Through our extensive work in the Data & Information space, we’ve identified a number of recurring pain points across packaging, pricing, and commercial strategy which, once addressed, can typically unlock 10-30% incremental ARR.
Packaging not aligned to needs or willingness to pay
Packaging (how you divide up your content, functionalities, and services into sellable units) is the foundation of effective pricing. Good packaging will reflect the differing needs and willingness to pay levels across your customer base, typically comprising accessible entry points and a range of structured up- and cross-sell pathways.
A common failing we observe is businesses with overly simplistic packaging, particularly when selling into diverse markets with wide ranging needs and levels of willingness to pay. This “one size fits none” approach typically results in prices too high for some (leading to discounting) and too low for others (leading to money left on the table).
At the other end of the spectrum, overly granular packaging is typically a recipe for confusion on the part of both sales and customers, leading to low levels of up- and cross-sell.
Best practice typically involves a small number of pre-defined packages, aligned closely to the needs and willingness to pay of your key customer segments, combined with a selection of add-ons, for high-value niche items.
Pricing not aligned to value
A key characteristic of Data & Information businesses is that the value derived by customers can vary widely depending on the characteristics of their business, the nature and extent of their usage, and the specific use cases they’re working on.
Traditional user-based pricing models often fail to capture this nuance, leaving significant amounts of money on the table. At the same time, user-based pricing often stifles adoption across and within organisations.
Best practice involves aligning your pricing to key value dimensions: the extent of access/usage, the nature of that usage, and the nature of the customer’s firm. Transitioning from user-based to team-based or even enterprise-wide access has the added benefit of encouraging adoption, leading to greater embeddedness and value delivered.
Under-monetization of APIs / machine-to-machine delivery
Monetizing machine-to-machine and programmatic access is particularly challenging. On the one hand APIs, data feeds, and cloud warehouse integrations can unlock higher-value, more embedded and “sticky” use cases. On the other hand, it’s harder than ever to track and quantify value.
Again, best practice involves aligning your pricing to key value dimensions: the extent of access/usage, the nature of that usage, and the nature of the customer’s firm, but the impact can be particularly material here vs. traditional modes of delivery. You can read our deep dive on API monetization here.
Excessive discounting
Discounting is a fact of life in most B2B environments, but Data & Information businesses are often particularly prone to it. The first order effect of discounting is simply money left on the table. The second order effect (often forgotten about) is the “price contamination” of bad pricing across the wider customer base.
Best practice involves three key elements. First, set well calibrated packages and list prices that are tailored to different use cases, customer types, and geographies, reducing the need for discounting in the first place. Second, ensure sales have the right tools, capabilities, and incentives to defend those prices. Third, ensure the right monitoring and governance is in place to ensure guidelines are adhered to.
Incoherent channel strategy
Not all customer segments will be desirable or cost-effective to serve directly. Channel partners can be an efficient and effective mechanism for expanding your reach into smaller or non-core customer types. Too often though, we see incoherent channel strategies leading to channel conflict and arbitrage opportunities.
Best practice involves having a clear channel strategy (which segments to target via which routes to market) which all partnership deals adhere to, together with central governance of all partnerships.
No clear strategy for AI
AI poses both risks and opportunities for Data & Information businesses. Those with unique/proprietary data are well placed to capitalise on the increasing demand from both customers and third-party technology players, but care is needed to avoid cannibalising existing revenues. Those reliant on collating, cleansing, and combining publicly available data are more precariously placed, and need to think hard about how they defend their position with analytical and workflow tooling, and added value insights, data interpretation, and forecasts.
As we discuss here, many businesses have not yet fully understood their value layer, and thus the likely risks/opportunities posed by AI.
Summary
In the age of AI, Data & Information businesses (particularly those with unique/proprietary data) have strong tail winds for growth. Time and again though, we see suboptimal packaging, pricing, and commercial strategy failing to capture value effectively. Our projects in this space typically unlock 10-30% incremental ARR through deep customer understanding and close alignment of packaging and pricing to value.
If you would like to discuss your packaging, pricing, and commercial strategy further, don’t hesitate to contact our Data & Information sector experts to arrange a call.
