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Welcome to the Predictive Enterprise

At any given moment within your organization, hundreds, perhaps even thousands of critical decisions are being made.

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At any given moment within your organization, hundreds, perhaps even thousands of critical decisions are being made. Decisions that will affect your organization’s ability to generate revenue, control expenses and manage risk. Decisions that will directly affect your bottom line. A predictive enterprise utilizes predictive capabilities — data, information and analytics — to optimize decision-making, mitigate risk and exploit insights for competitive advantage. It is the foundation for transforming a company from a backward-looking, gut-based hierarchical organization to one where everyone in the organization uses data and analytical techniques to make better decisions.

Predictive technology will have a bigger impact on companies and governments than cloud computing. In a few years, having a complete picture of the past, present, and future will be a requirement for all executives. It will affect every single department in a company and the enterprise at large.

Making decisions in a cloud of data

This revolution has been in the making for more than 10 years. In the early 2000s, as platforms like Salesforce started taking hold, companies began entering critical sales, marketing, support, finance, HR and other business data into the cloud. Meanwhile, companies like Amazon and Google pioneered the use of machine learning to help power their companies. This, coupled with the decrease in cost of data storage, compute power and rise of open source technologies, has led to the rise of a handful of companies delivering predictive analytics services and the democratization of this technology. The predictive enterprise is no longer a pipe dream.

The execution gap

For most companies, converting the data into knowledge is not the problem. Converting knowledge into actions and making them operational using existing infrastructure is where they fall down. This is often the case where companies take a top-down approach, trying to utilize in-house data-science assets.

In almost every functional area of an organization, a packaged predictive solution is now available. This allows companies to take a bottom-up approach to becoming a predictive enterprise. The subject matter experts within their organizations can select best-of-breed applications. Delegation of decision-making will be key to making a successful predictive enterprise work, and this should apply also to the way that executives architect their organizations.

How to get first-mover advantage

Predictive enterprises will outperform their peers and competitors by fully leveraging their data assets. It moves organizations from a siloed and unscalable approach to one where every employee has access to the insights and the tools they use have consumer-Web-level simplicity. Human resources, finance, customer support, marketing and sales, security and IT, and the board of directors can all select applications today to help them achieve this goal.

  1. Human resources: Predictive technology in the HR space uses data to make educated predictions about when a candidate is likely (or willing) to move. Entelo, Gild and TalentBin, for example, monitor changes in personal and professional data from dozens of social sites, and use that to anticipate hiring opportunities. This is a significant edge over competitors, who wait until someone has actually applied for a job, or publicly quit their old one, to reach out.
  2. Finance: Forecasting to date has essentially been a guessing game. Adaptive Insights, Aviso and C9 create budgets and smarter forecasts by looking at signals around the sales pipeline. They provide a clearer idea of whether a company will miss, hit or exceed targets.
  3. Customer support: A company with $10 million in revenue and a 10 percent customer churn rate needs to acquire $1 million of new business each year, just to avoid shrinking. The fact is that existing customers have greater lifetime value for your company. Gainsight, Totango, Bluenose and Preact use signals such as social media sentiment, product usage metrics and customer support tickets to identify signs that a customer is likely to take away their business.
  4. Sales and marketing: If you’re a salesperson, it would be really great to be focused on the deals that have the highest likelihood to close. And if you are a marketer, you would like to be sure that you’re providing leads that are most likely to become buyers. This is the problem that we at Fliptop are tackling, along with other great companies like Lattice-Engines and Infer.

The sales forecast will become like the weather forecast

We have become used to being able to predict the weather, and very soon it will become normal to predict revenue, customer problems, sales and marketing opportunities, IT security threats and hiring opportunities. This is not to say that the road to get there will be easy — many enterprises will have to transform the way they collect and store their data, and also change the way they empower their employees with tools. The race is on to see which companies will fully embrace the predictive enterprise first.


Doug Camplejohn is the founder and CEO of Fliptop, a leader in predictive-analytics applications for business-to-business companies. Before Fliptop, Camplejohn founded two companies, Mi5 Networks and Myplay, and also held senior roles at Apple, Epiphany and Vontu. Reach him @camplejohn.

This article originally appeared on Recode.net.