PwC’s Analytics Solutions For The FMCG Sector

www.pwc.inPwC’s analyticssolutions for theFMCG sectorStrictly privateand confidential

OverviewIndian consumers are changing at a faster pace than expected. Today,FMCG manufacturers rely on consumers ‘pulling’ products through thesupply chain; thus, they require a better understanding of consumerbehaviour and choices. Consumers are well-informed about productinformation—in particular, promotions and price comparisons viathe Internet—which makes predicting behaviour very complex. Thisis where business analytics plays a very important role, as it allowsorganisations to derive predictive insights to enable competitive factbased decisions. Armed with deeper insights into consumer behaviour,FMCG manufacturers will be able to direct R&D investment, improvethe effectiveness of marketing and maximise supply chain efficiencies.Let’s look at a few scenarios where analytics plays a crucial role insolving the challenges encountered by FMCG companies.2 PwC

Business insights usingFMCG analyticsPricing insightsKnow what price point will drawcustomers and increase profitabilityCustomer insightsSell what customers want at theirpreferred point of purchaseSales insightsAdopt new ways to sell basedon how customers are buyingMarketing insightsDeliver personalised promotionsand optimised marketing spendPwCanalyticsSupply chain insightsOptimise inventory levelswith minimal stock-outsBusiness successDrive maximum revenue andprofitability through all channelsPwC’s analytics solutions for the FMCG sector 3

1: Trade promotion optimisationBusiness challengesAnalytics solution and results Identify the right price and discount point thatmaximises sales lift and return on investment (ROI) Optimise promotions to improve sales performanceof newly launched productsBuild a linear regression model to understand the impactof demand drivers on historical sales volume andcalculate base volume Calculate total ‘true’ cost of promotions based on theindividual components Calculate ROI for historical promo eventsThe results: Improved ROI on trade promotions Gained insights into the profitability of promotionsacross stores, regions and products Enhanced ability to benchmark scheme performanceSample snapshots and reports1Baseline volume calculation60Actual volume4020Base volume02SKUPromotion ROI calculation5ROI432104 PwCStoresRegionsProducts

2: Marketing mix modellingBusiness challengesAnalytics solution and results Calculate ROI of advertisement spend acrossvarious channels like television, print and web Develop an analytic model showing impact of variousmarketing campaigns on sales Understand consumer behaviour with regard toexposure to advertising Evaluate media effectiveness, ROI and simulatewhat‑if scenariosThe results: Media channel effectiveness Prioritised advertisement and promotion spend in favourof channels that provide better ROI Reduced the overall spend on advertising and promotionSample snapshots and reports1Identify channels, campaigns, causal factors that impact salesHistorical dataMarketspend dataMacroeconomicfactorsMarket mix model Parse out seasonality Create baseline Analyse drivers Identify lift from activityAssumptionsand businessconstraintsOutput2Develop model-driven scenario analysis to analyse their impact on salesTVPrintPaidsearchDisplayonlineStoredemoMedia spendingPwC’s analytics solutions for the FMCG sector 5

3: Vendor selection modelBusiness challengesAnalytics solution and results Find a vendor evaluation method to facilitate anobjective, unbiased selection process Analytic hierarchy process (AHP) is a prominent approachin solving multi-criterion decision-making problems. Identify the key metrics of vendor performance thatcan help during negotiation with vendors onspecific points The method allows the incorporation of judgements onintangible qualitative criteria alongside tangiblequantitative criteria. Find a robust framework that can measurevendors’ performanceThe results: A tool for vendor negotiation Helped avoid conflicts through collaborative decision making Generated a repeatable process that saves time Measured vendor performance among peers and across timeSample snapshots and reports1Defining and ordering criteriaVendor selectionCapabilityPackagingPricingQualitySupply desiredquantityManagement andorganisationVariety of SEU’sbase packagingMargin/costProductionquantityAdherence tostandards(ingredients-wise,formula, et al)AutomationVariety of deliverypackagingMinimum orderquantityHygieneDelivery networkDeliverylead timeCredit periodEvaluating vendors and selecting the best y6 iaLocal Combined Ranksweights ery network28.5%7.5%8Management very lead time30.2%6.6%11SKU packaging49.3%9.1%3Delivery packaging50.7%9.4%2Margin cost36.7%6.5%12Minimum orderquantity38.9%6.9%Credit period24.4%Product qualityHygieneWeights alloted to criteria30257.5%% 58.7%6.7%9.1%06.5%Capability Process Packaging uct qualityDelivery packaging9Credit periodSKU packagingManagement andorganisation4.3%13Deliver lead time50.0%7.9%6Minimum orderquantityStandards50.0%7.9%6QuantityDelivery network

4: Sales forecastingBusiness challengesAnalytics solution and results Identify a scientific methodology to accuratelypredict future sales volumes Develop a robust sales forecasting model throughaggregation and statistical analysis of data Improve target setting by identifying currentmarket conditions and their impact oncustomer sales Develop a structured what-if analysis mechanism to createmultiple scenariosThe results: Improved planning Predicted sales volumes based on critical demand drivers Improved decision making through structuredscenario analysisSample snapshots and reports1Identify the key events, causal factors that impact salesDemand metricEventsTrendsCausal variablesSales promo eventProduct pricesSeasonalityFestival seasonsAdvert expenseRandomnessCompetitor activityIncome, GDP, IIPForecasted salesDevelop model-driven scenario analysis to analyse their impact on future 10Sep100Jul102000Aug102PwC’s analytics solutions for the FMCG sector 7

