Our Advanced Analytics team can help you reimagine your operations and turn your data into a constant source of value, from customer experience and predicted NPS to enhanced go-to-market strategies and greater employee engagement.
At DPS we assist you in maximising the value of all your data assets, regardless of their location or format. Our analytics professionals work with you to solve your most complex problems, ensuring that you not only obtain results quickly but also develop the internal capabilities necessary to expand your new data prowess. We use the most up-to-date analytics methodologies and tools to assist you in generating insights that lead to improved, quicker decisions. Our Advanced Analytics team can help you reimagine your operations and turn your data into a constant source of value, from customer experience and predicted NPS to enhanced go-to-market strategies and greater employee engagement.
We apply big data and advanced algorithms to your business problems in order to yield a solution that is measurably better than before. By identifying, sizing, prioritizing, and phasing all applicable use cases, businesses create an analytics strategy that generates value.
How We Utilize Advanced Analytics To Help You Determine Precise Answers To Your Question
Advanced analytics is a valuable resource. It’s a catch-all term for a wide range of analytics approaches and technologies that, for the most part, function together in a predictive manner. Data mining, machine learning, prescriptive analytics, big data analytics, predictive analytics, forecasting, and, more broadly, detecting patterns in data are all examples of Advanced Analytics.
With our expertise in Advanced Analytics, we will help your business to derive considerable value from its data assets, regardless of where the data is stored or in what format. While utilizing Advanced analytics tools & techniques we can help you address some of the more complex business problems that traditional BI reporting cannot.
In order to build a contextual marketing engine, a consumer packaged product’s producer may need to consider the following:
- When is a customer likely to run out of their supply for an item?
- When are people most receptive to marketing promotions during the day or week?
- When marketing at that time, what amount of profitability is possible?
- What is the most likely pricing point for them to buy?
We can assist a business in determining precise answers to those queries by integrating consumption models with historical data and artificial intelligence (AI).