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[Audio] Welcome to Module 4, Business Models, Pricing and Adoption Barriers.

[Audio] Welcome back. In Module 1 you scored your outcome, in Module 2 you identified customers and defined value, and in Module 3 you checked what you own. Now we put these pieces together into a business model. A business model is simply the logic of how an organisation creates value for customers, delivers it, and captures part of it as revenue. It answers questions such as: who pays, for what, how often, through which channel, and what does it cost us to deliver? Why does this deserve a whole module? Because the same technology can lead to very different outcomes depending on the model built around it. Henry Chesbrough, who has written extensively on innovation, argued that a technology of average quality combined with a good business model can be worth more than a superior technology with a poor one. Chesbrough and Rosenbloom showed this in 2002, studying technology spin-offs from a single large company. Researchers tend to put their effort into improving the technology. Successful commercialisation often depends just as much on choosing the right model. In this module we will go through five things. We will use a standard tool, the Business Model Canvas, to describe a model. We will compare the main business model options in digital agriculture. We will look at pricing, from principles to testing. We will check the basic economics, to see whether the business can sustain itself. And we will examine the barriers that slow adoption and how to lower them. Let us start..

[Audio] To describe a business model on a single page, we will use the Business Model Canvas, introduced by Alexander Osterwalder and Yves Pigneur in their 2010 book Business Model Generation. It has nine building blocks. On the customer side, there are four. Customer segments: who you create value for, and you already worked on this in Module 2. Value propositions: what problem you solve and what benefit you deliver, which is the sentence you wrote earlier. Channels: how you reach customers and deliver the offer, for example direct sales, cooperatives, advisers, or distributors. And customer relationships: what kind of relationship each segment expects, such as self-service, personal support, or a long-term partnership. In the middle is revenue streams: how and how much customers pay. It might be a subscription, a one-time fee, a licence, a service fee, or a share of savings. On the delivery side, there are four more. Key resources: the assets you need, including the intellectual property and data you mapped in Module 3, as well as people, infrastructure and funding. Key activities: what you must do well, such as developing software, calibrating sensors, running trials, or supporting users. Key partnerships: who you need, for example technology suppliers, research partners, cooperatives, or machinery makers. And cost structure: the main costs of running the model. The strength of the canvas is that it shows connections. If your value proposition is a promise of local agronomic advice, then your channel and your key activities must support that. If your revenue depends on thousands of small customers, your cost structure cannot rely on expensive personal visits. One tip: do not try to fill in a single perfect canvas. Use it to compare alternatives. Draw two or three different versions, each with a different revenue stream or channel, and see which looks more plausible. We will do this in the exercise shortly, after a look at the main options..

[Audio] Let us look at the main options for a digital agriculture outcome. I will present them with their typical strengths and weaknesses. These are tendencies, not rules, and every case differs. The first option is software as a service: customers pay a subscription, monthly or annually, to use your software. The strengths are recurring revenue and scalability: once the product exists, adding customers costs relatively little. The challenges are real, though. You need onboarding, customer support, reliable hosting, and continuous updates. In agriculture, sales cycles are often long and seasonal, and subscriptions may be cancelled between seasons if value is not visible. Pricing and billing also need to match the farm's cash flow. The second option is licensing: you allow an established company, perhaps an input supplier, a machinery maker, or a software platform, to include your technology in its product, in return for payment. The strengths are speed and low capital need, because the partner already has production, customers and trust. The weaknesses are loss of control over how the technology is used and priced, and royalties are often modest compared with the full value of a product. Licensing works best when you have strong, clearly owned intellectual property, which is why Module 3 matters here, and when the partner has a strong incentive to adopt it. The third option is hardware plus service: you sell a sensor, device or kit, and often add a recurring service such as data analysis, calibration or alerts. The strengths are tangible product value and the potential for recurring service income. The challenges are heavier: you need capital for manufacturing, stock and logistics, you must handle warranties and repairs, and farm environments are tough on equipment. A hardware-as-a-service approach, where customers rent rather than buy, reduces their upfront cost but increases your capital need. Notice a pattern: the models that give you the most control and revenue potential also tend to require the most capital, time and capability. The models that are faster and lighter tend to give up some control. Neither is wrong. The right choice depends on what you have and what you can build..

