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Forecasting Product Performance Like A Meteorologist ProductCamp Boston 2012 #pcampBoston A.Chakravarty 6/9/2012
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Forecasting Product Performance060912

Jun 09, 2015

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Page 1: Forecasting Product Performance060912

Forecasting Product PerformanceLike A Meteorologist

ProductCamp Boston 2012

#pcampBoston

A.Chakravarty 6/9/2012

Page 2: Forecasting Product Performance060912

Problems are just as frustrating…

Jim Kosek – Funny Weather VideoThree Key Forecasts related to Product Mgt:• Domino Effect – Power outages, Traffic backup, Black Ice• Redundancy Effect - Snow Drifts, Windy• Anticipated Failure – Heavy Snowfall

http://www.youtube.com/watch?v=Awwg0SO4Nck

PPT Notes provide addt’l information.

Page 3: Forecasting Product Performance060912

Workshop Overview• Forecasting – why is it important

• Forecasting Impacts Decisions

• Limitations

• Metrics & Data

• Methods & Techniques

• Leveraging forecasts workshop

– Key Product Metrics

– Internal partnering for product success

– Method and Technique

Review – 20 min.

Case – 25 min.

DISCLAIMER: This session will not discuss how you should forecast, but you should walk away having a better understanding of what to think about and the complexity of good forecasting for new products.

Page 4: Forecasting Product Performance060912

New Product Forecasting• Error Rates are HIGH!

• So Why Forecast?

1. New Product Development and Forecasting Problems. R. Simon, Journal of Business Forecasting 2009-2010.

Error rate in Guessing a Coin Toss (50%)

Page 5: Forecasting Product Performance060912

Forecasting – Why?New Product Development (NPD)

On average ~20% of company sales are New Products1

Newer products typically command higher profit margins1

In the US, 50% of revenues and 40% of profits are from New Products1

~26% of revenue from Engineering companies are from products < 3 years old1

~70% of today’s manufactured goods will be obsolete in 6 years1

In Fashion and High Tech that’s closer to 2 years1

35%-45% of New Products fail immediately2

According to Herb Baum, Former CEO of Campbell Soup Company, in consumer business, “80% of all new products fail, only 4% reach the 20 MM level and 0.5% break the 100MM mark.”3

1. New Product Forecasting 2006. M. Singh. MIT2. Doing it Right: Winning with New Products. R. G. Cooper. Product Management Institute 2006.3. Journal of Business Forecasting C. L. Jain, Editor. Winter 2009-2010.

Reduce Market Costs of Failed Products

Drive Sales & Profit

Page 6: Forecasting Product Performance060912

Forecasting Impacts Decisions

Product Launch

Sales Support

See Notes

Cover/Clothing

Time/Resources

Emergency Planning

Event Planning

Weather Forecastslet us decide:

Product Forecasts let us decide:

Page 7: Forecasting Product Performance060912

Forecasting NPD Limitations

Short term Accuracy

Significant data collection

Many data points

Change Impact

New patterns emerge

Long term Impacts

Weather ForecastsAre limited:

Product ForecastsAre limited:

High failure rate

Almost no accuracy

Data collection is sparse, erratic, and not necessarily sales drivers

Incomplete/Unknown data points

Product Change Impact

Environment Change Impact, game theory, competitive pressures

Long term Impacts are strategic only

Forecasting is difficult!

Page 8: Forecasting Product Performance060912

Metrics for Product ManagersQ

uanti

tati

ve

Metr

ics • Empirical

Data• e.g. Sales• Unique

Visitors• Subjective

Data• Market

Research• Surveys

Qualit

ati

ve

Metr

ics • Subjective Data

• Delphi• Sales Force

Composite• Focus Groups

• Pattern Data• Pattern

Recognition• Look-Alikes• Scenario Analysis

Page 9: Forecasting Product Performance060912

Metrics for Business

Product Metrics

Decrease Expenses

Increase Revenues

Business Metrics

Product metrics must impact a business metricProduct metrics need to be translated into a P&L statement

Example: Unique Visitors X Est. Avg Revenue per Visitor = Revenue

Page 10: Forecasting Product Performance060912

Persistence Today = Tommorrow

Trends – Nowcasting Extrapolation of current variables

Climatology Historical Extrapolation

Analog Patterns Looks like June 6th, 1874 so…, Scenarios, Looks-Like

Numerical Weather Predictions Computationally heavy, multi-variable predictive algorithms – many

variations

Basic Meteorological ForecastingKey Weather Forecasting Techniques:

Page 11: Forecasting Product Performance060912

Methods & TechniquesThree broad sets capture dozens of methods

1. Managing the Mysterious: How to Forecast New Products. Logistics Summit & Expo, Mexico 2010. Kenneth Kahn

2. New Product Forecasting 2006. M. Singh. MIT

57%44%

39%

14%

30%

19%15%

MktResearch

ExecutiveJury

Sales ForceComposite

ScenarioAnalysis

Looks-LikeAnalysis

Trend LineAnalysis

MovingAverage

Subjective QuantitativeMktResearch

Top methods used are shown by percentage

Page 12: Forecasting Product Performance060912

Some things to keep in mind to forecast

• Timeframe

• Assumptions

• Units of Measure – atomic

• Project Timeline and Milestones – including failure to meet impacts

• Ongoing, Cost Structure Estimations (for P&L Pro-Forma development)

• Risks & Mitigation

• Estimation technique for revenue or cost savings

• Revenue Derivation algorithm

• Validation & Assumptions Check

Page 13: Forecasting Product Performance060912

Workshop Preparation…

• TASK 1: Break out into groups of 2-5 people each and introduce yourselves.

Page 14: Forecasting Product Performance060912

THE FORECAST CASEWORKSHOPWeatherCommerce.com is a clicks-only online business that sells Thermometers, Barometers, Hygrometers, and Weather stations. The site has recently launched a new ecommerce sales engine to sell a new line of product – the Baltimore Weather Gauge. We need to forecast product performance post launch for 12 months.

• TASK 2: Each team take 5 minutes and create a list of 3-5 Key Product Metrics to measure new launch success for the Baltimore Weather Gauge

EXAMPLES:

• Monthly Sales (Units Sold) for 12 Months

• Monthly Sales ($) for 12 months

Page 15: Forecasting Product Performance060912

THE FORECAST WORKSHOP

• TASK 3: Each team takes 5 minutes to list 3-5 Key Resources and Key Deliverables that they would reach out to develop their forecast.

EXAMPLES:

• Marketing - Customer Market Survey, Realistic Price Points, Existing Customer Interest

• Ecommerce Sales Operations – Site Traffic and Conversion Rates for similar products.

Page 16: Forecasting Product Performance060912

THE FORECAST WORKSHOP

• TASK 4: Each team takes 5 minutes to list one method they would rely on to forecast – and a quick justification of why they would choose it.

• Note: There are no right or wrong answers. Many companies use up to 3 methods at the same time for validation and cross-check.

EXAMPLE:

• Online Market Surveys –With many existing customers through an ecommerce platform, we can quickly gauge product sentiment.

YOUR ORGANIZATION

• TASK 5: What techniques would you use for your company’s products?

Page 17: Forecasting Product Performance060912

THANK YOU!

Ananda S. Chakravarty

@achakravartyhttp://www.linkedin.com/in/anandachakravarty