DtC model: How businesses can take intelligent risks

The business press glorifies risk takers, and it’s not hard to see the attraction. Take Elon Musk. Whether or not you believe he’ll ever succeed in colonizing Mars, there’s no denying that many of his ideas — solar roof panels, Tesla batteries — will have a beneficial impact on all of humanity. In other words, his risks are justified.

Ditto for many smaller enterprises, such as Dollar Shave Club and Allbirds, that took big risks. Bucking the conventional, multi-channel retail models, these companies sell just a handful of products directly to consumers. In a few short years, they’ve become “unicorns” in the startup world, meaning that the companies are valued at over a billion dollars each (last year Unilever purchased Dollar Shave Club for $1 billion, and Allbirds is currently valued at $1.4 billion).

Of course, Elon Musk is a maverick with a gift for raising money. Direct-to-consumer (DtC) brands are small and nimble, and don’t have hundreds of employees and thousands of customers whose economic well-being may be jeopardized as the result of a bad risk. How do most companies create a culture of risk-taking without causing undue harm?

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DtC model beyond Elon Musk

It’s a question I think about all the time, and I’m confident that I’m far from alone. Being accountable for both the Product Management and Software Engineering functions at a software company, I understand that survival demands constant innovation. All software executives are regularly faced with decisions that have complex outcomes: do we re-launch our product on this new breed of technology or wait to see how the market plays out? If we build it, will customers come? If they don’t, how do we make up for those lost expenses?

Risk taking is essential to moving the economy forward, and therefore the pundits are right to praise it. And yet we also need to be smart about it, which we can do if we leverage the plethora of data that is now available. Data can help us determine the potential impact of our decisions should we opt to move forward with a new idea, as well as mitigate the consequences if things don’t pan out as hoped for or expected.

I’ll give you an example: right now, DtC companies are getting a lot of press and VC money. The reason they are so heralded is the way in which they can minimize the traditional retail “steps” which therefore reduces the consumer price and maintains healthy margins for the business. In traditional retail, a product is manufactured, then bought by a wholesaler, which sells to a distributor, which sells to a retailer, and then can finally be bought by the consumer. In a DtC model, a product is manufactured, marketed, then sold to the consumer. This direct selling model seems like a no-brainer, right?

Pros and cons of DtC

Unfortunately, it’s much more complicated than that. So much of the success of a DtC model is reliant on the success of the marketing team, which from a finance perspective, means taking on a lion’s share of risk. However, data just happens to be one of the distinct advantages of the DtC model. Selling directly to a customer means that the brand owns the incredibly valuable first-party data on their customers and can request permission to use that data for marketing purposes – pursuant with new privacy regulations, such as GDPR and the California Consumer Privacy Act.

Companies, like Uniliver, which typically sell their products via third party retailers and wholesalers, aren’t positioned to collect first-party data, and that puts them at a marketing disadvantage. This leaves many retail enterprises asking if they too should launch a DtC channel (in fact, this is one of the drivers of the Dollar Shave Club purchase by Unilever). Data, in the end, allows an organization to assess this risk.

The first step is to identify the right data that will indicate whether the risk at hand is a smart one. In this case, the company’s net promoter score (NPS) might be very insightful. Are consumers likely to recommend your products to their friends and colleagues? A high NPS is a good indication that the risk is sound, as DtC companies depend and thrive on customer referrals. Another data point to look at is the relationship of customer satisfaction to price points. If CSAT is highest for your most premium products, it’s an indication that consumers will be willing to buy from your website directly because they value your brand.

DtC model and ecommerce

By combining these data points, you can determine if your hunch has a chance of succeeding; now onto assessing the risks.

Launching a channel that competes directly with your long-term wholesale partners may ruffle feathers, which could result in lost sales, on top of the expense of designing, building, testing, launching and advertising your new eCommerce site. A smart move would be to test the impact of lost sales on your financial statements.

In other words, how would a 10%, 20% or 30% decrease in sales affect your balance sheet? Will you have enough cash on hand to hire the staff to run your eCommerce channel if sales were to decline? This kind of what-if planning can quickly reveal your risk appetite, as well as drive your business decisions. For instance, let’s say that a 20% loss of sales means that your new DtC eCommerce site must ramp up quickly, doubling the number of sales each quarter. This reality may require you to invest far more in digital advertising than you currently spend, and you may want to hire a team with expertise in optimizing media budgets.

There are obviously many variables that determine the potential success and risk of a business initiative. By looking at the right datasets to help validate your decisions, and by imaging various positive and negative outcomes of your decision on your financial statements, customer relationships and current level of resources, you will be in a far better position to decide if a risk is worth taking or not.

About the Author

Adam Rice serves as Vice President of Product Development for Centage Corporation. With almost two decades of experience designing, building, testing and maintaining production grade software, he spearheads software development process and software engineering for Centage’s Planning Maestro, the cloud-native planning & analytics platform that delivers year-round financial intelligence. With Planning Maestro, Centage offers the sophisticated features needed by small and mid-market organizations to integrate budgeting, forecasting, and deep data analysis within one easy-to-use, scalable SaaS solution. Follow on LinkedIn or Twitter @centage.

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