Marketelligent Capabilities & Offerings for Sales Analytics

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  • 1. Marketelligent Capabilities and Offerings for Sales Analytics

2. Epicentre for share loss for a leading brand was the Private channel within the South region Sales tracker for enabling a real time understanding of the business performance to help identify outages in a timely manner and ensure immediate course-correction measures. Below is an example of how a leading alco-bev manufacture in India is tracking sales performance for all key states by regions/channels/outlets. The report helped the client identify epicenter of share loss for one of its leading brand in one of the top salient markets. Break-up of our brands overall 5% share loss in by Regions Break-up of our brands 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 3. Evaluate growth pillars for the category (regions, sub-categories) based on historic & forecasted growth trend Sales tracker for enabling a real time understanding of the business performance to help identify outages in a timely manner and ensure immediate course-correction measures. Below is an example of a leading OTC player is looking at drafting their annual strategic plan for all their core categories based on historic performance across markets and forecasted growth. Sales report & trend analysis across markets 4. Marketelligent PRISM calculates the incremental sales lift () because of various in-store promotions without building a predictive model. It is a real-time tool that provides continuous monitoring 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 Display Feature Consumer TPR Decomposed Lift () . . . Streaming sales data fed weekly or monthly as is available Promotion calendar fed into the system periodically Marketelligent PRISM Marketelligent PRISM 5. 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 Priceindexvs.competition Volumeshare 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 how sensitive our brands share is to our brands pricing. Based on the predictive model, create a simulator which 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 6. A good demand forecast helps improve sales volume, cash flow and hence the profitability, by optimizing inventory and by minimizing out-of-stock. Besides considering historical data, external factors like promotion, seasonality, price changes, macro- economic conditions are also considered for more accurate 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 7. The SKU Rationalization study evaluated factors such as overall revenue contribution, growth rates and profile of customers buying a particular segment. These metrics were compared with overall style group and color customer preferences YoY, and a comparison of reactivation behavior of returning customers whose SKUs were discontinued post 2010 v/s those whose SKUs were not. We helped the client arrive at a strategy that rationalized 30-40% of SKUs in various segments 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 8. Trade Promotion Optimization (TPO) uses advanced econometric modeling techniques to help manufacturers refine their trade promotion strategies. The optimizer measures the impact of the various sales promotions across channels , categories and helps the sales team reallocate the promotional spends to maximize the sales lift from promotions. The model below was used to optimize the trade spends 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 9. Week1 Week2 Week3 Week4 Week5 Week6 Week7 Week8 Week9 Week10 Week11 Week12 Week13 Week14 Week15 Week16 Week17 Week18 Week19 Week21 Week22 Week23 Week24 Week25 Volume decomposed by media and base volume Magazines are gaining importance in driving incremental sales Market mix modeling is a predictive modeling technique used to understand the impact of various marketing vehicles in driving incremental sales. It is then used to plan future marketing budget allocation by optimizing spends while generating a higher ROI. The case study below is for a leading manufacturer in the anti-ageing category. The analysis indicated that sales loss was because of internal cannibalization and could have been arrested if the media spends on magazine was not reduce d Volume,000units Mediaspend,000US$ 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 10. Marketelligent provides data analytics based consulting and outsourcing services that help you make smarter business decisions. The firm is backed by senior professionals with experience across Consumer focused industries - Retail Banking, Consumer Packaged Goods, Retail, Telecom and Media. We offer an affordable global delivery model leveraging the best of domain expertise and analytic capabilities. 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 TEAM GLOBAL EXPERIENCE. PROVEN RESULTS. Roy K. Cherian CEO Roy has over 20 years of rich experience in marketing, advertising and media in organizations like Nestle India, United Breweries, FCB and Feedback Ventures. He holds an MBA from IIM Ahmedabad. Anunay Gupta, PhD COO & Head of Analytics Anunay has over 15 years of experience, with a significant portion focused on Analytics in Consumer Finance. In his last assignment at Citigroup, he was responsible for all Decision Management functions for the US Cards portfolio of Citigroup, covering approx $150B in assets. Anunay holds an MBA in Finance from NYU Stern School of Business. Greg Ferdinand EVP, Business Development Greg has over 20 years of experience in global marketing, strategic planning, business development and analytics at Dell, Capital One and AT&T. He has successfully developed and embedded analytic-driven programs into a variety of go-to-market, customer and operational functions. Greg holds an MBA from NYU Stern School of