Modern Revenue Growth Management (RGM) is evolving rapidly, requiring businesses to shift from traditional tools and methods to AI-driven solutions. Companies striving for efficiency, accuracy, and agility in their RGM strategies must decide how to integrate these advanced technologies: should they develop a proprietary, in-house solution or adopt an existing tool/platform (e.g., from Software as a Service (SaaS) providers).
Given the complexity of RGM, this choice can significantly impact long-term growth and competitiveness.
A common approach when deciding whether to build an in-house tool or adopt a market-ready solution starts with understanding the problems and the needs of the RGM department as well as cross-collaboration with other departments.
The process begins by addressing key questions:
Many organizations follow a phased approach, starting small with an in-house-build prototype-like solution. However, as the project grows in scope, expanding beyond a few users and basic functionalities to a more enterprise-level deployment, the focus often shifts toward established Software as a Service (SaaS) solutions. This strategy helps companies stay agile while preventing the diversion of critical resources into maintaining complex internal systems that fall outside their core expertise.
This article delves into the advantages and disadvantages of buying a solution compared to building one in-house. It will help you make the right decision for your organization when the question of Buy vs. Build arises.
The decision between buying a SaaS solution and building an in-house platform hinges on factors such as time to market, budget, customization needs, and long-term strategic objectives. SaaS solutions offer speed, flexibility, and lower initial costs but may lack specific customizations. In contrast, building an in-house solution provides tailored features and greater control but requires a significant investment and longer development time.
The table below outlines seven critical factors to consider in this decision-making process. The listed pros and cons for both the “Buy” (Software) and “Build" (In-House) approaches represent general expectations.
Building an in-house tool offers unparalleled flexibility and enables precise customization to meet a company's unique needs. It also provides full transparency into the underlying model used for simulations. However, purchasing software from a service provider minimizes internal development time and costs while granting access to cutting-edge technology.
Additionally, software providers often have more advanced modeling and artificial intelligence capabilities since it is their core business. Many are also willing to share insights into their model's construction and conduct accuracy testing during the onboarding process.
This raises a key question: How relevant is it truly to develop an in-house solution?
Developing such a tool is a major investment, one that can take years to complete. By the time the first version is ready, evolving business needs may render it outdated or misaligned with current expectations due to the long development timeline.
On the other hand, purchasing a specialized RGM tool available on the market provides a solution that is already designed to meet the needs of RGM departments. While it may have some limitations that don’t fully align with a company's unique requirements, third-party integrations and vendor collaboration often help bridge those gaps.
Adopting a market-ready solution is also significantly faster. For example, onboarding with Buynomics' software typically takes 6 to 12 weeks, while other solutions may require a few months—both far quicker than the time needed to develop an in-house tool from scratch.
Cost is another crucial factor that makes buying a tool far more attractive than building one.
Consider this scenario: an RGM department decides to develop an in-house software as a proof of concept for one country, with plans to scale later. Here’s a high-level estimate of what that might entail over a two-year period—by which time the project may be completed, but there’s no guarantee it will still align with the department’s evolving needs.
Consider a mid-sized CPG company assessing both options over a two-year period. It’s crucial to note that costs will increase as infrastructure and operational needs grow. The figures provided in this calculation reflect a small-scale implementation, meaning larger deployments would require higher investment.
The costs can be categorized into initial setup, development, infrastructure, and maintenance.
Note: These figures are illustrative and can vary based on specific circumstances and regions. This example is based on the benchmark of the industry standard in the US.
While the decision between buying a SaaS solution and building an in-house tool depends on multiple factors, this illustrative cost analysis highlights a clear advantage in terms of financial investment on the side of the SaaS solution.
The significantly lower cost of the initial setup, combined with the cost inclusion of the development team and ongoing maintenance and support in the license fee, makes the SaaS solution an attractive choice for many organizations. While an in-house build allows for deeper customization, the associated expenses and time commitment can be prohibitive.
In the dynamic world of Consumer Packaged Goods (CPG), effectively managing all RGM levers is crucial for maintaining competitiveness and driving revenue growth. Many companies have learned to manage individual aspects of RGM using spreadsheet-based analysis or independent tools for Trade Promotion Management (TPM).
However, these approaches often fail to account for the many complex factors that influence RGM decisions, such as cannibalization, interaction with competition, differentiated pricing sensitivity, and promotion effect changes.
Developing an in-house solution to address these challenges requires more than just an engineering team and investment. It also demands expertise in data modeling and behavioral analysis. A dedicated team must first build a robust model that accurately reflects shopper behavior, simulates millions of potential scenarios, and identifies the most probable outcomes.
Additionally, data granularity plays a crucial role. The deeper and more detailed the data analysis, the more informed and effective the decision-making process becomes.
Modern AI-based solutions offer a more sophisticated way to simultaneously consider all RGM levers, accounting for market dynamics and competitor cross effects.
Buynomics is the leading Revenue Growth Management (RGM) platform that combines all RGM levers for holistic optimization across all revenue levers.
Understand the Buynomics Virtual Shoppers
The Buynomics solution empowers RGM teams in enterprise organizations to make faster, more profitable decisions.
By integrating multiple data sources with cutting-edge AI technology, Buynomics is a single source of truth for all shopper-centric revenue decisions. The intuitive interface and actionable dashboards make it easy for non-technical users to derive insights and apply recommendations, reducing reliance on technical teams.
Trusted by global companies like Danone, General Mills, Unilever, L’Oreal, and Nestlé, Buynomics boosts gross profits by 2%- 4% and reduces decision-making time by up to 80%.
See for yourself why buying beats building. Request a demo today to see how Buynomics supports your RGM team and helps you make data-driven decisions!