Beyond Meat - A Marketing Plan Beyond the Sales
Bibliographic record
Abstract
Launched in 2009, Beyond Meat is one of the world leading producers of plant-based meat substitutes, operating in a highly dynamic market of alternative meats that is projected to steadily grow over the coming years and is predicted to reach the value of $31 billion by 2026. As such, Beyond Meat is one of the players in this market, competing directly with companies such as Impossible Foods and MorningStar Farms. Despite being one of the pioneers of the product and one of the major industry players, Beyond Meat has not been reaching the desired profit and market share. They are still largely relying on investment funds to grow, and have only had one profitable quarter in Q3 2019, after which the company reported negative profits again. Additionally, despite being one of the most prominent alternative protein producers, Beyond Meat has been seeing a decline in consumer awareness, as Google searches have been persistently falling since its IPO in June 2019. Furthermore, brand awareness is quite low amongst consumers. Beyond Meat failed to reach even top 9 meat substitute brands most often used by consumers in 2019; they have a 39% cued, and only 13% uncued brand recall value; and just a 1.2% social media engagement rate, falling below the industry standard. To increase market share as well as raise brand awareness and consumer loyalty, a marketing strategy has been developed that exploits Beyond Meat’s strengths and opportunities. Beyond Meat is particularly recognized for their environmental friendliness and has a strong brand reputation as the industry pioneer. Additionally, they have strong partnerships and are operating in a highly dynamic promising market. To exploit these, Beyond Meat should engage in the following marketing activities, to ensure their thriving success on this market: Highlight their USP as a non-GMO plant-based and gluten free product, that is good for the environment and delivers great taste; Introduce offers and promotions to increase market share. New packaging and promotion tactics (such as special Earth Day offers or different packaging bundles); Consumer loyalty and new customer inclusion should be promoted through the new proposed “Beyond Expectation” loyalty programme; Enter Asia for market share growth and strengthen their position in the USA and Europe by exploiting existing partnerships and engaging in new ones; Increase their YouTube presence and exceedingly collaborate with Instagram influencers to promote new recipe ideas and cooking experiences to increase consumer engagement and awareness; Increase collaborations with tangent brands to expand consumer reach. By engaging the above strategies, within 5 years Beyond Meat will reach: 20% market share in the USA; Reach 25% uncued recognition and social media engagement of 5%; Increase customer loyalty and the perceived value of the brand.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.073 | 0.025 |
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".