Optimizing the Customer Experience through Product Analytics

The gap between success and failure often comes down to which company can deliver a delightful and differentiated customer experience, and your product is the centerpiece of most customer interactions. In this presentation, you’ll learn not only how to deconstruct your customer experience but also how to leverage a product analytics solution to optimize it.

Transcript

1. Optimizing the Customer Experience through Product Analytics
2. 100% of product failures are also customer experience (CX) failures
3. A Tale of 2 CX’s A • functionality dictated by channel • one size fits all user experience • static, point-in-time interactions • user acquisition as the end-goal • customer value as an afterthought B • unified cross-channel workflow • personalized user experience • multi-platform, dynamic journeys • user engagement as the end-goal • value reinforced at every touchpoint
4. Be a B
5. because B has optimized the CX
6. Multi-Time CEO “There is an important trend going on, where a product is no longer just software. SaaS products consist of software, data, human input, APIs, integrations, etc. The definition of ‘product’ is changing. PMs need to see this.”
9. Customer Journey Awareness • Email Campaigns • Organic Search • Product Reviews • Brand Evangelists • Online Advertising Advocacy • Support Forums • Loyalty Programs • User Conferences • Partner Ecosystem • Customer Testimonials
10. product lives at the ◎ of the core customer journey
13. User Journey Activation • conversion and sign-up • first-time user experience • onboarding to aha moment • time to value realization • non-usage to habit loop Sophistication • progressive disclosure • high-value use cases • premium features • value dissemination • basic to advanced usage
14. product analytics unlocks the key ↻ in the user journey
15. Product Analytics optimizes CX • figure out critical path(s) • improve journey completion • get non-users activated • get novice users to pro • understand value drivers • avoid low-leverage bets
16. A Tale of 2 CX’s A • sporadic usage of analysis • incomplete view of the customer • lag between insight and action • cycles wasted on dead-end bets • mixed track record of decisions B • culture of self-serve insights • 360 view of the customer • efficient insight to action loop • focused on high-leverage bets • track record of quality decisions
17. Be a B
18. because B has enabled decision velocity
19. Optimizing the Customer Experience through Product Analytics