[Audio] Let us continue with further options. A data or analytics service sells insights rather than software: for example benchmark reports, risk assessments, or monitoring dashboards. This depends on solid data rights, which we covered in Module 3, and on trust in the quality of the analysis. Advisory, training and certification services use your know-how directly. This is often the quickest route to first revenue, because it needs little development. It can also help you learn about customers. The limitation is scale, since it depends on people's time. Many successful ventures begin with services and gradually productise what they learn. Outcome-based pricing means customers pay a share of the verified benefit, for example a part of the savings achieved. It aligns your interests with the customer's and lowers the perceived risk, which is attractive in a sector where trust is so important. But it requires reliable measurement and a clear baseline, and it exposes you to variability, such as a bad season for reasons you cannot control. A platform or marketplace connects two sides, for instance farmers and service providers. It can scale strongly, but faces the so-called chicken-and-egg problem: each side joins only if the other is already there. It is a demanding model that usually needs significant funding, and I would advise caution unless there is a clear reason for it. And open core or dual licensing, which we touched on in Module 3, offers a free or open community version and a paid version with premium features, hosting or support. How do you choose? Compare options against seven criteria. How do your target customers prefer to buy? How much capital does each model need? How quickly does each reach first revenue? How well does it scale? How much control over your intellectual property does it give you? Does your team have the skills it requires? And are suitable partners available? Often the answer is a combination: for example, start with paid pilots and services to learn and earn, then productise into a subscription, and license to a larger partner for broader reach..

[Audio] Time for our fourth working exercise. Please pause the video when I finish. Step one: draw two Business Model Canvases for your outcome, on paper or on a screen. Make them differ in a meaningful way, for example a subscription sold through a cooperative, versus a licence to an established company. Use your beachhead segment from Module 2 and your value proposition sentence. Step two: fill in all nine blocks as best you can. Do not worry about precision. If you do not know the answer, write your best guess and mark it with a question mark. Step three: for each canvas, identify the single riskiest assumption. This is the thing that, if wrong, would break the model. For example, "farmers will accept paying per hectare", or "a machinery company will integrate our module". Step four: for each risky assumption, define one low-cost test you could run within the next month. It might be five customer conversations, a price offer, or a meeting with a potential partner. Take ten to fifteen minutes. Pause the video now..

[Audio] Pricing is where many research teams hesitate, and often they underprice. Let us put some structure around it. There are three classic approaches. Cost-plus pricing takes your costs and adds a margin. It is simple, but it ignores what the product is worth to the customer, and it can leave a great deal of value uncollected or, for expensive products, set the price above what customers will pay. Competitor-based pricing looks at what similar products charge. It is a useful reference, but for a new kind of solution there may be no direct competitor, and copying prices means copying other people's assumptions. Value-based pricing, which is generally recommended in the pricing literature, for example by Nagle and Müller in their book The Strategy and Tactics of Pricing, starts from the value the customer receives and sets a price that captures a fair part of it. Think of it as a corridor. The floor is your cost of serving a customer. The ceiling is the value the customer gets. The price sits somewhere between, and the better your evidence of value, which you built in Module 2, the higher in the corridor you can credibly place it. Next, choose a price metric, meaning the unit on which you charge. Options include per hectare, per animal, per user, per device, or per outcome. A good metric has three qualities. It is linked to the value: if value grows with area, a per-hectare price is intuitive. It is easy to understand and predict, because farmers dislike surprise bills. And it does not discourage use: a per-click price may make users avoid the very behaviour that creates value. Timing matters too. Farms have seasonal cash flow, so consider billing at points that suit them, for example at the start of a season for planning tools, or after harvest for outcome-based fees. Tiers can help: a basic tier for small or cautious customers, a standard tier for most, and a premium tier with extra features or support. They let customers choose their level of commitment. A quick illustration, using the invented figures from Module 2. If the tool saves two treatments per hectare at thirty euros each, the value is sixty euros per hectare. A price of fifteen euros per hectare captures a quarter of that value and leaves the grower with three quarters. Some practitioners suggest that customers should keep a clear majority of the benefit, but you should test what works in your own market rather than rely on a rule of thumb..

