Qlik Case Studies Increasing marketing efficiency and optimizing lead scoring with Qlik AutoML

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	Increasing marketing efficiency and optimizing lead scoring with Qlik AutoML Qlik

Increasing marketing efficiency and optimizing lead scoring with Qlik AutoML

Qlik

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	Naylor has identified several areas of ROI that extend beyond the traditional measurable results of dollars saved or loss decreased.
Impact #2	Working with the customer service and data science teams at BigSquid.ai, Naylor has been able to optimize lead scoring and significantly reduce the number of fields required in the CMS.
Impact #3	This has saved their salespeople from gathering unnecessary data and wasting precious time filling in unnecessary fields.

Impact #1

Naylor has identified several areas of ROI that extend beyond the traditional measurable results of dollars saved or loss decreased.

Impact #2

Working with the customer service and data science teams at BigSquid.ai, Naylor has been able to optimize lead scoring and significantly reduce the number of fields required in the CMS.

Impact #3

This has saved their salespeople from gathering unnecessary data and wasting precious time filling in unnecessary fields.

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Overview

Increasing marketing efficiency and optimizing lead scoring with Qlik AutoML

Operational Impact