Technology companies seeking to increase growth and market share through an indirect channel of business partners need to master many disciplines. The most important are still the classic virtues: understanding the business case, generating revenue, and creating and retaining satisfied customers.
In this article, aimed primarily at B2B software companies in the lower third of the market penetration curve, I examine the classic virtues that, in my view, will matter most in the years ahead. Along the way, I show where AI can add genuine business value.
The lower part of the market penetration curve
VCompanies looking to establish or expand their partner channel often seek inspiration from the large technology companies. In business software, Microsoft and Salesforce are obvious role models.
However, copying their partner programmes is a mistake.
The result is often a large and complicated programme that becomes bogged down in bureaucracy and diverts attention from what really matters. You will not succeed by copying a concept that has evolved over many years and been adjusted countless times as the products have achieved widespread market penetration and partners have come and gone.
Market dynamics also vary considerably depending on where your product sits on the penetration curve. In the lower part, which is the subject of this article, you should concentrate your efforts on fundamental business principles.
AI can already add value here. By combining publicly available information with your own CRM data, you can analyse potential partners far more quickly than before. You can examine their market position, capabilities, customer segments, technologies, employee profiles, and existing partnerships to identify the companies most likely to match your partner profile.
But being selective from the outset is not always the right strategy. In some markets, it may be more effective simply to recruit anyone willing to sign up, provided that the cost of onboarding and ongoing support remains limited. It can be extraordinarily difficult to predict who will actually succeed. A partner that looks perfect on paper may never generate any revenue, while a less obvious candidate may develop into one of your best partners.
In such cases, actual market performance may be a better indicator than the initial screening. Give partners the opportunity to prove their worth, measure their activity and results, and later weed out those that fail to generate pipeline and revenue. AI can also support this process by continuously identifying which partners show potential and which remain passive.
AI can significantly reduce the research effort. But it cannot determine whether the necessary mutual trust and commercial interest are present. That still requires people.
Hygiene factors
I use the term hygiene factors for conditions that can impede growth if they are absent, but whose presence does not in itself accelerate it. They are also often referred to as prerequisites.
The most important hygiene factor is the product or service itself, which for simplicity I shall refer to as the product.
A product that has yet to achieve more than 20 per cent market share and is intended to be sold or serviced through a partner channel needs a very clear value proposition and razor-sharp positioning. Ideally, it should also reach the market at a time when a major wave of change is creating demand for new solutions.
The more value partners can create around the product, the more attractive it becomes for them to sell and service it.
If these conditions are not met, attracting good partners will be very difficult. Only when a product has achieved substantial market share and therefore features in many customers’ buying considerations does demand for the product itself become an argument for partners.
If the product condition is met, the partner programme should be simple, onboarding should be well organised, and ongoing support should rely on self-service wherever possible. Partners’ day-to-day activities should be supported by modern tools such as e-learning, digital certification and PRM systems.
This is where AI will play an increasingly important role.
A partner should, for example, be able to ask questions about products, pricing, competitors, contractual matters, implementation and sales processes, and receive an immediate answer based on the vendor’s own approved information. AI can also generate individual learning paths based on the partner’s role and previous activity, suggest relevant material for a specific sales opportunity, and identify areas where a partner lacks expertise.
This reduces the need for the vendor’s channel managers and product specialists to repeatedly answer the same questions. At the same time, the partner can get help when it is needed.
There are now so many mature platforms and service providers available that it no longer makes sense to develop partner programmes that require manual registration in spreadsheets, extensive classroom training or the storage of information in basic document libraries.
Business management – the classic virtues
Recruiting successful partners requires a fundamental understanding of their business.
You may be able to spark curiosity by presenting an attractive product, but the motivation to invest emerges only when the potential partner can see how the relationship will positively impact their business – and how quickly that impact can be achieved.
The tool I use for this purpose is called The Business Partner P&L.
The Business Partner P&L is the financial representation of your onboarding and partner programme. It shows the financial consequences of what you call a partnership and answers the fundamental questions:
- Why do you want to build a partner channel?
