Prescriptive analytics is the last step in the analytics process for enterprises. The concept of business optimization lies at the heart of it. So with our post on prescriptive analytics already in place, we will go through some of the best tools and techniques available at your disposal.
Basically, these techniques and tools will help business owners and strategists alike to make better decisions when venturing into prescriptive analytics.
So, let’s jump right in, starting with some of the best prescriptive analytics tools according to tons of reviews across the net.
Prescriptive Analytics Tools
The following are tools you should give some level of attention to as you scale through the world of prescriptive analytics.
#1. Improvado
Improvado is a data analytics solution for business marketing departments. It allows them to get all of their data in one place and in real-time via automated dashboards and reports.
Mostly, data is pulled from a variety of marketing systems, including Google Analytics, CRMs, email platforms, Facebook, and others. This information is then “piped” into any data warehouse and visualization platform you like.
Who benefits from Improvado?
Improvado is great for analytics and marketing, as well as business owners that require a solution to gather data from all of their marketing platforms into one location. This is one of the most popular prescriptive analytic tools. This is because, according to reviews, it has saved businesses millions of dollars and hundreds of hours of manual reporting work.
Furthermore, the platform’s integrations go all the way down to the ad and keyword level, extracting comprehensive data. You may even view ad creatives from the dashboard, giving you a complete overview of your statistics. Improvado is a simple, straightforward, and easy-to-use solution that eliminates the need for developer support with installation or adjustments
Read Also: Prescriptive Analytics: Definition, Real-World Examples, How it Works
Integrations
Improvado features over 150 different integrations. They’ll create custom integrations for any data source you specify.
#2. Alteryx
Alteryx is one of the few prescriptive analytics tools that combine data blending and analysis into a single tool. With a few clicks, the platform produces deployable analytics, using a repeatable procedure. It then shares the generated analytics to provide deeper data insights.
For the most part, this solution is ideal for both data analysts and data scientists since it allows them to integrate and restore data from cloud apps, spreadsheets, data warehouses, and other sources quickly and easily.
Furthermore, without having to write any additional code, the platform integrates the data and then does a prescriptive, statistical analysis. This provides scalable analytics, which will eventually contribute to your business’s success.
#3. RapidMiner
RapidMiner provides enterprises with artificial intelligence and predictive analytics via open and comprehensive data analytics techniques. Basically, users generate, maintain, and provide predictive analytics using this centralized tool, which has a sophisticated and robust graphical interface. The solution also supports scripting in a variety of programming languages.
Also, RapidMiner is a data science platform for analytics teams that unifies the whole data science lifecycle, from data preparation using machine learning techniques and models from prescriptive analytics.
Meanwhile, pre-built data connectivity, workflow components, and machine learning are all included in the platform’s visual interface.
Pricing for RapidMiner
RapidMiner Studio is priced per year and requires a three-year commitment. A free version with limited capabilities is also available from the company.
#4. Sisense
Sisense makes it simple for customers to turn their data into visually attractive interactive reports. The tool’s visualization features include a drag-and-drop basic user interface that makes it simple to build charts and more complicated images, as well as interactive visualizations.
Who Benefits from Sisense?
Sisense is a fantastic fit for analytics teams seeking a comprehensive view of their data with minimum IT support. The prescriptive analytics tool delivers useful information that leads to data-driven decisions. Furthermore, users can also connect to relevant apps or databases directly, mash up numerous data sources, and visualize data.
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Sisense Pricing and Integrations
Sisense charges on a case-by-case basis. Hence, for a price quote, you’ll need to contact the company.
Furthermore, over 100 data ports are available in Sisense. You could go through a complete list of these connectors on their official website.
#5. Birst
Birst is a web-based business intelligence and predictive analytics solution that combines insights from diverse teams, helping businesses to make better-informed decisions and automating the entire BI process.
Basically, every user is connected to a shared analytical network that can be readily accessed and extended, thanks to the platform’s unique architecture.
Who Benefits from Birst?
The tool’s shared network of analytics allows business users to be more agile while yet preserving IT control. Mostly, Birst focuses on one of the most difficult problems in data analytics. This includes building trust in data from several sources within an organization.
Pricing and Integrations
Birst’s tool is available for a free trial. However, you will need to contact the company for pricing information.
