programming language out there. It emphasizes clarity, simplicity, and versatility, putting developers in the best position for high productivity.

The fast edit-test-debug cycle makes it useful for Rapid Application Development, too.

What’s really fueling enterprise growth is Python’s data science applications. Companies that want to stay competitive need to make the most of their data.

As a flexible, high level programming language, Python can create the machine learning tools and analytics software to help turn that data into actionable business insight.

Front End Technologies

ReactNative

In a mobile market where companies have to balance development speed, platform coverage, and budget, it’s easy to see why ReactNative is gaining ground.

It takes an innovative approach to cross-platform development by using native user interface (UI) building blocks and assembling them with React’s special brand of JavaScript.

Apps look and feel like native apps because they render like native apps.

Besides providing near-native performance, ReactNative has the same economical appeal as hybrid apps.

Developers can build one app, then tailor it to cover multiple devices with only minor changes.

Maintaining that single code base is both easier and less expensive than juggling a collection of separate native apps.

AngularJS

Angular, part of the enterprise-oriented MEAN stack, is a flexible tool for building organized mobile and web apps.

It focuses on simplicity as well as ease of testing and construction.

The newer versions come with a variety of “starter seeds” for different purposes, and there are in general a lot of ways to do the same thing.

That gives developers the flexibility to design exactly what they need. Privacy-conscious customers also like that Angular is optimized for security.

Angular is suited to CRUD client-side apps, though Single Page Apps (SPAs) are the most popular applications right now (especially those that require a lot of data retrieval).

Plus, as a Google property it’s a solid choice for projects which rely heavily on other Google technologies.

Vue.js

This JavaScript library has a narrow focus with broad impact. As its name suggests, Vue handles only the view layer of the user interface.

It’s lightweight, easy to learn and use, and integrates well with other JavaScript applications.

Vue can be used to add interactive elements to an existing project or expand a page’s functionality instead of building a whole new SPA.

Progressive Web Apps (PWAs) are a common Vue application.

Although it isn’t a complete framework like React or Angular, it shares benefits like faster development and lower costs.

In addition, Vue’s small size and lazy loading of components gives it an edge in speed.

It’s a perfect example of a tool that does a few things well rather than dividing its focus across a huge feature set.

Building a Business-focused Stack

While these are all enterprise-friendly stack options, keep in mind that there’s no magic technology that fits every business plan.

Each project has unique priorities.

Sometimes performance is the overriding concern. Sometimes it’s more important to cover as many platforms as possible in the least amount of time.

Be sure to choose stack technology that supports the client’s business goals. It may take more consideration up front, but it’s the best way to avoid the hassle of being stuck with an ill-fitting stack.

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5 Ways to Build Internal Support for Your BI Initiative

business-intelligence

Business intelligence may have transformative potential, but it’s also a significant investment.

Too often, that investment goes unrewarded. Last year Gartner found that 70% of corporate business intelligence initiatives fail before reaching ROI.

Even when projects succeed, they are used by less than half of the team.

The lesson to be learned from this isn’t to avoid business intelligence, though. There’s too much to be gained from using data to build a dynamic, factual model of operations and customers.

Instead, executives should address one of the root causes of BI failure: internal resistance and a general lack of adoption.

Try these approaches to build team support for business intelligence.

Use Success Stories to Build Enthusiasm

Employees have a full set of regular duties to handle. Learning and using business intelligence adds more to their slate.

A well-designed system will save them time and effort once established, but they need to be motivated to put in the effort to learn new tools.

Business intelligence seems like an esoteric concept to some. It can be hard to see a direct connection between data and results.

Instead of throwing out dry statistics, frame business intelligence in terms of what it can do for the team using real examples.

Before early initiatives, find success stories from competitors or comparable organizations. Use those to build excitement for the upcoming project.

Once each phase of the business intelligence project is finished the results can be marketed to the internal team to keep that positive momentum going.

When pitching business intelligence to the team, keep reviews specific but short. Choose clear metrics that demonstrate the actual effects of the project without getting bogged down in details.

For example: “Sales teams closed 23% more contracts last quarter using the new lead management system.”

Integrate BI into Daily Workflows

There’s no incentive to change if staff can default to the old system. People get comfortable in a routine, even when it isn’t effective.

They prefer to stick to what they know rather than learn new procedures.

Nudge resistant team members out of their rut by removing the option to use old systems whenever possible.

Don’t disrupt everything at once, but do have a schedule for phasing out old tools and switching to new ones. Publicize the schedule so it isn’t a surprise when old programs won’t open.

At the same time, make it easy to adopt business intelligence.

Be sure users are properly trained on the new tools, to include putting reference materials where they can be easily accessed by everyone.

Sometimes resistance stems from embarrassment or unfamiliarity, so also refrain from criticizing team members who need extra training or refer to training material frequently.

Create Business Solutions, not just High-Tech Tools

Misalignment between business needs and tool function is a leading reason for lack of adoption.

IT gets an idea for something they can build to collect new data, but it isn’t geared towards an actual business goal.

The product becomes busy work that distracts staff from core functions.

Business intelligence tools need to address specific pain points order for the team to use them.

They should have a clear purpose with an established connection to existing business goals. It’s also important that the new tool is demonstrably better than the current system.

If the tool takes ten minutes to update every day and the old system took five minutes twice a week, it won’t be adopted.

Along the same lines, favor simplicity in function and design. Don’t build an overly complicated multi-tier system only engineers can understand.

Aim for a unified dashboard with intuitive controls and a straightforward troubleshooting process.

Remember that the Team are Vital Stakeholders

Finally, don’t overlook the value of employees as stakeholders in any business intelligence initiative.

They have “on the ground” knowledge of internal operations that can guide the creation of a more targeted system. Take advantage of their expertise early in the development process.

Include key internal team members when gathering stakeholder input during discovery.

Go beyond management and choose representatives from the groups who will use the tools after release. Solicit and give serious attention to team feedback, both during and after release.

Bringing the team in from the beginning does more than build better software. It creates a company-wide sense of ownership.

When team members feel they had a hand in creating business intelligence tools, they become enthusiastic adopters.

Build Support, Not Resentment

Above all, keep the process positive. Encouraging adoption of business intelligence doesn’t have to be a battle of wills.

Focus on potential gains, not punishment for failing to fall in line. Bring the end users in early, listen to their feedback, and build a system that helps them as much as it helps the company.

When the team is excited – or at least convinced of the product’s value – they’re much more likely to adopt business intelligence in the long run.

Every level of operations can benefit from business intelligence. If you have a project in mind, we can help make a compelling case for BI that encourages everyone to get on board. Sit down with one of our experienced developers to find out more!

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