Imagine if making business decisions were as easy as peering into a crystal ball, and seeing what works and doesn't work in the future? This seemingly far-fetched scenario is closer to reality than many realize, thanks to the science of predictive analytics.Predictive analytics involves gathering information from existing data sets and recognizing patterns to predict future trends or outcomes. According to Predictive Analytics World, "Predictive analytics optimizes marketing campaigns and website behavior to increase customer responses, conversions and clicks, and to decrease churn." Naturally, businesses find the ability to harness this information and make data-driven decisions about the future attractive. Predictive analytics implies that business leaders will acquire insights into their customers' behavior patterns. This insight will, in theory, improve sales. Should all business leaders jump aboard the predictive analytics bandwagon? Is it really a cure-all? Like most things, predictive analytics can be valuable. But as well as having positives, predictive analytics also embodies negatives. In short, businesses should approach predictive analytics with their eyes open to its possibilities and limitations. When Does Predictive Analytics Work? For any analytics project to achieve actionable insights, a business must clearly define its goals, objectives, and user needs. A telecommunications company might want to address customer retention while a retailer might seek information on future buying trends. Identifying appropriate data sets to be analyzed and recognizing relationships among them is also critical for successful analysis. Moreover, the quality of data is another important factor in achieving actionable insights. For example, if you're using customer feedback surveys as a data set, those surveys must be well-designed and offer a complete look at customer sentiments. How will predictive analytics help your business? "At the end of the day, predictive analytics benefits are all about understanding the behavior of your customers and of your potential customers," writes Cary Comer in an article for The Digital Bridge. "By analyzing data, you can better anticipate the behavior of your audience, and when you can anticipate that behavior, you can make a better case for audience participation in your campaign." The Shortcomings of Predictive Analytics Despite the many pros of predictive analytics, it still has limitations. The Harvard Business School quotes Arie Goldshlager who said, "'Predictive analytics can and do produce remarkable results,' but they can also produce many false negatives and positives that have to be anticipated and proactively managed." A few barriers exist that can thwart successful predictive analytics. For example, lack of useful data, and false or outdated assumptions are the two major factors. "People establish strong patterns of behavior that they usually keep up over time," writes Tom Davenport in the Harvard Business Review. "Sometimes, however, they change those behaviors, and the models that were used to predict them may no longer be valid." Moreover, while predictive analytics can offer valuable insights, it cannot guarantee a statistical rise in leads or conversions. If an analytics vendor makes promises about the ROI of predictive analytics that sound too good to be true, guess what? They probably are. It is also important to note that predictive analytics will provide no benefit if the customer ignores the insights, or is unable to leverage the insights. How Solution as a Service Can Help With Analytics It is possible to avoid the common pitfalls of predictive analytics by hiring a capable analytics vendor. Solution as a Service (SolaaS) is a unique approach to analytics, which through a single vendor combines a powerful analytics engine and customizable frontend; with the industry's best data scientists. These data scientists identify, wrangle, and analyze the best possible data sets that are relevant to a given business's objectives. Analytics engines are complicated, and need constant tweaking and upkeep. When working with predictive analytics, it is necessary to address any changes in customer behavior which might affect assumptions. As customer behavior patterns shift and assumptions change, the SolaaS vendor's data scientists tweak and manipulate the backend, and identify new data sets, if necessary. Flexibility is key with SolaaS. A vendor like PolyVista offers POC (proof of concept), as well as one-time, monthly, and multi-month contracts to meet their clients' needs and budgets. Summary Predictive analytics helps businesses make data-driven decisions about future endeavors, but it isn't a perfect cure-all. Predictive analytics requires quality data, but can fail if the wrong data is analyzed, relationships among data sets aren't recognized, or assumptions prove false and outdated. A Solution as a Service vendor alleviates these shortcomings by combining a top-of-the-line backend with the services of highly trained data analysts.