Pelin Thorogood Presentation “Minds on Metrics” Powerful Thinkers Series

UNLOCK BIG MARKETING INSIGHTS FROM BIG DATA

PELIN THOROGOOD

Copyright 2013, Anametrix, Inc.

• New media marketing and analytics expert

• Former CMO, WebSideStory

• “20 Women to Watch” in sales lead management in both 2011 and 2012

• Executive-in-Residence for Johnson Graduate School of Management, Cornell University

PELIN THOROGOOD

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President & CEOAnametrix


“Just like oil powered the industrial revolution. . . data is going to power this business revolution.

Data is the core lubricant, the core asset fueling every single industry vertical, but also socio-economic models.”

IBM CEO TO THE COUNCIL ON FOREIGN RELATIONS

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~ VIRGINIA ROMETTY, CEO


63% of CMOs believe return on marketing investment (ROMI) will be the most important measure of their success by 2015. IBM Global Chief Marketing Officer Study

86% of marketing decision-makers surveyed believe successfully integrating multiple channels under a single integrated marketing strategy is critical to long-term success. Forrester Research

THE NEW MARKETING CHALLENGE

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http://turbotodd.wordpress.com/2011/10/11/ibm-chief-marketing-officer-2011-study-get-fast-get-social-gain-more-influence/


95% of CMO.com respondents said the single most important area that they and their colleagues need to learn is. . .

“How to operationalize the entire company for speedier gathering, interpretation and application of data-driven insights.”

Lindsay, Stone & Briggs with CMO.com


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Yet…more than 70% of CMOs feel they are underprepared to manage the explosion of data and “lack true insight.” Forbes


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http://www.forbes.com/sites/davidwilliams/2012/04/16/if-you-think-a-web-analytics-tool-is-enough-think-again/


• 48 hours of video posted on You Tube

• 571 new websites created

• 3,125 new images on Flickr

• 100,000 tweets

• 2,000,000 Google searches

• 204,000,000 emails sent

• $272,000,000 of purchases on web

THE DATA EXPLOSION

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EVERY

MINUTE

of

EVERY

DAY


THE DATA EXPLOSION

We’re at the 15% mark of what in three years is going to be a flood

of data.

It’s going to be a different world. . .

VIRGINIA ROMETTY, CEO


• Mobile devices

• Apps

• Geo-location info

• Growth of consumers pro-actively offering personal data

• Random new gadgets. . .

WHERE WILL THE DATA COME FROM?


• Goal: Understand customer engagement across silos

• Solution: Multichannel data integration

• Challenge: Multichannel data integration

THE MULTICHANNEL FOCUS

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SEM Social

WebsiteEmail


• Multichannel ROI



• Goal: Focus on top-line performance within the necessary context to create true understanding of the complex interrelationships leading up to them

• Solution: Segmentation

• Challenges: Data Granularity & timeliness


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Copyright 2013, Anametrix, Inc.13

COMPARING MULTICHANNEL CONVERSION RATES


• Goal: Capture the state of the system and identify the actual levers of change

• Solution: System reporting with multichannel integration and embedded segmentation

• Challenge: Data quality


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PAID VS. EARNED MEDIA


ANALYTICS EVOLUTION

Copyright 2013, Anametrix, Inc.17

Reduce customer churn, and increase average revenue per customer across all product categories, by analyzing purchase activity by customer segment, across all channels and campaigns

Email

Contact Center Phone,Email

Contact CenterOnline Chat

Web Analytics

SEM

Transactional Data andMarketing Spend

Affiliate Tracking

HOW ONE ETAILER DOES IT…


Proposed promotion calendar for the next month included in order to forecast next month’s expected daily net revenue

Historical revenue numbers, historical promotion calendars, and common calendar events used to fit time-series model

FORECASTING


Forecasts by ─ Purchase types:

discounted and non-discounted

─ Product categories: books, cards, prints, and others

Examine revenue trends by decomposition─ Purchase type:

discount trend varies greatly, small changes for non-discount

─ Product category: large, rapid changes for books & prints, variability for all others together is moderate

