What should startups learn from the current Big Tech slump?

Startups can learn from the financial problems of big tech companies like Twitter (Photo: Souvik Banerjee)

GUEST BLOG. After a total euphoria in the field of technology, we are now witnessing the rapid downfall of most large technology companies. Think of Elon Musk’s acquisition of Twitter, which is already talking about the possibility of bankruptcy, or the 11,000 employees who lost their jobs at Meta, Facebook’s parent, in recent weeks. Unfortunately, I could go on and on with the list for a very long time.

Now, what can we take away from this technological crisis? Here are three lessons we can learn from this situation.

1 – Forecast customer demand to help you choose where to invest the money.

Big Tech’s cost structure was poorly managed compared to other industries, as hiring was no longer driven by demand and revenue potential, but by the desire to attract new talent, fresh talent, ahead of its competitors. In the field of artificial intelligence, many even claimed that the evaluation of companies was directly linked to the number of doctors hired, without any connection with the quality of the product or the company’s revenue. This competitive pressure to hire at all costs and as quickly as possible has led to a misallocation of resources for Big Techs and this is reflected in their profits!

Ideally, startups hire on a need basis, driven by customer demand. For example, the vast majority of venture capitalists will refuse to invest in a startup looking to raise money to generate more demand before it has some market validation. This is what is called in the industry “product-market-fit”, which is itself the correlation between the product and the actual demand of customers to pay for that product.

Hiring a mass of talent, even if they are among the smartest in the market, without knowing exactly what you are going to do is highly likely to lead to failure or put you in the same situation as Big Techs who must quickly proceed with massive cuts. Consider, for example, Element AI, which for a while was perceived as the next unicorn and was home to the biggest AI brains in Montreal. The problem: She was hiring all the time, probably thinking she’d end up with great products. For all its talent, the company never managed to create a product with real commercial potential, and after hundreds of millions invested (including a lot of public money), it was sold at a discount for about $500 million to ServiceNow, well less than your last review.

2 – Create a detailed plan to reach your income goals

What’s the best way to forecast customer demand? It sure is having a detailed plan to reach your income goals. Many Big Techs took advantage of the low cost of capital, as well as the pandemic, without properly planning their revenue targets. Many companies have estimated their revenue growth based on “learning new behaviors” during the pandemic, rather than having a plan in place to ensure revenue is met. This is the case of Shopify which, after seeing the very rapid increase in sales on its e-commerce platforms, mistakenly believed that this trend would become a fact and continue after the pandemic. Result: it hired massively and made very optimistic revenue forecasts, which it obviously failed to achieve.

For example, in Connect&GO, we expect to double our turnover in 2023, despite the possible recession. This is a very ambitious goal and requires a lot of planning. It’s easy for the CEO to say, “We need to double our revenue next year,” but it’s much harder to plan how we’re going to make sure we hit the target.

It is also this exercise that allows us to quickly see if our hypothesis is really plausible, as well as to foresee scenarios that could significantly affect the downside or upside of the forecast (for example: if in the first quarter we do not have X contracted revenues, our chances of reaching target will be reduced by 50% and therefore we will have to make the necessary decisions immediately).

While it’s almost impossible to perfectly predict how much revenue we’re going to achieve in the coming year, having a clear plan increases our chances of achieving it, but most importantly, don’t do like Big Tech and wait until it’s too late to make the necessary decisions.

3 – Recurring revenue models are more useful in crises

Many of the tech companies that are struggling right now are not based primarily on recurring revenue models. In fact, if we think about Facebook, Twitter, Snap and many others, their main business model is online advertising and they have almost no guaranteed recurring revenue. In the case of Shopify, in addition to the platform subscription (which helps them), another part of the revenue is based on a percentage of transactions on their e-commerce sites. A drop in your performance will immediately affect your own performance!

Tougher economic cycles like the one we’re facing now remind us why investors are so obsessed with subscription-based revenue models. While value-based pricing models are starting to gain traction and still make sense in many businesses, it is certain that in the coming months investors will come back to appreciate the certainty of recurring revenue even more.

So don’t do like Elon Musk and Twitter who are trying to hastily impose a recurring revenue model on their users, but think from the beginning how your project can generate stable, predictable and recurring revenue. This will help you a lot in any crisis!

For your part, do you have any other learnings or observations about the current downfall of big tech companies? Do not hesitate to write to me to give me your point of view!

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