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A data quality report that outlines the usability and accuracy of the data. Create a new search...
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LeapThought has found 115 LeapLines
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LeapLine |
Trend, Type, Function, Action | |
 |
A data quality report that outlines the usability and accuracy of the data. |
data analytics,
deliverable description,
data analytics,
z - no action
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A basic 'counts and amounts' customer base report based on 5-10 key customer-facing dimensions over one specific timeframe. |
data analytics,
data report elements,
data analytics,
z - no action
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Counts and-or indications of null or incomplete records |
data mining,
data report elements,
data analytics,
z - no action
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Basic frequency distributions and calculations, showing means, skews, spikes, exceptions and standard deviations |
data mining,
data report elements,
data analytics,
z - no action
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Tables and graphs showing counts and percentages of customers for each dimension |
data analytics,
data report elements,
data analytics,
z - no action
|
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Cross-tab tables and graphs for each meaningful combination of the key customer-facing dimensions |
data analytics,
data report elements,
data analytics,
z - no action
|
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Usability Testing - Assist setup-coordination of usability testing of the prototype |
total quality management,
web development activities,
QA,
assist
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List of fields required for analysis, including recommendations focused on data quality and data management processes (e.g., summary problems, extract issues, justification) |
database marketing,
deliverable description,
consulting,
z - no action
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Ongoing updates for extraction, transformation and load for updated data |
data analytics,
data analysis techniques,
data analytics,
z - no action
|
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Archive and store data and models |
data mining,
data analysis techniques,
data analytics,
archive
|
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Return data and data transfer materials |
data analytics,
data analysis techniques,
data analytics,
return
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Prototype Build - Assist to integrate elements to develop a working prototype for usability testing |
total quality management,
web development activities,
internet,
assist
|
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Analyze and report |
z - no trend,
measurement activities,
data analytics,
analyze
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Reconciling customer-base records with response data |
direct response,
data analysis techniques,
data analytics,
reconcile
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Recommendations on how to focus remainder of data mining project effort |
data mining,
deliverable description,
data analytics,
z - no action
|
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Repeatedly train model until confidence level is acceptable |
data mining,
data analysis techniques,
data analytics,
train
|
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Transform data analytic findings into tactical marketing actions |
data analytics,
project activities,
data analysis,
transform
|
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Set standards and practices for delivering data analysis content and results |
data analytics,
process development,
data analysis,
set
|
|
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Outline components of data mining deliverables |
data analytics,
project activities,
data analysis,
outline
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Data management and analytics |
data analytics,
data analysis techniques,
data analytics,
z - no action
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Finding meaningful dimensions |
data analytics,
data analysis techniques,
data analytics,
find
|
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Capture and reconcile data |
direct response,
data analysis techniques,
data analytics,
capture
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Recommended next data analytic steps |
data analytics,
recommendations,
data analytics,
z - no action
|
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Highly valued data on agents, agent preferences and experiences, and extremely valuable proprietary aggregated data on client preferences and experiences |
CRM,
organizational component,
data analytics,
z - no action
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Deliver data criteria (model) to company marketing operations |
data mining,
marketing execution activities,
data analytics,
deliver
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Data mining |
data mining,
business trend,
data analytics,
z - no action
|
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Articulate the key tasks data mining strives to achieve |
data mining,
development activities,
data analysis,
articulate
|
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Marketing data analysis and data mining |
data mining,
campaign element,
direct marketing,
z - no action
|
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Explain which tools are appropriate for which types of data analysis |
data analytics,
development activities,
data analysis,
explain
|
|
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Data model |
z - no trend,
document type or element,
data analytics,
z - no action
|
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Scored list of customers |
segmentation,
data deliverable,
data analytics,
z - no action
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Data collection practices |
data warehousing,
system processes,
IT,
z - no action
|
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Perform directed data analysis |
data analytics,
data analysis techniques,
data analytics,
perform
|
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Data and learning intensive - the data-intensive environment of the Internet is ideal for tracking client and prospect behavior, both on how they behave as individuals and how broader populations behave on aggregate. No other marketing activity can enable this level of accountability and trackability (with the possible exception of direct sales calls) |
leadership development,
internet benefit,
data analytics,
z - no action
|
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Define process for drawing conclusions to data analysis results |
data mining,
process development,
data analysis,
define
|
|
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Quality Assurance |
total quality management,
business trend,
operations,
z - no action
|
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Data mining |
data mining,
data analysis techniques,
marketing analysis,
z - no action
|
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Transfer data from sources |
data analytics,
technology activities,
data analytics,
transfer
|
|
|
Explain which types of data analysis are appropriate for which types of tasks |
data analytics,
development activities,
data analysis,
explain
|
|
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Evaluate and review potential prospect lists and data appends |
z - no trend,
marketing planning activities,
data analytics,
evaluate
|
|
|
Understand the importance to testing data for inconsistencies, anomalies and biases. |
data mining,
development activities,
IT,
understand
|
|
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Define an approach to standardizing, normalizing and preparing data for analysis |
data analytics,
planning activities,
IT,
define
|
|
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Define an approach to repairing and modifying erroneous or incomplete data |
data mining,
development activities,
IT,
define
|
|
|
Response data integrated into customer files |
direct response,
data analysis techniques,
internet,
z - no action
|
|
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Understand existing data structures, formats and conventions |
data analytics,
assessment activities,
IT,
understand
|
|
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Refine models and scoring schemes. |
data mining,
improvement activities,
data analysis,
refine
|
|
|
Data mining and analytics |
data mining,
activities,
data analytics,
z - no action
|
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Predictive modeling |
data mining,
data analysis techniques,
marketing analysis,
z - no action
|
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Revise and refine model |
data mining,
technology activities,
data analytics,
revise
|
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Identify sources of data |
database marketing,
technology assessment,
data analytics,
identify
|
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Results from the LeapThought © Document Database: A data quality report that outlines the usability and accuracy of the data. LeapThought has found 92 files.
