Monthly Report on HR Trends

Data Driven Product Management allows for each feature set to have a conceptual Business Case, ARR, and P&L.  Data Driven Product Design also allows us to Drive A/B Testing to Assess the Overall Utility of the SAAS Platform, and the Marginal Utility Derived by the Feature Set Change.

Anyway we slice it, Data Driven Design is the Glue that Binds SaaS;  <Development> <Product Management> <Product Marketing> <Customer Success>.

The following could be a short punch list to build a Product Data Set that can be Leveraged by AI;

  • Build Rich Data Points at the Feature Set and Application Utilization Levels:  Need to have the Data First.  
  • Create a Data Equation:  Leverage a Weighted Average Feature Set Model to Analyze the Sum of the Parts.
  • Identify the KPIs for AI to Feed into:  Know What to Change – Baselined A/B Testing Against ARR and Retention.
  • Create Agile Communication Flows: Get the Matrix’ed Silos Focused on the Same Change – at the Same Time.  
  • Mandate Regular Product Updates:  Create the Urgency to Execute the Change.  
  • Incentivise the Team on the Delta ARR:  Keep Chasing the Change – and the ARR.
  • Create Dynamic In-App Marketing and Feedback Mechanisms:  Create additional Product Marketing Channels to support Account Management and Customer Success.

AI can be a catalyst to executing on a Robust Data Driven Product Management Strategy, by Centralizing and Benchmarking  the Goals and Objectives (KPIs) of your SaaS Platform.

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SaaS Sales today have become profoundly more competitive. First Movers are often replicated/reverse engineered with easy access to talented and robust Global Development Teams and Surgical Marketing Expertise.

SaaS Stickiness is the Key to Long-Term Monetization. An investment approach to engaging and retaining your customer base could be based on; Platform Awareness, Feature Set Simplicity, Native Integrations, Clearly Defined Pricing, Lengthy and Feature Rich Free Trials and Assessments, Full Customer Success and Enablement.

Platform Awareness: Be seen.  The identification and eventual success of a SaaS platform depend more on brand awareness than any other criterion. SaaS platforms that are heavily Marketed, and Established with the appropriate Channels and Aggregators will be exposed to a greater number of Trials.

Feature Set Simplicity: Less is more.  While a Robust SaaS Feature Set is often needed to support the Value Proposition.  Simple, straightforward navigation with clean Use Case experiences support SaaS product adoption.

Native Integrations: Make it fit – easily.  Native SaaS integrations, while challenging, increase the adoption of the platform.  Well-defined Use Cases and strong Third-Party partnerships support the Interoperability of the SaaS solution and simple adoption with the Client Ecosystem.

Clearly Defined Pricing: How much?  Online marketing frequently excludes pricing information. With a vast array of competing products readily available, Go-No-Go decisions are often made based on Price and or Pricing availability.  Price is a very strong influencer as the market shifts toward “Perfect Competition.”

Lengthy and Feature-Rich Free Trials and Assessments: Give it all away.  The Trial Period is the first step in establishing Stickiness.  The Longer the Trial Period, and the More Extensive the Available Feature Set, the greater the probability of establishing a solid Recurring Revenue Stream.

Full Customer Success and Enablement: Show them how to get there – fast.  The faster your user base adopts the full feature-set of the application (super-user), the higher the probability of organizational stickiness.  Customer Success and Enablement is an Investment in obtaining recurring revenue which is often not made during the Free Trial Period.

Developing a Product within a Business Ecosystem that drives High Customer Stickiness is a more robust Competitive Advantage than First Mover.

The SaaS Investment Cycle goes beyond Product Development extending well Past Customer Success and User Adoption.

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