marketelligent capabilities & offerings for sales analytics
DESCRIPTION
TRANSCRIPT
Marketelligent Capabilities and
Offerings for Sales Analytics
Epicentre for share loss for a leading brand was the Private channel within the South region
Sales tracker for enabling a real time understanding ofthe business performance to help identify outages in atimely manner and ensure immediate course-correctionmeasures.
Below is an example of how a leading alco-bevmanufacture in India is tracking sales performance forall key states by regions/channels/outlets. The reporthelped the client identify epicenter of share loss for oneof its leading brand in one of the top salient markets.
Break-up of our brand’s overall 5% share loss in by Regions Break-up of our brand’s 3.2% share loss in South Region by Channels
SKU 1 is driving the share loss in South Region Pvt. channel
• Most of the share loss was coming from their leading SKU in the South region in the Private channel. We also highlighted the top salient outlets that needed to be addressed on priority basis.
• Based on this, the client was able to draft out the counter strategy for the priority outlets.
Benefits to the Business
Sales report & trend analysis
Evaluate growth pillars for the category (regions, sub-categories) based on historic & forecasted growth trend
Sales tracker for enabling a real time understanding ofthe business performance to help identify outages in atimely manner and ensure immediate course-correctionmeasures.
Below is an example of a leading OTC player is lookingat drafting their annual strategic plan for all their corecategories based on historic performance acrossmarkets and forecasted growth.
Sales report & trend analysis across markets
Marketelligent PRISM calculates the incremental saleslift (µ) because of various in-store promotions withoutbuilding a predictive model.
It is a real-time tool that provides continuousmonitoring and evaluation of promotion effectiveness.
Marketelligent PRISM for measuring sales lift from streaming sales data
Framework for continuous monitoring and evaluation of trade and marketing programs
Streaming sales data(Weekly or Monthly)
Sales and Marketing promotion calendar
Sales and Marketing promotion Spends
Weekly trends of promotional activity• Lift• Incremental sales
After activity reports• Total lift• Total incremental sales• ROI
Quarterly Reports• Cross Category / Channel /
Geography summaries
INPUT OUTPUT
Continuous monitoring & evaluation
Learnings fed back for future planning
µ Display
µ Feature
µ Consumer
µ TPR
Decomposed Lift (µ)
.
.
.
Streaming sales data fed weekly or monthly as is available
Promotion calendar fed into the system periodically
Marketelligent PRISM
Marketelligent PRISM
0
10
20
30
40
50
60
70
80
90
100
110
120
Wk-
1 (
'09)
Wk-
6 (
'09)
Wk-
11
('09
)
Wk-
16
('09
)
Wk-
21
('09
)
Wk-
26
('09
)
Wk-
31
('09
)
Wk-
36
('09
)
Wk-
41
('09
)
Wk-
46
('09
)
Wk-
51
('09
)
Wk-
4 (
'10)
Wk-
9 (
'10)
Wk-
14
('10
)
Wk-
19
('10
)
Wk-
24
('10
)
Wk-
29
('10
)
Wk-
34
('10
)
Wk-
39
('10
)
Wk-
44
('10
)
Wk-
49
('10
)
Wk-
2 (
'11)
Wk-
7 (
'11)
Wk-
12
('11
)
Wk-
17
('11
)
Wk-
22
('11
)
Wk-
27
('11
)
Wk-
32
('11
)
Wk-
37
('11
)
Wk-
42
('11
)
Wk-
47
('11
)
Wk-
52
('11
)
Price index vs. competition Volume share
Optimum price corridor
Pri
ce in
de
x vs
. co
mp
eti
tio
n
Vo
lum
e sh
are
Simulator for predicting volume share movement based on price changes in the market
Updated price to be entered for
each brand
Simulator predicts the updated volume share
for the brand
Price Corridor to play for maintaining your share vs. competition
Building a predictive model to understand howsensitive our brand’s share is to our brand’s pricing.Based on the predictive model, create a simulatorwhich can be used for identifying:- Optimum price band to operate in given
competitor price changes while minimizing share
low- Price thresholds for our brand beyond which there
is considerable share change- Price threshold with respect to competition
beyond which there is considerable share change
Pricing Simulator
A good demand forecast helps improve sales volume,cash flow and hence the profitability, by optimizinginventory and by minimizing out-of-stock. Besidesconsidering historical data, external factors like
promotion, seasonality, price changes, macro-economic conditions are also considered for moreaccurate forecasts.
Different statistical techniques used for sales forecasting:
Sales Forecasting
Forecasting sales for a leading alco-bev manufacturer
0.0
1.0
2.0
3.0
4.0
5.0 Actual Sales Forecasted Sales Base Line Sales
ARIMA (Autoregressive integrated moving average) Holt-Winters Forecasting
Year on Year Growth Rate model
• Inventory Control• Minimizing Out of Stock• Improving product
freshness & warehouse efficiency
• Maximizing warehouse space utilization
• Capitalizing on peak sales weeks
Benefits to the Business
The SKU Rationalization study evaluated factors suchas overall revenue contribution, growth rates andprofile of customers buying a particular segment.These metrics were compared with overall style groupand color customer preferences YoY, and a comparison
of reactivation behavior of returning customers whoseSKUs were discontinued post 2010 v/s those whoseSKUs were not. We helped the client arrive at astrategy that rationalized 30-40% of SKUs in varioussegments with no risk of a revenue impact.
