Machine Learning use in Sales & Marketing

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USE CASE – Affinio: Audience insights analysis

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Affinio – Audience insights analysis:EFFECTS OF USAGE

• Allows clients to understand customers as people, based on their interests and passions, by leveraging the social graph;

• Helps clients to develop data-driven content that will resonate with the targeted customers;

• Identifies the best channels to distribute and promote the client’s content (like websites, social networks, brand partners, influencers, and celebrities).

Source: http://www.affinio.com/approach

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USE CASE – Percolata: Helping retailers predict in-store customer traffic

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Utilizing physical big data to dramatically improve how brick and mortar businesses work

Percolata: Helping retailers predict in-store customer traffic: EFFECTS OF USAGE

• The free traffic counters contributes to tracking the customer count for retail stores and then project traffic going forward for the website.

• The plug-and-play sensors and predictive analytics give the most accurate occupancy rates. Major benefits include saved manager time, increased revenue, and the improvement of customer loyalty.

• The process takes two weeks to get started and less than a month to see tangible return of investment.

• The platform helps the increase of sales and cut out of costs with the auto-schedule feature that eliminates the problem of both over- and understaffing.

Source: http://www.percolata.com/

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USE CASE – Prelert: Behavioral analytics for payment security

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Prelert automates behavioral analytics allowing its customers to discover real-time insights while minimizing the upfront

investment

Prelert - Behavioral analytics for payment security: EFFECTS OF USAGE

• Machine Learning (ML) are at play to flag any malpractice in very high volume high frequency data transactions / communications.

• ML powered systems can now detect a possible insider trading in a stock market, also ML can flag a rogue customer transaction as a fraudulent transaction in high volume business doing market place websites.

• Among the benefits are:

– Analyze Operational Metrics

– Discover Root Cause

– Track Business KPIs

Source: http://info.prelert.com/products/anomaly-detective-engine

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USE CASE – Azure ML: Sales Forecasting

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Powerful cloud based analytics

Azure ML – Sales Forecasting: EFFECTS OF USAGE

• The platform comes loaded with many different samples that include models to predict credit risk, customer churn, flight delays, and many others which will help you predict different scenarios.

• Azure ML allows you to include multiple prediction scenarios in the same experiment and components to easily compare the results.

• The platform provides many tools for data analysis, but the most comfortable choice is to do most of the work in excel and just upload .csv file into the workspace.

• Azure ML offers the possibility to test different sets of columns and different algorithms so that you can compare the results and pick the best performing model.

Source: http://www.skylinetechnologies.com/Insights/Skyline-Blog/February-2015/Sales-Forecasting-using-Azure-Machine-Learning

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USE CASE – Predicting Performance of Fundraising Campaigns

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Two major components:

1)Linguistic features, which include a)Uni, bi and tri-grams from the project description and the section on risks, for predictive phrases. b)Psycholinguistic features (LIWC - Linguistic Inquiry and Word Count) for categories (cognitive, inhibition) c) Sentiment scores from comments on project page

2. Non-text metadata, as provided in the image:

Predicting Performance of Fundraising Campaigns: EFFECTS OF USAGE

• The platform tackles the potential success of a fundraising campaign (for example, using kickstarter), based only on the initial description of the project - something that the project creator has full control over.

• It focuses on the language of the project description - in particular, phrases which are predictive of success- and their psycholinguistic qualities - in addition to other metadata.

• Training and dev sets are used for experimenting with different estimators and model selection for the campaigns.

Source: http://cs229.stanford.edu/proj2015/239_poster.pdf

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USE CASE – SmartReply: Computer Virtual Assistant

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Google launched Smart Reply, a deep learning network that writes short email responses for you.

SmartReply - Computer Virtual Assistant: EFFECTS OF USAGE

• The Smart Reply System is built on a pair of recurrent neural networks, one that is used to encode the incoming email and one to predict possible responses.

• The system can automatically determine if an email is answerable with a short reply, and composes a few suitable responses to that.

• The use can edit or send with just a tap.

• The Smart Reply feature was especially designed to the same rigorous user privacy standards any user would want: no humans reading your email.

• The email chat box has come to understand the semantic similarity between two responses and now it's possible to suggest responses that are different not only in wording but in their underlying meaning as well.

Source: http://googleresearch.blogspot.ro/2015/11/computer-respond-to-this-email.html

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USE CASE – RichRelevance: omnichannel personalization

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The RichRelevance personalization

products work across mobile, web, in store, and other channels to

create relevant experiences that span the continuum of the customer lifecycle.

RichRelevance: omnichannel personalization: EFFECTS OF USAGE

• The platform helps you leverage the omnichannel data to create a 360-degree view of your customer’s shopping behaviors and preferences, so that you can engage them with highly relevant experiences on your website.

• The RichRelevance platform allows you to to personalize the entier shopper journey from engagement to product discovery to checkout on mobille devices.

• Marketers can now focus on introducing customized, curated content and messaging through personalization.

Source: http://www.richrelevance.com/omnichannel/

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USE CASE – Yelp: Making user-generated content valuable

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Yelp hosts tens of millions of photos uploaded by Yelpers from

all around the world. And this wide variety of photos provides

a rich window into local businesses.

Yelp - Making user-generated content valuable: EFFECTS OF USAGE

• Yelp is mainly used for checking out the atmosphere for a special event or navigating to a venue or a new place to eat. The business detail pages show a set of “cover photos” which are recommended by our photo scoring engine based on user feedback and certain photo attributes.

• This develops a photo understanding system which allows us to create semantic data about individual photographs.

• The data generated by the system has been powering our recent launch of tabbed photo browsing as well as our first attempts at content-based photo diversification.

Source: http://engineeringblog.yelp.com/2015/10/how-we-use-deep-learning-to-classify-business-photos-at-yelp.html

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USE CASE – Barilliance: Personalized product recommendations

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Targeting customers with personalized recommendations

across multiple pages and multiple channels

Barilliance - Personalized product recommendations: EFFECTS OF USAGE

• Product recommendations are setup to take domain specific attributes that are important for your site (i.e. size, color, brands) matching their design.

• The platform makes it possible for emails with product recommendations to be sent to users based on their most recent activity on your site.

• You can decide on which pages the content will be visible and do it yourself in a few minutes without IT involvement.

• The system is configured to capture data instead of asking the IT department to send it.

• Once the system starts to collect data, there is a learning period in which data is analyzed (2-4 weeks depending on your traffic and catalog size).

Source: http://www.barilliance.com/product-recommendations-engine/

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USE CASE – Lumidatum: Improving Customer Service

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Smart Data Discovery for the Customer Experience

Lumidatum: Improving Customer Service: EFFECTS OF USAGE

• The platform uses algorithms to discover deep insights about your customers and then turning the data into a competitive advantage by identifying what offer, when to send it and to whom.

• The Lumidatum machine learning system let's you find new revenue streams fueling the growth of your business.

• You can use data science to optimize merchandising, make product recommendations and identify your best customers.

• The platform helps you provide a dynamic and personalized experience for your customers boosting loyalty and engagement.

• It enables data preparation and cleansing through model building to utilizing and launching predictions in your own apps and platforms.

Source: https://www.lumidatum.com/

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