Leveraging Insights and Advanced Analytics to Succeed with Amazon and Other eCommerce

Retailers

3

E-COMMERCE: AMAZON PROJECTED REVENUE

Statista, 2018

Projected total revenue of Amazon from 2002 to 2022 (in billion U.S. dollars)

4

E-COMMERCE: AMAZON PROJECTED REVENUE

Kantar Retail, 2017

5

E-COMMERCE: CONSIDERATIONS TO BE AN EFFECTIVE TRADING PARTNER

Source: J&J Omni-Channel research Aug/Sept 2016; Powered by Ipsos

6

E-COMMERCE: IMPERATIVES

What Go-To-Market Strategies Will Help Me to “WIN BIG” and “GO FASTER” in e-Commerce?

How BIG Will e-Commerce Be for My Business?

What Capabilities Do I Need to Effectively Manage and Execute my e-Commerce Business?

What is The Right Size For My Digital Media, Retail Media and Search Spending?

What ROI, ROAS and LIFT Do My Digital Media, Retail Media & Paid Search Give?

7

Historically …

CPG has driven growth though momentum and strong relationship marketing needed for the Brick and

Mortal environment

And …

Amazon uniquely dominates eCommerce with its data and

analytics

Now …

eCommerce represents a tremendous shift to leveraging

analytical, data-driving marketing

How do you fight and win against Amazon’s robotic algorithms?

• Find new and unique ways to gather and interpret data

• Become fluent in developing insights and more responsible to the pace of eCommerce

• Leverage information to drive and influence across internal teams and external parties

8

WHAT WAS YOUR FIELD SALES COUNTERPART DRIVING WHEN …

9

We can’t beat Amazon at its game

But we have a shot if we play to our strengths

10

SCALE IT UP!

70 Billion

10 Billion

Hourly

Weekly

SPEED IT

UP

DRILL IT

DOWN

Person

HH

Case Study 1: Deploy Digital Shelf

Insight to Drive Search Execution

12

E-COMMERCE: CATEGORY MANAGEMENT AND INSIGHTS HAVE SHIFTED

No aisle, shelf, category or planogram

No barrier to new item entry

SEM – Paid Search

Shoppers seek by keyword, not category

Shoppers seek by keyword, not category

Retailer recommendations - Amazon’s Choice / Best Seller

Shopper Ratings and Reviews

National & DC level inventory management, not store

Alexa and voice

e-Commerce Category Management Digital Shelf

13

E-COMMERCE: CATEGORY MANAGEMENT AND INSIGHTS HAVE SHIFTED

Real time response to Reviews and Q&A

What about the Product Detail Page (PDP)?

High frequency PDP optimization

1-Click to Buy, Prime, Same Day

Sales Platform (Price, Promotions, Subscribe and Save)

SEO: Keywords and Search Terms

Marketing Platform (FMOT, Awareness, Education, Equity)

Digital not physical

3P Sellers

LBB

14

BUSINESS QUESTION: HOW IS MY DIGITAL SHELF HEALTH

15

CAPABILITIES REQUIRED

1

5

Skills and knowledge Technology and Systems

• What paid search campaigns are running?• When are the start/end dates?• Gain alignment to the metrics to be used

• Expert on Paid and Organic Search

Data required• Search: Ranking by key work, competitive products• Competitive retailer: data on key words• Conversion metrics

Data integration – ideally a dedicated technology platform

• Data integration• Advanced analytics

16

DIGITAL SHELF HEALTH … BRAND LEVEL

1

6

17

PAID SEARCH CAMPAIGN PERFORMANCE

1

7

18

DIGITAL SHELF HEALTH … DRILL DOWN TO PAID SEARCH

1

8

Clicks

Conversions

19

Organic SearchRank

ORGANIC SEARCH RANK

20

ORGANIC SEARCH FOR KEY ASIN AND KEY TERM IS DOWN

151 47 56 18 137 18

21

EXAMINE THE DATA

2

1

40.6%

73.1%

YAG Current

Chg. Glance Views

+33 ppts

127.7%

183.4%

YAG Current

Chg. Conversion vs LY

+56 ppts

7,637

11,735

YAG Current

Page Views (000)

+54 %

2,823

5,034

YAG Current

Sales (000)

+78 %

22

CAPABILITIES REQUIRED

22

Skills and knowledge Technology and Systems

• Which pages were optimized?• When did the new page go live?

