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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Is JSON Schema the Tool of the Future?

json-schema

JSON Schema is a lightweight data interchange format that generates clear, easy-to-understand documentation, making validation and testing easier.

JSON Schema is used to describe the structure and validation constraints of JSON documents.

Some have called it “the future for well-developed systems that have nested structures”.

There’s some weight to those claims; it’s definitely become a go-to tool for those who get past its steep learning curve.

Reviewing the Basics

JSON, which is the acronym for JavaScript Object Notation, is a lightweight data-interchange format.

It’s easy for humans to read and write, and equally easy for machines to parse and generate.

JSON Schema is a declarative language for validating the format and structure of a JSON Object.

It describes how data should look for a specific application and how it can be modified.

There are three main parts to JSON Schema:

JSON Schema Core

This is the specification where the terminology for a schema is defined.

Schema Validation

The JSON Schema validation is a document which explains how validation constraints may be defined. It lists and defines the set of keywords which can be used to specify validations for a JSON API.

Hyper-schema

This is where keywords associated with hyperlinks and hypermedia are defined.

What Problem Does JSON Schema Solve?

Schemas in general are used to validate files before use to prevent (or at least lower the risk of) software failing in unexpected ways.

If there’s an error in the data, the schema fails immediately. Schemas can serve as an extra quality filter for client-supplied data.

Using JSON Schema solves most of the communication problems between the front-end and the back-end, as well as between ETL (Extract, Transform and Load) and data consumption flows.

It creates a process for detailing the format of JSON messages in a language both humans and machines understand. This is especially useful in test automation.

Strengths of JSON Schema

The primary strength of JSON Schema is that it generates clear, human- and machine-readable documentation.

It’s easy to accurately describe the structure of data in a way that developers can use for automating validation.

This makes work easier for developers and testers, but the benefits go beyond productivity.

Clearer language allows developers to spot potential problem faster, and good documentation leads to more economical maintenance over time.

Weaknesses of JSON Schema

JSON Schema has a surprisingly sharp learning curve.

Some developers feel it’s hard to work with, dismissing it as “too verbose”. Because of the criticism, it isn’t well known.

Using JSON Schema makes projects grow quickly. For example, every nested level of JSON adds two levels of JSON Schema to the project.

This is a weakness common to schemas, though, and depending on the project it may be outweighed by the benefits. It’s also worth considering that JSON Schema has features which keep the size expansion down.

For example, objects can be described in the “definitions section” and simply referenced later.

What Else Is There?

Some developers prefer to use Mongoose, an Object Document Mapper (ODM) that allows them to define schemas, then create models based on those schemas.

The obvious drawback is that an extra abstraction layer delivers a hit to performance.

Another option is Joi, a validation library used to create schemas for controlling JavaScript objects. The syntax is completely different, though, and Joi works best for small projects.

Sometimes developers jump into a new MongoDB with a very flexible schema. This inevitably dooms them to “schema hell”, where they lose control as the project grows.

When JSON Schema Is the Right Choice

Performance is undeniably important. However, there are times when the cost of recovering from mistakes is far higher than the cost of taking the speed hit that comes with schema validation.

For those times the performance drop isn’t large enough to justify the risk of bad data entering the system, and that’s where JSON Schema comes into play.

JSON Schema is proving itself as a development option, but there’s no single “best tool” for every project. Concepta takes pride in designing a business-oriented solution that focuses on delivering value for our clients. To see what that solution might look like for your company, reserve your free consultation today!

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How Anago’s Mobile Sales Platform Boosts Efficiency

success-story-anago

Anago is a company on the rise. The well-respected commercial cleaning services company consistently ranks among INC magazine’s Fastest Growing Private Companies in America.

They service the hospitality industry, medical offices, retail locations, religious centers, and even handle event cleanup.

The company is also one of the most prominent franchisors in the country, appearing 68th on Entrepreneur.com’s Franchise 500 List.

Anago branches have opened throughout the United States as well as internationally.

With success, though, came some scaling challenges that made sales and data transparency a hassle.

Systemic Bottlenecks and Opaque Data

Anago suffered from a long sales cycle. Because they serve such a wide range of customer types, they can’t base quotes on square footage and tasks required.

An in-person visit is required where a sales representative meets with a site manager to walk through the location.

After the walk-through the representative would collect information from the site manager, then return to the office and build a quote.

This involved a great deal of back and forth communication to sort out inaccurate information and debate services. The quote was then emailed to the prospective client.

If the client accepted, there was another in-person visit or complex email exchange to get the contract signed.

The system had a lot of flaws. Anago had a hard time communicating quickly with clients, and it was too easy for human error to drag down the quote process.

Tracking data like closing ratios took up a lot of labor hours without satisfactory results.

Also, every layer of complication created another place for prospects to call out of the sales cycle.