- How much does the partner need to invest to get started?
- How quickly will that investment be recovered?
- What do the numbers look like over a three-year period?
- What will you contribute to make the relationship successful?
You should approach this discussion as though the partner were a potential investor. The dialogue should therefore take place at management level and only once it has been established that there is a good match between the product, the market and the partner’s business.
This is another area where AI can add considerable value.
In the past, such business cases were often based on a small number of standard assumptions. You can now develop scenarios based on the individual partner’s situation far more easily. AI can help structure the partner’s publicly available financial information, customer segments, employee profile, geographical coverage and existing product portfolio, and turn that information into assumptions that can subsequently be validated with the partner.
You can quickly model different scenarios:
- What happens if the partner invests in one, two or five salespeople?
- What is the impact of different conversion rates?
- How sensitive is the business case to a longer sales cycle?
- How much revenue does the partner need to generate to break even after six, twelve or eighteen months?
- What impact do repeat purchases, subscriptions and services have on the economics
AI can make calculations and analysis much faster. But it does not change the fundamental reality: the partner is investing real money and employee resources. The assumptions must therefore remain defensible to the people on both sides of the table.
Revenue generation
When I argue that the partner programmes of large technology companies should not be your role models, the primary reason is their market position.
A large market share generates demand in its own right. At the same time, these companies have had many partners pass through their programmes over the years. Those that could not build a viable business have dropped out. The remaining partners are the successful ones, and new partners often join because their existing customers are already asking for the products.
When your market share and market awareness are low, the situation is entirely different.
Your most critical success factor therefore becomes your ability either to generate the leads that your new partners need to work with or to help the partners find them.
As I explain in my book Building Successful Partner Channels, you cannot expect your first partners to both possess the necessary capabilities and have a sufficiently strong incentive to work out for themselves how to get the business around your product off the ground.
You need to invest time and money in helping the first partners win their first customers.
The sooner this happens, the stronger the partner’s incentive to commit additional resources to your product, and the easier it becomes to recruit the next partners.
This is precisely where AI can have a major impact.
AI can significantly reduce the work that has traditionally made outbound lead generation expensive. Combined with modern data sources, the technology can be used to:
- identify companies that match a specific ideal customer profile
- detect signs of organisational or technological change that may trigger a need
- research a company’s situation before the first contact
- identify relevant decision-makers and stakeholders
- develop hypotheses about potential business problems
- prepare personalised messages and questions
- analyse previous sales processes and identify patterns in the types of prospects that convert
- prioritise leads according to probability and potential value
This changes the economics of the early stages of partner development. To a much greater extent, the vendor can provide the partner with researched account lists, background information and talking points rather than simply handing over a product sheet and wishing them good hunting.
You can go a step further and establish a joint AI-supported demand-generation engine, in which the vendor develops the methods, prompts, data, content and campaigns while the partner contributes its local market knowledge and customer relationships.
The partner’s sales organisation still needs to talk to customers, understand their situation and build trust. AI can, however, eliminate a large proportion of the preparatory work.
Most vendors assume that a new partner’s first sale will be to an existing customer. This possibility should, of course, be explored first. But there is no guarantee that the customer base has a latent need for your particular product when you are courting the partner.
Moreover, the product’s ability to help the partner acquire new customers will often carry considerable weight when the partner assesses the attractiveness of the relationship.
If you can present both a convincing Business Partner P&L and a method for systematically identifying new prospects, you have removed two of the partner’s biggest risks.
Happy customers become your ambassadors
B2B software almost always needs to be adapted to the customer’s specific environment and circumstances. A successful implementation requires domain knowledge, product expertise and rigorous project management.
These capabilities are built through experience with customers and products.
Your risk is that failed projects will be blamed on your product. This is particularly likely when you have a small market share and only a few successful partners to point to.
The next classic virtue is therefore to support partners as they build experience.
