In this edition of Behind Product Lines, weโre covering two topics:
A Product Managerโs Evidence Ecosystem: how info travels to a PM
How Products can help customers make better decisions.
How Ideas Travel to a PM through an Evidence Ecosystem
Product Managers constantly swim in an ocean of evidence.
PMs need evidence to make informed decisions, prioritize, build strategies & devise solutions.
Evidence comes in different shapes & forms. This can be data from an analytics software, results from a customer interview, qualitative feedback, a market trend or even an anecdote.
The graphic below shows how evidence evidence travels through to a PM.
There are 11 sources where Product Managers gather their intel from:
Customer/Prospects share their problems, aspirations & ideas.
Sales work with customers to surface customization requests & objections.
Customer Success share business review feedback & feature wishlists.
Customer Support file support tickets & escalates high-priority issues.
PMs work with Marketing to retrieve demand trends, ICP research, interview feedback.
PMs tap into Market Intel to capture macro trends, third party reviews & competitive inf.
Product Analytics & dashboards inform PMs about the health of the product from adoption to retention.
External forces like partners, suppliers & government share important intel on processes, market shifts & new regulations.
The Community contributes to a PMโs idea bucket with feature suggestions & discussions.
A PM learns a lot through dogfooding the product.
A PM can learn a lot from structured user research e.g. focus groups, surveys & other methods.
Typically, there are two primary paths for evidence:
๐ In some cases, Product Managers PULL evidence from a source. For example, PMs may conduct regular discovery cycles, analyze product metrics, conduct interviews, carry out surveys/experiments etc. to proactively gather clues about the job-to-be-done.
๐ In other cases, evidence is PUSHED to a Product Manager. For example, sales teams share objections or customization requests, customer success conveys product feedback/wishlists & customer support escalates common problems users are facing.
๐ซ Let's pause here for a moment.
With so many conduits of information leading back to the PM, let's appreciate how HARD it can get for a PM to deal with this. It's a tedious challenge to distill all those inputs to decide what to do next.
This challenge only becomes bigger as PMs try to maintain some sort of balance between PULL and PUSH evidence paths.
In most product cultures, one supersedes the other (usually it's push >> pull) and that can lead to a myopic view of the ground reality.
Why?
๐ฉWhen PUSH inputs are disproportionately larger than PULL inputs >
๐นProduct teams become overly reactive & opportunistic.
๐นIt's difficult to plan strategies for the future.
๐นPMs lose all their time to firefighting.
๐ฉAnd when no PUSH inputs are coming in & it's all PULL >
๐นCustomers either aren't invested enough in the product to issue feedback to customer-facing teams.
๐นsupport teams are blocking off feedback loops creating blind spots for PMs.
๐นCustomer fires keep burning, resulting into eventual churn.
What kind of evidence pools do you tap into?
How Products Help Users Make Decisions
Great products equip users with the tools to make sound decisions.
According to Sheena lyengarโs "The Art of Choosing", customers 'flee' when confronted with a myriad of choices.
They enter a stalemate of sorts which gives rise to anxiety. Putting users into a state of indecision in your product poisons user activation & retention.
That's why boasting a vast inventory of listings isn't enough to guarantee the traction you're looking for. Now, of course, making choices isn't always a rational process. People are affected by biases. There are several documented types like bandwagon effect (what others are doing), loss aversion & confirmation bias (seeking out info you already believe).
So, what do products do to help users manage choice?
๐ One baseline technique is categorization. Categories create choice silos.
Ex: Audible: rather than reviewing a flat list of 500 titles, users first choose a category like business or tech to pick out an audiobook.
๐ Another technique is whittling down the supply to reduce choice.
Ex: When Proctor & Gamble reduced the SKUs for their Head & Shoulders shampoo line from 26 to 15 products, sales lifted by 10%.
Here are some other approaches products use:
๐ญ. ๐ฆ๐๐ฟ๐ณ๐ฎ๐ฐ๐ฒ ๐ฃ๐ฎ๐๐ ๐๐ต๐ผ๐ถ๐ฐ๐ฒ๐
"What did you use before?"
Products surface decisions taken in the past to short-circuit the process. Ex: Instacart frontloads all the items you've bought in the past to ease re-orders.
๐ฎ. ๐๐ ๐ฝ๐น๐ผ๐ฟ๐ฒ ๐ฃ๐ฟ๐ผ๐ ๐ถ๐บ๐ถ๐๐
"What are things that look like what you opted for before?"
Products use ML to suggest potential choices based on past behavior & similarities.
Ex: Spotify & Netflix push out recommendations based on what you've listened to or watched before.
๐ฏ. ๐ฆ๐ผ๐ฐ๐ถ๐ฎ๐น ๐๐ป๐ฑ๐ถ๐ฐ๐ฎ๐๐ผ๐ฟ๐
"What did similar people use before?"
Ex: Amazon tells you what other people who bought an item your purchased bought along with it OR Zomato surfaces reviews & social recommendations to encourage a purchase.
๐ฐ. ๐๐ถ๐ด๐ต๐น๐ถ๐ด๐ต๐ ๐๐ผ๐๐ ๐๐๐ฒ๐ฟ๐๐ถ๐ผ๐ป
"This is popular & might go away soon."
Ex: Booking.com highlights how a listing has a great price but also how many people are looking at a listing encouraging them to act fast.
๐ฑ. ๐๐ถ๐ฎ๐ด๐ป๐ผ๐๐๐ถ๐ฐ๐
"What is your buying criteria? What are your dealbreakers?"
Offering contextual faceted search or a deep survey wizard to fetch viable options.
Ex: Airbnb offers several search options on their facets to get you to destinations you'll like. OR Noom uses long surveys to recommend exercise plans.
๐ฒ. ๐๐ผ๐บ๐ฝ๐ฎ๐ฟ๐ถ๐๐ผ๐ป๐
"How does this offer compare with others?"
Ex: Autotrader & Truecar guide users on whether a listing is too pricey or just right to "aid" your decision to buy. Above all, learn how users of your product make their decisions. Figure out the factors they look at, the people they ask & the research they do. "Choice navigation" can well be a product differentiator.