Initial Few Days as a Product Owner in AI-Analytics Product Development
The importance and required skills that a product owner in AI analytics product development needs to bring.
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We have received a fresh product owner for our AI-Analytics Product team.
We build analytics software for the insurance market.
I have helped him with onboarding for the initial few days, as well as answered all his questions.
First, what he did,
Competitive Analysis to Deliver Better Value
He was asking: How can we build & deliver stronger product Value by gaining inspiration from our competitors?
According to him, we can gain a lot from our competitors.
We also need to have a competitive advantage that competitors can quickly imitate.
It is advantageous to watch out for our competitors every now and then and reboot if required.
He looked at the competitive analysis to identify what we are doing to safeguard the business’s success.
He was searching for a strong advantage we have as a product strategy?
Can this be the deep domain expertise we have? Can we have an established known brand in the market due to legacy trust?
How about product availability and pricing strategy?
How about location, technology, and owing to these maximizing the value generation?
Can these be long-term value? Availability, price, optimal end-to-end experience?
What are some more competitive advantages after doing competitive analysis?
Trust data: where can we find those?
Price strategy: How to find those
Availability of the product service: How can we measure those
Brand Value: How can we measure those?
Technology usages: How can this be part of the product strategy?
Simplicity: How can we measure this?
Let's look into the landscape, we vs. them, and how well we are positioning ourselves in the landscape.
All these questions were examined by him aggressively to figure out the product strategy.
Next, he was seeking with me to arrange for the below information.
Consumer Behaviour Data Analysis for Value Generation
It seems, according to research cited by McKinsey, organizations that leverage customer behavior data to generate behavioral insights outperform peers by 85 percent in sales growth and more than 25 percent in gross margin.
In a digital world where customer-centricity, personalization, and customer experience separate the winners from the losers, it’s no coincidence that these companies thrive.
Customer behavior refers to an individual’s buying habits, including social trends, frequency patterns, and background factors influencing their decision to buy something. Businesses study customer behavior to understand their target audience and create more-enticing products and service offers.
We know customer behavior can be influenced by three types of factors: personal, psychological, and social.
He was looking at our product and how current we are personalizing for every customer?
Where is the data through which we can predict consumer behavior? I have been enabling whatever I could locate.
For him, customer behavior analysis is a qualitative and quantitative observation of how customers interact with our product.
He is segmenting our customers based on these parameters and providing an offering to them. His analysis gives an idea of the motives, priorities, and decision-making methods considered during the customer’s journey. This analysis facilitates his understanding of how customers feel about our product buying experience, as well as if that perception aligns with their core values.
Our product owner was studying to discover if we are doing everything for customer acquisition and customer retention policies.
He told that Salesforce’s 2017 state of Marketing Report found that 67% percent of marketing leaders say creating a connected customer journey across all touchpoints and channels is critical to the success of their overall marketing strategy.
He was asking me if I had the below information
• Are you hooked up with your consumer to understand their needs?
• What data are you using for validating some of your assumptions?
• How would you like to change your product’s features based on the consumer’s changing behavior?
• When you coach your product manager, what would you like to observe and recommend for change?
Next, he was enquired about our
Data Team’s Capability
He was asking for the data strategist and wanted to meet with him
He was saying that with high-quality data, businesses are able to gain insights for stronger business decisions and strengthen efficiency and productivity. So, Data is next to Oil, no confusion about this.
But we need to overcome many challenges to reach their goal of gaining the drop in oil.
He was checking about the data collection steps we are using from below
• Questionnaires and surveys
• Documents and records
• Focus groups
• Oral stories and records
• Financial Reports
• Sales Records
• Retailer/Distributor/Deal Feedback
• Client Personal Information (e.g., name, address, age, contact information)
• Business Periodicals
• Government Records (e.g., poll, tax records, Social Security intelligence)
• Industry/Business Journals
• The internet
He asked for detail about the challenges we are facing here
Challenges With Data Collection:
• Data collection standards are not up to the level
• Complexity with the data collection
• Intelligence about the data collection
• Appropriate users are missing or are not able to feed the information
Challenges With Metadata:
• Missing Effective metadata technology
• Costly implementation
• Substandard technology selection
• Disparate information sources
• Authenticity and standard of the data
Challenges With Data Quality:
• Data Duplication
• Outdated data
• Hidden data
• Ambiguous data
• Inconsistent data
• Too much or too fewer data
• Data changes too fast
Challenges With Data Governance:
• Data are siloed
• Data are not controlled
• Poor leadership
• Data quality
• Poor data Documentation
• Data Ownership
• Organization structure
• Data Opacity
Data Security Challenges:
• Find what data needs to protect
• Remote work
• Deal with multiple security solutions
• Deal with Physical security
• Insufficient security configuration
• Fake data
• Data Manipulation and wrong data injection
Data Design Challenges:
• Design standards
• Data Processing speed
• Focusing on end-to-end data requirements
• Poor documentation
Data Architecture Challenges:
• Evolution of methodologies & culture
• Adapting to changing architecture
• Complex data environments
• Data quality
• Business focus
• Dealing with Large Volumes of data
• Data Integration
• Data technology selection
• Scaling data
Data Science Challenges:
• Find the right data
• Data cleaning
• Understanding business problem
• Collaboration with diverse stakeholders
• Understanding KPI & Metrics
• Imbalance data
Data Strategists need to work with all the teams to overcome these challenges. What are we doing?
Share your thoughts when you work with Data/Analytics team; what else they are doing to overcome those challenges?
He shared that we must be doing all and wanted to know what % is true as of now
• She compiles information to take the decision
• She discovers insights and strengthens knowledge from that data
• She tracks business performance and progress
• She generates early warnings about her business
• She deals with vendors and suppliers with this data for better collaboration
• She shares the data report with all the individuals who are partnering with her
• She optimizes flow wherever required
Next, He was inquiring about Digital Touchpoints; how well those are taken care of? He has requested all these questions to design the digital touchpoints.
- Do customers have a place to ask questions before going to support?
- Can your customers find all the resources they require in one place?
- Can customer update their own profile on the website?
- Can customer exchange their feedback?
- Can an online site be built based on customer needs?
- Can customers help themselves?
- Can customers have different devices to deal with the solution?
- Can customers access all their data points easily?
- Can a business onboard customers easily?
- Can customers easily contact us?
- Can businesses be able to easily figure out if there are any issues and promptly be able to solve back the customers problem?
- Can we have all the customers analyze the customer behavior?
- Can we have a personalization option designed for all the customers?
- Can we have customer emotion captures from the business site?
Our product owner journey has started, but all these he asked in one week of his joining.
We are optimistic that we will be able to build better product features to dominate the market with these approaches.
Published at DZone with permission of CHANDAN LAL PATARY. See the original article here.
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