Product Selection and Company Strategy
We all know better than ask this loaded question (I surely hope so!). Sometimes, though, this is exactly the question to ask.
Jokes aside... Oftentimes, managers find the need to compare the value and potential of their different products, so they can take rational decisions, about which products and services to focus on, which products will be deprecated, or what business units are to be divested.
This post tries to demonstrate such analysis. Your mileage, however, may vary: It is the mindset that counts, while each combination of industry, market, and company, will define specifics.
Data driven decision making
Being human, we tend to be emotionally invested in products we created. Those products (also services) came to be with great effort, considerable energy, and risk.
But there comes a time when we are required to sift through several products we are selling, to set course for the future to come.
But wait, how would you compare apples to oranges?
In this analysis, I discuss HES Inc., a Hypothetical Enterprise Software company, with products catering to quite dissimilar audiences:
Product Alpha solves for financial institutions, running as a backend service, providing critical operational intelligence. The annual license fees for this on-premises product are, for sake of this analysis, $80k per customer, the serviceable market is of 10,000 consumers with the needs and budget. Alpha has some alternatives, but no direct competition. Although it has sold just a few times already, it passed the harshest requirements, and is well regarded by the users.
Product Omega, in contrast, serves software development teams, ensuring 3rd party code origin and integrity. The hypothetical monthly subscription for this SaaS product is $49 per seat, payable by credit card. The market is significantly larger, with 100,000 teams in the target market, each using an average of 5 seats. Competition is rife, which set the price range, and also imposes a non trivial churn rate of 20% annually, compared to the minimal churn rate for Alpha, 5%, which we set preliminary: No one ever churned on this product, yet. The product is live for six month, and it already has amassed a couple hundreds of customers, currently averaging on 2.3 seats each.
So who's mommy's baby? Or, more formally, which product is to be prioritized?
Wait, I haven't yet provided the full picture: A thing to know about financial institutions, is that when coming to critical systems procurement, they love to take their time. Sales cycles can take 6-18 months. People to meet, RFPs to fill, committees to humor... A dedicated sales manager can close 10 such deals per year, on average, then get their 4% commission. Product Alpha, by the way, requires lengthy integration, which customers happily pay for. Three months per installation, at $24K, is what it takes.
Product Omega sells with much less friction, to be honest. With a digital marketing campaign in place, leads are accumulating fast enough. At 30% conversion rate, users install their time limited trial, and of these, a decent 25% sign on to subscribe. What usually starts as a single seat, slowly grows within the first year to three seats, and no further data is available, although marketing department tells me the potential averages at 10 seats in small to medium development teams.
So again, which one is it going to be? In a side note, I guess that you have been wondering how on earth these two products came to being within the same startup. But this chasm between different types of offering can be quite common, especially with products emerging from project companies, with a homegrown technology.
Crunch Time!
The first thing to acknowledge is that these products dictate very different business models, as well as growth models - leading to different behavior, cashflow, and company DNA.
1. Product Alpha is an "old school" B2B product, run on customers' servers, on their private cloud, or even on HES's cloud. At any rate, though, this is not a self service product, as it requires significant customization, integration, certification, and training.
The revenue streams behaves differently. Typically, a lump sum is paid in few installments at the beginning (signage, delivery acceptance, and perhaps more milestones), and then maintenance fees at the range of 20% are paid annually, once a one year guarantee expires. Although there are opportunities for paid training, additions, and modifications, I will live them off this analysis.
The cost structure is skewed as well: Pre sale efforts may consist of sales reps meeting stake holders along the target organization's echelon, a tender may take place, and technical features must be integrated into the prospective workflow - some times within a pilot, prior to the actual sale. Another aspect of such sales process is that they take time. The first approach by the customer may take place several quarters before the actual purchase, and this process must be sustained by costly personnel.
And now we must take into account the total number of customers, the rate in which they accumulate, and the costs associated in acquiring them:
2. Product Omega is a classic SaaS product, where the dominating metrics will be CLTV (Customer Life Time Value).
Our task breaks down to several stages:
Define product Alpha monthly revenue stream
Deduct serving fee and everything to do with delivery costs, to reach monthly profit contribution.
Then, assess the life time, in months, of the average customer. This is done by inverting the churn rate: If 5% (1/20) of your customers abandon you every month, it means that the average lifespan of customers is 20 months.
Finally we deduct the cost of acquisition for these customers: How much we paid for advertising, on boarding, initial support, and sales commissions, if applicable?
The next thing would be to layout a growth plan, based on our forecasts of the marketing funnel. For product Alpha, a SaaS, the funnel is quite short:
Lead acquisition
Free install
Paying customer
The conversion rates from stage to stage, combined with the amount spent on lead acquisition will determine the number of new customers every month, while the churn rate will dictate the number of leaving customers. Add these two factors to the number of existing customers, and you have yourself a growth model.
Note the tabular form of the calculation, in which each new customers from each period are regarded as a cohort, dwindling over time:
So, on an annual basis, the gross profit we forecast for product Omega is just shy of $800K.
*Note: For the sake of this example, I have not taken future years into account, so I have not built growth estimations, nor did I discount the revenue streams.
Conclusion
In this analysis I have omitted many crucial elements, which change from one industry to the other, and differs companies and products. The gist is our ability, as analysts, to reduce the complexities folded in the multiple datapoint we are given, and boil down a rational comparison of the bottom line for both products.
Not only have I not included critical parameters in this hypothetical exercise, neither have I drawn any decision. Typically, management vision of the market, company's mission, and strategy will dictate such decision, not merely the snapshot of one year's forecasts.
Hypothetical Enterprise has to develop such strategy. A fair question is whether both products could co-habit within the same organization.
I hope this post helps you develop your own strategic product line assessment.
Are you are interested in developing your own product strategy?
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