Haave is a data science consulting firm, discover how our combination of experience and training can help grow your business

We are uniquely skilled, academically-trained PhDs that are comfortable in chaos. Our expanding team of data scientists leverage cloud and Hadoop infrastructures, ingest and clean data, produce intuitive visualization, perform feature engineering, develop models ranging from logistic regression to advanced neural networks, and are adept at real-time streams processing and deep learning.

Our work has helped CMOs in better understanding audience behavior, Product Managers in finding better ways to commercialize products, CFOs in building better forecast models, and CISOs in better information management and security measures. Our team moves you closer toward the goal you aspire, no dream is too large for us to assess, model, evaluate and deploy.

Case Study Highlights

A few ways we have delivered value to enterprises

Natural Language Processing to Improve Call Center Efficiency

Implemented Natural Language Processing (NLP), a form of text analysis, to look at the causes of client dissatisfaction and determine problem resolution in a call center setting. Using advanced data science tools and techniques, identified common themes for grouping the right questions to best address customers’ needs. Streamlined process allowed for reduction in call length and increased on-call resolution resulting in fewer service technicians needing to be deployed in the field. Anticipated cost savings are expected to exceed $50 million per year.

Sentiment Analysis to Increase Customer Satisfaction

Company challenged with understanding customer sentiment and was losing business to the competition. Customer complaint feedback was voluminous and employees were inundated with making sense of the data. Performed text, sentiment and topic modeling to assess the feedback of former customers and understand why they were unhappy; analyses helped determine where best to focus efforts. Result was a reduction in customer churn and an improvement in workplace efficiencies, revenues associated with the project exceeded $8M per year.

A/B Testing to Decrease Website Cart Abandonment
Sporting Goods Retailer

Company experiencing higher than normal cart abandonment on the web. Website had become cluttered and the shopping process was unclear – customers didn’t know where or how to checkout. Haave believed shopper processes and workflows were to blame and performed web analytics to test and validate the assumption. An alternative checkout process that was more clear and concise was developed which proved the assumption to be correct. Improvement of these web checkout processes resulted in mammoth change as customers had more clarity on checkout – top line web revenues were increased by $20M per year.

Data Engineering to Improve Time to Market
Consumer Electronics

Prominent wearables firm needing to stay abreast of and drive the customer experience. The viability and workability of wearables and smartwatches involves connectivity across multiple platforms, such as mobile and cloud, as customers upload data, and this needs to be synced and analyzed by wearables providers. Multiple platforms, as well as Bluetooth connectivity, pose challenges for the movement of such data. Haave created data solutions that allowed for easy migration of the tonnes of data and at much greater speeds. This allowed the engineers to take new business processes out to market more quickly – time to market was reduced by 30% – resulting in a better customer experience as quality and frequency of features were made available more quickly.

Customer Segmentation and Gap Analysis for Retailer

As is common for many firms, company didn’t have a thorough understanding of who its customers were and what its most profitable customers looked like. These data were crucial to drive effective branding and marketing campaigns and, ultimately, top and bottom line results. Haave produced and distributed a detailed survey and performed cluster analysis on the results. Customer personas were identified and previously unidentified high profit segments surfaced, as part of the effort. Analytical results and recommendations were distributed to executives and lines of business, allowing the teams to drive targeted, improved and effective marketing efforts and campaigns.

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