In the Year 2007, I started my Product Management journey with eBay Enterprise. The first lesson I learned during my Product Journey came from my mentor Bill Roth, who said, “people (internal and external stakeholders) remember the dates and not the features you missed in the product so, always deliver your product on time.” Post that, I went through an intensive training program with Pragmatic Institute (https://www.pragmaticinstitute.com/). This highly reputed institution nurtures the next generation Product Managers and Product Marketers. During the program, they explained their proprietary Pragmatic Marketing Framework (https://www.pragmaticinstitute.com/framework), which was the core of the entire training program and became the most widely used framework in last 13 years of my corporate journey.
Those who are a part of the Product Management fraternity know that it’s a roller-coaster ride, and it pushes you to the corner multiple times and makes you pivot at different checkpoints of your Product Development journey. One of my mentors in Silicon Valley said that “the Product Management journey is a journey of perseverance and makes you an infinite and explicit learner.”
Regardless of whatever business problem, I confronted since 2007; the fundamentals of Product Management remained the same, and after 13 years, the seven fundamental questions I still struggle to answer are:
- What business problem are we trying to solve, and does that problem pervasive in the market?
- Are we solving an urgent need in the market or creating a demand?
- Will the prospective customers pay for our product upfront, or expect a FREEMIUM model?
- Are we building a product for an industry, a business function, or a specific persona?
- Does our product classify into DIY (Do-it-yourself), DIFM (Do-it-for-me) or DIWM (Do-it-with-me)?
- Do we want to license it, or offer it as a SAAS (software-as-a-subscription) based product?
- Does our product part of a discretionary spend or a part of a mission-critical value chain?
The above seven questions are incredibly critical and fundamental to any product or solution journey we involve in as a Product Manager. As our digital ecosystem is evolving and becoming complicated year over year, it has become challenging for a newly launched product to get a modest scale and adoption. As a Product Manager, we have continually battling to fit into the ever-changing landscape of our target personas whose day in the life is full of operational challenges and reactive decisions. Unlike the older days, our target personas are hyperconnected, distracted, expect instant gratification, speed, predictability, transparency, and faster decision making.
In this convoluted maddening corporate ecosystem, if you want to attain a respectable adoption, you need to invest in multi-modal consumption mediums and continuously invest in Relevancy, Contextualization, and Actionability. In my experience, we need to start formulating our adoption strategy quite early in the Product Development cycle because the cost of fitment post-deployment is significantly higher and impacts customer satisfaction measures.
Below mentioned are few of the important learnings which I gathered in recent years to boost the adoption of your digital products:
Immerse yourself in Day in the life of your target persona – As a Product Manager, we often don’t spend good quality time with our target personas and validate our hypothesis. There are lots of assumptions that are made based on heuristics and past experience and that becomes one of the major reasons behind lower adoption. Imagine you have decided to build a data product (e.g. Dashboard, Mobile App, Portal etc.) that is targeted to the CMO function in the Hi-Tech sector. Prior to building this product, it’s extremely important to get the following questions answered:
- What are the responsibilities of the CMO function in the Hi-Tech sector?
- What kind of decision-making process he/she needs to go through on a weekly, monthly, and quarterly basis?
- What are the various day-to-day operational challenges he/she has to go through and how these challenges can be clustered into People, Process, Technology, Business Model, Marketing Strategy, and Data?
- What KPIs he/she is accountable for and what are some of the adjacent KPIs he would like to see in the data product?
- How does he/she consume the various KPIs and what are the common data dimensions he leverages to slice-and-dice the data?
- Besides the Marketing measures (Impressions, Clicks, CTR, CPA, ROAS, and ROI), would he/she interested in seeing the Financial, CRM, Sales Funnel, Clickstream, and Contact Center measures?
- How important is the various audience segments part of the story?
- What is the expected attribution model he/she would like to leverage to attribute the credit of the conversion?
- What kind of decisions he/she usually takes at the Channel and Campaign level on a weekly and monthly basis?
- What is the expected data granularity and recency?
- What would define the relevancy and context based on his persona?
- What kind of predictive and actionable indicators he/she would like to see in the story?
- What metrics he/she would like to proactively forecast, predict, and detect anomalies against?
- What would be the process to bring the agency stakeholders into the decision-making process as they control the spending?
- While consuming the story if the CMO needs additional insights, what would be the process?
- What if the CMO wants to bring both the Digital Marketing and Market Research worlds on the same page? How would you do that?
- What is the current availability of data, the current data quality, and the known gaps?
Clarify your assumptionsaround adoption during Product Development – This is a very important and often overlooked step in the Product Development cycle, and I would highly recommend investing time on this. Let’s take the above example and augment our questionnaire for the CMO:
- What would define a good adoption from the CMO perspective?
- What parameters of Relevancy, Context, and Actionability the data product needs to satisfy?
- What are the known factors which might influence the adoption of the data product?
- What would make the day/week really good?
- What if the certain day or week has turned out to be bad, what kind of actionable indicators CMO would expect in the data product?
- What if the engagement with the data product starts dropping, what kind of early warning signals the CMO would expect?
- What if the data product needs to be further extended to the team? If yes, what would be their expected storyline?
- What kind of training and evangelization sessions would be expected pre and post-deployment?
- What if one of the consumption mediums is not able to entertain the question from the CMO? Are there alternative mediums that can fill that void?
- What kind of support would be expected and the acceptable SLAs?
- What are the acceptable data granularity and recency?
Invest in a multi-modal consumption model – Our business users are living in a hyperconnected world, and they would expect multiple consumption mediums for any data product they would engage in the future so; it’s very important for any Product company to empathize with the world of their target persona, and bring the power of App, Portal, Chatbot, Voicebot, Email, WhatsApp, PPT, Doc, XLS, and even integration with collaboration platforms like Teams and Slack. This is going to be table stakes in the near future.
Think beyond Facts and bring the flavor of Augmented Analytics – Almost every business user in today’s ecosystem demands smarter anomaly detection with root-cause analysis, early warning signals, adaptive forecasts, prescriptive recommendations, and human-friendly insights. Since 2018, the augmented analytics sector has grown significantly and has attracted multi-million dollar investment from the VCs, and gave birth to a few Unicorns. Some of the players who have done really well in this space are ThoughtSpot, DataRobot, Answer Rocket, Course5 Discovery, and Outlier.
Execute your Product Strategy in a nimble and agile manner – Don’t surprise your target audience with a bloated and over-engineered offering because they are already too distracted and have committed to various habit-forming products. Your product needs to have that stimulation which rewards the business users throughout the engagement. To better understand your target audience, their consumption behavior, their decision-making process, and their frustrations, its always better to deliver your digital products in an agile manner and keep on evolving based on a closed-feedback loop.
Setup a closed-feedback loop and productize the knowledge of your human capital– We have entered into an era where organizations need to start embracing the idea of productizing the qualitative knowledge that resides in different business functions and build an enterprise-wide Knowledge Graph (a smarter and logical representation of the different qualitative attributes in the form of an ontology). This is a much smarter way of structuring the knowledge than parking of hundreds of documents into a Knowledge Management Portal with a belief that, a large % of the workforce will check out the document, get themselves evangelized, and check-in back with the newer information. By structuring the knowledge into a graph, we can quickly transform it into a vector that can be leveraged as an input by a Machine-Learning model. This would be a more natural way of setting up a closed feedback loop between business and analytics.
In the end, I would like to conclude this post with a saying that, Adoption is not a Post-Deployment Challenge to solve.