Predictive analytics and predictive marketing

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  • FROM DATA COLLECTION TO BEHAVIOUR PREDICTION:WHATS AFTER BIG DATA AND SMALL DATA

    STELIANA MORARU | PAOLO VINCENTI

    STRATEGICA INTERNATIONAL CONFERENCE2016

  • CURRENT ENVIRONMENT

    The Big Data revolution is upon us (LaRiviere,McAfee, Rao, Narayanan & Sun, 2016)

    Characteristics: volume, velocity, variety, scalable

  • BENEFITS - BIG DATA

    Analyse operational & transactional dataGlean insights into the behaviour of onlinecustomersBring new & exceedingly complex products tomarketDrive deeper understanding from machines &devices within organisations

  • Technology companies are using big data toanalyse millions of voice sample to delivermore reliable and accurate voice interfaces

    Banks are using big data techniques toimprove fraud detection

    Retailers are harnessing a wide range ofcustomer interactions, both online and offline,in order to provide more tailoredrecommendations and optimal pricing

    IN PRACTICE

  • The first use case involves predicting demand for consumer products that are inthe long tail of consumption

    Firms value accurate demand forecasts because inventory is expensive to keepon shelves and stockout are detrimental to both short-term revenue and long-term customer engagementPredictive analytics

    WHAT'S NEXT AFTER BIG DATA

  • DefinitionPredictive marketing is the practice of extracting information fromexisting customer data sets to determine a pattern and predict futureoutcomes and trends.

    Relies on using data to make more business wise marketing decisions bypredicting which marketing activities are more likely to be successfuland which one are more likely not to be.

    Example: Online merchants gain essential profits from instant, personalizedproduct recommendations based on predictive analytics

    PREDICTIVE MARKETING

  • PREDICTIVE MARKETINGIN PRACTICE

    the predictive marketing relies on a continuum of analyticscapabilities: descriptive (what happened), diagnostic (why ithappened), predictive (what will happen) and prescriptive (what todo next)

    predictive capabilities can help professionals to forecast with anacceptable level of reliability what customers are the best fit forthem and for the business

    offers the possibility to marketers to make smarter decisions basedon the actions buyers actually take and have more insight than everbefore into their pipeline

  • PREDICTIVE MARKETING INPRACTICE

    89% of marketers have predictive analytics on theirroadmap for 201649% of marketers are using predictive analytics today 40% plan to implement analytics in the next 12 months 78% of marketers see their prospects buying journeysbecoming more complex and nonlinear. 80% of survey respondents agreed that buyers areincreasingly going online to educate themselves aboutproducts and services

  • Has a well-earned reputation for using the information it gleans about itscustomers to drive everything from the look of the service to the shows inwhich it invest

    Example:

    House of Cards -> Netflix has used data from all the 27 million subscribersfrom the United States. They could observe that movies directed by DavidFincher, the director of The Social Network, were watched from beginning toend.

    Based on the big data and small data, Netflix identified Kevin Spacey as one ofthe most popular actors, while the British version of House of Cards was avery well-received mini-series in United Kingdom.

    NETFLIX

  • AMAZON

    Predictive analytics was a practice the companyhas installed since their beginning. Example:recommendations appear under a product apotential buyer is considering of adding in hercart, is part of what makes Amazon such an e-commerce powerhouse todayUses Big Data to monitor, track and secure its 1.5billion items in its retail store that are layingaround it 200 fulfilment centres around the world

  • a jeans and apparel brand retailer has successfully used the predictivemarketing program to level up their sales and brand awareness

    they acquired a cloud-based predictive marketing solution in 2009

    they consolidated, cleaned and de-duped their customer data on a dailybasis -> start using data gathered to make hypersonalized campaigns.

    Example: The company has used predictive analytics to find groups of peoplewith distinct product preferences - product-based clusters. They identified at least three different clusters: customers who favoredmostly woven shirts, customers who favored beachwear, andcustomers who were more inclined to high fashion. Having the data,Mavi has launched a reengagement campaign for lapsed customersand managed to reactivate 20% of them.

    MAVI

  • OBSERVATIONS

    The need for a customer-centric approach has alwaysbeen present in the marketing strategyPredictive marketing is bringing more valuable results tocompanies, by better understanding the customer dataand the potential to provide customized content. The personalised content equates to a higher retentionrate, reduced attrition rate, and knowing what thecustomer want before they even know it

  • THANK YOU!