perfect price - silicon valley pricing webinar

alex@perfectprice.io chris@svpricing.com

Silicon Valley Pricing + Perfect Price

Copyright 2017 Perfect Price, Inc. All rights reserved.

Introductions

Alexander Shartsis CEO, Founder Perfect Price

Chris Herbert Founder

Silicon Valley Pricing

PERFECTPRICE

Agenda

Nobody is selling anything here

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Logistics

Ending promptly at 10:55 Ask questions in GoToWebinar chat How to ask more questions 1:1 afterwards

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Feedback

Survey at the end via email Topics for future webinars Improvements to webinar format Takes only 2 minutes!

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Case Studies: Pricing Today

Pricing yesterday

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Pricing today

1993: Charge against GM earnings

$744,000,000

1995: Sold by General Motors

$1,200,000,000

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Enterprise locations in NYC (just the first 20 of 60+)

Data from everywhere

Dynamic pricing everywhere

Just the USA price

How can humans keep up?

“There is a gap between where we are and where we should be Every minute we are behind, we lose money.”

- CEO, Fortune 500 Company

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Perfect Price solution

Behaviors (JS, API)

Hadoop Cluster (AWS)

Raw Data (API)

Raw Data Aggregate Data Intelligence Insight Decisions Action

Demand Model

Using Artificial Intelligence

+ Machine Learning Price Testing

Strategic Decisions

Cost/Profit Model

Outside Data

Strategic ChangesVisualizations

Dynamic Pricing

Customer Perfect Price

SAP, Oracle, etc.

Forecast

Predictive Analytics

Dynamic Pricing

3 steps to know what you’re losing

Clean up data Model demand Quantify losses

Collect required data from across your organization

Cluster and segment Build demand model as if you were repricing

Compare new prices to current Quantify losses at all levels

Step 1: Clean up data

Myth: We have all the data

Fact: Right data doesn’t exist

Lots of data, none of it is in a usable format

Missing data in a $300mm retail company

64%

36%

Source: Baby Center 2015 US Mobile Moms Report

0%% of moms who

check prices on mobilemobile app data

captured by this company

3mmMonthly App Users

Data collection timelines

Simple

Complex

50 days 100 days 150 days 200 days

Perfect Price Experience

Return on effort?

How long should data cleanup take?

It depends on number of channels and availability of data Most companies can get a sample together in <2 weeks Raw data is usually ready in ~4 weeks, sometimes less Processed data can be blocked by engineering roadmap

Step 2: Model demand

Myth: Fact: Not every hypothesis turns out to be right The costly part is knowing the approach

Excel + 1 analyst + 1 weekend = The Answer

The way Perfect Price models demand

Global data(Weather, &c)

Industry data(Aggregator)

Your data(Views, prices,

conversions, &c)

Machine learning engine

Pre-processing Clustering Demand

predictionRevenue

mgmt

you.com

Your product$75.00

Buy now

Explaining machine learning to laypeople

Scrutinize the model

weekday

weekend

Scrutinize the model

ideal prices (vertical lines)

dots = raw data

Quantify losses

You’ll know you’re ready when…

You know exactly how much you’re losing You know exactly where you’re losing it You feel confident in making a change Budget is thrown at you to fix it ASAP

Comprehensive ≠ all knowing

ClusteringProduct, customer, and location Enables accuracy with limited data

Relatively static

Long modelPrices for every product, time combination Powers dynamic pricing, forecasts

Burst modelRerun every ~5 min Captures the unpredictable Enables long model to be simple Captures max value

What Clean Data Looks Like

The Same

Product

Totals/Avg.

Combining internal and external data

Powerful search

Quick filtering

At-a-glance competitive environment

Focus on the important

Make the important accessible

Share data with stakeholders

Testing and Experiments

The Perfect Price testing cycle

Get results Monitor

Analyze

Plan Create

Run

“We just want to test it”

Difference in differences made simple Output where stakeholders or end users need it Enabling industry professionals like Optimizely in A/B testing

Easily track, access and collaborate

Centralized control of price testing

Google centralizes all testing in 1 team, to prevent bad data Perfect Price is 1 test dashboard for the entire company Be warned whenever trying to add a product that is in a test

Key study results, easy to read and share

Enabling the Business

The future: predictive analytics

Summary & Conclusions

3 step process for starting out Powerful, dedicated system for pricing only Clean, centralized data for fast decision making and experiments Simple, powerful testing framework enables more testing Dual model approach for real time pricing applications

alex@perfectprice.io chris@svpricing.com

Q&A