Running an Effective Win Loss Program in 2024

This blog was written by Andrew Cloutier, Partner at Anova.

If It Sounds Too Good to Be True, It Probably Is. In general, teams are being asked to do more with less, both in terms of budget and FTEs. As such, the automation of processes is an important consideration in terms of cost and time savings.

The rise of automation and generative AI has created exciting opportunities across industries, including win / loss. While the potential of AI is undeniable, it’s important to approach it with a realistic mindset. An advanced AI model, no matter how sophisticated, cannot compensate for flawed data sources or poorly designed processes.

Practitioners of win / loss need to have a point of view and plan for taking advantage of automation and AI-driven opportunities, both for operational efficiency and insight generation purposes. But it is also important not to engage in “magical thinking” as it relates to the fundamental blocking and tackling of gathering and analyzing win / loss data. The most advanced generative AI model in the world is useless if its data source is faulty, and a fully automated survey process isn’t particularly helpful if the survey isn’t getting sent to the right people or generating meaningful responses. In short, if it sounds too good to be true, it probably is.

Qual Matters – High-Quality, Qualitative Feedback from Qualified Respondents

The best win / loss outcomes come from the programs with the highest-quality inputs and highest-quality deliverables.

  • High-quality inputs require buy-in from disparate stakeholders within an organization (especially sales) and a mechanism to source and vet in-scope win and loss opportunities with accurate decision-maker contact information in a reasonable post-decision timeframe.
  • Whether analyzed by human stakeholders or parsed by AI for buyer sentiment, the most actionable win / loss insights will be derived by the most detailed and nuanced data set. Live qualitative interviews, with their conversational flow and the opportunity to ask clarifying probes, remain the gold standard for generating this data. When the interview is conducted by an independent third party, it adds objectivity and removes potential bias.
  • Context matters – for example, if 42% of a company’s losses cite a perceived lack of value as a reason for not moving forward, is that better or worse than comparable firms? In addition to absolute trends, which can be tracked over time, it can also be helpful to understand how results compare to industry benchmarks.