17 Best Predictive Analytics Software for Small Businesses Reviewed In 2024
Best Predictive Analytics Software for Small Businesses Shortlist
Here's my pick of the 10 best software from the 17 tools reviewed.
Our one-on-one guidance will help you find the perfect fit.
Predictive analytics software for small businesses is a valuable asset for any newly emerging or growing company. With the right software, teams can improve decision-making and enhance efficiency. But finding the right predictive analytics software for your small business can be a challenge, especially with so many options available.
Using my experience with revenue operations software, I tested and compared several of the top predictive analytics software for small businesses available. Then, I compiled my results into detailed reviews to help you find the best software for your needs.
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Best Predictive Analytics Software for Small Businesses Summary
This comparison chart summarizes pricing details for my top predictive analytics software for small businesses to help you find the best one for your budget and business needs.
| Tools | Price | |
|---|---|---|
| Mixpanel | From $20/month | Website |
| Domo | Pricing upon request | Website |
| Improvado | Pricing upon request | Website |
| Sisense | Pricing upon request | Website |
| Qrvey | From $2500/year | Website |
| Google Cloud Big Query | Pricing upon request | Website |
| TIBCO Platform | Pricing upon request | Website |
| Marketing Evolution | Pricing upon request | Website |
| FICO | Pricing upon request | Website |
| SAS Viya | From $0.55/SU/hour | Website |
Compare Software Specs Side by Side
Use our comparison chart to review and evaluate software specs side-by-side.
Compare SoftwareHow to Choose a Predictive Analytics Software for Small Businesses
It’s easy to get bogged down in long feature lists and complex pricing structures. To help you stay focused as you work through your unique software selection process, here’s a checklist of factors to keep in mind:
| Factor | What to Consider |
| Scalability | Ensure the software can grow with your business, handling increased data volume and user demand without performance issues. |
| Integrations | Check if the software connects with your existing tools, like CRM or marketing platforms, to streamline workflows and data sharing. |
| Customizability | Look for options to tailor dashboards, reports, and analytics models to suit your team's specific needs and preferences. |
| Ease of Use | Opt for software with an intuitive interface that your team can quickly learn and use, minimizing training time and maximizing productivity. |
| Budget | Assess total costs, including subscription fees, setup charges, and any additional expenses. Make sure it fits your financial plan. |
| Security Safeguards | Verify the software has strong data protection measures, like encryption and compliance with regulations, to keep your information safe. |
| Support | Consider the availability and quality of customer support, including live chat, email, and phone assistance, to help resolve any issues promptly. |
| Reporting Capabilities | Ensure it offers advanced reporting tools to analyze data science trends and generate insights that drive decision-making. |
Best Predictive Analytics Software For Small Businesses Reviews
Below are my detailed summaries of the best predictive analytics software for small businesses that made it onto my shortlist. My reviews offer a detailed look at the key features, pros & cons, integrations, and ideal use cases of each tool to help you find the best one.
Mixpanel tracks user interactions across web and mobile applications. It provides insights into customer behavior, engagement, and retention, allowing businesses to anticipate user actions, make proactive decisions, and drive growth.
Why I picked Mixpanel: Mixpanel offers in-depth behavioral analysis, letting you see how users interact with your product at every stage. It has funnel tracking for pinpointing where users drop off and cohort analysis for grouping users based on specific behaviors, helping you refine engagement strategies. Mixpanel’s retention reports also make it easy to track how well your product holds user interest over time, giving you valuable insights for enhancing the product experience.
Standout features & integrations:
Features include event tracking, which captures specific user interactions within your product, cohort analysis to help you group users based on their behaviors, and A/B testing capabilities.
Integrations include Salesforce, HubSpot, Google Analytics, Slack, Zendesk, Segment, Shopify, Optimizely, Marketo, and WordPress.
Pros and cons
Pros:
- Cross-platform tracking
- Cohort analysis capabilities
- Real-time data analysis
Cons:
- Basic forecasting tools
- Limited anomaly detection
Domo enables real-time visualizations and predictive insights through AI and machine learning. It offers end-to-end solutions, predictive analytics, and performance alerts for optimizing operations and improving strategic outcomes.
Why I picked Domo: Domo’s advanced business intelligence suite allows your team to access and analyze data in real time. This feature tracks business metrics as they change, helping your team respond quickly to trends. Additionally, the software’s data preparation tools also allow you to organize and clean data efficiently, while its user-friendly design ensures your team can explore data and find insights without needing advanced technical skills.
