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AI Performance Analytics Strategy

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by

Jonathan Ray

on 30 September 2013

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Transcript of AI Performance Analytics Strategy

Analytics
Strategy
2013

“People's deep confidence in their judgements and abilities is often at odds with reality.”
Michael J. Mauboussin
The Mission
To provide all business managers at AI with timely reporting and analysis from the data we collect, offering detailed insight into our audiences, marketing and product performance
Objective
Coordinate with Pivot Team to automate data flows from customer data sources into a consolidated customer database
Objective
Work with each business group to clearly define unique performance factors that are associated with their revenue growth
Objective
Develop a comprehensive set of revenue-based KPIs with a strong cause and effect correlation to sales, customized for each product line by brand
Objective
Build new KPIs into multi-layered dashboard reports with custom views for executives and business managers
Objective
Create performance analysis for each business group, highlighting successes, challenges and opportunities
Objective
Maintain constant communication with business groups to keep them informed and assist with performance optimization
Our Data must be...
Accessible
Organized
Reliable
In order to be successful...
Challenge
We have too many disconnected databases
Challenge
There are many like products at AI and the tactics one group deploys to generate revenue compared to other groups can vary greatly.
Challenge
We utilize hundreds of metrics in our reporting yet few of them show a consistent correlation to an increase or decrease in sales
Opportunity
Current reporting does not include important non-financial metrics that offer valuable insight into how and why our products perform. By expanding our KPI set we are able to better understand and react to changes in market conditions and more accurately target prospects who are primed to convert.
Technology
AI utilizes many tools to collect, store and manipulate data. Many of these tools are redundant. We should evaluate and condense our tool-set for all groups to help streamline data flows and increase reporting efficiency. When evaluating new tools, we should first approach the technology/service with a need and verify that the solution provides us with new information or efficiencies that are not already available with an existing tool.
Resources
JRay
Stacy
Intern
Matt G.
Nikhil
Technical/Tracking Configuration
Part Time
Part Time
Temp
Full Time
Map KPIs
All KPIs used should be clearly
mapped to revenue
Dashboard Reports
Delivered monthly and serves as the starting point for managers to view performance and initiate action

Customized for each brand and product type, only displaying the most relevant information important for the team and management

Includes high-level analysis and measurement action scenarios, organized to promote better communication with team members and encourage action

Designed to compare metrics and KPIs across the company
Opportunity
Data without narrative is left to interpretation, which can often be wrong. We can help groups navigate reports to pinpoint opportunities and areas that require action
Opportunity
With an expanding analytics team we can provide extended support to business groups, reducing wait times on tracking set-up and reporting
Data Aggregation/Reporting/Optimization
What is the reality?
Now more than ever, mature businesses are reliant on data to drive their growth strategies. Companies that are not equipped to handle and report from large amounts of data are not able to see opportunities that could be right in front of them.
Dashboard Report Model
September
November
January
Phase I: Group Consultation
Meet with each business group to review their product lines, revenue strategies and factors for growth.
Phase III: KPI Development
Create an expanded map of all KPIs for revenue by brand. Business groups will contribute to these metrics and should be managed overtime. We should develop metrics that show a strong cause and effect relationship to revenue.
Phase II: Data Mapping
Based on the information gathered from group meetings, create a map of data sources and gain access to systems to prepare data extracts. The longer term goal here will be to automate data flows from standalone customer DB into an aggregated customer DB, from which we will extract data from more efficiently.
Phase IV: Dashboard Modeling
Produce dashboard models to include KPIs and analysis for the executive and manager viewer. Dashboards should allow viewer to drill down into source data and compare against like products from other business groups. Technology necessary to power the dashboards TBD by models.
December
Phase V: Dashboard Testing
Once the dashboards have been constructed, the analytics team will work with group managers and executives to test the reports and tailor data points.
September
Launch!
The goal is to launch the first version of the dashboard reports at the beginning of the year.
August
Dashboard Reports Timeline
Full transcript