PEAK: Flexible Reporting of Multi-Rater Employee Evaluations

PEAK Reports (Performance Evaluation And Knowledge) is our family of Excel and online applications for managing the production of multi-rater evaluation feedback reports. It offers outstanding time and process benefits to those involved in recruiting, screening, evaluating, managing, and developing employees. It frees HR coaches and organization development staff to concentrate on their primary tasks. The application provides for the production of several commonly used evaluation feedback reports, which can be customized to the needs of each client.



Behind the reporting engine is a multidimensional data cube based on data sets derived from any evaluation instrument. MIG processes this raw evaluation data using each client's business rules and evaluation objectives to produce the multidimensional PEAK data cube.



PEAK Reports adapt to a wide range of performance evaluation paradigms, and are not tied to a particular approach to performance evaluation. They accommodate any type of performance evaluation or employee screening process, including prescreening, interviewing, 90-, 180-, or 360-degree evaluations, gap reporting, skill assessments, etc.



The PEAK Reports reporting engine greatly reduces the time and effort that employment recruiters, HR staff, and other managers spend organizing evaluation activities, analyzing data, and preparing feedback reports.



The PEAK Reports application facilitates the quick turn-around of one-time, annual, or continuous reviews. It is available in turn-key, service-bureau, or blended configurations as a workstation, WWW, or server based tool, and requires no additional IT Investment. The output is hardware independent.

Because of its flexible structure, Peak Reports can be used for organizing and reporting on any activity or program for which consistent evaluative data is available. This might include treatment programs, standardized testing, benchmarking, evaluation of presentations and workshops, etc.

Maintaining Your Customers: Data Driven CRM

Customers today are very demanding and retailers must be able to anticipate customer needs or risk losing that customer.

For example: I grew up in a small town on the east coast of the United States. Every year before school started my mother would take me school shopping at the two or three local specialty stores in town. We knew the store owners by name and the owners knew our families. There was a sense of loyalty and the relationship between business and customer was a long standing one. If we did not find what we wanted or if we needed another size, the store clerk could place a special order and we waited, sometimes two weeks, for the product.

Today, back to school shopping is a very different process. We may go to one or two national retailers. We rarely know the store owners. Many times we shop online because it is possible to have access to regional, national or global retailers’ right in the privacy of our own home. Even though we do not know the merchants personally, we expect the retailers to understand and meet our needs. If we have to wait or if the retailer can not provide personalized customer service we will move on to the next merchant or supplier.

“We as customers want what we want and we want it now and at the right price.”

Fortunately today we have analytical tools and customer and product data to better serve and personalize the retail experience. Businesses know that it is less expensive to maintain a customer than to find a new customer. Therefore, customer relationship management (CRM) is key to a successful retail business. One CRM strategy that is gaining popularity is data mining.

Data mining is a process of knowledge discovery that employs statistical modeling to large amounts of transformed transactional and historical data, in an effort to understand and predict customer behavior.

The data utilized in the data mining process is typically massive and transactional in nature. It is opportunistic. The data was not collected with analysis in mind. Instead it was collected to monitor process control and track inventory. The data is usually messy and lacking integrity.


Right now ask yourself the following questions: Do you have access to large amounts of data? Is this data operational and/or transactional in nature?

If the answer is yes, you are lucky. You have the data needed to better understand your customers, hence making you a more responsive business partner. By uncovering patterns and relationships among your current customers, you will be able to put promotions and advertising in place that speaks directly to those customer segments. Once the power of your transactional data is unleashed you will be able to predict when a customer is about to churn. This will allow you to be proactive in the market place. Furthermore, you will be able to cross sell products and up sell products to existing customers.

You have the data what’s next? What is next is a process of data cleaning and data warehousing. You need to organize your transactional data so that you can build predictive models. The diagram below outlines the process. Stay tuned for the next edition of this newsletter when I will tell you how to household and prepare your data for the data mining process.