• Be intimate with what the PDP is and does• Expert on Paid and Organic Search• Know terms like “glance views”

Data requiredPDP compliance metrics: Images, key words, hierarchy, A+Search: Ranking by key work, competitive productsPerformance: Glance views, LBB, % Rep OOS, Page Views, Conversion

Data integration – ideally a dedicated technology platform

• Data integration• Advanced analytics

Case Study 2: Evaluate Campaigns

24

DETAILED CAUSAL CHARTS

Sales Drivers by Week:

• Base• Incremental• AMG• AMS• AVG Selling Price• Promotion Days• Digital Coupons• Google Paid Search• Seasonality• Youtube

25

MACHINE LEARNING ANALYZES HUNDREDS OF SCENARIOS AND

VARIABLESTested Variables Legend

Total_RSU Pantry_RSU Base_RSU Fresh_RSUTrend_Base_u

nits

Total_Order_

Units

Pantry_Order

_Units

Base_Order_U

nits

Fresh_Order_

Units

Seasonality_I

ndex

Weighted_AS

PPantry_ASP Base_ASP Fresh_ASP Volume/Units

Pantry_Shippa

ble_Units

Base_Shippab

le_Units

Fresh_Shippa

ble_UnitsTotal_Units Fresh_units Pantry_units Base_units

AMG_BtS_DP

V_Camp

AMG_BtS_Uni

tsSold

DML_Bann_Im

press

DML_Bann_Cli

cks

Halo_Bann_Im

press

Halo_Bann_Cli

cks

Halo_Bann_Cli

cksBase Factors

Halo_NIVid_I

mpress

Halo_NIVid_Cl

icks

FB_Total_Enga

ge

FB_LinkPage_

Enage

FB_PhotoPage

_Engage

FB_VideoPage

_Engage

Bing_Branded

_Impress

DotComDispla

y_US_Impress

DML_Video_I

mpress

DML_Video_C

licks

DML_NIVideo

_ImpressPrint_TRP

DotComVid_U

S_Impress

Bing_Branded

_Clicks

Offline

Marketing

Efforts

DML_NIVideo

_Clicks

Bing_Unbrand

ed_Impress

Bing_Unbrand

ed_Clicks

Bing_Unbrand

ed_Cost

Google_Brand

ed_Impress

Google_Brand

ed_Clicks

Google_Brand

ed_DOLS

Google_Unbra

nded_Impress

Google_Unbra

nded_Clicks

Google_Unbra

nded_Cost

Google_Hisp_I

mpress

Google_Hisp_

Clicks

DML_IBVid_Cl

icks

HookLogic_Bo

dy_Impress

Online

Marketing

Efforts

(OffSite)

HookLogic_Bo

dy_Clicks

HookLogic_Bo

dy_DOLS

HookLogic_Fra

nchise_Impre

ss

HookLogic_Fra

nchise_Clicks

DML_IBVid_I

mpress

GoogBing_Un

branded_Impr

ess

GoogBing_Un

branded_Click

s

AMG_Winter_

Impress_Cam

p

GoogBing_Bra

nded_Impress

GoogBing_Bra

nded_Clicks

GoogBing_Bra

nded_DOLS

AMG_Winter_

Click_CampTV_DML_TRP

TV_DML_Impr

ess

Online

Marketing

Efforts

(OnSite)

