Imagine you have the power to see into the future and predict whether your next product or service will be a success.
If you’re a product manager, marketer, or analyst you’ve probably used the Ansoff Matrix to evaluate growth and risk strategies when launching new or existing products into new or existing markets. But there’s always an element of guesswork and risk involved.
Launching an existing product into a new market usually consists of an extension or feature change so you will have some prior understanding of how the product behaved when exposed to previous customers.
But launching a new product into a new market is an entirely different risk as you have no prior understanding of how people will react to this new product. How can you predict its success? And how can you best use machine learning to make your route to market smoother?
Track your audience in real-time
Historically, machine learning models have relied on prior data to predict how an audience will react to new products or new markets. The problem with this approach is that this data is frozen at a point in time, so it doesn’t account for crucial factors that can significantly impact your results. Factors such as changing human behaviour, people’s most recent transactions or the latest viral trends.
A dynamic approach on the other hand, that involves a real-time engine in production can roll out new recommenders that give you the capability to evolve with ever-changing human behaviour or sudden surges in popularity. Your business is no longer bound by the fixed nature of a deliberate design that cannot adapt or isn’t relevant at that moment in time. You gain real-time insights, see trends or new segments as they unfold, and can take decisive action based on how your customers respond to your offers, messages, or engagements.
The reality of real-time technology
Every company running a successful digital campaign already has real-time capabilities and knows the importance of tracking customers in real-time. Anything that is engagement based or sales driven should have a real-time engine for inbound engagements. That means the moment someone comes to you, you should engage with them based on your knowledge of who they are and what they’re most likely to do next. Most companies are not doing this however.
One of the biggest challenges is having tooling out of the box that can do the work for you. The upfront investment in technology and skilled resources can be debilitating.
Having a platform like ecosystem.ai – that can handle 80% of your requirements with a no-code solution, where the other 20% can be handled by your team as a low-code option – addresses all of your biggest concerns. This can fast-track your pathway to successful launches and campaigns with real-time results in milliseconds.
Why guess when you can dynamically test?
If you already have historical data that gives you an understanding of what’s been done before, shouldn’t you use that?
Of course, you can decide to use some of this data as input, or you can decide to use none of it. Instead, you can start testing with a cold-start scenario where you learn dynamically what the outcome will be. Dynamic experimentation lets you learn from your customers feedback in real-time and thereby radically reduce your risk, cost, and time to market. It allows you to remove the guesswork, streamline your processes and concentrate on winning products or services that turn customers into loyal fans.
Companies know the core services or products people will engage with, and this forms the basis of the historical data they gather.
The ability to experiment with all the other things your customers will want over time is where it gets interesting. To figure out all those elements manually is virtually impossible though. A real-time engine lets you start testing how people will behave in the context you have set up, so you can learn dynamically, rather than using prior situations to predict for the future.
