How Prescriptive Analytics Transforms New Product Launches
For most CG companies, new product launches are high risk due to unknowns about product fit, how much to spend, how to go-to-market and what results to expect. CG companies don’t just need to know what might happen in a launch; they need strong data-driven recommendations on how to go to market to achieve revenue targets. That’s been the missing piece to the new product launch puzzle.
Proving what works before CG companies invest
The Risks
New product launches bring well-known risk:
About HALF of all new product launches fail.
FACT:
Just ONE of these could torpedo project success:
Product Name
SOURCE: Frost & Sullivan
New product introductions are critical to success, yet come with:
High cost
FACT:
McKinsey showed NO CORRELATION between these:
Amount invested in launch ≠ Success
Average frequency of launches ≠ Success
Experience alone doesn’t improve the process.
SOURCE: McKinsey
Here is where current new product launch processes fall short:
The Problem
Fragmented analytics give competing answers
Delivery of decision support is manual and costly
Predicting outcomes is slow and limited
FACT:
Among global manufacturing firms:
have no NPI* system to connect to sustaining cost systems and processes
The solution
The solution lies in prescriptive analytics. Prescriptive analytics is the final step in the Gartner Analytic Ascendency model, one step up in sophistication from predictive analytics. Here's the difference.
Predictive analytics:
Tells you what is likely to happen in the future
Example: Demand forecasting
Prescriptive analytics builds on what predictive analytics does — forecasts outcomes — and then recommends STEPS TO MAKE IT HAPPEN.
1
Replicates the behavior of a consumer market in a simulated environment
Harmonizes disparate analytics in a common model
2
Automates this process
3
Learns and improves over time.
4
How Prescriptive Analytics Changes the New Product Launch Process
How Prescriptive Analytics Changes the New Product Launch Process
Let's see how CG companies might use prescriptive analytics to plan the launch of a brand new product.
The prescriptive analytics platform creates a mathematical twin of a market – a set of virtual consumers who think like real ones.
User poses questions to consumers’ virtual brains via self-serve queries.
The platform shows what would happen and recommends changes.
Platform recommends new tweaks weekly after launch.
Cost
Packaging
Formulation
Marketing
Distribution
High risk
High failure rate
High uncertainty about competitor’s actions
Source: LevaData
still use spreadsheets as the primary data source for phase gate reviews and analysis
Predictive vs Prescriptive Analytics
Prescriptive analytics:
Tells you which action to take to gain a future advantage or mitigate a threat
Example: Recommendation systems used by Netflix or Spotify
Descriptive
Diagnostic
Predictive
Prescriptive
Source: IThappens.nu
44%
70%
*NPI = New Product Introduction
1
2
How will our competitor’s new product impact our launch?
3
4
vs.
Prescriptive analytics fills in the gaps in current new product launch approaches by unifying data, enabling collaboration, speeding and automating processes and eliminating uncertainty.
FACT:
Product launch leaders are 2.5 times more likely to have the right analytical talent and coordination across marketing silos.
Transforming New Product Introduction
Applying prescriptive analytics TRANSFORMS new product launches:
FACT:
Test multiple ideas virtually without costly in-market experiments
Consumer Goods new product launch success is most associated with:
Planning upcoming launches
Team collaboration
Incorporating market
insights
57%
45%
33%
Prescriptive analytics replaces the phased launch/learn/refine/repeat process with an optimized go-to-market plan the first time out, so CG companies know what works before they invest.
Taking the Uncertainty Out of New Product Launches
The Concentric platform delivers faster, lower cost answers to questions about your new product launch — and lots of other questions too.
Click here to learn more
Gartner’s Analytic Ascendency model ranks analytics types in order of their increasing value and difficulty:
Only those ideas with the highest likelihood of success survive evaluation
Provides near-time course corrections after launch based on in-market results
Leverage the
same process
over multiple products
and markets
Source: Bain
Cost efficient:
mucH lower risk:
Continuous
Repeatable, testable, and scalable:
Source: McKinsey
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