Optimize Pack

Optimize Pack will help you evaluate new pack ideas, identify winners, and optimize them directly with your shoppers to achieve in-shelf success. 

It is a monadic test, meaning that each of your packages is evaluated by a separate group of respondents. This is the most reliable way to test packages as the respondents' reactions are not impacted by your other ideas.

Contents

Key Measures

A holistic pack evaluation: Assess all package components from brand recall, communication, functionality, and sustainability, with the ability to test your package messaging through custom questions. Option to evaluate the side and back views of the pack too. 

Package and brand profiling: Understand what drives your package performance and how well the pack fits with and impacts your brand.

Heatmaps: Get extensive open-ended feedback to identify the best and weakest components of your package.

Flexible stimuli: In addition to the front of the package, you will have the ability to include side and back panels of your package as appropriate.

A robust sample size: A recommended sample of 300 or 400 (depending on your subscription terms), nationally representative purchasers leveraging strong demographic and consumption profiling.

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Using the Optimize Pack Framework

Optimize Pack uses an analysis framework that helps you understand if your package performance meets the criteria for in-market success. It includes the following key metrics that you can utilize to profile your pack: 

  • Package Performance Summary: Profiles package performance based on brand recall, pack driven shelf impact, recall lift potential, and other key performance metrics.
  • Package Profiling: Deep dive into package performance indicators and actionable attributes to highlight areas of strength and weakness.
  • Brand Profiling: Understand the impact and fit of the pack on brand perceptions.
  • Diagnostic Feedback: Key messages, heatmaps, and areas for improvement.

Lite Shelf add-on: If you opt to include our lite shelf add-on to your pack test, respondents also provide:

  • Spontaneous Brand Recall
  • What catches attention
  • Product Most Likely To Purchase
  • Other Products/Brands To Consider
  • Products/Brands Never Purchased

Questionnaire flow and key metrics definitions: 

  • Category purchase behavior: These questions determine whether the respondent makes purchasing decisions for their household, and respondents are asked about their purchase behavior in relation to the target category, as well as where and how they purchase the products
  • Pre-exposure brand choice: Before exposed to the package being tested, respondents are asked to select the brands they would purchase from a list including the target brand and the competitor brands set up during the configuration
  • Stimuli exposure + Brand recall: Respondents are shown the main image of the package on its own page for 5 seconds. This is immediately followed by the open-ended brand recall question - “which brand was the package from?”
  • Brand evaluation: Respondents evaluate how well they felt the package fits with the brand, and how the package changed the way they feel about the brand.
  • Key messages: This open-ended question asks respondents “what is the main thing that this package is trying to tell you?”
  • Further package evaluation: Respondents rate the package they just saw in terms of overall appeal, the influence of the package on likelihood of purchasing the product, how likely they would be to purchase this package if the brand or package they usually buy wasn’t available, distinctiveness, advantage, and ease of finding on shelf compared to other products in the category.  
  • Communication/ Messages: Respondents rate the clarity of the package’s communication of the information, features and benefits of the product.
  • Heatmaps: Respondents evaluate the images of the package that have been uploaded by clicking on up to three areas that they like and three areas that they dislike - with open-ended comments added for each like and dislike. This creates a likes and dislikes heatmaps, backed up with verbatim responses. 
  • Ideas for improvement: Respondents as asked in open-ended question  to give ideas on how to make the package better.
  • Sustainability questions: Respondents rate how much they agree that the package is environmentally friendly / sustainable.

Lite Shelf add-on: If you opt to include our lite shelf add-on to your pack test, the add-on is inserted into the questionnaire after the pre-exposure brand choice question. 

Upload individual images of package stimuli in the configuration. Include your main target package in the image set, as well as competitor packs and/or owned alternatives.

After a short intro, respondents are exposed to the collection of stimuli (competitor and owned) organized in shelf-like 2x8. They are given a 5 second flash of the collection, and then again as a clickable image-set. Introduction to the individual pack stimuli is then presented and the remainder of the Optimize Pack survey is completed as configured.

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Configuration checklist 

  • Audience: Custom Audience or Zappi Audience selected (list of available Zappi Audiences can be found on the platform)
  • Pack image: JPG/PNG (height of 860 PX max) 
    • You can test between 1-5 packages at once
  • Competitive brands: Add a list of competitive brands that will be used by the pre-exposure brand choice question
  • Name of retailers: Add the name of retailers in your category
  • Time period: Add the time period for the retailer visit. It will read as: “Where have you bought [category] in the last [time period]”
  • Attributes: Up to 20 in total:
    • You will have a list of attributes to choose from, which for non-English markets are available to you already translated, including:
      • Accessible, Aspirational, Authentic
      • Boring
      • Cheap, Clever, Complicated, Confusing, Convenient, Cool
      • Easy to understand, Easy to use, Elegant, Embarrassing, Engaging
      • Feminine, For people like me, Friendly, Fun, Funny
      • Great tasting, Great value
      • High tech
      • Innovative, Inspirational
      • Luxurious
      • Masculine, Modern
      • Not cool
      • Refreshing, Relaxing
      • Sexy, Simple, Sleek, Stylish, Sweet
      • Tasteful, Thoughtful, Top quality
      • Uncool, Unique
      • Youthful
    • You can use your own custom attributes, but you will need to provide translations for the non-English market
  • Side and back views of the package (optional): If chosen, it will include an image exposure and heatmap evaluation of likes and dislikes
  • Tagging: Tagging your stimuli allows for better organization of your stimuli and meta-analysis of your concepts later on. Don’t worry if you can’t think of your tags now, they can always be added later. 
  • Lite Shelf add-on: If you opt to include our lite shelf add-on to your pack test, you'll need additional stimuli:
    • Images only
    • Upload up to 16 images of shelf package images.
    • Include your own target stimuli, as well as a common range of your owned brands and competitors.

Click here to see the step-by-step guide on how to configure your study.

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Best practices in pack testing 

Best practices involve comparing new designs against an existing design or a close competitor’s design. We recommend always testing multiple cells, using the existing package design (or a close competitor) as the control cell to compare against. 

Packaging updates can be utilized for many things - increased brand awareness, emotional connection, conveying new messaging, etc - it is important to make sure that the metrics that are trying to be moved perform well against what is already in the market while not doing any harm in other areas.

Normative Comparisons in Optimize Pack

Norms will not be able to tell you whether you have maintained existing parity while moving the needle on your area of focus.  There's too much variability in packaging that gets tested to make a norm a meaningful comparison. Instead, test against an existing or similar pack and use it as the benchmark - the test pack should do at least as well on relevant metrics.

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If you are looking for any additional information, please check out the links below:

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