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Archive and store data and models Create a new search...
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LeapThought has found 106 LeapLines
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LeapLine |
Trend, Type, Function, Action | |
 |
Archive and store data and models |
data mining,
data analysis techniques,
data analytics,
archive
|
|
|
Operational data store (ODS) |
CRM,
systems,
IT,
z - no action
|
|
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Refine models and scoring schemes. |
data mining,
improvement activities,
data analysis,
refine
|
|
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Train and develop attrition models |
customer loyaltyj,
marketing execution activities,
data analytics,
train
|
|
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Measure results of models and scoring in the customer arena |
modeling,
improvement activities,
Data analysis,
measure
|
|
|
Archive and storage |
z - no trend,
company department,
operations,
z - no action
|
|
|
Review and finalize analytical instruments for analyzing business models |
business planning,
data analysis techniques,
research,
review
|
|
|
In-store events (e.g., 'test drives') |
event marketing,
activities,
events,
z - no action
|
|
|
Ongoing updates for extraction, transformation and load for updated data |
data analytics,
data analysis techniques,
data analytics,
z - no action
|
|
|
A data quality report that outlines the usability and accuracy of the data. |
data analytics,
deliverable description,
data analytics,
z - no action
|
|
|
Top-line financial models that illustrate revenue potential and costs savings |
ROI,
business case activities,
finance,
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
|
|
|
Return data and data transfer materials |
data analytics,
data analysis techniques,
data analytics,
return
|
|
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Define delivery models |
z - no trend,
planning activities,
operations,
define
|
|
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Develop liquidity, revenue and governance models for operating the website |
change management,
business case activities,
internet,
develop
|
|
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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
|
|
|
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
|
|
|
Repeatedly train model until confidence level is acceptable |
data mining,
data analysis techniques,
data analytics,
train
|
|
|
Recommendations on how to focus remainder of data mining project effort |
data mining,
deliverable description,
data analytics,
z - no action
|
|
|
Reconciling customer-base records with response data |
direct response,
data analysis techniques,
data analytics,
reconcile
|
|
|
|
|
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
|
|
|
Outline components of data mining deliverables |
data analytics,
project activities,
data analysis,
outline
|
|
|
Data management and analytics |
data analytics,
data analysis techniques,
data analytics,
z - no action
|
|
|
Capture and reconcile data |
direct response,
data analysis techniques,
data analytics,
capture
|
|
|
Finding meaningful dimensions |
data analytics,
data analysis techniques,
data analytics,
find
|
|
|
Recommended next data analytic steps |
data analytics,
recommendations,
data analytics,
z - no action
|
|
|
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
|
|
|
Deliver data criteria (model) to company marketing operations |
data mining,
marketing execution activities,
data analytics,
deliver
|
|
|
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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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
|
|
|
Customer Needs Assessment - Identify key user segments and prioritize their needs and use models |
segmentation,
customer research,
research,
identify
|
|
|
Explain which tools are appropriate for which types of data analysis |
data analytics,
development activities,
data analysis,
explain
|
|
|
Marketing data analysis and data mining |
data mining,
campaign element,
direct marketing,
z - no action
|
|
|
Data model |
z - no trend,
document type or element,
data analytics,
z - no action
|
|
|
Scored list of customers |
segmentation,
data deliverable,
data analytics,
z - no action
|
|
|
Data collection practices |
data warehousing,
system processes,
IT,
z - no action
|
|
|
Perform directed data analysis |
data analytics,
data analysis techniques,
data analytics,
perform
|
|
|
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
|
|
|
Define process for drawing conclusions to data analysis results |
data mining,
process development,
data analysis,
define
|
|
|
Site Requirements - Summarize priority use models and site feature and functionality requirements |
internet,
web development activities,
internet,
summarize
|
|
|
Transfer data from sources |
data analytics,
technology activities,
data analytics,
transfer
|
|
|
Data mining |
data mining,
data analysis techniques,
marketing analysis,
z - no action
|
|
|
Explain which types of data analysis are appropriate for which types of tasks |
data analytics,
development activities,
data analysis,
explain
|
|
|
Evaluate and review potential prospect lists and data appends |
z - no trend,
marketing planning activities,
data analytics,
evaluate
|
|
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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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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 repairing and modifying erroneous or incomplete data |
data mining,
development activities,
IT,
define
|
Page 1 of 3 >> Go to page 1 2 3
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Results from the LeapThought © Document Database: Archive and store data and models LeapThought has found 83 files.
| Definition of Data Analytics - Data mining overview- 1 Powerpoint slide |
 |
This mixed process/entity diagram shows a high-level, marketing view of how data analytics operates.
SEE DETAIL PAGE and DOWNLOAD
|
| Data analytics investment cycle- 1 Powerpoint slide |
 |
This chart shows how financial benefits of data mining projects rise as their costs shrink over time.
SEE DETAIL PAGE and DOWNLOAD
|
| Multi-track project approach: Marketing and data analytics- 1 Powerpoint slide |
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This plan shows the steps/phases in a combined marketing strategy and data analytics project.
SEE DETAIL PAGE and DOWNLOAD
|
| Understand customer behavior: Finding answers using historic data- 1 Powerpoint slide |
 |
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
|
| Marketing and data analytics project approach- 1 Powerpoint slide |
 |
This 9-step marketing project approach takes a group through the marketing planning phases while simultaneously conducting a data analytics (data mart) project.
SEE DETAIL PAGE and DOWNLOAD
|
| Market analysis project activities and timing- 1 Powerpoint slide |
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This Gantt chart shows the steps and timing for a market analysis for internet products and business development.
SEE DETAIL PAGE and DOWNLOAD
|
| Critical success factors to risk/reward models- 1 Powerpoint slide |
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This graphic list shows nine critical success factors to launching risk/reward models in conducting business.
SEE DETAIL PAGE and DOWNLOAD
|
| Market Analysis Project Activities and Timeline- 1 Powerpoint Slide |
 |
This Gantt chart shows the steps and timing for a market analysis for internet products and business development.
SEE DETAIL PAGE and DOWNLOAD
|
| Market Analysis Project Activities and Timeline- 1 Powerpoint Slide |
 |
This Gantt chart shows the steps and timing for a market analysis for internet products and business development.
SEE DETAIL PAGE and DOWNLOAD
|
| Current situation of database marketing initiatives- 1 Powerpoint slide |
 |
This chart shows a very top level database implementation approach (presumably an analytics or marketing database). The approach is repeated four times to represent different business units. One phase is shaded in.
SEE DETAIL PAGE and DOWNLOAD
|
| Understand customer behavior: Finding answers using historic data- 1 Powerpoint slide |
 |
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
|
| Customer information teaches companies how to provide the right experiences- 1 Powerpoint slide |
 |
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
|
| Benefits of data analytics- 1 Powerpoint slide. |
 |
This illustration shows how a comprehensive data analysis program can deliver benefits as related to customer acquisition and retention.
SEE DETAIL PAGE and DOWNLOAD
|
| Data Management is central to Marketing Operations- 1 Powerpoint slide |
 |
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
SEE DETAIL PAGE and DOWNLOAD
|
| Acquisition and retention map- 1 Powerpoint slide |
 |
This simple map shows sources of data to fuel a data analytics program and types of analysis that might be completed.
SEE DETAIL PAGE and DOWNLOAD
|
See more document results...
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