Friday, March 29, 2024

Apple vs. Lenovo: a Contrast In Concepts

Datamation content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More.

This month I spent some time going over Lenovo’s rich product set and I was again reminded of the dramatic difference between how Lenovo, and most OEMs, differ in strategy from Apple. 

Apple’s approach seems to be, based on valuation, far more lucrative but it does have some severe risks which are starting to emerge as Apple market growth stagnates. You can see how Steve Jobs made Apple so successful, but also see Tim Cook’s unwillingness to continue that strategy, which is largely unique in market, which is contributing to that firm’s weakening prospects. 

But I can also see a path that would make the more common path that Lenovo is taking more successful. And this path, given that Apple appears to be pivoting to that more common model, might help them as well.  

Let’s talk about that this week. 

The Conflicting Models

Apple’s model, at least under Steve Jobs, was to build few products but do massive demand generation to convince people that a one-size-fits-all solution was ideal for them. The more common model is to build lots of product variants and hope to match better unique customer needs to one of those products.  

Apple overlays their product strategy with a lock-in model so that once you are in Apple’s ecosystem it is very hard to migrate out. That allows the company to charge higher prices because, in effect, they are a monopoly to their installed base, while those using Lenovo’s model have to compete constantly for customers, even those they already have. 

The advantages to Apple’s model is far less unsold inventory, far less customer churn, and far higher control over their revenue and profit. The disadvantage is a tendency to take their customers for granted, a far lower need to innovate, and a tendency to create an unstoppable adverse migration wave when the customers figure out they are being overcharged and under-appreciated.

A lock-in strategy, to be strategically successful, requires an unwavering prioritization of customer satisfaction, but the power it conveys always assures a shift in focus, placing margins ahead of that satisfaction and building a foundation for failure.  We saw that with IBM in the 1980s and again with the Microsoft browser last decade. 

On the other hand, building a lot of products and emphasizing choice forces lower margins, confuses customers, and dilutes demand generation efforts below where they might adequately execute – particularly for the most innovative offerings. 

And innovative offerings require a lot of marketing muscle to convince people the benefits are worth the change (we don’t like changing from what we already know to work). 

Optimizing Lenovo’s Model

One thing that is emerging we didn’t have before is AI and micro-targeting. If you can instrument your customer and better match them to the unique solution that best suits them, then you can not only optimize your product mix against the population of customers you already have but micro-target those you haven’t yet acquired. And this is an ideal problem for an AI to solve. 

I often get the question of what PC (mostly laptops) a person should buy. And to answer that question I need to know a bit about them in terms of their skills, how they want to use the product, and what their priorities are. From that, I can then point to options that will best meet their needs.

Granted, right now this is vastly easier with Apple because their customer’s primary requirement is that it be an Apple product, vastly reducing the complexity of the decision. But an AI would shift the load from people like me, be able to handle massive levels of product and customer diversity while providing even better-targeted answers.  Properly implemented this focus on better matching needs to choice should be vastly better for the majority of customers who are, in mass, very different from each other in terms of needs. 

In theory, using an AI to accurately match the customer with the unique product that was designed for them should allow a company using Lenovo’s model to approach Apple’s success while avoiding the problems with lock-in – and the marketing-centric scaling issues with the Lenovo approach.

In effect, Lenovo would market the process and the more attractive result and not need to market individual products, allowing them to aggregate their marketing effort. This would make it more effective while still providing unmatched choice. 

This approach will be critical when we finally get to the point of being able to 3D print complete products, because marketing every potential variant will be economically unfeasible. 

Wrapping Up

We have a potential model between the unique focused model that Steve Jobs created for Apple and the more common model used by Lenovo and other OEMs. Even Apple appears to be pulled into this more common model, and given the lack of success with products like their HomePod and iPad Pro, both performing well below potential, they are having a difficult time transitioning. 

To optimize this more common multi-product model micro-targeting and AI assistants will eventually be used. So that a new or existing customer will only see the few products that best meet unique needs. This would allow the firms to market the process rather than the product, ideally creating what may be the best blend of both approaches.  Now we wait to see who figures this out first. 

I should point out that this analysis came to me after reviewing Motorola’s (a subsidiary of Lenovo) product line, and realizing their $300 Motorola One Active was better for me than Apple’s $1,200 iPhone. This result got me thinking that if you could scale this, most people would likely prefer a less expensive phone that better fit their unique needs over a vastly more expensive one-size-fits-all phone that didn’t. 

Subscribe to Data Insider

Learn the latest news and best practices about data science, big data analytics, artificial intelligence, data security, and more.

Similar articles

Get the Free Newsletter!

Subscribe to Data Insider for top news, trends & analysis

Latest Articles