Predictive Analytics is Key to Building World-Class Sales Organizations

Is there any fantasy more alluring for business leaders than the ability to peer into the future? To know which strategies, employees, and models will produce the most revenue, drive growth the fastest, and propel profitability far beyond targeted quotas?

While seeing the future in the traditional sense remains in the domain of imagination, predictive analytics and intelligence are increasingly becoming  known as the ‘crystal ball for businesses.’

As marketplaces reach new competitive heights while customer engagement and retention grow more challenging than ever, actionable forecasts from predictive analysis and intelligence have become exponentially more popular across industries.

Companies like PerceptionPredict drive continuous advancements in the predictive analytics field and create new opportunities for non-traditional businesses to implement predictive analysis and intelligence in new business models and industries. 

The marketing, insurance, and credit industries have employed this branch of artificial intelligence and statistics for decades to anticipate future performance.

Now the AI buzz is intensifying around the business world. Sales organizations stand to reap some of the largest rewards from predictive intelligence. The adoption of AI and similar technologies among sales professionals is projected to grow 139% by 2022, and for good reason.

Sales organizations that implemented artificial intelligence tools like predictive intelligence and analysis increased leads by over 50% while reducing costs by a staggering 40-60%.

What Is the Definition of Predictive Intelligence and Analytics?

The definition of predictive intelligence and analytics is a variety of statistical techniques from data mining, predictive modeling, and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. 

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture the relationships between innumerable factors to accurately assess risk or potential upsides associated with a set of conditions. This provides an opportunity for data-driven, strategic decision-making about candidate transactions.

Is Predictive Analysis a Part of A.I.?

Predictive analysis and intelligence are subsets of artificial intelligence. Unlike most AI disciplines, predictive analysis eschews autonomous programs and tools for interactive software that allow humans to interact with data. As a result, individuals can query data, identify and analyze trends and patterns, and test assumptions. 

Autonomous AI tools can make, test, and adjust assumptions precisely and rapidly but lack the deeper introspection that predictive analysis offers. 

How Does Predictive Analytics and Intelligence Work?

Predictive analysis and intelligence require an advanced understanding and command of state-of-the-art technology, mathematics, artificial intelligence, machine learning, and statistical and computational modeling.

To lay the foundation for predictive intelligence, analytics, and modeling, companies like PerceptionPredict gather mountains of relevant data, including key performance indicators, historical performance, transactional history, and human attributes. The result is a massive body of raw data for their tools to crawl and analyze. 

Artificial intelligence and machine learning tools learn using data sets that feature known outcomes to establish a baseline for forecasting and modeling.

Then, they intake raw test-set data to analyze the information and hunt for patterns and anomalies that are useful for building predictive models.

After identifying relevant data, predictive intelligence and analytical firms use advanced statistical algorithms to create a computational model of the predictions made during analysis. 

In simpler terms, the process of predictive analysis and intelligence gathering looks something like this:

  1. Gather relevant and necessary historical and transactional data to develop a ‘test set’ of data.

  2. Train machine learning and AI tools with a “ground-truth” dataset that contains the outcome of interest, allowing the tools to learn and develop insight into past events.

  3. Introduce the new dataset, absent outcome data. This allows the predictive tools and software to extrapolate historical data to establish trends and patterns for accurate forecasts.

  4. Introduce your new datasets to highly advanced and precise statistical algorithms to develop a computational model of the future.

  5. Analyze the model for actionable insights that empower companies to introduce or enhance data-driven and strategic decision-making.

While this example oversimplifies the process to a degree, it provides a keen perspective on the complexity of predictive analysis and intelligence. 

Fortunately, companies don’t have to introduce, implement, and manage this resource-intensive process themselves. Predictive intelligence firms like PerceptionPredict specialize in harnessing the power of predictive analysis to create actionable intelligence for companies worldwide, including industry leaders like Mercedes-Benz and Crowdstrike.

What Are the Business Benefits of Predictive Intelligence

From sales talent selection to high-performer retention, a sales organization thrives or crumbles based on hiring decisions. And yet, sales hiring methods remain a subjective, informal, and flawed process that creates costly turnover, heightened opportunity and training costs, and slower growth. 

Unfortunately, many companies are unaware of data-driven talent acquisition’s significant benefits, including enhanced productivity, performance, morale, and revenue.

Poor sales hiring selections create high early-tenure attrition, slow ramp, lost leads and revenue opportunities, poor customer loyalty, and uninspiring customer experiences. PerceptionPredict’s sales Performance Fingerprints enable sales leadership to proactively and decisively reduce poor hiring choices and their costly impact.

Examples and Applications of Predictive Analysis

Predictive analysis and intelligence may have vastly improved over recent years thanks to emerging technology, but it is by no means a new practice. Predictive analysis has a long history across a variety of industries, including:

  • Marketing 

By analyzing past customer behavior, market indicators, and historical and transactional data, predictive market intelligence tools can forecast engagement and campaign success before launch.

  • Credit Scoring 

Predictive intelligence and analytics allow lenders and other credit-based companies to analyze a customer’s credit history, loan application, and other available data to predict the likelihood of individuals making future credit payments on time.

  • Business Management  

Predictive analytics significantly improves customer relationship management, performance and revenue predictions, and customer and employee retention. 

  • Insurance 

More than 65% of insurance companies credit predictive analysis and intelligence for reducing underwriting expenses, and 60% say the resulting data has helped increase overall sales and profits. Insurers use a wide-ranging dataset to determine the risk associated with prospective clients and develop profitable policies that minimize risk.

Other industries known for employing predictive intelligence to support their mission include:

  • Policing and Law Enforcement

  • Telecommunications

  • Travel 

  • Healthcare

  • Sports Management

  • Social Networking

  • Actuarial Sciences

  • Pharmaceutical Research and Development

  • Recruitment and Talent Acquisition 

As artificial intelligence and surrounding technology continue to advance exponentially, predictive analysis and intelligence will likely become a common practice for all businesses as they discover the significant impact it can have on their profitability and growth.

PerceptionPredict Performance Fingerprints: Next-Level Predictive Intelligence Analysis

PerceptionPredict is a firm composed of world-class data scientists, renowned psychologists, and industry-leading industry consultants. Together, they built unique and highly effective predictive analytics and intelligence tools that facilitate specialized candidate profiles called Performance Fingerprints. 

Performance Fingerprints analyze all relevant data points to create a candidate success model. This sales profile, or ‘sales DNA,’ accurately predicts future performance, turnover likelihood, and company fit. Implementing Performance Fingerprints enables the sales organizations to:

  • Track the impact of poor hiring decisions

  • Treat each new hire as an investment decision, with analytical data predicting the return on investment for each candidate pre-hire.

  • Increase accountability and reduce unfair or biased hiring practices, allowing them to attract a wider talent pool.

  • Accelerate the hiring process and make it more agile by enabling recruiters and hiring managers to prioritize their focus on high-value recruitment prospects.

  • Challenge the view of turnover as an unavoidable cost of doing business.
  • Empower sales leadership to make more strategic and measured decisions, utilizing data, specialized tools, and predictive intelligence.

Performance Fingerprints aren’t just for hiring. Implementing role-based Performance Fingerprints equips the sales organizations with new tools that proactively engineer performance outcomes to support overarching goals. 

Performance Fingerprints can also help predict, identify, and rectify common resource-draining issues companies face, like absenteeism and high turnover.

Book a demo today to check out how PerceptionPredict’s Performance Fingerprints use predictive intelligence to take your hires and your business to the next level.

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