People benchmark things all the time, often without realizing it. Consider purchasing a TV: Which model should I choose? How does the cost affect my decision? Will it fit in my space? Can I tell the difference between 1080p and 4K? Is it aesthetically pleasing? Usually, most of these questions don’t matter, and you end up buying the most expensive TV within your budget. Or at least, this is my experience.
Business decisions are often made within a similar framework. Expensive choices are perceived as higher quality, while cheaper options are considered only if resources are limited. For instance, Salesforce is costly, yet widely adopted despite numerous affordable alternatives. These decisions are usually made with limited information, and experiences can vary greatly between companies.
To improve business decisions, it's valuable to assess the resources allocated to your team. Internal resources often come with abundant data and business-centric KPIs that align with organizational needs. Benchmarking is an effective method to enhance decision-making by determining what matters to an organization and optimizing resource value.
Having conducted many benchmarking exercises across various companies, I've observed a consistent theme: businesses need to feel heard. While data teams support the business, they often favor purely technical solutions. However, a purely technical solution will be rejected if the business does not understand and align with the benchmarking process. Here are two ways to begin building bridges to incorporate the business perspective:
Most benchmarking projects use standard KPIs: uptime for manufacturing, close-rate for sales, impressions for digital marketing. These are straightforward to connect with business importance. However, some industries require a deeper understanding. In one project with a dental practice, we spent about twenty hours with a dentist to understand what was crucial in their field. This approach ensured the business team knew we were focusing on what mattered to their industry because it came directly from them.
Starting with the business’s KPIs is essential for them to feel comfortable with the analysis. They need to trust the foundation to accept the derived information.
Sales teams often emphasize their regional knowledge, claiming one region is tougher than another. Metrics alone don’t capture the whole story. At one firm, we created a metric to proxy competitiveness since all regions aren't equal, yet everyone complains about their territory's difficulty.
It's important to acknowledge biases rather than dismiss them. Allowing a portion of the score to reflect business input helps balance insights and feelings. In a recent project, we split benchmarking 60% quantitative and 40% qualitative. This approach made the business feel their input was valued while the data team provided critical performance feedback.
Data Envelopment Analysis (DEA) is a big, clunky piece of mathematical jargon. More specifically, it is an algorithmic way to benchmark resources against each other. And while I think DEA is a great tool, I have also stopped using the term in front of the business side. The business doesn’t need to know or want to know what it is called. So, I have started referring to the two methods as Business Weighted and Mathematically Weighted benchmarking. The good news is that I don’t have to explain them; the bad news is that the Business Weighted approach usually wins, because… branding.
For simplicity, consider a toymaker with ten teams making different toys. The toymaker is concerned with four metrics: cost per unit, profit per unit, turnover time, and scrap rate.
In the Business Weighted method, the data provides answers while the business sets major inputs. For instance, profit per unit might be weighted at 40%, scrap rate at 30%, cost per unit at 20%, and turnover time at 10%. Teams are rated by performance for each KPI.
Decision-making involves setting an anchor point. Either the top performer sets the benchmark, or the top three performers do. This affects how points are distributed. I recommend a higher anchor point, though finding the optimal point requires trial and error.
Sum the weighted scores to benchmark the teams.
The Mathematically Weighted, or DEA, method posits that multiple teams can reach optimal performance. The DEA requires classifying KPIs as inputs or outputs and deciding whether to focus on input efficiency or output maximization.
Using our toymaker example, an output-oriented DEA would provide insights on improving team efficiency. Input data into the pyDEA package to benchmark teams. The algorithm reveals which teams perform best overall, even if they aren't perfect in all metrics. The package can also identify areas for improvement, called slack.
Benchmarking is underutilized due to its time-consuming nature and soft ROI. However, the ROI from better decision-making is significant. Benchmarking projects also build relationships between data and business teams, showing a commitment to integrating business input while moving towards data-driven decision-making. Start with the BW method to familiarize teams with the process, then automate with DEA for deeper insights.
Mathematically Weighted: Digging for a New Business Model
Business Weighted: Influencing Influencers
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