1
SenseBox MIT Sloan G-Lab 2016
Gabriel Awad, Nikolaos Legmpelos, Julia Hawley, Maher Shaban Bogota, Colombia
February 16th, 2017
Young&party Lookingforastyle Matureprofessionals
• 25%ofcustomers• Highlike/mediumexp.
• Formalstyle• Highincome
• 20%ofcustomers• Mediumlike/lowexp.
• Naturalstyle• lowincome
• 55%ofcustomers• Highlike/expertise
• Sexystyle• lowincome
• Youngwomenwhoenjoyusingbeauty&cosmeticsproducts.
• Theyareveryknowledgeabouttheproductsandliketolooksexy.
• Theymaynothavemuchmoneylayingaround,butarewillingtospendonproductsthatmeettheirneeds
• Youngprofessionalswhoareredefiningtheiridentitythroughtheirlooks
• Theyarenotveryknowledgeablewithbeauty&cosmeticsproductsthatfittheir
• Theyhavesignificantmoneytospendonproductsandarelookingtofindabalancebetweensexyandformal.
• Olderwomenwhoenjoyusingbeauty&cosmeticsproducts.
• Theyaresomewhatknowledgeableabouttheproductsandpreferamoreprofessionallook
• Theyhaveplentyofmoneytospendonproductsthatmatchtheirtaste.
~28yrs.~23yrs. ~32yrs.
GeneralassumptionsCostofcapital:y%Grossmargin:x%
DataprovidedbyFernando
Costofcustomeracquisition:~zk COPAveragevaluefromthedataprovided
byFernando
Case-specificassumptions
Customerchurn(afteradjustments,before%ofpayingusers)Datafromlastsection
CostsharingcaseDecreasegrossmargintoS %
intheconsolidatedcase
A B C
FE%ifpayingusers
Datafromlastsection
G
Revenueperbox:24kCOPD
Results Math
Cashflowperperiod: Amountofcashexpectedperconsumerinagivenperiod
NPV: Profitpercustomerafteradjustingforthecostofcapital
Money-over-money:expectedprofitpercustomer(asamultipleofCOCAinvestment)
Internalreturnrate(IRR):expectedreturnofmarketinginvestment
𝐶𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑝𝑒𝑟 𝑝𝑒𝑟𝑖𝑜𝑑 𝒊 = 𝑫 ∗ 𝑨 ∗ 𝑮 ∗ 𝑬𝒊6𝟏 = 𝑯
𝑁𝑃𝑉 = −𝑪 + 𝑁𝑒𝑡 𝑝𝑟𝑒𝑠𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑯
𝑀𝑜𝑛𝑒𝑦_𝑜𝑣𝑒𝑟_𝑀𝑜𝑛𝑒𝑦 = 𝑆𝑢𝑚(𝑯)/𝑪
𝐼𝑅𝑅 = 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝑟𝑒𝑡𝑢𝑟𝑛 𝑟𝑎𝑡𝑒 𝑜𝑓 (−𝑪 + 𝑯 𝑝𝑒𝑟 𝑝𝑒𝑟𝑖𝑜𝑑)
3-clusteranalysis
3clusters Before After Increase Before After Increase
Young-highlike-highexpert 29% 100% 3.4x 3% 100% 36.2xMiddle-lowlike-lowexpert 18% 100% 5.5x 2% 100% 43.7xOld-highlike-middleexpert 23% 100% 4.3x 0% 100%
BrandAwareness BrandTrial
3clusters Before After Increase Before After Increase
Young-highlike-highexpert 46% 100% 2.2x 6% 100% 16.1xMiddle-lowlike-lowexpert 40% 100% 2.5x 2% 100% 40.3xOld-highlike-middleexpert 37% 100% 2.7x 2% 100% 41.0x
ProductAwareness ProductTrial
PurchaseIntent3clusters Promotors Detractors NPS %ofCustomers Yes No Increaseinsales
Young-highlike-highexpert 43% 22% 21 48% 3% 52%Middle-lowlike-lowexpert 48% 20% 27 45% 4% 55%Old-highlike-middleexpert 38% 34% 4 40% 4% 60%
Satisfaction SalesLift
Findings
a) Lowgeneralbrandawarenessandmiddle/highproductawarenessMayindicatethatproductsdonotvaluethebrandandbuytheproductforitsprice
b) MiddleclusterhastheleastawarenessbutthehighestratioofpromotorsandNPSTargetadvertisementstrategiesshouldbemoreprofitableiffocusedonthispersona/cluster
c) OldclusterhasthehighestnumberofdetractorsandlowestNPSTargetadvertisementstrategiesshouldavoidthispersona/cluster
Photos: SenseBoxOffice
Cartagena
BogotaGolfClub
Chia
Analytics report for suppliers (1/3) - Overview
Howclusterizationofdemographicdatacanincreasemakedatavisualizationeasier/moreinsightful
Currentpresentation Dataclustering
• Hard-to-interpretinformation• Highlycorrelateddatapresentedseparately
Twowaystogroupthedata
Subjectivecriteriamanagementdefineshowtoclusterthedata(e.g.,urbanheavyusersofmake-up)
ObjectivecriteriaStatisticaltoolsdefinehowtoclusterthedata(e.g.,urbanheavyusersofmake-up)
• Intuitivevs.data-drivenapproach
Output
Urbanheavyusersofmake-up(35%)
Topclusters(example)
Low/mediumuserswithformal/classicstyle(30%)
Young,party/sexystyleusers(20%)
• SmallernumberofgroupswillproviderbetterunderstandingofSensebox’s customerbase
Others(15%)
Findings
Performancewithingroup
Awareness Intent
80% 30%
20% 90%
60% 50%
60% 60%
• Group1:Lowreturnonmarketinginvestment• Group2:Highreturnonmarketinginvestment
1
2
3
4
Analytics report for suppliers (2/3) - Personas
Analytics report for suppliers (3/3) - Metrics
Clusterization of demographic and psychographic data enables SenseBox to improve their understanding of their client base
SenseBox has 3 types of customers with varying ages, make-up expertise, styles and hobbies
Evaluating the product metrics for each persona/cluster improves SenseBox’s value proposition to its suppliers
Calculating the Customer LTV will help SenseBox optimize its marketing and investment strategies
Financials - Customer lifetime value
IT systems
IT systems applicable to SenseBox’s business model were recommended; a CRM and ERP platform were set up.
Insights with Google Analytics