How Predictive Analytics can add Value to Applications "Product people - Product managers, product designers, UX designers, UX researchers, Business analysts, developers, makers & entrepreneurs 27 February 2019 True Artificial Intelligence, Data-Driven, Data-Driven Product Management, Machine Learning, predictive analytics, Product Management, Mind the Product Mind the Product Ltd 764 Product Management 3.056

Every software application today is fighting for space in an increasingly crowded market, so software vendors need to differentiate their offerings with valuable features to avoid losing out to competitors. An increasing number of application teams are turning to predictive analytics as a source of competitive differentiation and additional value for end users. In fact, in a recent survey of 500 application teams that we ran, predictive analytics was the number one feature currently being added to product roadmaps.

Why Predictive Analytics?

Because most applications focus on what’s happened in the past – showing dashboards and reports with historical data – rather than providing insights into what will happen in the future. Predictive analytics uses historical data, machine learning, and artificial intelligence (AI) to help the application end user act in a preemptive way. It answers this question: “What is most likely to happen based on my current data, and what can I do to change that outcome?”

In a software market where features have become commodities, predictive analytics is helping application end users do things like reduce customer churn, detect fraud, reduce machine downtime, and boost sales through targeted promotions. It can help you to set your application apart, make your product more valuable, and potentially find a new source of revenue for your organization.

Getting Started With Predictive Analytics

For a product manager, one of the first questions to ask when starting a predictive analytics initiative is who will be using your application, along with what questions they are trying to answer and what actions they’ll need to take as a result. Product managers should also think of how they can help their end users to act on future insights once predictive analytics is added to the application. Integrating workflow capabilities and other actions into the application means users will not only see predictive insights in context of where they work, but also be able to take action on those insights – for instance, kick off a new workflow, inform a colleague, or send certain customers an email notification.

Consider an application end user who handles accounts receivable. The key question for this end user is how many customers did not pay their bill or were late in paying their bill last month, and therefore which customers may pay their bills late or not at all. A modern predictive analytics solution can feed historical data into a mathematical model that considers the key trends and patterns in the data, and then predicts what will happen next. Some tools will also allow the user to predict what effect the different actions they take will have on the end result. So the model may predict that a direct phone call one week before a bill’s due date will be more effective than monthly reminders in getting customers to pay on time.

The Road to Predictive Analytics Doesn’t Have to be Complex

Predictive analytics is a complex capability, and implementing it is a complicated process littered with pitfalls. When companies treat predictive analytics like any other analytics venture, they can hit roadblocks.

Below are a few of the pitfalls you should watch out for when choosing a predictive analytics solution to work with:

The most common challenge with predictive analytics is expertise.

Predictive solutions are typically designed for data scientists who have deep understanding of statistical modeling, R, and Python. This is inherently limiting. In fact, most application teams can’t even begin to approach predictive analytics without first hiring a dedicated data scientist (or two or three!). Fortunately, new predictive analytics solutions are now emerging that are designed for almost anyone to use, even without expertise in statistical modeling, Python, or R.

Other common challenges include adoption and user engagement.

Predictive analytics is notoriously difficult to use, often living as a standalone tool. This means users have to switch from their primary business application over to the predictive analytics solution.

They may also fail to truly empower end users. Some predictive tools deliver information, but don’t let users take action without jumping into yet another application – ultimately wasting time and interrupting their workflow.

Successful application teams consider what capabilities will provide users with value now and into the future. By embedding predictive analytics inside their applications along with intelligent workflows, application teams are empowering their end users to see insights and take action all within the application – saving a lot of time and frustration. And when you add predictive analytics capabilities to your application, you create a product that users value and which stands apart from the competition.

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