Before we delve into this topic, let’s define what productizing means. “Productizing” services is supplanting the value, skill, and suggestions that a consultant or freelancer provides with a digital product. The benefits of repackaging services as products are enhanced business scalability and the ability to assist myriad of clients without giving up additional time.
How do consultants strategize?
If you speak with anyone who has hired a consultant from the Big Three to grow their business they'll tell you that the model they use isn't rocket science. Astoundingly, the problem-solving practice is straightforward but extraordinarily disciplined.Consultants follow a templatized framework. They typically start by understanding their client’s profile by asking a set of questions depending on their industry vertical. Based on the response, they fit into a predefined stereotype. For every stereotype, they have a recipe/cookbook. Every cookbook has a set of rules or suggestions for the client to improve their business.Majority of these suggestions are generalized ideas and strategies accrued from years of experience. They are not data-driven and they aren't specific to the firm. The suggestions would apply to any client who fits in that stereotype. The book Bulletproof Problem Solving: The One Skill That Changes Everything from two former McKinseyites, Charles Conn and Robert McLean elucidates the same. It defines the rules of thumb, frameworks, and approaches for synthesizing the business problem as a seven-step recipe presented in a circular framework(through iterations).
The Need for Productization
Not every business can afford to hire a strategy consultant. But most of them use software systems to run and manage their daily workflows. My contention here is that if these tools are actually smart enough these businesses might not need these consultants in the first place. And when I say smartness I don't mean artificial intelligence or machine learning, but proactive features as dictated in the recipe books of the consultant's repertoire. The initial goal is to suggest templates, measure deviations and communicate potential loss based on simple rules to entice users to instigate the right actions to improve their businesses.Let me elucidate this through a simplistic example. Your phone hangs a lot recently and you get a message saying "Increase your phone space/Add an SD card". Let's say you approach a consultant to clean up your phone space and want recommendations for the best things you should be doing. He would start by enquiring your phone model, apps that you use, the operating system that your phone runs on, etc. He would then match this with a pre-defined stereotype based on your answers. Eg: A 2016+ android smartphone with essential millennial apps installed.Now the recipe book would contain suggestions like:1. Millennials are susceptive to having apps installed which they don't use. Identify these and uninstall them.2. Clear memes files sent via chat apps like WhatsApp3. Sync photos to the cloud since the user owns an android phone4. A millennial is likely to use social apps like TikTok, Instagram, etc, hence clear old image and video files from these appsBut all of the above can be done by a simple mobile app if the application is designed in a smart way. There is a successful mobile app from Google called Google Files which is smart like your mobile consultant you wanted to hire and tells you how to manage your phone and optimize for storage. No Machine Learning and AI services here, this simply works with hardcoded rules and simple rule-based algorithms.
With Time, Productizing Overshadows Consulting
These smart digital products can only deliver the partial value of what a consultant can provide initially. But with usage, if they are designed well, they could capture the data and use AI to recommend suggestions that are specific to your business and requirements. And eventually when more clients start using the product they can augment to provide insights across multiple clients. Eg: We've identified your operations be to 20% slower when benchmarking with similar firms. These are our 5 recommendations to expedite your process.
Compelling Solutions Come with Vertical Depth
Productizing consulting starts by identifying and picking the right vertical. As you go vertically narrower it will allow you to build a much more useful and compelling solution. The reason is obvious, when your product teams focusses on a specific market its becomes easier to do in depth to understand the the specific needs of a user base and customize a solution for them. Skipping the all-things-to-all-people approach helps product teams to capture a larger market share more quickly than if they were trying to build out a broad solution. Look at the valuations of a few vertical focussed digital SAAS companies below.
Eg: Files by Google picked the mobile phone space optimization vertical. It doesn't cater services like messaging, lending or payments space. It's able to deliver a clear value proposition because of its narrow vertical focus. However, the downside of going vertically deeper is the reduction in the total addressable market(TAM). Vertical markets can be limited in size. You need to find the sweet spot to deliver enough value and simultaneously serve a wider segment to generate enough revenue.
Summary
Consulting is productizable. Great products should emulate management consultants doling out proactive suggestions. Ideally advising customers on how they should be running their business efficiently. By and large, we don't need the fancy Machine learning an AI alchemy. Simple smartness(rule-based frameworks) and narrowing down on the right vertical(under-penetrated market + unsophisticated users) can make products a heavy hitter.
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