Increasing marketing efficiency and optimizing lead scoring with Qlik AutoML
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- Qlik AutoML
- BigSquid.ai’s Kraken
Tech Stack
- Machine Learning
- Data Analysis
- CMS
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Productivity Improvements
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Professional Service
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Replenishment
- Predictive Quality Analytics
Services
- Data Science Services
About The Customer
Naylor Association Solutions is a company that empowers professional associations to deliver greater value, engage with its members, and increase non-dues revenue by providing a variety of services. They help their customers with things such as member communications by delivering magazines, newsletters, or directories to association members. They also help associations provide career solutions for their members, plan and execute trade shows and events, association management software (AMS), and assist association members to maintain certifications.
The Challenge
Naylor Association Solutions was facing a challenge in their marketing-sales processes, particularly in lead scoring and qualification. The company was using a CMS that required account executives to fill out nearly 30 different data fields, which were then used by the marketing automation platform for scoring and qualification. This process was time-consuming and frustrating for the salespeople, who viewed many of the fields as unnecessary. On the other hand, the marketing team couldn't provide a better justification for the data other than they needed it for their processes.
The Solution
Naylor Association Solutions decided to adopt machine learning to improve efficiency in lead scoring, lead generation, and data analysis. They evaluated several options, including self-service platforms, providers that would build a 100% custom algorithm for their specific use case, and automated machine learning solutions like BigSquid.ai’s Kraken. They chose BigSquid.ai due to the ease-of-use of the platform and rapid time to value. The implementation of this solution allowed Naylor to optimize lead scoring and significantly reduce the number of fields required in the CMS, saving their salespeople from gathering unnecessary data and wasting time filling in unnecessary fields.
Operational Impact
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