Anago needed a more effective process.

Shifting Forms to The Field

As a company known for incorporating advanced methods into their cleaning style, it’s no surprise that Anago wanted a technology-based solution.

They partnered with Concepta to build a management system that connects the office with their sales team in the field.

Representatives can now build quotes in the field, search and generate reports, add notes and further information to proposals, and update existing clients who are interested in additional services.

Prospective clients get their quote immediately, and if it’s agreeable they can sign and submit the contract via iPad.

To address the need for data transparency, Concepta designed the system to integrate with Anago’s CRM and customer apps.

Clients can send instant messages to their sales rep or the office. If there’s an issue, either party can upload pictures to make sure everyone is on the same page.

A Future-Focused Solution

Using the new system closes many of the gaps where clients dropped out of the sales cycle.

50% of proposals are now done through the field app, freeing agents from multiple trips back to the office.

The improved customer experience is winning over clients, who appreciate better communication and faster responses from their sales contact.

Anago’s franchise program benefits, as well. The new high-tech process holds major appeal for future franchise owners, and the company’s value has risen accordingly.

Overall, it’s a system that will keep generating ROI through increased efficiency and higher franchise sales for years to come.

Mobile App Development Company in Orlando

Concepta is one of Orlando’s most renowned mobile development agencies.

We have over a decade of experience helping their partners overcome business challenges, grow revenue, and improve their processes.

For Anago, Concepta created a solution to support all those three goals.

You’ve seen what we did for Anago – now find out what we can do for you! Schedule your free consultation today!

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The Easiest Way to Implement Business Intelligence For Enterprise

Business-Intelligence-For-Enterprise

The benefits of business intelligence are clear to see. Using data makes companies more efficient and highly agile, positioning them to take advantage of opportunities as they arise instead of racing to keep up with the competition.

What isn’t so obvious is how to make the shift towards making data-driven decisions. There are so many BI tools on the market that deciding where to start can seem overwhelming.

The easiest way to stay focused is to build around specific business goals rather than choosing a trendy tool and trying to make it fit. Having a roadmap and a destination keeps business intelligence efforts on track, even when making adjustments as needs evolve.

Every roadmap will be different, but there are some guidelines every company can use to put together a practical, effective business intelligence plan.

Get Your “Data House” in Order

It can’t be said too often that business intelligence is only as good as the data feeding it. Bad data turns into flawed analysis, which leads to wasted time and money.

The first step of any business intelligence project should be conducting a comprehensive assessment of the company’s current data situation. Be sure to include:

  • Data sources available for use
  • Current data management practices
  • Potential stakeholders in a business intelligence project (both major and minor)
  • Wishlist for data or analytics capabilities

The goal is to clarify what the company has now and what would best help push performance to the next level.

This is also a good time to recommit on a company level to good data management. Business intelligence leads to a stronger flow of incoming data, and having familiar policies in place early will help staff take it in stride.

Work in Phases

Set a list of priorities and work in self-contained, cumulative phases to spread business intelligence across the organization. It may be tempting to just start fresh with a whole new system, but there are two compelling reasons to favor a modular approach.

Cost

So much goes into launching a business intelligence initiative. The costs go beyond buying or building software. Companies must also consider the cost of integrating it into their existing workflows and improving the data pipelines that feed the analytics.

Starting small both reduces the initial investment and allows the benefits of early projects to help pay for later ones.

Building support

One of the biggest killers of business intelligence projects is a lack of internal adoption. Maybe the product doesn’t fit into existing workflows, or staff aren’t convinced of its benefits.

It doesn’t help that sales teams for BI solutions tend to oversell their software. As a result executives expect too much, too soon, and when the desired results don’t materialize on schedule they become disenchanted.

A phased adoption plan allows the first success stories to build excitement for the business intelligence process. It serves to help manage expectations. Everyone can see how the first project played out and knows what they stand to gain.

Some areas show results more quickly than others, making them better choices for building support. For example, it’s easy to demonstrate the value of email marketing analytics or intelligent customer profiling and lead scoring. Both make staff’s jobs easier while noticeably increasing revenue.

Start with Market Tools

Don’t rush to build business intelligence software from the ground up right away. Needs may be unclear in the beginning; only thorough experience will companies discover does and doesn’t work. It can be frustrating to realize an expensive new suite of software requires an equally expensive overhaul of related workflows.

There are plenty of analytics tools and software on the market to experiment with while getting a feel for business intelligence. Options like Google Analytics, Salesforce, MailChimp, and User Voice offer an impressive suite of tools powerful enough to see real results.

As these prove their worth, companies can have custom software built to organize the various data streams into customized dashboards. These dashboards bridge the gap between the moment when companies are getting all the analytics they need but managing the results is too unwieldy and the point where their needs can only be met with a fully custom solution.

