Simple Relevance CHICAGO AMA BIG DATE presentation deck
We Make Your DigitalMarketing Awesome & LucrativeCreate More Revenuea lot
Erik SeveringhausCEOLong time in Email(Founding team for iContact)Big Data NerdPatent #20080320482A1“Management of Grid ComputingResources Based on Service LevelEnvironments”Marketing KnowledgeKellogg MBAAbout The Speaker:email@example.com | 312-569-9431 | @SimpleRelevance
About SimpleRelevanceFounded by a co-founder of market-leadingESP iContact and Partner in IBM’s ITOptimization consulting team with 10+ yearsexperience in digital marketing optimization.Funded by top Venture Capitalists, incubatedat the 1871 technology center in Chicago.Patent-pending algorithm from University ofChicago researchers developed over 2+ years.Market leading industry partners & Fortune500 clients.
012.52537.550Mobile Social Display Search EmailReturn on Investment per $1 SpentEmail is Still KingSource: Direct Marketing Association
Math You LikePersonalizedTotally Data DrivenAutomated (“Simple”)30-300% Improvementin Email Metrics
The ProcessSegment at the Individual Level to improve messaging andconversion with personalizationClose the Loop using data to continuously, automaticallyoptimize campaign with a iteration cycle measured in minutesDeﬁne Dynamic Workﬂows to move prospects through thefunnelReportingDeploy CampaignSetup CampaignIdentify Segment
Full Loop Marketingcrm profilessocial shares,likes, profile info,email opens &clickscart purchases and clicksFirst Party DataThird Party Dataauto content creation,demographics,other metadataTaking Actionoptimized,automated,emails, tweetsandothermarketing
Use CasesOptimize all email sends for time of day based onlikelihood of conversion.Optimize all email subject lines for likelihood to open.Optimize creative, location, subject line, and time ofday personalization for cross-sell messaging.Optimize oﬀers, creative, subject line, and time of daytargeting for upsell communication.
ProfileLi-Ning is the third largest sportswearcompany in the world, a market leaderin Asia and emerging brand in theUnited States receiving endorsementsfrom superstars such as DwayneWade.ChallengeWhen Li-Ning launched in the U.S.they put tremendous emphasis onestablishing a relationship with theircustomers. Because they werediﬀerent, Li-Ning wanted topersonalize their experience.SolutionAfter launching, Li-Ning introducedSimpleRelevance datapersonalization into their emailcampaigns52% increase in revenue20+% AOV increasesImprovement
ProfileThe johnnie-O line has evolved into anapparel and lifestyle brand distinctlydeﬁned as "west coast prep". Items formen, women, and children includeshirts, belts, hats, socks, sweatshirts,and specialty items.Challengejohnnie-O wanted to increase onlinesales by sending data driven,personalized email marketing to itscustomersSolutionj o h n n i e - O i m p l e m e n t e dSimpleRelevance’s productrecommendation engine into theirbranded emailsImprovement 40-50% increase in revenue per email campaignSimpleRelevance quickly became johnnie-O‘s mostsuccessful marketing strategy from an ROI perspective
ProfileA successful Fortune 500 carmanufacturer with headquartersoverseas.Produces standard as well as luxurymodels.ChallengeThe manufacturer wanted toincorporate SimpleRelevance’s dataanalytics into their social mediamarketing, speciﬁcally Twitter, toinﬂuence customer’s purchasedecisions when car shopping.SolutionSimpleRelevance created tailoredlanding pages and integrated smartanalytics into a custom twitterm o n i t o r i n g t o o l t h a t a l l o w e dSimpleRelevance to deliver custom,targeted tweets to users who hadtweeted about car shopping.Improvement $160,000 in sales in the ﬁrst month“Those who get an oﬀer [from SimpleRelevance] closed2x more often than those who didn’t.“ – VP of Marketing
Get In TouchEmail: firstname.lastname@example.orgCell: 202-431-5699Oﬃce: 312-569-9431Twitter: @SimpleRelevancehttp://www.simplerelevance.com
How It WorksThe SimpleRelevance engine tracks and analyzes every interactionbetween each customer and your email marketing.For each message, we track a plethora of data points including:Time Of Day and Day of Week“From” Address, Subject Line, Word CountWhat Creative & Content Was IncludedWhat Links Were ClickedWe correlate those responses with each customer, as well as the existingdemographic and psychographic data to build a predictive model foreach individual customer.We continuously learn with every interaction, so the system gets smarterwith time.Because the algorithms do the optimization, you don’t need to do it allmanually.
Bayesian LearningThe system is constantly evaluating each of the data points that areintegrated into our system, looking for both data points that resonateacross segments of users as well as predictive elements that stand outfor certain users.With each new data point, the entire system becomes smarter.The engine regenerates itself nightly, evaluating new data and updatingpropensity models to take into account new information.The system can be tuned for diﬀerent types of interactions and objects,optimizing on any number of variables so long as metadata is structuredcoherently.