FORECASTING: PRODUCT & CUSTOMER TYPES

0 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Non-discountedDiscounted

Actual Forecast

0 4 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

CardPrintOtherBook

Actual Forecast


Project expected number of New and Repeat customers

Project expected Revenue per Customer

Multiply and add to compute total expected revenue for the next 12 months

Define target revenue for the next 12 months

Compare expected revenue and target revenue; compute target numbers for:─ New customers─ Repeat customers

FORECASTING: CUSTOMER ACQUISITION

Total Target Revenue for 12 Months

Defined by Retailer

2014Customers Revenue / Customer Revenue Revenue

New Repeat New Repeat New Repeat Total

Jan n1 r1 x1 y1

Feb n2 r2 x2 y2

Mar n3 r3 x3 y3

Apr n4 r4 x4 y4

May n5 r5 x5 y5

Jun n6 r6 x6 y6

Jul n7 r7 x7 y7

Aug n8 r8 x8 y8

Sep n9 r9 x9 y9

Oct n10 r10 x10 y10

Nov n11 r11 x11 y11

Dec n12 r12 x12 y12


Projected view shows predicted values for new/repeat customers, revenue per customer and total revenue

Expected Revenue 12 Months

1,198,050

Based on the revenue target of 1,500,000 optimization finds required number of additional new & repeat customers

2014Customers Revenue /

Customer Revenue Revenue


Jan 800 2,850 29 28 23,200 79,800 103,000

Feb 550 2,950 19 22 10,450 64,900 75,350

Mar 450 1350 18 19 8,100 25,650 33,750

Apr 650 3,700 23 20 14,950 74,000 88,950

May 550 2,100 19 21 10,450 44,100 54,550

Jun 450 2400 22 21 9,900 50,400 60,300

Jul 600 2,850 22 23 13,200 65,550 78,750

Aug 550 3,100 21 21 11,550 65,100 76,650

Sep 500 2,350 19 21 9,500 49,350 58,850

Oct 450 1900 23 23 10,350 43,700 54,050

Nov 1,600 5,400 31 30 49,600 162,000 211,600

Dec 1,550 8,100 33 31 51,150 251,100 302,250

New Expected Revenue 12 Months

1,500,093

2014Customers Revenue /

Customer Revenue Revenue


Jan 1,120 3,138 29 28 32,480 87,864 120,344

Feb 770 3,370 19 22 14,630 74,140 88,770

Mar 630 1842 18 19 11,340 34,998 46,338

Apr 910 4,270 23 20 20,930 85,400 106,330

May 770 2,725 19 21 14,630 57,225 71,855

Jun 630 3061 22 21 13,860 64,281 78,141

Jul 840 3,552 22 23 18,480 81,696 100,176

Aug 770 3,824 21 21 16,170 80,304 96,474

Sep 700 3,102 19 21 13,300 65,142 78,442

Oct 630 2697 23 23 14,490 62,031 76,521

Nov 2,240 6,517 31 30 69,440 195,510 264,950

Dec 2,170 9,682 33 31 71,610 300,142 371,752

FORECASTING: REVENUE STREAMS


Total Revenue

$XMaterial Margin

$Y

Refund Rate

# Orders

AOV

# Customers

New

Repeat

New

Repeat

Customer Management Category Management

CoGS

Pricing

Product Mix

New

Repeat

CONVERGE INTO A “LEVER” MODEL


VISUALIZE

OPTIMIZE

PREDICT

OWNED MEDIA

REFERENCE

CORPORATE

DISCOVER

SOCIAL MEDIA

PAID MEDIA

REAL-TIME MARKETING ANALYTICS


Pelin ThorogoodCEO

[email protected]

THANK YOU

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Technology

Pelin Thorogood Presentation “Minds on Metrics” Powerful Thinkers Series