Meanwhile, on their website, Birst does not publish a list of data integrations. So, to get that information, you may have to speak with a customer care representative.
#6. Knime
Knime is a business intelligence (BI) application for data integration, reporting, and analytics. It has a visual interface with nodes for a variety of tasks. This ranges from data extraction to data presentation. Statistical models are the platform’s primary focus.
Furthermore, the analytics platform is primarily intended for data scientists, and it includes statistical functions, complex machine learning, and predictive algorithms, workflow control, and other features.
In addition, Knime can be used with a variety of data science tools, including Python, R, Hadoop, and H2O, to name a few.
Pricing and Integration
Knime is a free and open-source software platform. In other words, you do not need to pay any fee to access the tool.
Meanwhile, Knime’s platform has a wide range of integrations. You can view all of those integrations on their official website.
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#7. Talend
Talend is a data and marketing platform well-known for its versatility. For the most part, it can communicate with some of the most well-known cloud service providers, including Amazon Web Services, Microsoft Azure, and Google Cloud.
Talend collaborates with industry leaders across a wide range of industries to develop cutting-edge software for big data access, data integration, and data enrichment. The software also includes some of the best data integration and management tools on the market according to reviews from Reddit.
Pricing and Integrations
Talend offers a variety of price options, including a free open source model that is available to customers all over the world. The service can either be hosted locally or in the cloud.
Meanwhile, Talend has over a hundred data connections. All of Talend’s integrations may be found here.
#8. AIMMS
The AIMMS Prescriptive Analytics platform uses mathematical optimization techniques and data modeling to provide quantifiable results. Consequently, this provides a competitive advantage for businesses. The software’s versatility and robustness allow it to operate with a wide range of business strategists to help solve practically any problem.
Furthermore, AIMMS provides a safe, adaptable, and quick answer to almost any business problem. Interactive dashboards then deliver statistics and decision support to the entire organization, allowing for better results.
In addition, the drag-and-drop simplicity and visualization choices in this prescriptive analytics solution make it simple to design analytic models. And then the proprietary language makes it simple to build solutions without having to bother about programming.
Pricing and Integrations
The AIMMS website does not provide pricing information. It does not also publish a list of integrations on its website.
#9. Looker
Looker is a web-based platform with a proprietary modeling language. The application runs entirely in-database and uses the newest and quickest analytic databases to provide real-time verdicts. In simple terms, this makes it very simple for users to select, build, and configure a wide range of interactive visualizations by offering a large number of graphs, charts, and graphs.
Furthermore, for teams across nearly all business departments, the platform provides a useful BI and prescriptive analytics tool. Looker is especially useful for companies who desire a simple application that nonetheless gives a lot of information in a visual format.
Pricing and Integraitons
Pricing is determined by your specific business needs. So, you’ll need to get in touch with the company for a quote.
Meanwhile, Looker works with over 50 different data sources.
#10. Tableau
Tableau is a business intelligence platform that assists companies in turning data into actionable insights. The user-friendly platform makes it simple to connect to data stored virtually anywhere and in almost any format. Basically, users may construct interactive dashboards with advanced visual analytics using Tableau’s drag-and-drop capability.
1frthermore, Tableau can be used by data analysts, or really anyone, to gain valuable insights. The platform is a very handy way for businesses with a lot of clients to keep track of their progress. And more importantly, it doesn’t require any coding knowledge to utilize.
Moving on, you have the options of either installing the software on-site or cloud-hosting the data analytics tool on Tableau’s server.
Meanwhile, the latter may be an excellent choice for enterprises that don’t want to install more software on-site.
Pricing
Tableau has two pricing options for its product; one for the on-site installation and the other for Tableau hosting.
Integrations
Tableau allows users to connect to a wide range of data sources.
Prescriptive Analytics Techniques
Before we look at the various prescriptive analytics techniques, it’s important to grasp the importance of an analytical decision model. The components involved in making a decision are represented by the model:
- The Final Decisions: Basically you’d need to ask some questions like; What decision do we need to make, and what options do we have? What other options do you have if you don’t want to make the decision?
- Important Information to make the decisions: Questions like—Are we in possession of knowledge that will assist us in making the best decisions possible? Does the call center representative, for example, who is a decision maker, have access to the customer’s profile?—need precise answers.