[Audio] How do you find out what customers will actually pay? Not by asking a direct hypothetical question. As we saw in Module 2, "Would you pay fifty euros?" invites a polite and unreliable answer. There are better methods. One is the price sensitivity approach developed by the Dutch researcher Peter van Westendorp in the 1970s. Instead of asking one price question, you ask four: at what price would this be so cheap that you would doubt its quality; at what price is it a bargain; at what price does it start to feel expensive but still possible; and at what price is it too expensive? Plotting the answers from a group of potential customers reveals a range of acceptable prices. It works best with a reasonable number of respondents in one segment. But the strongest tests are real offers. Give a customer a written quote. Propose a paid pilot with a modest fee. Invite a pre-order or a letter of intent that states a price. How the customer reacts, whether they accept, negotiate, delay or refuse, tells you far more than any survey. This links directly to the evidence ladder in Module 2. Notice the value of charging something for pilots. A completely free pilot attracts people who are merely curious, and they may not give you the time and attention you need. A small fee, even symbolic, filters for people who have a real problem and shows you which price signals they accept. Always compare your price with the customer's alternatives. These include competing products, paid advisers who currently give similar advice, the cost of doing the work manually, and the option of doing nothing. If your price is much higher than a trusted adviser's fee for similar advice, you need a strong explanation for why. Finally, listen to objections and discount requests. "It's too expensive" may mean the value is not clear, the budget is held by someone else, or the price metric is wrong. Dig into what lies behind it. Each objection is information about how to improve your model..

[Audio] A business model must also work financially. You do not need an accountant's detail at this stage, but you do need to check the basic economics, because a model that cannot sustain itself is not a business, however useful the product. Start with contribution per customer: the revenue from one customer, minus the costs that vary with serving them, such as hosting, support, hardware, and delivery. What remains pays for everything else. Then the fixed costs: the costs you carry regardless of customer numbers, such as salaries, development, and administration. Divide fixed costs by contribution per customer and you get the break-even number of customers. Next, a pair of measures from the software world. Customer acquisition cost, or CAC, is how much you spend, on events, demonstrations, pilots, and sales effort, to win one customer. Lifetime value, or LTV, is the contribution a customer gives you over the whole time they stay. A common rule of thumb in the software industry is that lifetime value should be at least about three times acquisition cost. Treat that as a conversation starter, not a law. Let me walk through a worked example. The figures are invented, only to show the method. Suppose the average customer farms forty hectares and pays fifteen euros per hectare: six hundred euros a year. Variable costs of hosting and support are one hundred and fifty euros, so the contribution is four hundred and fifty euros. If your fixed costs are ninety thousand euros a year, break-even is two hundred customers. Now suppose it costs nine hundred euros to win each customer, and eighty percent of customers renew each year, which implies an average life of five years. Lifetime value is four hundred and fifty times five, or two thousand two hundred and fifty euros. The ratio of lifetime value to acquisition cost is two and a half, a little below the rule of thumb. What do you do with that result? You ask which lever to change. Could you lower acquisition cost by selling through a cooperative that brings many farms at once? Could you raise retention with better onboarding? Could you adjust the price, or add a premium tier? The numbers do not tell you the answer, but they show you where to look. Remember also that agriculture runs in seasons. Costs arise throughout the year, but revenue may arrive in a few moments. Plan your cash flow across the full cycle, and not just the annual totals..

