leveraging insights and advanced analytics to succeed with ... · best practices and insights...
TRANSCRIPT
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