Update 29 July, 2024: We tweaked this article fresh in 2024 to reflect recent changes in the marketplace for predictive analytics.
You can’t expect to make consistent progress and grow your business without analyzing your decisions. That kind of analysis should be based on the data you get from various analytics tools, measuring who you’ve interacted with, the most profitable customer acquisition channels, changes in your team’s productivity, and so on.
The problem is that most analytics tools are designed to track data and suggest solutions based solely on your past performance, which often fails to account for velocity and the internal rate of change.
What you need to prepare for the future is a tool that can predict it with a reasonable degree of accuracy. In other words, you’re looking for a predictive analytics platform.
Empower Your Decisions with Predictive Analytics
Traditionally, businesses have relied on data and analytics to measure performance and make decisions. This approach still works, but it doesn’t work that well for a few reasons:
- Analytics only tracks historical performance
- The amount of data coming from all sources is too large
- The data can’t be effectively synthesized by humans into actionable suggestions
While gathering traditional analytics was the best that businesses could do for decades, today, predictive analytics software opens up a new world of possibilities by forecasting trends, course-correcting your team on any decision-making in real time, and improving outcomes by focusing on the most actionable and impactful steps first.
The Foundation of Predictive Analytics
What Are Predictive Analytics Tools?
Predictive analytics models are simply the most advanced types of analytics available to companies today. They don’t just reflect and categorize past data — they make predictive judgements based on a combination of statistical models and algorithms.
While the predictive algorithms are trained on historical data, the goal of predictive models is to assess the likeliness of future events rather than accurately display past achievements.
Leading companies in a variety of industries, from finance to healthcare, leverage predictive analytics capabilities to streamline processes, find optimization strategies, and double down on growing market segments.
Why Would My Business Need Predictive Analytics?
In a business context, there are a variety of use cases in which predictive analytics can analyze data and make suggestions:
- Increasing efficiency within established processes
- Reducing external exposure risks
- Reducing customer churn
- Improving customer experience
- Identifying the best pricing model
- Forecasting sales
- Optimally growing your team
To take advantage of these sorts of decisions, you first need to choose the right predictive analytics tools and train their statistical models. The good news is the selection of predictive software gets wider every year, from established companies like IBM and Oracle to small but fast-iterating tech startups.
Key Features to Look for in Predictive Analytics Tools
The predictive analytics market is growing and servicing more industries and niches day by day. That is to say that you shouldn’t assume that all predictive analysis is the same, since different tools cater to different market segments.
These are some of the key predictive analytics features to consider when looking at a new platform to adopt:
- Data modeling: Predictive analytics techniques differ based on the type of data modeling they use, which can range from classification or regression models to time series analysis to Bayesian inference.
- Machine learning models: All the leading predictive analytics tools use machine learning to process vast quantities of data. The exact type of models, however, can have a significant impact on the results, so make sure to know how the data is being analyzed.
- Ease of use: Having an intuitive tool at your disposal that doesn’t require extensive training means having more people using it for their day-to-day needs.
- Integration options: Any tool you choose must integrate with other platforms you’re using for seamless transfer of data. Otherwise, the input will quickly become outdated, which will affect the quality of the results as well.
- Scalability: Even if your company is small today, as it grows, you’ll be dealing with increasing volumes of data, more complex models, more users, more requests, etc. Making sure that the predictive analytics platform of your choice can scale well is key.
How to Evaluate Tool Effectiveness
The first step to getting the best out of any predictive analytics tool is clearly defining your goals and the current situation (constraints).
Then try every tool within your range on a sample project, from which you can deduce its accuracy and processing speed.
Finally, reach out to customer support with a list of questions specific to your company and see how quickly they respond and how helpful their replies are.
Not sure where to start? Let’s take a look at the best predictive analytics platforms in 2024. Notice how the last tool in the list describes the current gap in predictive analytics and mentions the way it can be addressed.
Top Predictive Analytics Tools Reviewed
1. IBM SPSS
You simply can’t talk about enterprise data software and not mention IBM. SPSS is a multifaceted software platform that can be used for anything from statistical and text analysis to big data deployment and predictive algorithms.
There are three IBM SPSS products available: Statistics, Modeler, and Watson Studio Premium. For predictive analytics, take a closer look at IBM SPSS Statistics, which combines ad hoc and geospatial analysis, as well as hypothesis testing. If you want a graphical representation of your outcomes, you can easily do so in the SPSS Modeler.
2. RapidMiner Studio
If you just want to try what predictive analytics has to offer, RapidMiner Studio can be a great choice as the top predictive analytics software with a free plan available.
Other benefits of RapidMiner Studio are its ease of use, with a drag-and-drop interface, and a library of nearly 1,500 preset algorithms for a variety of use cases. The downside of RapidMiner is that once you need more features, its paid plans start at over $7,000 per user a year.
3. Alteryx APA
When you try to match data science and automation, you get Alteryx. Its analytic process automation (APA) platform strives to make data science easy even for people who can’t write proper queries. Alteryx is user-friendly, delivers actionable data insights, and can handle unstructured data. Besides, it has the best collaboration features for those who need multiple user accounts.