- The Decision’s Goal: Finnally you’d need answers to questions like—What do we want to see happen as a result of this decision? Can the decision, for example, help us enhance sales in the current quarter?—at this point.
Moving on, prescriptive analytics techniques vary. For starters, optimization, game theory, simulation, decision-analysis approaches, and control systems are just a few examples. However, it has been discovered that most organizations adopt the following two types:
#1. Predictive Analytics Plus Rules
An analytical decision model is common in all prescriptive analytics techniques. To put it another way, they contain all three elements that go into making a decision.
The ‘Predictive Analytics Plus Rules’ methodology, on the other hand, is more straightforward. Without examining all possible outcomes, it mixes projections with business-defined rules and assumptions. As a result, it may not always recommend the optimal course of action. This technique often comes in handy when making operational decisions.
Let’s consider this scenario; (Making an offer to the customer is the action point🙂
For each offer, a predictive model can be designed to determine the optimal offer that can be presented to the customer. This will determine how likely each customer is to respond to the offer. So in line with a rule, every consumer could be offered the proposition with the highest customer reaction.
This technique is prescriptive due to business-defined rules. However, due to the use of data and analytics, it is prescriptive analytics. The rules get smarter and the analytics become actionable as a result of this combination.
#2. Optimization
The ‘optimization’ approach is the second prescriptive analytics technique. This is generally used to make strategic and tactical decisions. However, it could also be a tool for making operational decisions.
It uses an analytical decision model to calculate all possible outcomes for each option. It further assesses the extent of compromise between several goals. In other words, in scenarios with varying levels of uncertainty, it evaluates the best use of limited time and resources.
There is, however, an issue with the optimization approach and it is divided into three main components:
- Objective: The metric that needs to be optimized is called an objective. For example, maximizing earnings for the current fiscal year could be a objective.
- Decisions: These are choices that must be taken in order to reach the goal/objective. A decision on the quantity of financial investment, for example, could be made.
- Constraints: These are limitations or restrictions that can impact a decision. The amount of a budget, for example, can limit the coverage area of a decision.
Consider the illustration that was used to demonstrate the first technique. Against the action point (make an offer to the consumer), the analytical decision model would present all potential options (for example, send offer A to the customer by email today) and outcomes (for instance, response probability). So to find the best action point, the user can compare one combination (action point, option, and projected results) to all other combinations.
Consequently, as the complexity grows (more action points and outcomes), the demand for advanced algorithms that can compute efficiently follows suit. Meanwhile, this need has been met thanks to significant advancements in the field of optimization algorithms.
Real-World Examples of Optimization in the Retail Industry:
- Optimize Promotional Programs: Businesses can choose which campaigns to run and for which products, taking into account factors such as budget, channels, inventory, and resources.
- Optimize Product Selection: In order to generate the most value, businesses can determine the optimal proportion of premium, medium, and low-cost products. Basically, product cost, demand, and substitution impacts are all factored into the model.
- Optimize Layouts: To boost sales, some retailers employ the prescriptive method to determine the ideal store layout.
- Optimize Pricing: Because the prescriptive method assists organizations in identifying and understanding patterns and insights, pricing decisions can be made with confidence.
What Is Prescriptive Analytics Tool?
Prescriptive analytics is a statistical method that uses data to find the best way to move forward or the best action to take in a certain situation. Both descriptive and predictive analytics are used in prescriptive analytics, but the focus is on actionable insights rather than data monitoring.
What Are Prescriptive Analytics Techniques?
Prescriptive analytics use a combination of methods and tools, such as business rules, algorithms, machine learning (ML), and computational modeling procedures. These methods are used on many different kinds of data, such as historical and transactional data, real-time data feeds, and “big data.”
Which Is an Example of Prescriptive Analytical Tool?
Email automation is a clear example of the use of prescriptive analytics. Marketers use email automation to sort leads into groups based on their goals, mindsets, and plans, and then send them emails based on those groups.
How Does Netflix Use Prescriptive Analytics?
Prescriptive Analytics focuses on the present by taking into account the different variables that will influence the future. It assists Netflix in understanding the aspects that influence the purchase decisions of customers. This strategy is then utilized to make timely decisions.