[Audio] The best business model still fails if people do not adopt the product. So let us look directly at the barriers and what you can do about each. A well-known European study by Kernecker and colleagues, published in 2020 in the journal Precision Agriculture, explored what farmers across Europe expect from smart farming technologies and what they experience. One of its findings is that farmers' perceptions of such technologies vary with the characteristics of the technology and with the farming context. For you, the message is that you cannot assume one set of barriers for every farm. Your interviews in your own segment tell you which ones matter. Still, some barriers recur, and each has typical responses. Upfront cost and uncertain return. Reduce the risk of the first step: offer a trial season, a pilot with a clear target, pay-after-results or outcome-based pricing, or turn a large purchase into a subscription or lease. Connectivity. Many rural areas have patchy coverage, so design for offline use, with synchronisation when a connection returns. Interoperability and lock-in. Farmers fear being trapped in one supplier's system. Provide open interfaces, standard data formats, and the ability to take their data away. This also fits the data principles from Module 3. Skills and time. Farmers are busy, and often cannot spare time to learn a complicated tool. Keep onboarding simple, provide training in the local language, and offer responsive support, ideally through trusted local people such as advisers or cooperative technicians. Trust and data worries. Write clear data terms, explain simply who sees what, and let people control their data. Show results through demonstration farms and references from peers, as we discussed in Module 1. Fit with routines. A tool that requires a different way of working than the farm already uses faces resistance. Design with users, and fit into existing tasks. Cooperative purchasing and adviser bundles can also help: when a trusted organisation recommends and sometimes co-pays for a tool, farmers' risk and effort decrease. Finally, find out whether regional or national programmes offer investment support for farm digitalisation. Such schemes exist in many places, through rural development and agricultural policy programmes, but conditions vary a lot, so check with local authorities or advisers rather than assume. Mentioning available support in your offer can reduce the price barrier..

[Audio] Let us summarise Module 4. We started from the idea that a product is not yet a business, and that the same technology can lead to very different results depending on the business model. We used the Business Model Canvas to describe a model on one page and, more importantly, to compare alternatives. We reviewed the main options: software subscription, licensing, hardware with service, data or advisory services, outcome-based pricing, platforms, and open core. We saw that models giving more control and revenue potential usually need more capital, time and capability, and that many ventures combine them over time: services first, then product, then licensing for reach. We discussed pricing: to base it on value, to choose a price metric that is clear and aligned with value, to align billing with the season, and to test willingness to pay with real offers such as paid pilots and written quotes, not hypothetical questions. We worked through the basic economics with an invented example: contribution per customer, break-even, and the relation between acquisition cost and lifetime value. And we examined adoption barriers and how a business model can lower them through trials, flexible payment, open interfaces, local support and references. If you completed the exercise, you now have two draft canvases, a riskiest assumption for each, and a low-cost test to run. That is a real step toward a business plan. In Module 5, our last main module, we ask how to reach your customers and how to fund the journey. We will look at channels and partnerships, at pilots that become references, and at the funding pathways available to research outcomes: from follow-on grants to licensing deals, spin-offs and investment. Let us continue..

References. Kernecker, M., Knierim, A., Wurbs, A., Kraus, T., & Borges, F. (2020). Experience versus expectation: Farmers’ perceptions of smart farming technologies for cropping systems across Europe. Precision Agriculture, 21(1), 34–50. https://doi.org/10.1007/s11119-019-09651-z Osterwalder, A., & Pigneur, Y. (2010). Business model generation: A handbook for visionaries, game changers, and challengers. Wiley. Chesbrough, H., & Rosenbloom, R. S. (2002). The role of the business model in capturing value from innovation: Evidence from Xerox Corporation’s technology spin-off companies. Industrial and Corporate Change, 11(3), 529–555. Chesbrough, H. (2010). Business model innovation: Opportunities and barriers. Long Range Planning, 43(2–3), 354–363. Nagle, T. T., & Müller, G. (2018). The strategy and tactics of pricing: A guide to growing more profitably (6th ed.). Routledge. Van Westendorp, P. (1976). NSS Price Sensitivity Meter (PSM): A new approach to study consumer perception of price. Proceedings of the ESOMAR Congress..

[Audio] Thank you!. thank you!. TALLHEDA has received funding from the European Union's Horizon Europe research and innovation programme under Grant Agreement No. 101136578. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Executive Agency (REA). Neither the European Union nor the granting authority can be held responsible for them..