| Role of different research methods in business decision making- 1 Powerpoint slide |
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This matrix rates the effectiveness of different research types as they support core marketing objectives.
SEE DETAIL PAGE and DOWNLOAD
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| Understand customer behavior: Finding answers using historic data- 1 Powerpoint slide |
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This is a top-level process flow diagram that shows how to conduct market data analysis by ‘training’ a data model.
SEE DETAIL PAGE and DOWNLOAD
|
| Definition of Data Analytics - Data mining overview- 1 Powerpoint slide |
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This mixed process/entity diagram shows a high-level, marketing view of how data analytics operates.
SEE DETAIL PAGE and DOWNLOAD
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| Customer information teaches companies how to provide the right experiences- 1 Powerpoint slide |
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This combination list/map/case shows many different types of customer data and how they can be used for management decisions as well as transactional or customer-based decisions.
SEE DETAIL PAGE and DOWNLOAD
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| Benefits of data analytics- 1 Powerpoint slide. |
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This illustration shows how a comprehensive data analysis program can deliver benefits as related to customer acquisition and retention.
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| Data Management is central to Marketing Operations- 1 Powerpoint slide |
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This slide provides an illustration of how data sources can be managed and analyzed to benefit marketing operations. This illustration compares the process to a funnel, with raw data entering the system and applied data exiting as useful marketing to
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| Acquisition and retention map- 1 Powerpoint slide |
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This simple map shows sources of data to fuel a data analytics program and types of analysis that might be completed.
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| Data analysis improves the odds - Like counting cards- 1 Powerpoint page |
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This illustration explains that data analysis, although very analytical by marketing standards, is very much like counting cards. This is the central story of why data mining returns propensity vs. definitive answers.
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| Customer data implications of projected growth- One powerpoint slide |
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This slide shows how interim and permanent data analysis programs would be implemented as a company ramped up customer acquisitions.
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| Establishing CRM priorities- 1 PowerPoint Slide |
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This classic Harvey ball chart shows the progress of different CRM initiatives against three different business units. It is used here to establish CRM priorities.
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| Data analytics-ready organizations - Internal and external drivers, new enablers- 1 Powerpoint slide |
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This slide makes a case for data analytics for customer-driven organizations. It asks the question “Why data analytics now?” and answers the question using external drivers, internal drivers, and new enablers.
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| Understand customer behavior: Finding answers using historic data- 1 Powerpoint slide |
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This slide illustrates how a trained data model can guide two fundamental marketing decisions: determining what the right tactics are (such as creating a specific offer) and identifying the right customers to market to (i.e., picking the right lis
SEE DETAIL PAGE and DOWNLOAD
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| Six stages of data mining- 1 Powerpoint slide |
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This is a top-line view of a data mining methodology. Its six stages are Assess, Build, Experiment, Mine, Deliver, and Measure.
SEE DETAIL PAGE and DOWNLOAD
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| The six points of data failure within the cycle- 1 Powerpoint slide |
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This handsome slide lists, in a process, the six areas of data processing and (in this case) where data becomes erroneous or otherwise broken. It lists success factors, or process steps, first, and then shows failure points.
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| Structure of data analytics-ready organizations- 1 Powerpoint slide |
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This graphic shows the layers of a company that is ready for marketing-focused data analytics.
SEE DETAIL PAGE and DOWNLOAD
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