At this stage, your primary concern should not be to ensure that you are paid for every single consulting hour the partner consumes from your organisation. What matters is that the first customers experience a professional implementation and achieve the expected business value.
The partner should, of course, have an incentive to build its own capabilities rather than rely on your support indefinitely. But you do not solve that problem by charging exorbitant rates for assistance when the partner is still on the learning curve.
AI can shorten the learning curve here as well.
Project plans, solution designs, customer requirements, support histories, implementation notes and previous projects can provide the foundation for AI assistants that help consultants identify known problems and recommend approaches. A less experienced partner consultant can thereby gain access to experience that previously existed only in the heads of a handful of experts within the vendor’s organisation.
AI can also review project materials and highlight missing information, unclear requirements, unusual configurations, and circumstances that have caused problems in previous projects.
The technology should not be held responsible for the implementation. But it can act as an additional control mechanism and make the combined experience from the vendor’s and partners’ previous projects available to those working on the project at hand.
This is particularly valuable in the early stages, when every single reference customer counts.
Scaling
The overriding reason for using an independent partner channel to sell and service your products is the potential for scale.
To realise that potential, two conditions must be met.
The partner channel must already exist
There must be an established channel with many potential partners that already serve the same customers as you and for whom your product is a close fit with their existing capabilities.
If you need to develop such a channel first, you should wait until you have achieved a solid market share. Otherwise, the work will take too long and cost too much.
AI can help map an existing channel in far greater detail than was previously possible. Companies can, for example, be analysed by geography, vertical market, technological capabilities, customer types, employee profiles and existing vendor relationships.
This makes it possible to approach partner recruitment more systematically while also identifying white spaces where the desired channel does not exist.
You must be able to avoid excessive channel overlap
AAs far as possible, your partners should serve different market segments so they do not constantly run into one another.
This allows your product to exploit its market potential more effectively while preventing partners from wasting energy competing for the same customers.
In the beginning, this is rarely a major problem, and you may even be able to offer your first partners some protection as they establish their businesses.
In the longer term, you will most effectively exploit the market potential by helping partners specialise.
Here too, AI can make the work more data-driven. By continuously analysing the pipeline, won and lost deals, customer types, capabilities, and geographical coverage, you can identify the segments in which each partner has the greatest probability of success.
The objective is not necessarily to give partners exclusive territories. Rather, it is to create a division of labour in which each partner invests in the areas where the probability of building a profitable business is greatest.
Concluding thoughts
When I am asked to share my views on current trends in partnerships, I am invariably presented with a list of the latest buzzwords. This time, they include artificial intelligence, platforms, automation and globalisation.
There is, however, a considerable distance between new terminology and mature concepts. But as technologies become genuinely useful, they should of course be adopted.
Hardly anyone today would launch a serious partner programme without supporting it with a PRM portal. Most training programmes are offered as web-based self-learning, with completion documented through digital certificates specifying the competence achieved, the issuer and, where relevant, the expiry date.
AI is the next layer on top of this digitalisation.
The technology can already assist with partner research, onboarding, personalised training, business cases, lead generation, sales material, opportunity analysis, implementation support, partner support and segmentation.
These are substantial productivity improvements that should be exploited.
But they do not change the fundamental economics of the partner relationship.
Potential partners will certainly raise an eyebrow if they are expected to attend all training in person, receive certificates on paper, register leads in a spreadsheet, search for documents on a SharePoint server and communicate about every practical matter by email.
AI-based self-service and automation can make the experience considerably better.
But even the world’s most advanced partner portal cannot rescue a poor business case.
Conversely, a partner will be willing to live with many practical inconveniences if the business opportunity is sufficiently attractive. If an investment of one million kroner this year has a strong probability of becoming five million in three years, that is the calculation that drives the decision.
That is the discussion you need to have with your potential partners.
And the more you can reduce the risk in that business case through your product, your go-to-market model, your own investment and, now, AI, the sooner the partner can make a decision and get started.
AI, in other words, does not replace the classic virtues.
It enables you to practise them better.