5: Pricing recommendationBusiness challengesAnalytics solution and results Understand the impact on sales for a given changein price (pricing elasticity) across products andunderstand the impact on the contribution margin Build a pricing model to enable an effective pricingstructure for various product categories Ensure a consistent scientific methodology isbeing applied to pricing decisions acrosscategories/outletsOptimise pricing to improve margins andbottom‑line profitabilityThe results: Improved planning Data-driven pricing suggestions for greater sales andincremental contribution marginSample snapshots and reports1Establishment of price elasticity across the product portfolioPrice elasticity by categoryPrice elasticityHigh price elasticity forproducts usually treated asa discretionary purchaseLow-price elasticity driven by‘needs’ purchase; provides‘bundling’ opportunity whencombined with impulse purchasesABCDEFGProductsSuggest optimal pricing to drive immediate contribution margin100%300250Gross margin (%)80%20060%15040%10020%0%8 PwC50ABCDEFGHIJKLMNOPQRST0Gross margin ( in USD)2

6: Sentiment analysisBusiness challengesAnalytics solution and results Web crawlers to capture unstructured data across varioussocial media platforms A text mining model for parsing conversations into positive,neutral and negative buckets Capture customer feedback across various socialmedia platforms and derive meaningfulconclusions, which could be sent to relevantfunctions within the organisationImprove brand strength and engage with customersin a meaningful wayThe results: Instant customer feedback Sentiment analysis can help to track consumer behaviourin real time across channels, monitor online brand healthand also uncover the levers that can have a significantbusiness impact.Sample snapshots and reports1Capture customer conversation on social media platformsCompanyCompetitorsMarket2Develop sentiment analysis for business insightsPwC’s analytics solutions for the FMCG sector 9

7: Inventory optimisationBusiness challengesAnalytics solution and results Align inventory planning, forecasting andexecution capabilities across the organisation Employ statistical modelling techniques to performinventory stock level vs lost sales scenario analysis Obtain insights from vast volumes of data at theSKU location on a weekly/daily level to improveinventory forecasting Develop robust demand forecasts through statisticalanalysis of data across outletsThe results: Improved inventory management Suggested order quantity recommendations to reduceout-of-stock frequency Optimised balance between inventory stock and lostsales based on the economics and competitiveenvironment of the businessSample snapshots and reportsPerform inventory stock vs lost sales scenario analysis330Starting inventory/lost sales300220More optimal sales vs.inventory trade-offscenarios200120 115Current2Inventory195ModerateAggressive75 Lost salesConservativeOptimise inventory levels taking into account all supply and demand variablesSKUSuggested orderActual orderSuggested orderActual orderSuggested orderActual Total42453539504610 PwC

8: Product launch benchmarkingand cannibalisationBusiness challengesAnalytics solution and results Leverage analytics to identify factors critical to newproduct success Structure a set of indicators to measure a product launchbased on multiple analytical and modelling techniques Understand how sales and contribution margin ofa new product should be benchmarked toassess performance Estimate the impact of the product launch on overall marketshare through statistical analysis and field analytics Analyse the level of cannibalisation from a newlylaunched productThe result: Recommendations to steer early launchexecution to ensure greater success or to limit investment onlaunches likely to failSample snapshots and reportsAssess new product comparativelaunch momentumCumulative volume (%)Existing product volumeForecast vs. actual2.5100ForecastActual2.0806040Different products havedifferent early launch profiles(function of category, brandpower, market demand, etc.)200123456789 10 11 12 13WeekIncremental portfolio valueUnderstand cannibalisation ofexisting productsNew product launch momentum12032Volume (MM)11.51.00.50.0ABCExisting productsDDevelop insights to steer the success of the newlylaunched productKeepScaleRefineinvestmentKillTotal cost of launchPwC’s analytics solutions for the FMCG sector 11

9: Price and pack analyticsBusiness challengesAnalytics solution and results Define the right brands, packs and prices for thespecified channel/customer to meet targetedconsumer and shopper needsUsing modelling, PwC’s price and pack analytics optimiseschannel performance, package diversity, pricing and otherkey value drivers.The result: Increased SKU efficiency Increased revenue across the portfolio Increased market share and value share The right packs and prices by outletSample snapshots and reports1Identify SKUs for potential deletion based on a variety of factors, from current contribution marginto time in the market and substitutabilitySKU volumerankSKU revenuerankGross profit(in thousandUSD)New or le?(Y/N)Componentof growthvision?(Y/N)Keep 12NewNYNY2472554.8NewYNYY* New products defined as introduced in past six months2Understand pricing distribution across brands as a percentage of volumeSingle serve transaction splitby price range, by manufacturer50%% of category volume40%30%20%10%0%Less than 1 USD1 USD–2 USDBrand 112 PwCBrand 2Brand 3Over 2 USDBrand 4