Evolving into an AI Product Manager
It has become custom to be data-driven in all our approaches. Product Management is one field where being data-driven can result in wonderful results. The renowned AI expert Andrew Ng in his “State of AI” lecture at Emerging Tech Conference last year spoke about AI Product Managers. As Product Managers most of us are very eager to start creating wireframes for UI once the product idea is formed. Andrew suggested a different approach. Data driven Product Managers must first create a data set that represents the problem space. Only then they must ask engineering to iterate on the problem to deliver 90% accuracy for the problem to be solved.
Every product needs to start with strategic data acquisition as the very first step. Ng told the story about Blue River, who painstakingly collected data on heads of lettuce until they had the most unique data set for the problem. This data set along with robotics, helps them determine how to best allocate resources to grow lettuce and autonomously thin the crop to maximize yield. John Deere snapped them up for $305M as a result of having a solid and unique data set built up over time. Examples like these confirm the theory that Data is indeed the new oil!
At Commerce AI we firmly believe that data-driven decision making leads to optimal results. You need to monitor and measure user feedback before making product changes that will delight your customers. A leading customer of ours in the wireless router industry has been using this approach to good effect. By continuously monitoring ratings and reviews of their products they are able to know the pulse of their audience.
“Smartly assembled datasets and insights lead to effective product roadmaps, support initiatives and marketing programs.”
Continuous monitoring of ratings and reviews is one tangible way of measuring these feature enhancements and marketing/support initiatives. And the virtuous cycle continues.
This virtuous product cycle is one where a good event feeds on itself to improve product / business further. It is a positive feedback loop. This virtuous cycle can drive a product’s strategy for decades.
Unfortunately, It is not uncommon for Product Managers in most big companies to have the following modus operandi for tracking customer feedback and/or product performance- every month download data from an internal system into a CSV, import that into Excel and make charts, paste the charts into PowerPoint and make slides and bullets, and then email the PPT to 20 people. If you tell this person that they could switch to Google Docs, they'll laugh at you; tell them that they could do it on an iPad and they'll fall off their chair laughing. But really, that monthly PowerPoint status report should be a live SaaS dashboard that's always up-to-date, machine learning should trigger alerts for any unexpected and important changes, and the 10 meg email should be a Slack channel. Now ask them again if they want an iPad.
Product Managers - if you identify with any of the below challenges, AI could be your friend:
- You cannot make the connection between customer feedback and sales
- You are constantly playing catch up with competition
- You have no idea who your influencers are
- Your current promotions are not working
- ROI is hard to quantify
And you are an AI Product Manager if you
- Look at your problem space and collect the data set first
- Have a training data set that is unique and differentiated
- Know your AI performance/benchmark data
- Have user interaction traces/experiences shaping the product roadmap
- Are constantly looking for additional sources of data that will continue to improve your product
So embrace the change and let AI help you succeed.