Evaluate, Adjust, Reassess

Schedule periodic assessments to review the business intelligence process as a whole.  Get feedback from all stakeholders, including weighing adoption rates by department to check for inconsistencies that could signal a problem.

Measure performance results against meaningful yardsticks. It’s not enough to say something general like, “Reports increased by 60%”.

Instead, assess the actual impact on productivity and budget with specific instances: Time spent managing leads dropped by 35% while successful sales calls increased by 15%.”

Business intelligence is a dynamic process. Remember to leave room for adjustments going forward. Look back on previous phases to evaluate their long-term value. How are they integrating with new technology? Have they met expectations, or is their performance trailing off?

Don’t be afraid to replace a component that doesn’t work. It’s important to give tools enough time to show ROI, but that doesn’t mean sticking with solutions that are causing problems.

This constant evaluation and correction process is the key to staying on the business intelligence roadmap without getting caught up in costly detours.

What can business intelligence do for you? How can you work BI tools into your workflows in a way that makes sense? To get recommendations about business intelligence software and learn how to organize your data into insights that drive real-world revenue, set up a free consultation with Concepta today!

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4 Habits That Are Holding IT Executives Back

Habits-That-Are-Holding-IT-Executives-Back

The best IT professionals are passionate about their work. They read trade publications, hang out on forums, and contribute to code repositories and other shared resources.

That passion makes them good at what they do, but it can also lead them astray in the workplace. Sometimes developers become so fixated on their favorite aspects of technology that they make well-intentioned decisions that ultimately hurt the company.

It falls on senior leadership to monitor these IT obsessions to make sure they don’t get out of hand.

Here are some of the worst offenders:

Trend Hopping

Staying on top of the latest technology is part of being a good IT professional, and the First Mover Advantage is real. Investing in promising technology early has the potential to provide a serious competitive edge.

However, some take it too far. These people jump to adopt exciting new trends that aren’t quite market-ready, essentially volunteering their companies as test subjects for unproven technology. If they haven’t done their homework, there could be major issues that negate the first-mover advantage.

The highly competitive automotive industry is an excellent example. In an industry-wide survey, JD Power found that new car owners most often complained about the cutting edge features that were meant to be market differentiators.

Unfortunately, these features weren’t ready for wider use. Voice recognition, which is highly popular and increasingly reliable now, caused a full 10% of new car complaints in 2015. Waiting just a little longer would have allowed the technology to mature enough to meet customer expectations.

There’s another risk with trend hoppers. Without oversight they may discard tools that show promise as soon as (or even before) they achieve a respectable ROI in favor of the “next best thing”. Besides lowering the lifetime value of tech investments, this inhibits the adoption of future projects.

Staff become reluctant to use new technology for fear it will be suddenly replaced. They don’t want to be constantly learning new tools for novelty’s sake; they want to be using the tools that work best. A lack of support can kill even well-planned projects before they start.

To keep trend hoppers in line,  emphasize the need for more than the “cool factor” when considering new technology.

There must be other factors, such as:

  • Issues with the current process;
  • Demonstrated better results with a new solution;
  • Low risk to testing a new solution.

Hanging onto Hardware

There’s beauty in a well-built, well-maintained server room, but insisting on physical hardware can damage a company’s agility. New projects can’t begin development until construction on the necessary hardware systems is complete.

Plus, physical infrastructure requires significant investment. There’s the upfront cost of actually buying, installing, and configuring hardware. Maintenance and security (both digital and physical) raise operating expenses even more.

Equipment naturally becomes outdated and needs replacement. When the company expands, all that equipment has to be taken down, moved, and set up again.

In the vast majority of cases, these are unnecessary hurdles. Cloud storage and computing solutions are maturing into more viable solutions than maintaining in-house hardware for most purposes. They’re easy to set up and come with built-in vendor maintenance.

If the company moves, there’s no hardware to transport or interruption to workflows while technicians get the system running again.

Initial costs with cloud storage are relatively low, too. Companies can buy only what’s needed, then add capacity as required. Ongoing costs operate much like a utility. As a result, software built using cloud solutions begins paying for itself much sooner than its hardwired counterparts.

Data Hoarding

Data has the potential to find or create incredible opportunities. Hoarding data without putting it to work wastes that potential. It costs money together, scrub, store, protect, and maintain data. If it’s not being used, it represents a liability instead of an asset.

That’s what a frustrating number of companies are experiencing, though. 40% aren’t using their data to generate insights, despite spending an average of 20 hours a week gathering it.

What’s behind this hesitation?

  • Options paralysis: There are so many ways data can be used that it’s hard to know where to start.
  • Unusable data: Data is collected but never prepared for use in analytics.
  • Lack of support: Executive leadership isn’t backing adoption of data initiatives.
  • Data silos: Data gets caught in silos where only a small group of people can access it.

