Marketing per i newborns: come Analytics e IoT aiutano le aziende a targetizzare le nuove generazioni - SAS Italy
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SAS Italy - Disegnare strategie di marketing che siano efficaci, soprattutto quando si parla di nuove generazioni, dipende innanzitutto dall’individuazione del giusto target. Una volta individuato, è necessario estrarre il valore che proviene dall’intersezione di tre componenti chiave: data, discovery e deployment. Da dove partire quindi? Il marketing path delle nuove mamme è un percorso caratterizzato da un mix di tecniche di marketing tradizionali e digitali: in questa presentazione scopri come le aziende sono in grado, grazie agli Analytics e all’applicazione dell’IoT, di diventare rilevanti per loro.
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Marketing per i newborns: come Analytics e IoT aiutano le aziende a targetizzare le nuove generazioni - SAS Italy
- 1. Copyright © SAS Institute Inc. All rights reserved. Marketing for newborns: how Analytics and IoT reflect on the way companies target new generations Cristina Conti, Analytics Solutions Manager Global Technology Practice, SAS
- 2. Copyright © SAS Institute Inc. All rights reserved. Some intro I’ll be using mom- familiar situations to present powerful analytical capabilities that can generate a strong business outcome I have a 4 months old daughter whose birth gave me the idea for this presentation I work in SAS Global Practice, supporting Analytics and Data Science
- 3. Copyright © SAS Institute Inc. All rights reserved. Gen Z, who are they? One day they’ll rule the world… or are they already? Most often, they are targeted through they romantically embezzeled mothers
- 4. Copyright © SAS Institute Inc. All rights reserved. Why are mothers a good target? Without too much psychology, mothers are unsure how to treat new babies Thus, they are very responsive to some very old style, yet very powerful marketing rule… find a need that you didn’t know you had!! In most mother’s case, this rule is even more powerful as moms are always looking for a magic wand
- 5. Copyright © SAS Institute Inc. All rights reserved. New mothers marketing path Traditional word of mouth: you have it, I want it Modern word of mouth: online blogs Images: I do, you do, we do Customer segmentation Contextual marketing Internet of things Optimization
- 6. Copyright © SAS Institute Inc. All rights reserved. Data DeploymentDiscovery To get VALUE out of any analytics endeavor, 3 key components have to be considered: • DATA; • DISCOVERY; and • DEPLOYMENT.
- 7. Copyright © SAS Institute Inc. All rights reserved. Data is the foundation Structured Data Online / Digital Data Machine Data Social Media Data
- 8. Copyright © SAS Institute Inc. All rights reserved. What we do with Data: DISCOVERY Visualization Prediction Machine Learning Optimization
- 9. Copyright © SAS Institute Inc. All rights reserved. How we Operationalize the results: DEPLOYMENT Data Warehouse CRM / Call Center Mobile Channel Devices
- 10. Copyright © SAS Institute Inc. All rights reserved. How? word of mouth and online blogs… *Mom Consulting Central, 2014
- 11. Copyright © SAS Institute Inc. All rights reserved. Ok, so exactly how do we capture them? From huge unstructured content To actionable analysis that can lead to selling our own product
- 12. Copyright © SAS Institute Inc. All rights reserved. From a practical standpoint Natural Language Processing Context JL Machine Learning H Discovery Human Input Topics, Insights, Relationships, Taxonomies, Scored Documents Unstructured Text Data Operations Further Analysis Text Analytics
- 13. Copyright © SAS Institute Inc. All rights reserved. From a practical standpoint
- 14. Copyright © SAS Institute Inc. All rights reserved. From a practical standpoint Model social relations 1 Detect the network 2 Assign the roles and classify the community 3 Social Network Analysis
- 15. Copyright © SAS Institute Inc. All rights reserved. Picture this: you have it, you do it, I want it *Mom Consulting Central, 2014 83% of influencers have «visual» accounts: Pinterest, Instagram… Why pictures matter? Sometimes we are the pics we post… and you’ll know exactly what I’m going through!