4. Leadspace CDP
Yet another type of predictive analytics is available through Leadspace’s customer data platform (CDP). Focusing solely on the B2B market, Leadspace connects all the data points from any channel, creates unified customer profiles, and leverages its artificial intelligence algorithms to provide clear next-step recommendations and even integrate into other sales and marketing solutions.
5. TIBCO Spotfire
One of the easiest solutions to come out of any predictive analytics platform is TIBCO’s one-click predictions. The Spotfire tool automatically clusters and pre-programs most data, establishing appropriate connections and relationships between various data points. As a result, the platform features insightful data visualization which can show you the most important information at a glance.
6. Sisense
Started mainly as a platform for developers and data scientists to create apps for data mining, Sisense has since dramatically improved the accessibility of its platform so that everyone can benefit from its modelling and predictive analytics. Business analytics features also extend into data visualization and easy KPI monitoring to help you come up with the right strategy at the right time.
7. Lift AI (and why it’s different)
Most predictive data analysis tools are too broad in scope. They are not using pure machine-learning models in real time, but instead ask you to create a desired outcome, feed it data, and then use some elements of statistical modeling and machine learning to help you. However, it’s often your responsibility to train those models correctly, and they require huge amounts of data to become useful.
Machine learning and the computing power available to execute it allows you to look at thousands of data points when you don’t even know if they will be informative or causal to the outcome. Only machine learning can assess such a huge set of data points and then compare every permutation against billions of already known outcomes to train the models.
It works best if it’s pre-trained and ready to go.
That’s what Lift AI uses, and it applies machine learning to determine the buyer intent of your website visitors based on a model built from millions of existing data points.
Lift AI can detect potential buyers on your website, even if they’re anonymous (who can make up to 98% of all visitors) instantly by way of their behavior — which is the likelihood to purchase or convert into a lead. Lift AI’s machine-learning model predicts this by scoring each individual visitor’s behavior on the website in real-time.
With this information in hand, you have the power to make real-time decisions and take action through your existing toolsets. Visitors could either be connected directly to one of your BDRs via any chat platform you have in place or met with a nurturing bot or a self-help bot to assist them depending on their purchase intent — low, medium, or high.
That’s just one example, but Lift AI scores can also be integrated with your CRO, CRM, SEP, MAP, and ABM tools to make them more effective and efficient.
It’s common for Lift AI to identify around 9% of your web traffic as having high buyer intent, so you can reach out to them and increase your conversion rates.
Pricing and Value
The majority of tools and related platforms you see above (e.g. SAP analytics cloud) are offered as subscription services. Some of them post their pricing online, others require you to get a quote from their sales team. Ask for quotes from all the providers you’re interested in to be able to compare pricing vs. value.
If you’re a small company with high potential for growth, look for providers that charge based on usage instead of a simple monthly or annual subscription.
Choosing the Right Predictive Analytics Tool
Identifying Your Needs
The best way to see how predictive data analysis can be helpful to you is by scanning through all the incoming data, whether it’s customer records, sales, phone calls, emails, expenses, demand fluctuations, etc.
Once you have different types of data in front of you, ask yourself which ones have the potential to be leveraged by predictive analytics to show possible future outcomes. It’s a good idea to start with those that have the most outsized impact on your business.
Implementation and Integration
Getting started with predictive models can be a daunting task, especially if they need to be trained on your data specifically. Make sure to inquire about onboarding when you talk to customer support and look for automated integrations into the existing data tools you use. Starting with those can give you actionable data with little time invested upfront.
Success Stories: Predictive Analytics Tools in Action
Depending on how you build predictive models within predictive analytics, some past data might result in an instant conversion to revenue while other data might need to keep accumulating before any insights can be derived from it.
With Lift AI, significant results tend to show up within the first 90 days, with some companies reporting up to 10 times increase in chat conversion rates.
For example, Chronos, one of Lift AI’s customers, got 85% more leads from its pipeline than before. Truckstop increased its sales efficiency by 3 times (9.72% vs. 3.28%). Drift had its conversions go up 9 times.
Future Trends in Predictive Analytics
Leveraging predictive analytics will soon become the integral part of any decision-making process in business. By embracing some of the leading predictive analytics platforms today alongside machine-learning solutions like Lift AI, you still have a unique opportunity to get a first-mover advantage in your competitive landscape.
Overall, real-time data, automated analytics, and complex statistical analysis are only going to grow in importance, and building predictive models that deliver actionable insights based on company data will attract more and more innovative solutions.
Getting Started With Your Predictive Analytics Journey
To get started with leveraging advanced machine learning to your advantage, try Lift AI risk-free today. Sign up for a proof of concept that Lift AI offers free of charge to companies with over 20,000 monthly visitors.
Reach out to the Lift AI team and see how your sales opportunities and monthly revenue can grow with little to no action on your part — thanks to the right analysis of your historical and current data.
In addition to Lift AI, start organizing data and trying out statistical techniques like regression analysis and categorical data analysis from multiple data sources using the demos and trials of the predictive analytics tools mentioned above. Once you get the hang of various predictive analytics models, choose the one that seems to give you the best predictive analytics capabilities automatically.
