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Case Studies - Customer & Marketing Analytics for Retail


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Case Studies - Customer & Marketing Analytics for Retail

  1. 1. CUSTOMER & MARKETING INTELLIGENCE SERVICESCustomer Targeting Strategy DevelopmentCase Studies
  2. 2. Customer IntelligenceIdentifying Most Valuable CustomerCase StudyClient: A US based high end luxury retailer in the space of apparels and accessoriesBusiness Context & Client Problem:The retailer has several high end luxury retail stores in key cities across the US and sell luxury apparels and relatedaccessories. They want to identify the most valuable customers within their existing customers based on relationshipand purchase patterns for proactive relationship management purpose. They also wanted to assess the profile of suchbest customers and catch them young in their lifecycle.Impact on Business:The study not only identified the sweet spot of the business , but also created detailed understanding of best customerprofile and characteristics and helped the retailer to create effective CRM programSolution: Customer segmentation based on relationship quotient and identification of demographic andbehavioural sweet spot for most valuable customersRelationship Segmentation Segment Drill down Identify Look Alikes• Analysing various transactionpatterns, cycles, mix etc.• Design segmentation schemebased on Recency, Frequencyand Monetary factors• Identify the Most ValuableCustomer segment covering80% of the business value with20% customersUnderstanding behavioral anddemographic characteristics of the bestcustomers against restCreate scoring model to identifycustomers who look like the mostvaluable customers, however not yetreaching the status
  3. 3. Customer IntelligenceTargeted Cross SellClient: A leading global technology companyBusiness Context & Client Problem:A global technology giant wanted to cross-sell profitable docking stations to some of their existing SMB (Small &Medium Business) customers. Due to budget constraints, the company wanted to be focused on reaching out to theright set of accounts based on their propensity of buying a docking station in the near future. This could be derivedthrough the relationship quotient, product purchase sequence as well as the estimated need of the company.Impact on Business:A targeted cross-sell campaign based on relationship, product purchased so far and industry dynamics helped the salespeople to not only focus on high RoI accounts, but also helped them customise their communicationSolution: A scoring model to prioritise the set of accounts based on R-F-M segmentation as well as natural productassociation between docking station and other productsRelationship Segmentation Product Association Analysis Scoring Model• Analysing requency, frequencyand monetary aspects ofrelationship with the accounts• Design segmentation schemebased on RFM characteristics• Identify sweet-spot for bestcustomersIdentifying association between variousproducts based on how often they arebought by same customerWeighted Model incorporatingrelationship quotient, installed baseassociation and industry dynamicsCase Study
  4. 4. Customer IntelligencePrediction of New Product Sales Trajectory Leveraging SocialMedia Buzz & SentimentsClient: Global marketing organisation of a leading manufacturer of personal computersBusiness Context & Client Problem:In a market where product life-cycles are a few months long and competition is heavy, waiting for and relying solely onpoint-of-sales data was less predictive and constraining in terms of quick course corrections. The PC manufacturerwanted to utilise the market buzz and indications obtained from social media on early days of launch to predictpotential growth path of the product.Impact on Business: The solution provided initial insights on key social media indices to track for assessing performanceof a product and react quickly to potential corrective actions. Such solution is expected to be technology enabled andoperationalised across various productsSolution: Crawled data from social media sources like Twitter, Amazon, Google etc to create predictive indicesaround market buzz and consumer sentiments on key features to correlate with potential sales trajectory oflaunched product.Creation of Social Indices Build Predictive Model Operationalisation of Solution• Crawling of mentions, reviews, comments fromvarious sources like Twitter, Amazon & Googlereviews, CNET for 9-10 products launched inlast 2 years• Advanced text mining to identify key featuresand scoring sentiments displayed• Creation of social indices around mentions,promotion, average reviews, sentiments acrosskey features for each product lifecycle• Standardisation of growth trajectory ofvarious similar products through parametriccurves• Creation of an advanced panel regressionmodel to relate the social indices and trendswith the growth observed over time forvarious products• Assessing most predictive factors for relatingwith growth trajectory and build a scoringmodel involving various social indices• Developed set of indices which arehighly predictive about productperformance• Operationalising the technologysolutionCase Study
