How AI powers modern product management

This article was originally published on VentureBeat in May 5, 2022

Product management has long been an elevated science with frameworks, rules and significant research work conducted to ensure the product in question is fit-for-market and valued for its price. 

While most Consumer Goods organizations adopt a product engineering mindset approach to their product management cycles, sometimes using a structured framework that references to digital product management and agile methods, they may fail to analyze and incorporate the most important aspect of modern product management: products need to address a real customer pain. And, in order to do so, product managers need to constantly listen to consumers and understand what they want. 

But not every company is using the full suite of tools available to tap into the collective wisdom of the consumer base when making product decisions. Developing a product that can make or break your organization is too important to get wrong or approach without sufficient intelligence.

As McKinsey wisely stated, digital product managers "are increasingly the "˜mini-CEO' of the product," responsible for many different facets and held accountable for success, regardless of whether a failure had to do anything with the actual product creation. The sad reality is, by many measures, 80%-95% of all products fail.

At every step of the product management process there are meaningful contributions from AI-powered product analytics and feedback intelligence platforms to create, optimize, and market products better.

Here are the five recognized stages of the product management cycle and some specific ways the right product intelligence platform can give organizations the best chance to maximize their return on investment.

Ideation

The ideation phase incorporates assessing trends and opportunities, surveying the competitive landscape, and identifying white space opportunities. While many companies rely on simple social listening and human assessment, AI-driven product intelligence is another level of guidance. When relating this to digital product management, this would be the equivalent to product discovery: assessing user needs and identifying what is missing for an outcome to be reached.

Instead of latent indicators caused by surfacing reading of comments today, product intelligence platforms can crunch the totality of conversations to understand where customer preferences are going. The end result is creating a product that appeals to today's market and future-prepares the organization.

Definition

Once the ideation process finishes and a product is conceptualized, the product teams must get down to brass tacks and productize features and establish product leadership attributes to become a winner. This is where good ideas can die if they fail to get the specifics right. 

A product intelligence platform ensures this definitional phase focuses on product attributes customers will want and need while also understanding which attributes your competitors' products have that customers love or hate.

This is not easily achieved by generic feedback analytics or customer experience tools that can parse surface-level meanings on user feedback. By focusing on a simple aggregation of public comments with no measure for scale or influence or deeper context, companies can make the wrong decision, rendering a product unwanted or obsolete within a year. Considering that 45% of product launches are delayed, tapping into real-time feedback is a huge opportunity to keep the process moving while always staying on top of how consumer preferences are changing. 

Product development

Now the "real work" begins via the development cycle. Companies without the right intelligence tool at this point go heads down and build out a product over several months or years, confident that their pre-development insights remain valid.

Here is where product intelligence helps physical product manufacturers behave more like their digital counterparts, which use the minimally viable product (MVP) methodology to release foundational products and iterate as additional development is needed. While physical products do not allow as many iterative releases, they can still use intel to course correct. Companies continuously monitoring product intelligence can keep an eye on the billions of daily conversations to ensure the development roadmap is correct and begin identifying new functionality to incorporate in future releases. 

Launch

Once your company has ideated, defined, developed, and optimized your product, the time comes for launch. Many amazing products never had a chance to change consumers' lives because the launch failed, either due to poor messaging, timing, or go-to-market strategy. Pre-launch, brands identify target personas and define launch strategy and positioning. Post-launch, they monitor successes and compare them to previous product launches or the ones from their competitors.

While the product has been built by this time and therefore cannot be altered, how a product is positioned can often have as much effect on success on how it was built. The right intelligence platforms can tap into existing conversations to understand existing customer perception both about the anticipation of this launch and consumers' ongoing opinions about the product category and competition "“ which can also help to identify product issues & crises early on. It allows you to contrast your product against the competitive set, as well as identify channels that could help get your product in front of a much wider audience.  

Optimization

Companies monitor product issues, address safety or liability concerns, and test product performance in the optimization phase. Again, this is all happening within the organization and specific to the developed product. 

The right insights platform absorbs conversations about customers' initial impressions of your product and validates or calls into question your marketing strategy. By keeping insights in an always-on approach, you can course-correct any attributes that will be poorly received and add additional features that could make the difference between a failed launch and a once-in-a-lifetime success. 

Putting it all together: Product management and development

Companies that incorporate AI-powered product intelligence from online conversations are more likely to make smarter decisions at every stage of the product management cycle, likely leading to fewer delays and a higher chance of being in the minority of products that succeed.

When the cost of failure is so high, it's an obvious step every organization should take to protect their investments and maximize their chances of success. 

Are you looking to make your hardware product management process more agile and customer-centric?