Predictive Analytics is a ML-based form of advanced business analytics which uses machine learning technology to create predictive models for certain business tasks.
This technology is based on processing of current and historical data. These data, properly analyzed, serve as a basis for further predictions, for example, marketing campaign optimization, brand naming and positioning, customer behavior assumptions and much more.
This scheme provides simplified demonstration of how it works:
For years of work Silk Data has implemented various AI projects, and ML-based predictive analytics tools take their sufficient part. Though every project is unique and requires a specific approach, we have several main directions that can cover most of the client requests, including marketing organization, data security and cost estimations.
We have enough expertise and facilities to create a predictive analytics custom solution from scratch. You’ll get a full-scale software or application that includes both a properly trained predictive model and convenient interface aimed to cover the needs of advanced predictive analysis.
If you don’t have enough resources for end-to-end development or you already have your own corporate internal system that only requires improvements, this is also the task we can solve. We can recommend special extensions for your existing software, adding the predictive capabilities that will work with your corporate data. Our comprehensive experience ensures that the implementation goes smooth and flawless, without any productivity losses.
If you already have some predictive analysis tools but wish to improve them through up-to-date machine learning models, we may offer a solution. We’re capable of developing and training the models that will demonstrate highest efficiency working with your particular data. For instance, the ML usage can lead to recommendation system implementation. Analyzing user behavior patterns and past interactions with your product, the system provides personalized recommendations, improving customer satisfaction and engagement.
Sometimes, the large amounts of data that you’ve analyzed require days or even weeks of thinking, comparing and consulting to get to the proper final decision.The predictive data analytics speeds up these processes, providing ready to use suggestions.
Nowadays, about 60% of organizations worldwide use predictive analytics for better decision-making (Gartner) and the number is increasing. It’s especially useful for the marketing sphere. For example, you can choose the best logo option or optimize pricing policy through customer behavior or trends analysis. In other words, predictive analytics in marketing saves time and prevents from unfounded solutions.
Manually performed data gathering, processing and analysis can take hundreds of working hours for a team of specialists. Moreover, tiredness, incompetence and common working load can lead to mistakes and the results will be of a poor quality. Our predictive analytics solutions allow to automize the manual processes and free up corporate resources for other tasks.
Advanced predictive analytics is also valuable for ability to provide costs and risks estimations. The analysis of historical data and statistics allows to forecast potential risks before they actually occur. That’s why it’s recommended to use predictive analytics in finance and banking, for example, in credit scoring. Thorough analysis of the client's credit and income story helps to avoid incorrect decisions, while each of them may cost the bank a lot of money.
Using any predictive analytics solution, you can detect unusual network traffic or user behavior, tracking the historical and current data. Moreover, predictive analytics is a perfect technique for fraud detection. For example, applied to the e-commerce industry, the system can chase and analyze user operations and find unusual and suspicious activity. The ‘Deloitte’ research says that about 70% of financial and commercial institutions already use predictive analysis tools for fraud prevention.
We understand that every project is unique, and different requirements imply their own specific technological stacks and development stages. We follow different practices that allow to fulfil the clients’ needs and make any necessary changes without sufficient time and money losses.
You can always ask for initial model development with minimal functionality to get a visual demonstration of the future product. This approach helps to understand all the necessary specifics and prove, if the project concept resonates with the business needs.
At Silk Data we follow strict and consistent policy, referring to client corporate data. Your corporate or personal e-mail, database insights and other important information will never be transferred to third parties without your consent or critical emergency.
Every project starts with thorough data and business requirements analysis. We consider all your needs, resources and expectations, trying to figure out if the solution possible and what particular tools and technologies can be used.
Even when the solution is complete and the project is over, you can always ask for further help. Seeking for new versions, on-going functionality monitoring, additional functionality or auditing? Silk Data team is ready to help.
We practice the retrospective approach, when we get a critical look at our work, trying to improve crucial processes, making the working process more efficient.
"We focus on creating innovative products that not only meet but exceed customer expectations. Our development process is reinforced by both theory and practice, ensuring that every solution is built using the most efficient, scalable, and up-to-date methods and techniques available"
Nikolai Karelin - Head of AI
Predictive Analytics and Data Analytics are significant parts of a complicated process of advanced data analysis and insights extraction. However, each of them performs its own specific tasks, referring to different aspects of analysis.
While Data Analytics’ aim is to analyze present and historical data, such as trends, statistics and data relations and define certain patterns between them, Predictive Analytics focuses on making predictions and anticipations which are based on these patterns.
In other words, Data Analysis answers the question ‘What happened/is happening and why?’, while the Predictive Analytics says ‘What is likely to happen?’.
A professional development team will never say the exact number in advance. The final project price depends on a vast range of factors, including its complexity, access to data, data scale and additional tasks related to the solution implementation and business analysis.
At Silk Data every project starts with the requirements analysis and idea discussion. Then, the thorough analysis follows, when we present possible solutions and their estimated costs.
Indeed. Our expertise is enough to implement AI-based predictive analytics into your existing system, and this request appears to be popular among businesses. Through that you’ll be able to significantly improve your processes without sufficient productivity losses.