- 16. Copyright © SAS Institute Inc. All rights reserved. Step 3: Image Recognition Load Images and Resize 1 Convert Colors Apply noise reduction and binarization Apply edge detection Convert to data matrix for machine learning purposes 2 3 4 5
- 17. Copyright © SAS Institute Inc. All rights reserved. Segmentation, because my child is not like yours «needs» toys to learn how to crawl «needs» toys to manage teething «needs» sling to always be carried «needs» neck-ring to enjoy water
- 18. Copyright © SAS Institute Inc. All rights reserved. Modern targeting through recommendations Cluster method KNN method SVD method ensemble• User profile • Order history • Click history • Item view history Item recommendation s Not just that! Combine with initial mapping and profiling based on unstructured data to provide even more precise recommendations
- 19. Copyright © SAS Institute Inc. All rights reserved. 5 moms just finished the baby massage class It’s lunchtime and it’s raining They have plenty of time, no need to rush home Real-time geomarketing Give them what they need through geolocalized marketing
- 20. Copyright © SAS Institute Inc. All rights reserved. Still an end-to-end integrated process Descriptive Diagnostic Predictive Transactions Channel Interactions Opinions / Sentiments Listen / Filter / Trigger Listen: Detect Events Understand: Integrate Data Understand: Apply Analytics React: Take Action Online, Offline Structured, Unstructured Match, Merge, Purge First Party, Third Party Outbound Inbound / Real-Time Orchestrated Omni-Channel
- 21. Copyright © SAS Institute Inc. All rights reserved. Connected at birth (don’t tell them though)
- 22. Copyright © SAS Institute Inc. All rights reserved. Ok, maybe no demotic baby yet, but you can check out the demo-tic home at the booth
- 23. sas.com Copyright © SAS Institute Inc. All rights reserved. Cristina.Conti@sas.com
Editor's Notes
- http://www.i-scoop.eu/how-the-internet-of-things-impacts-marketing/ A’s = Angel baby B’s = Textbook baby C’s = Touchy baby D’s = Spirited baby E’s = Grumpy baby REFERENCES: Speak the language of your customer, simran + fiona
- This is the recipe, the winning formula. It is deceptively simple. We’ll go peel the layers one by one today, and explore what each component means, and why catering to each one is so important to ensure success out of your analytics endeavor. The framework is based on the 3 components: DATA, DISCOVERY and DEPLOYMENT. If you attempt analytics and consider only one area, you will likely not derive as much VALUE as you could.
- Let’s start with Data, because data is the foundation of everything we do. Today data comes from a wide variety of sources that are not created equally. Think of the differences between relational databases, machine data and social media platforms. All contain valuable data in very different formats. We need to not only access and consume that data, but we need to deal with the quality (or lack of!) and make sense of it. That’s why data is often the first challenge that you need to overcome, and that challenge comes in many guises. It’s in the wrong shape. It’s too complex. It’s arriving too quickly or it’s simply too big. In the past each of these challenges meant compromises, or worse, you simply didn’t even attempt to analyze the data because it was too hard. That’s not the case anymore. There was a time that we talked about BIG DATA – we don’t anymore because it’s a given; Data will always be big and it will keep growing. And that’s ok because we have capabilities available to address even the most extreme of cases, making sure that no matter the amount of data, the speed at which it gets created or the complexity of its structure (even free text found in tweets and Facebook pages) - we will always be able to access, prepare and analyze it. No amount or complexity or speed is insurmountable. You should handle structured data, semi-structured data, unstructured data and even machine data. You can use all of the raw material now. Data no longer needs to be summarized or sampled. We have the capability to work with data that is at its most detailed and granular – data that has the greatest analytic value. We can work at the transaction level! And we done this with some of the biggest companies in the world. Companies that receive more than 5 billion transactions a month. We can visualize that data in seconds.
- And then, the question is? What do we do with this data? And the answer is Discovery. Discovery is the act of finding something that had not been known before. And that is exactly what we need to do. We will embrace that raw material of Data, and with all of our creativity use it to discover patterns. Patterns that will allow us to develop and test prototypes. You are now freed from the bureaucracy of limitations and restrictions (whether it’s through software advances or fewer and simpler interactions with IT) to be creative. In fact, that is now the mandate of analytics. Creativity is key to discovering patterns that will lead to innovation. What are the techniques of your creative Discovery? Visualization Prediction Machine Learning Optimization And these techniques are available to empower everyone, including you. Whether you are data scientist or a citizen data scientist. We have the tools to help you Discover innovations. Innovations that are tested through prototyping. Trying out ideas, rapidly. Failing fast. Learning and then improving. It’s better to know that something is not going to work and know it quickly, rather than spending months on a failure. Instead, try things fast and learn.
- And then what? Deployment. This is third principle of Analytics in Action. Without deploying our efforts, there is no action possible. There is no way to show our innovation. So everything you did in Discovery, on all that Data, needs to be more than just an academic exercise. Deployment completes the picture. That is where we get the VALUE out of analytics. However, deployment comes with responsibilities and expectations. Deploying analytical insights into production systems like Data Warehouses, customer facing channels like CRM and call centers, mobile channels and other devices needs a quality Finished Product. Something that has an expectation of being polished and complete. That means that you’ll need to apply Governance to ensure your innovation assets are indeed ready for production. You’ll need a process that follows a workflow to ensure that the right checks and balances are met before rolling into production. It means that you need to be Enterprise-Ready – You need your solution to be Reliable, Steady and Scalable. These are the responsibilities and expectations of deployment.