AMG_DML_Im

press_Camp

AMG_DML_Cli

ck_Camp

AMG_DML_CT

R_Camp

AMG_DML_DP

V_Camp

AMG_DML_Un

itsSold_Camp

AMG_BtS_Imp

ress_Camp

AMG_BtS_Clic

k_Camp

AMG_BtS_CTR

_Camp

AMG_Winter_

CTR_Camp

AMG_Winter_

DPV_Camp

Total_AMG_C

amp_CTR

TV_DML_TRP_

AdStock

Total_AMG_C

amp_Clicks

Total_AMG_C

amp_Impress

Total_Sales Base_sales Pantry_sales Fresh_salesPantry_Avg_O

rder_Prc

Base_Avg_Ord

er_Prc

Fresh_Avg_Or

der_Prc

Pantry_SumC

oupons

Base_SumCou

pons

Pantry_SumPr

omo

J3KMediaRadi

o_DOLS

DotComDispla

y_US_DOLS

Bing_Branded

_Cost

DotComVid_U

S_DOLS

GoogBing_Un

branded_DOL

S

TV_DML_DOLSAMG_DML_D

OLS_Camp

AMG_BtS_DOL

S_Camp

AMG_BtS_DOL

S_Camp

AMG_Winter_

DOLS_Camp

Fresh_SumPro

mo

Fresh_SumCo

upons

Base_SumPro

motion

AMG_Winter_

UnitsSold_Ca

mp

Google_Hisp_

DOLS

HookLogic_Fra

nchise_DOLS

Fresh_PageVi

ew_Rank

Pantry_page_

views

Total_Page_Vi

ews

Fresh_page_vi

ews

Fresh_unique

_visitors

Base_page_vi

ews

Base_unique_

visitors

Pantry_uniqu

e_visitors

Pantry_Avg_R

ating

Total_Unique

_Visitors

Total_PgView

s

Base_page_vi

ews

Total_Unique

_Vis

Base_unique_

visitors

Base_CL_Sear

ch

Base_Avg_Per

Pg

Total_CL_Sear

ch

Pantry_CL_Se

arch

Pantry_Avg_P

erPg

Pantry_AvgRa

nk

Pantry_AvgSc

ore

Pantry_AvgSc

ore_Basic

Pantry_AvgPri

ority_score

Fresh_Avg_Ra

ting

Base_Avg_Rat

ing

Fresh_AvgRan

k

Total_PgView

_Rank

Pantry_PgVie

w_Rank

Base_PgView

_Rank

Fresh_AvgPrio

rity_score

Base_AvgRankBase_AvgScor

e

Base_AvgScor

e_Basic

Base_AvgPrior

ity_score

Fresh_CL_Sear

ch

Fresh_Avg_Pe

rPg

Fresh_AvgSco

re

Fresh_AvgSco

re_BasicPantry_lbb Base_LBB Fresh_lbb

Amazon_Prim

e_Days

Wghtd_Avg_O

rder_PrcHoliday

26

HBC CASE STUDY … PREDICTIVE

MODELINGP

OS

Un

its

Weeks

R-Squared = 0.94

27

VOLUME DRIVERS

28

MODEL DECOMPOSITION

29

PRICE ARCHITECTURE HELPS MANAGE MAP PRICING …

30

Investment effectiveness and ROI … the solution

Optimize J&J

expenditures across retail

media, media and search

vehicles to maximize

sales

31

BRAND LIFT BY VEHICLE …

32

BRAND ROAS BY VEHICLE …

ROAS

33

SIMULATED SALES, 10% INCREASE IN INVESTMENT

34

Demand Planning … the solution

STEP CHANGE IN DEMAND PLANNING

EFFECTIVENESS: REDUCE OOS,

REDUCE ALLOCATIONS AND IMPROVE

CUSTOMER UFR

35

UNIT FORECASTS

36

DOLLAR FORECASTS

37

WATERFALL CHARTS EXPLAIN THE SALES BUILD OVER TIME

38

E-COMMERCE: CATEGORY MANAGEMENT AND INSIGHTS HAS SHIFTED

Closed Loop Business Planning

Cycle

Consumption Forecast &

Due To Models

Retail Media and

Campaign Plan

Development

Retail Media and

Campaign Execution

Post Campaign

Performance Evaluation

Best Practices and

Insights Discovery

Consumption Forecast Business Applications:

• Sales• Demand Planning• Finance• Strategy

Post Campaign Drivers Performance & Evaluation:

• Investment ROI• Tactic Effectiveness• Benchmarking

Best Practices and Insights Discovery:

• Optimize Media and Search strategies

• Modify Business Plans

3

4 1

“What If” Planning Simulator• Forecast Sales and Media

Drivers• Assess Individual and

Combined Outcomes

2

39

E-COMMERCE: CATEGORY MANAGEMENT AND INSIGHTS HAS SHIFTED

B&M e-Commerce

Search

Robust syndicated data sources available

Multiple steps to transact

Limited marketing communication

High barriers to new item/brand entry

Space constrained

Slow to change in store experience Fast paced, dynamic changes in near real time

Many more and new analytics needed

Fast paced, dynamic changes in near real time

Low barrier to competitor entry

Substantial marketing opportunity

Multiple campaign vehicles

Easy 0-1 Steps to transact

Multi-sourced data required