Data-forward businesses use analytics to drive their decision making. The meaning behind those analytics requires teams to look at the past, the present, and the future. That’s where descriptive, predictive, and prescriptive analytics models come into play. Each of these analytics types serves a different purpose and is informed through integration. Let’s look at how we can fuel descriptive, predictive, and prescriptive analytics with integration for streamlined and simplified decision making.
Defining Descriptive, Predictive, and Prescriptive Analytics
Before we highlight each analytics approach in relation to integration, let’s first make sure we’re clear on what each type of analytics represents and what they’re used for.
Descriptive analytics paint a picture of what happened in the past by using data mining and data aggregation strategies. Businesses access descriptive analytics through stored data and summarize it with charts, dashboards, and other visuals to understand what it means. For example, retailers may use descriptive analytics to learn that most shoppers turn right when they enter a store. That data summarizes a pattern that they can now apply retroactively for future store layouts.
Predictive analytics are pretty much how they sound. They use statistical analysis and forecasting to predict what might happen in the future so the business can plan effectively. Descriptive analytics (like those uncovered by retailers in above example) show a definite trend, but predictive analytics are a bit more unpredictable.
They assume that those trends increase the probability of things like future demand, customer churn, employee turnover, etc. Predictive analytics algorithms capture dataset correlations from multiple systems to make educated decisions based on machine learning. For example, a credit score is partially informed by predictive analytics. Banks look to historical data to accurately guess how likely a candidate will be to keep up with payments.
Prescriptive analytics is all about what should happen next. It’s used to inform data-driven decision making in the here and now. While predictive analytics are looking down the road to guess future trends that might affect a company, prescriptive analytics use complex models from multiple data sources to help make well-informed hypotheses for the shorter term.
For example, you may use prescriptive analytics to inform product development. By gathering data on customer surveys, market research, behavioral data, and tests of the beta version, you’d identify which features are performing well and which may need to be reconsidered to optimize the user experience.
Using Integration for Descriptive Analytics
Descriptive analytics are informed by processing huge amounts of historical data. Integrating data warehouses into other application is one way to enable descriptive analytics.
If you don’t have a data warehouse, data can also be pulled from multiple applications like CRM, ERP, eCommerce, big data solutions, etc. Once systems are integrated together, the resulting analysis can be displayed as compelling visuals that tell the story of that data.
For example, you could combine ERP and CRM metrics to identify trends in customer payment cycles for different industries.
Using Integration for Predictive Analytics
Predictive analytics are probably the most common type of analytics used by businesses today. Organizations rely on this data to plan marketing actions, detail sentiment analysis, perform risk modeling, predict buying behaviors, recommend content, maintain quality assurance, and much more.
Various systems contribute to a company’s internal knowledge, and it’s by integrating those systems that deeper predictive analysis can be achieved. For example, to strategize upsell and cross-sell options, a company may want to review purchasing history, unique market segments, past campaigns, and revenue per customer to come up with a plan.
Data from the eCommerce website, the marketing automation, and the accounting solution could all come into play. Integration allows predictive analysis from all those necessary data points to suggest which cross-sell and upsell actions are most likely to increase customer lifetime value and revenue.
Using Integration for Prescriptive Analytics
Prescriptive analytics consider all the relevant factors of something to help decide what should happen next based on that information. For example, if your marketing team is automating an email campaign, they may use prescriptive analytics to review how leads in that campaign are reacting to certain messages. Based on that data, they can segment certain groups to receive a different set of messages and test what performs better. Integrating third party analytics tools with solutions like CRM and marketing automation is one way to use integration for prescriptive analytics.
Analyzing data from the past, present, and future highlights where a business is doing well and where it can evolve. Regardless of whether you’re a B2B or B2C organization, analytics are essential to gaining better understanding your business and supporting your scalability. That’s why so many companies turn to integration as a catalyst for their analytics.
Integration allows you to detangle the insights you already have buried in your systems and apply them in new ways. It’s a must for taking full advantage of your descriptive, predictive, and prescriptive analytics models.