How to use objective data metrics to benchmark your alliance performance

 

Benchmarking alliance performance in the technology sector requires disruption. Legacy partner program metrics are misaligned to the way end customers seek to procure technology solutions. The most influential technology players — namely, Amazon Web Services, Microsoft and Google — expect their ecosystem partners to transform away from legacy mindsets.

 

Further disruption stems from multiparty alliances where current benchmark metrics lean toward the sponsoring alliance partner rather than toward the ecosystem or the end customer. Past alliance management strategies and metrics simply will not work today.

 

TBR partner The Association of Strategic Alliance Professionals asked us to speak on this topic exclusively for its 1000s of members as part of the ASAP Webinar Series. Now, we’re extending the invitation to TBR’s community, with the bonus of a live Q&A with TBR’s strategic alliances experts, Practice Manager & Principal Analyst Patrick M. Heffernan and Principal Analyst Bozhidar Hristov.

 

In this FREE webinar you’ll learn:

  • Objective data each type of ecosystem participant — platform vendor, hyperscaler, systems integrator, large enterprise software vendor and small independent software vendor — should benchmark
  • Objective data metrics emerging to monitor ecosystem performance
  • How these measurements, in the form of contractual commitments, need to be simplified to suit the ecosystem rather than the participant’s operating model

 

 

 

 

Previous TBR webinars can be viewed anytime on TBR’s Webinar Portal. For additional information or to arrange a briefing with our analysts, please contact TBR at [email protected].

How to illuminate operational insights and increase value from your business model and estimates

Gimme 3 — Insight Interview with TBR’s Subject-matter Experts

In TBR’s new blog series, “Gimme 3 — Insight Interview with TBR’s Subject-matter Experts,” Principal Analyst Patrick M. Heffernan discusses our latest and most popular research with our analyst team.

This month Patrick chats with fellow Principal Analyst Ezra Gottheil about TBR’s approach to modeling financial performance and other business metrics, including what value comes from modeling and advice on doing modeling correctly.

 

Patrick: We use the term model very loosely around here, and I’ve been challenged on occasion by our readers to explain what we mean when we say we “model vendors.” What do you think a model is, and how do models provide operational insight?

Ezra: The models we are talking about are mathematical frameworks that help us and our customers better understand business processes. Basically, they are estimation tools. The model maintains the relationships among the numbers, and we populate the model with published numbers and estimates that reflect and illuminate the underlying business operations.

 

Everybody models. Figuring out when you must leave to get someplace on time is a model. A budget, even a back-of-the-envelope estimate, is a model. You put in a bunch of estimates, consider the implications, and adjust. That’s modeling. As you make those adjustments, you are thinking about your estimations — what drives the numbers and their implication. That is analysis.

 

The model, through its formulas and checks, preserves the integrity of the numbers, but it is the process of using the model that both requires and generates insight. It keeps you honest, and using the model increases your understanding of the underlying business. The model is an estimation tool, but estimates are not just numbers. They represent how a business operates. Making estimates requires understanding the business, but the process of estimation also expands this understanding. The numbers mean more than mere numbers, and everything you do with them deepens that meaning.

 

It is like the numbers in medicine — temperature, heart rate, blood pressure and the many numbers obtained from samples. These numbers give insight into how the body works. Business numbers reflect how the business works, what the company is trying to accomplish and how, and how well it is doing. This includes greater detail on what the numbers represent, like major expense categories. It also includes context within the company and within the industry, often reflected in the changes in values over time.

Patrick: Our customers use data from our models to help build their own models. Based on your experience, what advice can you give other modelers?

Ezra: Three points: start slow and with a limited scope; start with data you already have or can easily obtain, like the vendor data and insights from us; and start with clear definitions of what you have and what you want to know. We’ve been asked to help clients rationalize, clean up or update their existing models, and their challenges often come from trying to do too much, using too many sources and inconsistent sources for data, and not being clear about the data they have and the outcomes they’re looking for.

 

As I mentioned, modeling can show you how a business works, so if your modeling efforts don’t reflect what you know to be your own truths in your business, how can you trust your model to explain your competitors? One more piece of advice: be comfortable with estimates. You’re not always going to be right; you just need to know what you were thinking when you made your estimates, what changed, and why you didn’t anticipate how the changes would affect your model. So maybe the advice is, really: be humble with your estimates and be willing to adjust.

 

Patrick: How can customers get more out of their models, as well as from our data and insights?

Ezra: Models deliver value by giving the customer a greater understanding of a company’s competitive situation within the market. What works and what doesn’t? Where are the threats and opportunities? How is our company performing in specific areas in comparison to other companies. What should we learn from, and what should we avoid?

 

Models provide a consistent framework to shape that analysis, and TBR’s data reflects relentless updating and refinement of the inputs and the insights. I say “relentless,” because that’s really key to the value. You can’t model once and be done. You need to update constantly and within the framework you’ve established. TBR helps with quarterly data and analysis, so even if you’re only revisiting your own analysis every six months or just annually, you know the up-to-date inputs from TBR fit your model — your framework — in a consistent manner.

 

Got it and a good reminder that snapshot views definitely provide just a moment in time and it’s the consistency that draws out the value over time. One final thought: The vendors we examine, from telecom providers to infrastructure vendors to cloud hyperscalers to management consultancies, all have their own way of looking at their business and the competitive space they play in. By creating taxonomies around the various service lines and modeling the businesses consistently and relentlessly, we’re helping make better apples-to-apples comparisons even though we’re looking at a messy fruit salad.

 

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