Standout features & integrations:
Features include powerful data connectors that bring in data from multiple sources, real-time data updates, and mobile access, so your team can access data on the go, enhancing flexibility and responsiveness.
Integrations include Salesforce, Google Analytics, QuickBooks, Microsoft Dynamics, SAP, Oracle NetSuite, HubSpot, Shopify, Zendesk, and Slack.
Pros and cons
Pros:
- Scenario planning features
- Predictive KPI tracking
- Advanced data visualization
Cons:
- Minimal model customization
- Few advanced algorithms
Improvado centralizes over 500 sources, enabling small businesses to automate reporting and gain actionable insights. It provides a unified view of performance metrics and helps identify trends and optimize return on ad spends.
Why I picked Improvado: Improvado customizes data connections and automates reporting to consolidate all your data in one place. It offers customizable metrics and dashboards so you can shape reporting to match your team’s needs. The platform also automates data collection, while its data transformation tools standardize metrics for consistent reporting across channels.
Standout features & integrations:
Features include automated data extraction, customizable dashboards, and data transformation to ensure data is ready for analysis without additional formatting.
Integrations include Google Analytics, Facebook Ads, Twitter Ads, LinkedIn Ads, Shopify, HubSpot, Salesforce, Bing Ads, Adobe Analytics, and Mailchimp.
Pros and cons
Pros:
- Automated data normalization
- Real-time data integration
- Customizable data models
Cons:
- Lacks advanced algorithms
- Limited forecasting tools
Sisense is a business intelligence and data analytics platform that enables organizations to gather, analyze, and visualize data from various sources. Its data exploring and reporting capabilities help businesses make informed decisions and drive change.
Why I picked Sisense: Sisense lets you customize dashboards extensively, so your team can display the data that matters most. Its drag-and-drop capabilities can quickly build and modify reports to fit changing needs, while its data preparation tools help you organize information from multiple sources without heavy coding. Additionally, the software’s user-friendly interface ensures everyone on your team can access insights easily and make informed decision.
Standout features & integrations:
Features include in-chip technology for fast data processing, various data sources, and ad-hoc reporting tools, enabling you to generate reports on the fly as needed.
Integrations include Salesforce, Google Analytics, Amazon Redshift, PostgreSQL, MySQL, Oracle NetSuite, Microsoft SQL Server, Snowflake, HubSpot, and Zendesk.
Pros and cons
Pros:
- Real-time anomaly detection
- In-depth trend analysis
- Advanced data modeling
Cons:
- Basic forecasting tools
- Limited model customization
Qrvery is a cloud-native analytics platform that integrates predictive analytics into its applications for real-time insights and reporting. It ensures data security and compliance and is highly scalable for business operations.
Why I picked Qrvey: Qrvery offers flexible embedding options that let you integrate analytics directly into your software. With interactive dashboards, you can display data that aligns with your brand and user needs. Qrvey’s automation capabilities also allow your team to set triggers and workflows based on data analyst updates, keeping insights timely and relevant. Additionally, the software’s intuitive setup also ensures you can quickly adjust analytics without relying on extensive coding skills, letting you focus on delivering valuable insights to users.
Standout features & integrations:
Features include interactive dashboards that allow your team to engage with data effectively, real-time data processing, and various data input methods, ensuring advanced data analysis.
Integrations include AWS, Salesforce, HubSpot, Slack, Google Analytics, Marketo, QuickBooks, Zendesk, Shopify, and Microsoft Dynamics.
Pros and cons
Pros:
- Automated data profiling
- Multi-tenant data lake
- Custom data models
Cons:
- Manual data preparation
- No hyperparameter tuning
Google Cloud Big Query is a serverless, highly scalable data warehouse that allows users to visualize and interpret large amounts of data without complex data transfers. It supports real-time data analysis and runs SQL queries using Google’s processing power.
Why I picked Google Cloud Big Query: Google Cloud Big Query handles large-scale data analysis, letting you process and query massive datasets without delays. The software’s data warehousing options make storing and organizing complex datasets easy, and its query optimization ensures your analysis remains efficient, even as data volumes grow.
Standout features & integrations:
Features include real-time analytics, machine learning capabilities that allow your team to build models directly within Big Query, and data sharing.
