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3 tools for analyzing and optimizing consumer data

For more than two decades, decision support systems with data warehousing, business intelligence and statistical modeling techniques have been used to plan and optimize a wide variety of offline and online marketing programs.

However, these systems often require deep technical expertise for developing and maintaining data models, queries and algorithms, which could become very complex for cross-channel, multi-stage marketing programs.

As a result, large marketing organizations often use consultants or have dedicated in-house experts who mainly focus on assisting the marketers to plan and optimize their programs. Smaller marketing teams, however, often resort to less sophisticated tools, such as spreadsheets, that naturally have limited analysis and optimization capabilities thus leading to low-performance marketing programs and dissatisfied marketers.

Furthermore, the traditional decision support systems often fail to perform as effective decision automation systems because they cannot deliver accurate results in near real-time using very large volumes of consumer data collected from a wide variety of online sources. According to a 2011 Forrester research report by Boris Evelson on trends in business intelligence, “traditional or earlier-generation business intelligence approaches are often not flexible or agile enough to react and adapt quickly to constantly changing information requirements. Enterprises today need agile business intelligence technologies and applications that are unified, pervasive, limitless and automated, especially in the realm of untamed customer-facing processes like customer on-boarding, customer service, and marketing campaign management.”

The emergence of distributed cloud computing and super-fast data processing technologies for very large volumes of data (also known as Big Data) have led to a new generation of Software as a Service (SaaS) tools for collecting and analyzing a wide variety of consumer profile and behavioral data.


This new generation of analysis tools continue to evolve to meet the growing business requirements for higher accuracy, reliability and near real-time processing of data collected from the "Internet of things" used by consumers worldwide. A sizable portion of these tools is now focused on providing marketers easy-to-use analysis and discovery capabilities. These tools can consume various offline and online data about individuals and their activities and subsequently provide marketers insight to better plan and optimize their marketing programs.

The majority of new data analysis and discovery tools offer complete cloud-based solutions without requiring a separate on-premise data management infrastructure. Furthermore, their subscription-based pricing can accommodate a variety of marketing organizations with a broad range of budget sizes. Depending on the complexity of data and analysis needed and how quickly the results must be delivered, some companies may even combine the new cloud-based analysis tools with the traditional on-premise decision support systems.

Here’s a small sample set of SaaS data analysis and discovery tools that specialize in processing consumer data for marketing applications. More information about these and other similar products is available online and in digital marketing analyst reports.

1. BlueKai

BlueKai was recently acquired by Oracle to provide a centralized audience data management platform for the Oracle Marketing Cloud. BlueKai offers marketers a centralized hub to effectively integrate disparate audience analytics and media performance data (collected by BlueKai or other systems) to plan, model, target and optimize various cross-channel marketing programs. BlueKai's Audience Analytics Suite offers an intuitive user interface on top of their Big Data analysis system to enable marketers to easily perform these tasks:

  • Profiling customers and discovering new audiences;
  • Modeling and exploring target audiences before executing any campaigns;
  • Profiling and growing current visitors to generate look-alikes with demographic, geographic, interest, and in-market attributes;
  • Discovering non-intuitive attributes of converters for audience expansion;
  • Performance-based planning and recommending what works; and
  • Optimizing audience and media mix based on known first party performers who drive the most valuable parts of the marketing funnel.

2. Umbel

Umbel focuses on integrating Big Data analysis and graphical visualization tools to allow Web publishers to better measure audiences that view their content. It has a proprietary Digital Genome technology that processes billions of digital traits about individuals and converts them into insights that marketers can use to create better and more effective user interactions. Umbel also collects public data when users access Web properties through social sign-in and in turn provides publishers and their marketers real-time user data for running more effective marketing promotions.

Umbel correlates data about its registered users against 30 different data sources to provide composite audience information. As a result, it can provide publishers more granular visitor data that goes beyond just the usual demographics and geography data provided by most third-party measurement systems. That in turn helps publishers to better promote the value and interests of their audience segments to brands and marketing agencies.

3. AgilOne

AgilOne offers a marketer-centric predictive analytics platform to provide insights into current and future customer behavior by analyzing a wide variety of user data, such as social, interactions, conversion rates, etc. With AgileOne, marketers can get a complete view of their customers across different channels and predict how those customers will respond to specific types of promotions.

For example, AgileOne can help an online retailer analyze a wide variety of data about its customers who receive marketing promotions and shop on its eCommerce site. By discovering useful trends related to specific audience segments and pro-actively addressing potential issues, e.g., returned items or depleted inventories, marketers can better predict the customer behavior in order to more effectively re-engage with them. The complexity of the underlying mathematical models and machine learning algorithms embedded in AgileOne’s platform are encapsulated by a Web-based graphical user interface customized specifically for marketers and not data scientists.

I like to close by emphasizing that marketers still need careful, patient evaluation of computer analysis results to make important decisions about their marketing programs. Although data analysis and discovery tools are becoming more affordable and easier to use, they will never replace valuable human judgment gained through many years of experience.

Image courtesy of Wikimedia Commons

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