I agree with some of the other comments posted here that cost is a key factor in the purchase decision for razors, once quality has been established. I would be curious to know whether these razors achieve higher consumer quality outcomes than other options than traditionally manufactured products, and secondly whether the margin on the products will allow a price reduction, particularly on the razor blade side. The project also begs the question of whether consumers see razors as an on-demand purchase item, or a planned one. Will consumers care enough about their razors to request personalised design, or just pick up whatever is available in the nearest shop?
This is a really interesting usage of additive manufacturing. I suspect that, as the entire joint replacement market advances and patient outcomes improve, bespoke replacements will become even more important and poorly fitting replacements will no longer be tolerated. I would be interested to hear how this has developed over the past 10 years as these methods have come into play.
Fabulous piece. It makes a lot of sense for a consumer brand selling very personal products to be using crowdsourcing methods to connect more with its customer base. I would be curious to know how they manage IP for consumer-driven ideas, particularly in light of your comments on increased competition. If competing firms also look to crowdsource, will the differentiation of the brand be reduced? Also, I wonder how they manage to retain a consistent brand and product message to consumers whilst using open sourced ideas. It sounds as though they will still need an active innovation department in the future to manage this, even if they increase consumer engagement even further.
This is a really great article – thanks! Open innovation at Lego makes a huge amount of sense, both from the perspective of reducing internal R&D costs as well as increasing customer engagement. I would view the open innovation platform as a kind of inbound marketing, building customer interest in the product, which should eventually lead to sales. I suspect that the issue of public disclosure of decision-making process will only become important when teen and adult submitters of ideas are involved – children may be happy to just send their ideas in.
Great piece of analysis. I can see a real threat to lenders bypassing the traditional credit scoring system altogether, as FICO scores become increasingly divergent from their own models and seen as outdated and imperfect. LendingClub has seen this pattern in its own loan approvals as it has refined its model based on its increasingly large cumulative loan book. However, the FICO score still provides a “language” that the industry understands, and a standard that is measurable across all consumers. It would be challenging for a start-up to replace the industry standard in the short term, even if they do have a superior algorithm.
This is a really interesting business model. Due diligence processes in private investing world are highly manual and out-dated, relying on investment committee members’ previous experience and memory ahead of data-driven analysis. CircleUp needs to prove its business model further by realising a set of successful exits. I would be curious to hear what fees they charge on their funds – there is downward pressure on fees in the wider investing world, and the use of AI and big data could be an effective way for players like CircleUp to undercut existing players on fees through lower due diligence costs, and potentially squeeze lower-quartile investment funds out of the market. An alternative strategy could be to consider developing a SAAS platform providing such data to other asset managers. Whilst Blackstone is creating its own internal data hub, the vast majority of firms do not have the internal resources or deal track record to undertake such a challenge – there may be an opportunity to leverage the needs of those firms that get left out of the data revolution.
This is an important topic for a rapidly changing market. I would be interested to know whether Tala is already incorporating Kenyan customers’ M Pesa mobile money account transactions into its machine learning algorithms. Secondly, as an online lender, how does Tala deal with fraud detection? Does it verify information from borrowers manually or depend solely on smartphone data and assume the truthfulness of other information provided by consumers? It seems like their technology platform could be applied also in developed markets, or sold as a SAAS product to other lenders looking to incorporate additional datapoints into their credit underwriting process.