Applied Marketing Science evaluated all of our data—our customer surveys, our forums, everything. They organized it and told us what to focus on and why. It saved us months of work, maybe more, because we might not have done anything with it.
What if the answer to your greatest business challenge is in the customer data you already have? If your company is like many others, you have accumulated mountains of customer data. But how can you mine those mountains for the answers you need to make better decisions? How can you make the best use of big data?
The AMS Difference
We find answers to important questions in the customer data you already have—quickly—so you can be more agile in adapting to an ever-changing marketplace.
- What are the biggest complaints customers have with your product or brand?
- What are customers saying about your company, relative to the competition?
- What new features do customers ask for in the next generation of your product, and why do they want them?
Using the latest advances in machine learning, as well as sophisticated multivariate data modeling and other big data analytics, we rapidly churn through massive datasets to find the gems of insight that you need to design better products and deliver experiences that delight customers.
- We analyze customer-generated content from product discussion forums, blogs, social media, comments on your website and answers to open-ended questions in customer satisfaction survey data.
- We analyze survey datasets or secondary research your company has already purchased. We then synthesize everything into actionable insights that show you areas in need of attention and hidden opportunities to exploit for competitive advantage.
The End Result
Our reports provide clear and actionable answers, so your cross-functional teams can quickly make use of their new insights. We also provide recommendations on next steps, based on what we find.
Let us help you get more insight from the data you already have.
Find answers in your customer data that you didn't know were there. Let's Talk
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