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XP3 Bringing Business Intelligence to the Front Lines of Business
CPG CatNet.org
Series One
Tip 12
Data Independent Templates
Tip 11
Coordinate Efforts Between Management, Marketing and Sales Teams

Tip 10
Take the First Step to a Successful Analysis and Presentation Workflow

Tip 9
Adapted Data Views Create More Relevant Customer Insights
Tip 8
Automate the Dissemination of Content and Data
Tip 7
Create Ready-Made and Customizable Analyses
Tip 6
Automate the Generation of Intelligent Insights
Tip 5
Retailer/Manufacturer Relationship - How to Get the Work Done
Tip 4
Winning New Product Presentations - How the Product Will Affect the Category
Tip 3 - Part 2
A One-Size Fits All Approach to Consumer Centric Marketing
Tip 3 - Part 1
A One-Size Fits All Approach to Consumer Centric Marketing
Tip 2
Combine Wal-Mart and Syndicated Data for a Complete View of the Market
Tip 1
Demographic Data - It's Free





News & Events

DemandTec Announces DemandTec TradePoint Network Partner Program and Charter Partner Interactive Edge
Interactive Edge to offer time-saving presentation tool to consumer products firms building promotion plans with DemandTec's services

Interactive Edge Joins SAP's Industry Value Network Group for Consumer Products
This designation, based upon SAP customer feedback and nominations, reflects Interactive Edge's record of success with consumer products and recognition of Interactive Edge's ability to bring innovation and expertise to the Industry Value Network.

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Business Tip 10
Take the First Step to a Successful Analysis and Presentation Workflow

To produce insightful presentations often requires the contributions of many people of diverse talents. Defining roles and responsibilities for data analysis and presentation development if the first step in this process

Successful Analysis and Presentation Workflow

Creating insightful sales analyses and presentation requires the expertise and coordination of many people in an organization from data analysts to business managers to sales reps. Organizations acknowledge this, but many fail to define specific roles and responsibilities (workflow) and to select tools that adequately address the complexity of the sales analysis process. This leads to.

Data Redundancy

  • Data analysts pull the same data and re-calculate the same subtotals, wasting time and money with duplicate efforts
  • Data analysts use different tools, increasing infrastructure and training costs for multiple tolls that do the same thing

Presentation Redundancy

  • Different category or marketing managers develop presentations in different formats causing inconsistent, incompatible, or inefficient presentations
  • Lack of methodology and standards means that the message communicated is often inconsistent between locations

Limited Insights

  • Due to the disconnect between data providers, manufacturers struggle with sales analysis of departments, fine lines, and products
  • Sales personnel do not customize for markets or accounts because it is too difficult or time-consuming

A successful analysis and presentation work stream is needed to eliminate these and other problems … and defining roles and responsibilities is the first step in this process.

One possible workflow might look like this:

Manage Data

Role: Data Analyst
Responsibility: Manage Data

Let people with data expertise manage data by creating and maintaining systems to pull, define, clean, integrate, and aggregate data into a format that business personnel can use.

Develop Insights

Role: Category Manager
Responsibility: Develop Insights

Business personnel such as Category and Marketing Managers are best qualified to assess trends, draw conclusions, make recommendations, and to put insights into a format that sales personnel and customers understand.

Deliver Insights

Role: Field Sales
Responsibility: Deliver Insights

Sales representatives need customer presentations that are ready to go but that can also be easily customized for their own accounts or markets. Let sales people do what they do best – sell!

The data, products and accounts depicted in this example are fictitious. Any resemblance to actual data, products or accounts is purely coincidental.

 
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