When data hoarding becomes a problem immediate, targeted action can shake things loose. Find specific ways to use data, encourage adoption, and leverage those successes into creating a wider data-driven culture.

Using Square Pegs for Round Holes

Sometimes IT professionals “fall in love” with a tool. It works exactly how they like to work, and they want to use it for every possible purpose. They try so hard to make it fit that they overlook a better solution.

The result is unnecessarily complex software and workflows. Even when the favorite tool works for an unsuitable purpose, it takes more time and money to compensate for the bad fit.

For instance, while enterprise apps are wildly popular right now there are some situations in which they aren’t the best  solution. A repair company’s dispatch app could reduce inefficiency in daily workflows, but trying to patch in all partner vendors as well as internal staff would probably cause the app to fail.

Those requirements are too broad for an app, which is meant to provide excellent service over a narrow range of operations.

This can be the hardest habit to break because executives rely strongly on technology recommendations from their staff. In the earliest planning stages of any project, make a point of stepping back and considering several options dispassionately.

Get input from a variety of stakeholders. Make sure the final decision is based on the best fit, regardless of whether it is the coolest tool.

Perspective

One thing to keep in mind: these habits usually aren’t conscious choices. While they can have serious negative effects, IT professionals don’t mean to damage the company. Their bad habits are merely blind spots.

Executives will get the best results by avoiding a confrontational approach when working around these issues. This philosophy has the double benefit of finding the best solution for the company and helping the developer recognize the potential impact of their habit.

The best way to avoid becoming mired in bad IT habits is to encourage active communication and cooperation within a development team. At Concepta, we have 12 years of experience balancing the strengths and weaknesses of team members during the web development process. To find out how our approach could work for your next project, set up your free consultation today!

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Find Your Best Customers Through Intelligent Customer Profiling

intelligent customer profiling

In the past, digital marketing used the same techniques as print advertising.

Marketers made their best guess about the demographics and keywords relevant to their product, measured a campaign’s success, and refined their approach from there.

Machine learning technology has changed that.

Now companies can use predictive analytics to build intelligent customer profiles, allowing them to maximize revenue by knowing which customers to target and how.

Underused Resources

The biggest problem from a digital marketing standpoint isn’t acquiring useful data.

Companies make data every day.

Sales patterns, loyalty programs, website traffic, customer volume, social media activity- all of these result in data that could provide valuable insights.

The real problem is that companies have struggled to find a way to effectively interpret their raw data into marketing direction.

More data has been generated in the last five years than in all of human history before then, yet only 0.5% is ever analyzed and put to work.

Instead, data ends up siloed within different departments of an organization.

Parts of it are used, but the bulk of a company’s data ends up aging out of usefulness in databases.

Deeper Customer Understanding

Predictive analytics can take advantage of that existing store of information by using it to build models of customers based on data, a process called intelligent customer profiling.

Although customer profiles have been a popular marketing tool for years, until now their creation was subjective.

There was no reliable way to know who was buying a product and why, so profiles tended to be limited to demographics and whatever information could be gleaned from focus groups or customer interviews.

Intelligent profiling- sometimes referred to as predictive customer intelligence– is worlds beyond that approach.

It draws on data gathered through the course of normal operations to add refined demographic and psychographic details to the customer profile.

Targeted Marketing

Having a better grasp on customers has major benefits for a company.

For starters, it provides a reality check as to who uses their services the most and why.

Is it the ideal customer from the company’s buyer persona, or is there growing popularity among a completely different demographic?

Customer profiling also serves to connect the data-gathering back end of a business with the action-focused front end.

In other words, predictive analytics is where the business value of data science lies.

It provides concrete suggestions that can steer the marketing team in the most profitable direction.

These are just a few questions intelligent profiling can answer:

  • Who is most likely to be a repeat customer?
  • What types of customers offer the highest ROI?
  • Who will respond to an email?
  • Why do consumers choose this company over its competitors?
  • What indicators suggest a customer will qualify for a program or loan?
  • Is marketing reaching the right sort of customers?

These profiles are powerful tools for the marketing department.

They can help reach full potential revenue on existing customers by revealing what matters most.

Alternately, they can reveal opportunities to target prospective customers, ones that weren’t included by the subjective buyer personas.

Intelligent profiling benefits more than the marketing department.

It guides the sales team in creating the perfect pitch for every profile type, each informed by hard data that reveals what matters most to that customer.

If you’d like to know more about how predictive analytics can empower your sales team, watch this blog.

We’ll be posting the second part of our customer profiling series next month.

Parting Thoughts

Knowledge is power, but only when it’s used.

Intelligent customer profiling is the path from inert data to actionable insights.

Having trouble managing the output from a handful of marketing programs? Concepta can unify your analytics through a single custom dashboard to display all your data in one location.

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