andreabeja

[andreabeja]

Burberry

Burberry's decisions have had a significant impact on its vulnerability in the fashion world. For example, Burberry's expansion into new markets, such as China, and its focus on digital innovation have helped the brand adapt to changing consumer preferences and market dynamics. However, Burberry has also faced challenges such as counterfeiting, which can harm its brand reputation.

An LLM would say that brands like Chanel, Louis Vuitton, and Hermès are less likely to be vulnerable to counterfeiting. I don't necessarily agree, I think that brands that are taking an unconventional approach would be less vulnerable.

For example, Lululemon addressed the challenge of counterfeit products, specifically dupes of their popular Align leggings, by using a unique strategy. Instead of combating the dupes, Lululemon embraced them through a "dupe swap" event on TikTok. For those of us on TikTok, including myself, this was a very clever and bold strategy.

Source: https://www.marketingbrew.com/stories/2023/05/24/lululemon-gave-out-usd98k-worth-of-leggings-here-s-how-and-why

[andreabeja]

Forecasting Effectiveness and Product Diffusion

Forecasting product adoption is challenging due to the wide range of tools and methods available. Some products, like individually wrapped peanut butter slices, are likely to achieve rapid market adoption. These slices combine convenience with a clearly defined target market, benefiting from prior market testing.

Alternatively, products such as collapsible wheels might take longer to diffuse. Potential users need to see others using a product before they feel compelled to try it themselves. Similarly, luxury items like stave puzzles are expected to have a slower adoption rate. These puzzles cater to a niche market that values exclusivity over mass appeal, limiting their widespread uptake.

Polytrack stands out for its potentially big impact on the industry. With successful implementations across various racetracks and a proven ability to reduce horse injuries, Polytrack should be able to achieve rapid adoption. However, due to the conservative nature of the racing industry and the high switching costs, its widespread acceptance may still be delayed.

Effective forecasting is important for predicting how quickly a product will penetrate the market. The HBR article linked below emphasizes that the choice of forecasting technique should be carefully considered, taking into account available data and goals. Factors such as the purpose of the forecast, desired accuracy, time frame, cost-benefit analysis, and analysis duration can impact which forecasting technique is best. An example of strategic forecasting is seen in how Corning Glass Works tailors its forecasting methods to match the life cycle stages of its products (see image below), ensuring more accurate market predictions and better management of product launches and growth.

Source: https://hbr.org/1971/07/how-to-choose-the-right-forecasting-technique

[andreabeja]

Good Friction, AI, and Spotify

The concept of 'good friction' can be used to ensure that AI applications are implemented ethically, but that also allow consumers to have choices that affect their experience.  Good friction can be summarized as touchpoints that ensure customer agency and improve choice, allowing users to actively engage with AI rather than be sidelined. Companies should also watch out for “dark patterns” which is where ‘bad friction’ is being used to manipulate and nudge customers into undesirable situations. 

One example of good friction is Spotify's approach to onboarding new users. Rather than streamlining the process to ensure the lowest time to onboard, Spotify asks users to select their favorite genres and artists. This step achieves a dual purpose: it educates users on the use of their data in personalizing their experience and also gives Spotify customer data to refine its services. 

Another example is Spotify’s feature that pops up after a playlist and allows the user to exclude playlists like that in the future. While AI/ML is allowing this feature to be user- friendly, it still gives power to the customer to influence the direction of the behind-the-scenes algorithm.

https://engineering.atspotify.com/2023/10/exclude-from-your-taste-profile/

[andreabeja]

The Discipline of Business Experimentation

 Experimenting to conduct business decisions can be a very strategic tool if used correctly. It’s important to align the company’s goals into filtering criteria for deciding what proposals and experiments to conduct. Setting a hypothesis is important for making sure stakeholders are aligned on what is the purpose of the experiment and what the output should be. The experiment should also ensure that potential outside noise is addressed and isolated from the variables of interest. Randomization and blind tests are important tools that can address the issues of bias, as well as make the experiment more reliable. Once the experiment is completed, it’s important to look at all components involved in a change and what the ROI would be. 

A company that does experimentation really well is Duolingo. Below is a screenshot of their in-house experimentation tool. This tool was designed to streamline the creation, execution, and analysis of experiments, which shows Duolingo’s commitment to learning and adaptation. As the article discussed, when companies experiment, it is important for them to establish a clear purpose for experiments, ensure stakeholder buy-in, and outline the feasibility of the experiment. This structured approach to experimentation helps Duolingo develop a deeper understanding of the product and its users.

Reference: https://blog.duolingo.com/improving-duolingo-one-experiment-at-a-time/