• Color and style group preferences consistent YoY; safe to rationalize non performing categories• Reactivation levels for discontinued v/s continued SKU customers• Customer sub segments further analysed to exclude any SKUs from rationalization that may impact revenues
Secondary Analysis
• Segmentation of top 80% style groups, 80-98% style groups and bottom 2%
• Cap for top colors in top 80% style
Primary analysis
SKU Rationalization
Trade Promotion Optimization (TPO) uses advancedeconometric modeling techniques to helpmanufacturers refine their trade promotion strategies.The optimizer measures the impact of the varioussales promotions across channels , categories and
helps the sales team reallocate the promotionalspends to maximize the sales lift from promotions.The model below was used to optimize the tradespends of a large CPG company.
5 step framework for optimizing trade budgets
Simulator used to re-allocate trade spends across brands and activities
• The simulator was used by the sales operations team to allocate/ decide promotions across channels and categories
• The simulator could be used to predict the business impact of the various trade promotions
• The simulator could also be used to decide promotional slabs to achieve a desired volume
Benefits to the Business
Trade Promotion Optimization
We
ek
1
We
ek
2
We
ek
3
We
ek
4
We
ek
5
We
ek
6
We
ek
7
We
ek
8
We
ek
9
We
ek
10
We
ek
11
We
ek1
2
We
ek
13
We
ek
14
We
ek
15
We
ek
16
We
ek
17
We
ek
18
We
ek
19
We
ek
21
We
ek
22
We
ek
23
We
ek
24
We
ek
25
Volume decomposed by media and base volumeMagazines are gaining importance in driving incremental sales
Market mix modeling is a predictive modelingtechnique used to understand the impact of variousmarketing vehicles in driving incremental sales. It isthen used to plan future marketing budget allocationby optimizing spends while generating a higher ROI.
The case study below is for a leading manufacturer inthe anti-ageing category. The analysis indicated thatsales loss was because of internal cannibalization andcould have been arrested if the media spends onmagazine was not reduce d
Vo
lum
e, ‘0
00
un
its
Med
ia spen
d, ‘0
00
US$
0
100
200
300
400
500
600
700
800
900
0
2
4
6
8
10
12
14
16
18
20
Baseline sales Magazine incr. sales TV incr. sales Daily incr. sales
Magazine spend TV spend Dailies spend
1.58%
-0.30%
-2.47%
-1.88%
2.78%
-6.23%
-6.51%
-8.00% -6.00% -4.00% -2.00% 0.00% 2.00% 4.00%
Incremental Drivers
-1.18%
Base
Drivers
-5.33%
Cannibalization by new product
There is one point increase in Wtd. distribution
Marginal increase in avg. price
36% Decrease in spends
24% Decrease in spends
42% Increase in spends
Total volume change
Wtd. distribution
Magazine
Brand Y product launch
Base price
Daily
TV
Brand X source of sales decline 2011 vs. 2010
• Brand Y launch has cannibalized Brand X volumes• The decline could have been restricted if the magazine spends was not reduced
Market Mix Modeling
Marketelligent provides data analytics basedconsulting and outsourcing services that help youmake smarter business decisions. The firm is backedby senior professionals with experience acrossConsumer focused industries - Retail Banking,
Consumer Packaged Goods, Retail, Telecom andMedia. We offer an affordable global delivery modelleveraging the best of domain expertise and analyticcapabilities.
Reporting and Dashboard Custom Analysis Modeling
• Sales and Market Share tracking• KPI reporting• Category / Channel Trends• Web based dashboards
• Market Structure Analysis• Meta analysis / Data Integration• Global opportunity mapping
• Marketing Mix Optimization• Trade Spend Optimization• Structural Equation Modeling • Pricing analytics• Sales forecasting
Offerings in CPG Analytics :
MANAGEMENT TEAMGLOBAL EXPERIENCE.
PROVEN RESULTS.
Roy K. CherianCEORoy has over 20 years of rich experience in marketing, advertising and mediain organizations like Nestle India, United Breweries, FCB and FeedbackVentures. He holds an MBA from IIM Ahmedabad.
Anunay Gupta, PhDCOO & Head of AnalyticsAnunay has over 15 years of experience, with a significant portion focusedon Analytics in Consumer Finance. In his last assignment at Citigroup, he wasresponsible for all Decision Management functions for the US Cardsportfolio of Citigroup, covering approx $150B in assets. Anunay holds anMBA in Finance from NYU Stern School of Business.
Greg FerdinandEVP, Business DevelopmentGreg has over 20 years of experience in global marketing, strategic planning,business development and analytics at Dell, Capital One and AT&T. He hassuccessfully developed and embedded analytic-driven programs into avariety of go-to-market, customer and operational functions. Greg holds anMBA from NYU Stern School of Business
Kakul PaulBusiness Head, CPGKakul has over 8 years of experience within the CPG industry. She waspreviously part of the Analytics practice as WNS, leading analytic initiativesfor top Fortune 50 clients globally. She has extensive experience in whatdrives Consumer purchase behavior, market mix modeling, pricing &promotion analytics, etc. Kakul has an MBA from IIM Ahmedabad.
MARKETELLIGENT, INC.80 Broad Street, 5th Floor, New York, NY 10004
1.212.837.7827 (o) 1.208.439.5551 (fax) [email protected]
CONTACT www.marketelligent.com
About Marketelligent