Integrations include Google Analytics, Google Ads, Looker, Data Studio, Tableau, Informatica, Talend, Qlik, Power BI, and SAP.
Pros and cons
Pros:
- Real-time data streaming
- Built-in ML models
- SQL-based querying
Cons:
- Complex model deployment
- Manual data cleaning needed
TIBCO Platform offers strong data visualization, enabling users to create interactive dashboards and explore complex datasets without technical expertise. With these features, users can gain strategic insights to improve operational efficiency.
Why I picked TIBCO Platform: TIBCO Platform allows you to create highly customized, interactive visualizations that make data easy to understand. It has a drag-and-drop functionality for quickly building visuals without extensive coding and supports real-time analytics for making decisions based on current data. Additionally, TIBCO Platform’s dashboarding options let you organize data that aligns with your team’s needs, while its flexibility allows you to present data in an informative and visually engaging way.
Standout features & integrations:
Features include advanced data visualization tools, real-time analytics capabilities, and advanced customization options, allowing your team to adapt the platform to fit your needs.
Integrations include AWS, Salesforce, SAP, Oracle NetSuite, Microsoft Azure, Google Cloud, IBM Cloud, Snowflake, MongoDB, and Apache Kafka.
Pros and cons
Pros:
- Flexible deployment options
- Real-time analytics
- Advanced data visualization
Cons:
- Minimal model customization
- Few advanced algorithms
Marketing Evolution offers predictive analytics for optimizing marketing strategies and enhancing return on investment. It provides an advanced view of marketing efforts, allowing businesses to understand the full customer journey.
Why I picked Marketing Evolution: Marketing Evolution offers deep insights into consumer behavior and campaign effectiveness. It uses advanced data modeling to provide real-time feedback on marketing efforts and helps your team allocate resources by identifying high-performing channels. Additionally, its performance forecasting tools also give you a view of potential outcomes for future marketing plans.
Standout features & integrations:
Features include cross-channel attribution, audience segmentation tools that allow you to target specific groups with tailored campaigns, and predictive analytics to forecast future marketing trends and outcomes.
Integrations include Salesforce, Google Analytics, Adobe Analytics, Oracle NetSuite, Microsoft Dynamics, SAP, Marketo, HubSpot, Tableau, and IBM Watson.
Pros and cons
Pros:
- Cross-channel attribution
- Real-time analytics updates
- Person-level data insights
Cons:
- Limited model customization
- Limited third-party integrations
FICO specializes in risk management and decision-making solutions. It integrates advanced analytics and machine learning for real-time insights into potential risks, helping businesses anticipate and mitigate financial challenges.
Why I picked FICO: FICO provides specialized risk management features to help your team make informed decisions. It has customizable scoring models for assessing risk factors specific to your business needs and data visualization tools to simplify complex insights. Additionally, FICO’s scenario simulation allows you to test various outcomes, so your team can prepare for potential challenges in advance.
Standout features & integrations:
Features include credit scoring models, fraud detection tools to help safeguard your business against fraudulent activities, and compliance management features ensure that your operations adhere to industry standards and regulations.
Integrations include Salesforce, SAP, Oracle NetSuite, Microsoft Dynamics, IBM Watson, SAS, Tableau, Google Cloud, AWS, and Adobe Analytics.
Pros and cons
Pros:
- Real-time decision making
- Customizable scoring models
- Advanced risk assessment
Cons:
- Few forecasting tools
- Limited algorithm variety
SAS Viya performs advanced statistical analysis and predictive modeling. It helps small businesses prepare and cleanse data, and build and deploy machine learning models to support data-driven decision-making.
Why I picked SAS Viya: SAS Viya offers advanced statistical analysis capabilities, providing your team with the tools to conduct in-depth data exploration. The platform supports various statistical methods for complex data environments. You can also perform predictive modeling and machine learning to enhance the accuracy of forecasts. Additionally, SAS Viya's scalability ensures it can handle large datasets effectively, making it a reliable choice for growing businesses.
Standout features & integrations:
Features include comprehensive statistical analysis tools that support a variety of data models, machine learning capabilities to automate the process of building predictive models, and a collaborative environment.
Integrations include Microsoft Azure, AWS, Google Cloud, Hadoop, Teradata, Oracle NetSuite, IBM Db2, SAP, Salesforce, and Cloudera.
