Dove: Using New Product Development to Grow the Brand – A Case Study
Bibliographic record
Abstract
In the increasingly competitive environment of business, the development and launch of new products has become an important competitive tool. In a crowded marketplace, there is greater need for differentiation; in markets that are stagnant there is pressure to create excitement. The development and launch of new products helps in both situations. In certain industries like the personal care and beauty products industry, new product development has become critical to survival. A leading brand, offering a wide range of personal care products is Unilever. Unilever has more than 400 brands, 14 of which generate sales in excess of €1 billion a year. Many of these brands have longstanding, strong social missions, including Lifebuoy's drive to promote hygiene through hand washing with soap, and Dove's campaign for real beauty. The Dove brand started its life in 1957 in the US, with the revolutionary new beauty cleansing Bar. With its patented blend of mild cleansers and ¼ moisturizing cream, it is #1 Dermatologist Recommended brand in the US, Canada and France and strongly endorsed by Dermatologists across the world. Although it was a successful product, Dove recognized the opportunity to stretch the brand by investments that would: revitalize it extend and further develop its growth phase help to delay the onset of the maturity phase increase the overall brand equity Dove was convinced that such investment would help to maintain the brand's strength in a rapidly changing market place. Hence, Dove started researching on how the brand could be extended into a series of skincare and hair care products, at the same time keeping the core product strong, thereby grow the brand as a whole. Today apart from its moisturizing soap, Dove has extended the brand by launching many new products like: Body Washes, Hand and Body Lotions, Facial Cleansers, Deodorants, Shampoos, Conditioners and Hair Styling products. New product development had transformed the brand within growing Indian market. This in turn gave a great opportunity to roll-out other developments in other markets as well. This case-study will focus on key strategic issues related to brand extension. It will also emphasize the various benefits of brand extension: How brand extension increases brand awareness and profitability with offerings in more than one product category, and ultimately adding up to the brand equity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".