  5. 5. Marketing EffectivenessMeasuring Impact of Trade Discount & PromotionClient: An India based Consumer Packaged Goods GiantBusiness Context & Client Problem:The client is a conglomerate of diverse business lines with a significant focus on Consumer Packaged Goods, especiallyfood. In this scenario, the client wanted to measure effectiveness of various trade & consumer promotion on traderevenue with wholesalers, convenience stores and retailers segregating the impact of pricechange, promotion, competitive actions and cross SKU cannibalisation & Halo effects..Impact on Business:The analysis not only revealed hidden patterns of true effectiveness of different promotional spends and cross-categoryinteractions, but also provided enough insights for differentiated promotion strategy across segmentsSolution:Segment Data, Create Indices Modeling Decomposition of Impact Analyse Scenarios• Segmentation of outlets based on similarresponsiveness and product assortment• Creation of Price Indices, Promo calendar,competition indices and cross-categoryinteraction indices• Treatment of data for trend, seasonality, outlieretc.• Analyse underlying patterns based on first weekor last week of the month, start of year ,significant changes etc.• (Mixed Effect) Regression modeling ofvolume sold against price, promotion,competition and interaction indices• Decomposition of volume realised intobase volume, promo net impact,cannibalisation & competition impactetc.• Analyzing RoI of promotion spendsbased on incremental value• Identify optimal promotion & pricefor each channel & segmentsCase Study
  6. 6. Marketing EffectivenessOptimising Marketing MixClient: An online education company based in the US, which offers associate degree programs and othercertifications based on tie-ups with universities and self generated contentBusiness Context & Client Problem:The client organisation deploys a variety of marketing vehicles to generate awareness and demand for their courseofferings. These are both online like Display Advertising, Cost-per-action (CPA),Pay-per-click (PPC) arrangements andoffline activities like Branding initiatives. There is a need to understand the relative effectiveness and ROI from each ofthe marketing vehicles, so that the marketing mix can be optimised to get the best return on total marketing spend..Impact on Business:Based on the model and tool’s suggested marketing mix, there is an estimated lift of 10% in ROI which translates toapproximately $1.3 Million on an annualised basis for the current marketing budgetSolution: A market mix model that provides estimated ROI for each of the marketing vehicles and an Optimiser toolthat uses the ROI estimates to suggest the ideal marketing mix for a given marketing budget“Base” Sales & IncrementalEffectRoI Estimation for Mktg VehiclesOptimiser Tool for theIdeal Mktg Mix in a GivenBudgetEstimate “Base” sales and incremental effectdue to marketing vehiclesAverage and marginal RoI for marketingspend in each vehicleDirectional suggestion of marketingmix changes and incremental RoICase Study
  7. 7. Text Mining of Qualitative Inputs in Surveys withour Cloud Based App.Impact on Business:• A cost effective way to analyse unstructured text data fast and derive actionable insights from it• Increase speed of drawing insights• Simplification of analytics in the hand of business managersSolution: A comprehensive survey data analysis which support advanced requirements in predictive analytics andtext mining with an easy-to-use interface designed for business managersInput Data Interactive OutputsBusiness Context:Responses to open-ended questions in market research and customer surveys typically go unanalysed albeit there areimmense insights in them. Our team set out to frame the text analysis problem and create an application that canaddress all needs of survey analytics in one place. While the application has a broader survey analysis flavour, it has akey textual analysis module that addresses most of the textual analytics needs.Intended Clients: Market Research teams , Customer service , Brand management, Loyalty management, etc• Any kind of textual data withas many split-by variables• Capability to handle BIG Data• CSV, Excel formats• Capability to fetch dataautomatically from anydatabaseWord CloudThematicAnalysisSentimentAnalysisInteractiveChartsCase Study
  8. 8. Personalised RecommendationsObjectiveTo identify upto top 5 recommendationsfor cross-sell program for a retailerResultsThe improvement in precision was 8x using demographicinformation of customers and 7x using only purchase historySummary of RetailDataPrecision = Percentage of correct recommendations in all the recommendations# of Customers 8,419# of Products 1,559# of Transactions 58,308Avg # of Products 5Time Period (years) 2RandomRecommendations# of Hits Expected # of Hits Precision % Improvement # of Hits Precision % Improvement1 4,668 14 96 2.06% 686% 115 2.46% 821%2 4,668 28 185 1.98% 661% 204 2.19% 728%3 4,668 42 260 1.86% 619% 266 1.90% 633%5 4,668 70 362 1.55% 517% 374 1.60% 534%Based on Purchase Data only Based on Purchase and DemographicDataNumber ofRecommendationsCustomers inValidation DatasetRandom vs Our Recommendation Solution The approach includes identification of significantdemographic attributes influencing customer preferences Similar techniques could be used to predict customerratings (e.g. movie, CDs, games, etc.) The approach could be used to recommend onlinecustomers and could used in conjunction with click-stream dataCase Study
  9. 9. Thank you