Pros and cons
Pros:
- Real-time analytics capabilities
- Automated machine learning
- Integrated data management
Cons:
- Minimal model customization
- Few advanced algorithms
Other Predictive Analytics Software for Small Businesses
Here are some additional predictive analytics software for small businesses that didn’t make it onto my shortlist, but are still worth checking out:
- Lift AI
For real-time engagement insights
- Zoho Analytics
For user-friendly interface
- Alteryx
For data preparation automation
- Heap
For automatic data capture
- Fullstory
For user experience insights
- IBM SPSS Statistics
For creating valuable models
- Quaeris
For natural language queries
Predictive Analytics Software for Small Businesses Selection Criteria
When selecting the best predictive analytics software for small businesses to include in this list, I considered common buyer needs and pain points like ease of integration with existing systems and scalability. I also used the following framework to keep my evaluation structured and fair:
Core Functionality (25% of total score)
To be considered for inclusion in this list, each solution had to fulfill these common use cases:
- Data visualization
- Predictive modeling
- Automated reporting
- Data integration
- Trend analysis
Additional Standout Features (25% of total score)
To help further narrow down the competition, I also looked for unique features, such as:
- Customizable dashboards
- Real-time analytics
- Natural language processing
- Advanced machine learning models
- Mobile accessibility
Usability (10% of total score)
To get a sense of the usability of each system, I considered the following:
- Intuitive interface design
- Ease of navigation
- Minimal learning curve
- Customization options
- Responsive design
Onboarding (10% of total score)
To evaluate the onboarding experience for each platform, I considered the following:
- Availability of interactive product tours
- Access to training videos and tutorials
- Pre-built templates for quick setup
- Support through webinars and workshops
- Effective chatbot assistance
Customer Support (10% of total score)
To assess each software provider’s customer support services, I considered the following:
- 24/7 availability
- Multiple support channels
- Quick response times
- Quality of help documentation
- Personalized support options
Value For Money (10% of total score)
To evaluate the value for money of each platform, I considered the following:
- Competitive pricing against similar tools
- Transparency of pricing structure
- Flexibility of subscription plans
- Inclusion of features in base price
- Availability of discounts or offers
Customer Reviews (10% of total score)
To get a sense of overall customer satisfaction, I considered the following when reading customer reviews:
- Overall satisfaction rating
- Comments on reliability and performance
- Feedback on customer experience support quality
- User experiences with onboarding
- Opinions on feature usefulness
Trends In Predictive Analytics Software for Small Businesses
In my research, I sourced countless product updates, press releases, and release logs from different predictive analytics software for small business vendors. Here are some of the emerging trends I’m keeping an eye on:
- AI-Powered Insights: Vendors are using artificial intelligence to provide more accurate predictions and deeper insights. This helps businesses make informed decisions faster and improve sales strategies.
- Data Democratization: More tools are focusing on making business analytics accessible to non-technical users. This includes features like natural language queries, allowing anyone in a business to interact with data without needing specialized skills.
- Embedded Analytics: There's a growing trend of integrating analytics capabilities directly into business applications. This allows users to access insights where they work, without switching between platforms.
- Augmented Analytics: This involves using machine learning to automate data preparation and insight generation. It reduces the time spent on manual data analysis, helping businesses quickly get actionable insights and enhance user experiences.
- Privacy-Focused Features: With increasing data privacy concerns, vendors are implementing features that enhance data security and comply with regulations. This includes encrypted data storage and stringent access controls that protect sensitive business data.
What Is Predictive Analytics Software for Small Businesses?
Predictive analytics software for small businesses helps smaller-scale companies forecast future outcomes based on historical data. Business analysts, marketing teams, and sales professionals use these tools to gain insights into trends and make informed decisions.
Key features such as data visualization, predictive modeling, and automated reporting features help understand complex data, predict future trends, and provide significant value. This enables businesses to anticipate changes and optimize their strategies accordingly.
Features of Predictive Analytics Software for Small Businesses
When selecting predictive analytics software for small businesses, keep an eye out for the following key features:
- Data Visualization: Transforms complex data sets into easy-to-understand visual formats, helping users identify trends and insights quickly.
- Predictive Modeling: Uses historical data to forecast future outcomes and aid in strategic planning and decision-making.
