Building a Product Management Data StrategyDecember 16, 2015

1

Agenda

The product data imperativeSources of product dataQuantitative & qualitative data in the Pendo platformBest practicesQ&A

2

Building great products is hard

46%of new product launches fail

3

75% do not meet revenue goals

2Yr average lifespan of

“successful” products

Sources: Product Development and Management Association, 2004; Harvard Business Review, 2011

Are the best product managers truly clairvoyant?

4

Data can make a critical difference

Users

5

Features Journeys

Uncover and understand user needs to build meaningful cohorts

Guide roadmap and feature prioritization based

on real user behavior

Follow and optimize user funnels through the

product

Product data sources - internal objects

Key application stats such as users, licences, etc, that is stored as part of application data

6

! Often critical indicators of a product’s “health”

! Data is consistently collected and is stored / captured within the application

Challenges

! Can require development resources to extract and format

Product data sources - web analytics

Page-level and user data captured by instrumenting application pages with Google Analytics or other web analytics software

7

! Provides measure of usage volume, and visitor demographic information

! Track “conversion” events and other specific actions

Challenges

! Engineering work required to implement / customize

! Optimized for web visitors - does not provide user or feature-level detail

Product data sources - support cases

Current or archived support requests from help desk (kana, zendesk) or other repository software

8

! Users ask for help when they’re stuck - identify areas or features of the application where they struggle

! Level of support requests also indicates feature usage volume

Challenges

! Data is not summarized - requires extensive reading / digging to uncover insights

?

Product data sources - User testing & surveys

Qualitative user feedback from observed UX testing sessions and user surveys

9

! Captures direct input and feedback from users

! User testing allows direct observation of application use. Can gauge overall feature utility in addition to UI usability

Challenges

! Data is not detailed, but not necessarily representative

! Difficult to assemble and get responses from user groups

The cross-referencing challenge

10

Product data lives in different systems in many different formats

Capturing a consolidated view requires significant legwork

Product managers have to become cross- referencing ninjas

?

Using a platform for product analytics

Pendo is tailored specifically for rich, complex software products

11

Users Features Journeys

Track user / account-level activity across the activity

Solicit qualitative user feedback directly within the application

Tag specific features for analysis without coding

Insights are retroactive to install date

Define and measure drop-off across custom funnels

Follow aggregate, and individual user paths

Pendo analytics: users

Detailed insights into user and account activity. Create rich segments based on demographic and behavioral characteristics

12

Interactive polling directly within the application. Capture qualitative feedback, ratings, and additional user details

Pendo analytics: features & journeys

Detailed analysis on specific application features. In-interface tagging without additional coding / engineering support

13

Create and analyze funnels and paths. Understand how your users progress through the application and where they drop

Product data improves feature adoption

ChallengeNeeded to expand user adoption of new toolNo clear understanding of how features were used,

leading to difficulty prioritizing improvementsA New Approach

Instrumented feature set to measure usageTracked users across defined “funnel” to find

breakpointsRe-designed UI based on observed user activity

14

Product data provides rapid insight

ChallengeStruggled to capture actionable user dataMetrics and reports needed to be defined prior to

product release - any changes required development work and application updates

A New ApproachImplemented a product data platform to capture user

eventsNew feature / user tracking implemented in minutes

without any additional coding

15

Smarter decision-making balances data and insight

Data is critical, but it isn’t the answer to everything. A good product data strategy brings in additional insight without ignoring the flashes of intuition that can lead to transformative solutions.

16

Tenets of a successful product data strategy

1. Use fast, focused experiments: Build insight through multiple, short tests and prototypes

2. Share your data: Understanding and insights can come from anywhere. The entire product team should have access to data

3. Formalize product reviews: Don’t over-analyze, or get too close to the development process. Specific review cycles can help to balance insight and intuition

4. Be open to surprises: Product data isn’t just an answer to a specific question - it’s a way to openly observe users. Insights are often unexpected.

17

Questions

Eric BoduchFounderPendoeric@pendo.io

18

Michael Peach Product Marketing Pendo mike@pendo.io

Learn more at www.pendo.io