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How Data Can Help You Deliver Your Products More Efficiently

Even the best products need continual adjustment and optimization across the delivery lifecycle to hit that market sweet spot and attain true stickiness. 

Data can help product leaders act more intelligently on the roadmap, optimize the use of available resources (e.g. the unique strengths and expertise of your team) and ultimately, build more impactful products. Here’s how:

Use Data to Deliver Products On Time and Under Budget

Delayed release and budget overruns are all too common for product teams, and this can have pretty severe long-term impacts. 

 

In fact, in 2019 Gartner found that 45% of product launches are delayed by at least a month, from which many fail to meet internal targets within a year. That number is only expected to increase (quite substantially) in 2020. 

 

A root cause of this trend are tireless attempts at building a “perfect” solution, which often lead to unrealistic, unattainable targets. And yes, of course we’d love to fine-tune and polish a product for years til we achieve something close to perfection, but that would stretch resources thin and shrink any chances of success. 

 

Instead, data-driven PMs must redefine their vision of the “best” product – as an ROI ratio of impact vs. costs. A data-driven impact-cost balance can help you optimize your number of iterations, thereby pushing for quality without compromising your release schedules.

 

A crucial data metric to monitor here is burn rate – the speed at which your company or  team spends the capital or “resources” on hand. You can modify the burn rate KPI using a burndown chart to plot capacity utilization during a sprint, mapping ideal effort hours against your actual remaining efforts. This can significantly help with prioritization and planning.

 

Here are a few tips for PMs looking to use burndown charts for effective product delivery: 

  • Use dedicated burndown charts for costs and developer effort/time
  • Replace costs/ time/ efforts with the user pain points a developer is working on; this would better capture multi-tasking hours
  • Count the number of tasks and map it across sprints to get a more accurate view of productivity, apart from simply the number of hours put in

 

Using data analytics in this way enables better planning of the delivery lifecycle and more accurate, proactive forecasting of any delays or budget overruns. Data is particularly relevant for the newly emerging “Growth Product Manager.” Cost and time pressures have given rise to this whole new breed of PMs who are squarely focused on pushing internal goals. They often work hand in hand with “core” PMs to balance customer needs with business outcomes.

 

“Product teams are inheriting commercial responsibility at a rapid pace. This new commercially oriented reality poses a challenge for traditional product organizations that product teams are not used to carrying a quota or focusing on short-term business outcomes,” Eric Keating, VP of Marketing at Appcues, said. “In response, this role of the growth product manager has emerged.”

 

Now growth PMs and core PMs aren’t necessarily two separate people ! 

 

You can have a single owner with two lines of responsibilities, geared to cover the entire breadth of desired outcomes. So, whether you employ a growth PM or adopt a growth PM mindset, you need data to measure whether the product is reaching your ROI goals. 

 

“That metric or goal could be new user activation, free to paid conversion, adoption rate, retention, or expansion, to name a few,” Keating said. To improve the metrics they own, growth PMs typically rely on a series of short-term experiments to incrementally improve and increase efficiencies throughout the product experience.” 

 

Use Data to Measure Your Impact and Make Adjustments in Real Time 

 

A sustainable delivery plan draws clear lines between development effort and the impact that every change/feature has. Unfortunately, PMs often shy away from analyzing results in detail because: 

 

  • A busy roadmap and consecutive changes make it difficult to collect accurate data
  • There is fear that quantifying impact could lead to more challenging targets
  • There is no visibility into the ideal impact KPIs and how to measure them

The last point is the most common reason for putting change impact analysis on the backburner. However, the failure to analyze user impacts can confuse feature prioritization plans later down the line. PMs will struggle to allocate the right resources in the right direction without an accurate and complete understanding of the WHY behind it all. 

 

Some researchers have even come up with tested models for change impact analysis for software product lines (SPLs) – this underscores the severity and urgency of the problem!

 

Efficient delivery implies that every step and activity brings value, either for the business or the user (preferably both). You can’t achieve this without measuring impacts and redirecting product efforts accordingly. Some of the most easy-to-measure impact KPIs are: 

 

  • Increases in usage/engagement
  • Revenue gains from existing customers
  • Referrals leading to new customer acquisition
  • Improvements in NPS scores
  • Upticks in organic PR (social media mentions, analyst coverage, etc.) 

 

These metrics aside, you can also track long-term KPIs like increased market share, where the release of a particular change/feature may have spurred an upwards trend. It’s equally important to listen to negative signals – a negative response to a feature change indicates a gap where, in some scenarios, a PM might be on the wrong track entirely.

 

Impact data helps to realign efforts dynamically along the SDLC and keep the delivery aligned with user needs. 

 

To learn more about leveraging data to deliver better products:  

 

 

 

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