If you are involved in any way with producing packaging (whether production runs, test runs, or comps) for consumer goods companies, you know how important issues related to sustainability in packaging can be. Metrics are hard to come by, especially as they relate to comparing packaging types and media.

With the release of the Global Protocol on Packaging Sustainability 2.0, things just got a little more quantifiable.

The protocols are designed to look at issues of sustainability across a variety of business indicators at four levels:

1. Simple analysis using a single indicator to track a change, such as packaging weight, and cube utilization.
2. Optimization analysis for a given to the full functional package. For example, using a weight reduction indicator together with a cube utilization indicator to ensure that changes in one don’t offset the other.
3. Comparative analysis of one or more packaging formats/material across multiple formats for same functional unit, such as comparing drink packs from glass, plastics, metal or beverage carton to see trade-offs with each material choice.
4. Full system design and analysis comparing packaging formats/materials with information on the product.

The protocols also divide the indicators and metrics into three categories — environmental, economic and social — that can be used to address different business questions of important to your consumer packaging company clients. These include:

• where in the packaging design process the assessments are being applied
• how the results are being used
• where in the supply chain they are being applied

If you are serving consumer products companies in any capacity, whether producing full production packaging, doing test packaging, or comps, this is a resource that could serve you extremely well. Issues of sustainability are critical in this marketplace, and having a working knowledge of the most current protocols puts you in an excellent position as a valued supplier.

Download the protocols here

read more: The Digital Nirvana

Every database marketing program begins with a rhetorical question that the marketer already knows the answer to: How good is the data?

The answer is usually, “Not good,” because many companies overlook the essential first step of Knowledge Discovery in Databases (KDD):

Step 1 Data Cleansing  

Also known as data hygiene—this process perpetually cleans and updates the data as part of the sales and billing process. Companies that overlook data cleansing, give it a low priority or sweep it under the rug soon find themselves with dirty data on their hands. But organizations that keep their data squeaky clean have the best chance of mining their data successfully because they can check off Step 1, and head right to:

Step 2 Data Integration

Sometimes, it’s desirable to combine more than one set of data—such as customers and prospects or leads that are in various stages of the demand waterfall. You may also want to aggregate prospects from more than one source, including both purchased and rented lists. Although there are several steps involved in data integration, the most important is de-duplicating the records. This can eliminate a tremendous amount of waste. But you must establish rules that define which source is preferred when duplicates are found.

Step 3—Data Selection

The data selection team needs to determine thresholds, limitations and other selection criteria. For example, if firmographic attributes are the most important criteria, then only the data models that meet the minimum threshold for annual income or revenue would be selected. If psychographic data matter more, then records might be selected for specific interests such as camping, concerts or social causes.

Step 4 Data Transformation

Once the best data has been selected, it must be transformed into a uniform set and optimized for use in a marketing program or campaign. All the fields must be consolidated, merged and purged so that they will be easy to index and use for data mining. If you’re using personalization in your campaign—and you should—this step is essential to ensure accuracy.

Step 5 Data Mining

This process is exacting, but in a nutshell, it involves searching the various fields of the database for specific attributes. These are then used to identify trends that can be matched against the predictive models that represent the marketer’s ideal prospects. The process is complete when the mined data resembles the data models. The Predictive Model Mark-up Language (PMML) developed by the Data Mining Group enables uniform data mining processes and techniques across vendors.

Step 6 Pattern Evaluation

The patterns that emerge during the data mining process must be evaluated to determine which are relevant to the model and which aren’t. If one of the new patterns contradicts the original persona, revisiting the model is a good idea. If the two are consistent, the model is validated.  Pattern evaluation can lead to the discovery of trends that might not have been apparent to the team that created the original model. And using the knowledge that is revealed can have a very positive effect on the entire program.

Step 7 Knowledge Presentation

The proof is in the pudding. Once the final data are selected, a report that explains why the chosen data are the best for the program is delivered. Everything that was learned during the data mining process—including trends, patterns, and anomalies—is included in the knowledge presentation to the user. The key is to present the findings in a clear, easy-to-digest format.

While this has been a brief and simplified description of data mining, the entire process—which involves a number of different algorithms—is actually quite complex. Classification algorithms that predict one or more discrete variables, regression algorithms that predict one or more continuous variables, segmentation, association, and sequence algorithms are all used. When practiced correctly, database marketing, data mining, and predictive modeling can all yield maximum ROI.

via Part II: Data Mining For Hidden Treasures—7 Steps of Knowledge Discovery in Databases | The Kern Organization.

Marguerite Gardiner, the Countess of Blessington, wrote Conversations with Lord Byron in 1834.” In describing the poet, she said “Genius is the gold in the mine; talent is the miner who works and brings it out.” Today, if we substitute the word “data” for the word “gold,” the statement still rings true.Like a gold mine, a data mine contains valuable nuggets that need to be extracted from the dross that surrounds it. And techniques for excavating these treasures are  constantly evolving. What we now call data collection and database creation was made possible in the 1960s by computers the size of small buildings. During the 1970s and 1980s, database management systems led to hierarchical database systems, and later, to relational database systems.  With the ability to index databases, database technology increased geometrically, and new theories and practices quickly spread around the world. Query languages, user interfaces, pre-fabricated forms and reports, transaction management, data recovery, and online transactional processing (OLTP) all came into play.  And by the time the Internet emerged in the early 1990s, database technology was a booming industry. Web-based systems thrived, and data and web mining became sophisticated disciplines. Relational technology made efficient storage, retrieval and management of large amounts of data possible. And advanced data models—including  extended-relational, object-oriented, object-relational, and deductive—enabled spatial, temporal, multi-media, active, scientific, knowledge, and office information databases to flourish. In some ways, technology outpaced practical application, and in many cases “data rich, information poor” companies had no idea what to do with the reams of data they had collected. These massive repositories of dormant data became known as “data tombs.” Data mining—also known as Knowledge Discovery in Databases (KDD) —is how smart marketers extract meaningful data from these tombs. In order to convert facts into knowledge, analysts look for patterns within the data, then identify and categorize them. Using this information, they create a predictive model that flags people who resemble current customers  in key ways.  This is a simplified explanation of what is actually a very complex process, but you get the gist. Several scientific organizations, most notably the Data Mining Group (DMG), have pooled resources in an effort to create a uniform method for data mining using the Predictive Model Markup Language, PMML.  IBM, Microsoft, SAP, Oracle, NCR, and most major computer and software companies are members of this group. Advanced data mining can reveal insights about customers, former customers, prospects, and leads.  When combined with purchasing patterns and behavior, the data can be used to drive sales, reduce churn, and support cross-sell and up-sell initiatives. There truly is gold in them there hills, if you know where and how to look.  In part two of this article, we’ll explore the seven steps in KDD:

1) Data Cleaning 2) Data Integration 3) Data Selection 4) Data Transformation 5) Data Mining 6) Pattern Evaluation 7) Knowledge Presentation

via Data Mining for Hidden Treasures | The Kern Organization.