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Introducing Logi Predict: Embed Predictive Insights in Your Application

By Steven Schneider | September 6, 2018

Predictive analytics has been in the headlines for years. McKinsey says it’s transforming outcomes of software projects. Gartner says machine learning automation and predictive analytics lead to “better, faster decisions.” The Wall Street Journal says the models that fuel predictive insights will run the world.

The hype is real. Predictive analytics is now the number one capability on product roadmaps (according to our 2018 State of Embedded Analytics Report). Applications can’t be brainless products any more. They have to incorporate advanced capabilities that help their end users do things like:

  • Reduce customer churn
  • Detect fraud in transactions and invoices
  • Reduce machine downtime
  • Identify high-risk healthcare patients
  • Boost sales through targeted promotions

It’s clear predictive analytics has massive potential. But until recently, predictive has been out of reach for companies that don’t have vast resources or in-depth expertise.

That’s why I’m so excited to announce Logi Predict, a new product in the Logi Analytics Platform. Logi Predict is the first predictive analytics solution specifically designed to embed machine learning and artificial intelligence inside applications. It’s a game changer: By embedding predictive analytics in your existing application, your end users get the information they need without leaving your product—saving them time and frustration, and adding a ton of value to your application.

>> Read the Press Release Announcing Logi Predict <<

We built Logi Predict with typical product managers and developers in mind. Unlike traditional predictive analytics, you don’t have to hire a data scientist with R and Python expertise or learn complex statistical modeling. And Logi Predict can be deployed five times faster than building something yourself.

“After GroupFiO added Logi Predict to our product, our customers achieved a 22 percent sales uplift from targeted marketing campaigns,” said Ravi Srinivasan, CEO, GroupFiO. “Logi Predict analyzes detailed data at a customer level to identify promotions that yield substantial results. It’s like having a virtual assistant to make the shopping experience more enjoyable. In the past, only large online retailers with big budgets and teams had access to sophisticated predictive capabilities, but now Logi Predict helps bring this offering to smaller and medium size retailers.”

Logi Predict is the result of years of careful research and design. Our goal was to solve four of the most common challenges of predictive analytics:

Expertise: Predictive analytics is typically designed for data scientists who have a deep understanding of statistical modeling, R, and Python. That’s a big barrier to cross. Logi Predict was built specifically for application teams to use, even if they don’t have a deep understanding of R, Python, or statistical modeling.

Taking Action in the Application: Predictive analytics has typically fallen short in empowering end users. It would show information, but failed to let users take action then and there. Logi Predict solves this problem by embedding intelligent workflows inside applications that let users take immediate action or trigger another process without jumping into another system.

Distributing Information: Most application teams will tell you predictive analytics is a pain to embed, deploy, and scale. We set out to make Logi Predict easy to embed and white-label in your application, so predictive insights become part of your customers’ regular workflows.

Creating Predictive Models: Creating a predictive model usually takes at least 14 steps, and is so complex only data scientists can do it. Logi Predict’s intelligent wizard automates models and turns it into a three-step process.

I can’t wait to see how companies will use Logi Predict to add value to their applications and reach new levels.

Find out more in the press release announcing Logi Predict >

 

About the Author

Steven Schneider is the CEO of Logi Analytics, where he brings more than 15 years of technology leadership experience. Steven has previously served as both Chief Operating Officer and Chief Product Officer at Logi, where he led the sales, product, engineering, marketing, and customer success teams. Prior to Logi, he was a founding partner of OnDemandIQ, a Hosted Business Intelligence solution, and a practice manager at leading web technology company Proxicom. Steven holds a BS in Computer Science from Virginia Tech and an MBA from the University of Southern California.

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