- Automated Reporting: Generates regular reports without manual intervention, saving time and ensuring consistency in data analysis.
- Real-Time Analytics: Provides up-to-date data insights, allowing businesses to make timely decisions based on current information.
- Natural Language Processing: Enables users to interact with data using simple language queries, making data analysis accessible to non-technical users.
- Data Integration: Combines data from various sources into a single platform, ensuring comprehensive analysis and insight generation.
- Machine Learning Capabilities: Automates data analysis and model building, enhancing the accuracy of predictions and insights.
- Customizable Dashboards: Allows users to tailor data presentations to meet specific business needs and improve the relevance of insights.
- Privacy-Focused Features: Ensures data security and compliance with regulations, protecting sensitive business information.
- Mobile Accessibility: Provides access to data insights on the go, ensuring flexibility and responsiveness in decision-making.
Benefits of Predictive Analytics Software for Small Businesses
Implementing predictive analytics software for small businesses benefits your team and your company. Here are a few you can look forward to:
- Improved Decision-Making: By using predictive modeling and real-time analytics, your team can make informed decisions based on data-driven insights.
- Time Savings: Automated reporting and data visualization reduce the time spent on manual data analysis, allowing your team to focus on strategic tasks.
- Enhanced Accuracy: Machine learning capabilities improve the precision of forecasts and predictions, providing more reliable insights.
- Increased Accessibility: Features like natural language processing and customizable dashboards make data insights accessible to all team members, regardless of technical expertise.
- Better Resource Allocation: By understanding trends and making accurate predictions, you can allocate resources more effectively to achieve business goals.
- Data Security: Privacy-focused features ensure that sensitive business information is protected, maintaining compliance with data regulations.
- Flexibility in Operations: Mobile accessibility allows your team to access insights on the go, ensuring they can respond quickly to changing business conditions.
Costs and Pricing of Predictive Analytics Software for Small Businesses
Selecting predictive analytics software for small businesses requires understanding the various pricing models and plans available. Costs vary based on features, team size, add-ons, and more. The table below summarizes common plans, their average prices, and typical features included in predictive analytics software for small business solutions:
Plan Comparison Table for Predictive Analytics Software for Small Businesses
| Plan Type | Average Price | Common Features |
| Free Plan | $0 | Basic data visualization, limited data integration, and basic reporting. |
| Personal Plan | $5-$25/user/month | Advanced data visualization, predictive modeling, and automated reporting. |
| Business Plan | $30-$75/user/month | Real-time analytics, customizable dashboards, and enhanced data integration. |
| Enterprise Plan | $80-$150/user/month | Full data integration, machine learning capabilities, natural language processing, and data security. |
Predictive Analytics Software for Small Businesses FAQs
Here are some answers to common questions about predictive analytics software for small businesses:
How can predictive analytics be applied to improve performance in an organization?
Predictive analytics allows teams to anticipate outcomes based on historical data and statistical models. By leveraging these insights, you can develop data-driven strategies in market research, product development, and marketing. This approach helps optimize performance and drive growth across various business sectors.
What are the risks of predictive analytics?
While predictive analytics offers valuable insights, it does come with risks like data limitations and overfitting. Your team might face challenges with interpreting complex models and managing resource demands. It’s also essential to be mindful of ethical concerns, particularly data privacy and bias.
How does predictive analytics help business?
Predictive analytics tools help your business streamline operations, boosting revenue, and mitigating risks. It’s applicable across various industries, such as banking, retail, and healthcare. Augmented analytics, which incorporates big data and machine learning, further enhances decision-making processes.
How long does it take to see results from using predictive analytics software?
The timeframe varies depending on factors like the complexity of your data and the specific goals you’re targeting. Some businesses notice improvements within a few weeks, while others may take several months to realize the benefits. Consistent use and regular analysis will help you achieve better results over time.
How do businesses ensure the ethical use of predictive analytics?
To use predictive analytics ethically, your team should prioritize data privacy and transparency. Implementing clear data governance policies and conducting regular audits can help maintain trust. It’s also crucial to address potential biases in models and ensure compliance with industry regulations.
What are common challenges when implementing predictive analytics?
Implementing predictive analytics can pose challenges such as integrating existing systems and managing data quality. Your team might also encounter a steep learning curve and require specialized skills. Overcoming these hurdles involves investing in training and choosing tools that align with your business needs.
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