How predictive analytics solutions shape tomorrow
Everyone wonders about the future. And while we may not currently have a perfect future predictor, deep learning AI is helping us via predictive analytics solutions.
What are predictive analytics solutions?
Predictive analytics solutions combine a host of artificial intelligence technologies, including deep learning, data modeling, and sentiment analysis to provide trend data used in multiple industries for operational planning. Using this technology helps take the guesswork out of decision making. Especially for those industries dependent upon massive amounts of data collected through various methodologies.
Predictive analytics solutions in action
While used in many industries that handle large amounts of data, predictive analytics in sales, marketing, and industrial decision-making is among the forefront of AI. Immediate benefits can be achieved in these markets by finding “quick wins,” such as predicting customer purchase patterns or the life expectancy of machinery. The long-term benefits resulting from the reports based on various data sources is critical for planning for success.
In a sales environment, information is gathered on existing and potential customers from day one. Companies are looking to learn who their target is, and how to convince them to buy. Additionally, many companies have access to data above and beyond simple contact information. Website analytics is one such data source that provides an added layer of data. When combined with customer demographics and other information, a clearer picture emerges of the best way to compatibly reach specific consumers.
Today’s webmasters employ a large amount of tools to ensure a potential client takes the desired actions on their site. This includes the initial visit to the product and every navigation in between. Using add-ons that allow the recording of user interface actions, such as mouse movements, web designers can start to paint a clearer picture of how clients are using the website. With this information, webmasters can bring solid reporting to the table and begin testing the best layout to encourage purchases. Thus proving what aspects of the site’s various landing pages are more likely to attract attention from potential clients.
Combining this information with any demographic details collected during the transaction can allow companies to target a more finite range. By making sure they target the right group of people using the right price point, companies lessen time and money spent on campaigns that simply do not work.
Once a marketing piece gets a customer’s attention, companies want to use anything at their disposal to convert contact opportunities into a sale. Sentiment analysis is a technology that can be useful in predictive sales analytics to help with determining the best method to appeal to the consumer. By looking at customer satisfaction and purchase history of similar consumers, companies can offer targeted pricing and packages that are more likely to end in a sale.
The usage of predictive sales analytics goes much more in-depth than website conversions for products and services. An even larger picture of a product and its consumers is gleaned from a combination of other data sources. By analyzing past performance, competitor information, and customer interactions, sales teams can use predictive forecasting models to adjust aspects of their marketing.
No doubt you have received email from a company you’re familiar with. These emails are a source of very important data for marketing departments. Over time, a pattern of behavior becomes visible that can help determine key aspects of targeting consumers. At this stage, AI is critical in finding these patterns. For example, some companies use predictive analytics solutions in order to find the best time to send out a marketing blast to ensure a higher amount of consumers will open the message.
While the predictive analytics for marketing and sales sounds very similar, and does have some crossover, marketing is focused on finding opportunities and creating demand around a product or organization. Sales is focused on the actual purchasing patterns that close the deal.
Using analytics to find new value in data is one common route we see predictive analytics used as an investigative tool. The oil and gas industry harnesses vast amounts of historical and technical data to determine how to correctly handle a number of different aspects of the business, including pricing and demand forecasting. Things once managed by schedules that required human intervention can now be finely tuned by AI to maximize efficiencies.
Imagine the amount of maintenance required to operate an offshore rig operation to ensure safety and maximize profitability: miles of pipes, cables, and critical components that all require regular inspection. Using predictive analytics, operators can watch historical performance patterns and be proactive in their approach to scheduling this type of maintenance.
Utilizing predictive analytics on market data, savvy companies can forecast supply and demand to act accordingly with their resources. By focusing efforts on areas specific to their industry, companies are finding profits in areas not thought of before. A penny saved is a penny earned! By using data to run operations productively, waste is minimized and decision makers can find efficiencies that normally would go unnoticed.
Today’s solutions shape tomorrow’s decisions.
The concept of reporting on historical and real time data is not new. However, how large amounts of data are processed has undergone significant changes in the last decade. Those that wish to remain competitive in today’s data-driven market are taking note of how the use of predictive analytics can help critical decision-making based on market-tested algorithms. And those that aren’t taking note are already behind.
The tools used to handle this type of reporting are based on aspects of past machine learning along with traditional reporting via companies that specialize in big data analytics. But how does that help someone just starting in predictive analytics? Real time data is visualized in ways that make sense to decision makers and data scientists alike. By implementing aspects of artificial intelligence with this traditional approach, companies are able to leverage the use of predictive analytics solutions to make immediate impact in all aspects of their business.
The toolset chosen depends on a number of factors. Current experience with artificial intelligence in business, how a company’s data is organized, and ease of implementation. By leveraging Algorithmia’s full AI management platform that supports the cloud or on-prem, much of the heavy lifting is already done. Connections to existing data sources allow for an almost immediate return on investment. Especially considering the access Algorithmia provides to a large number of pre-existing models that can be used immediately or expanded on.
By partnering your predictive analytics efforts with Algorithmia, your data analyst teams will have all the tools they need to focus on their tasks and your DevOps can go back to doing what they do best.