A next generation AI-powered service helping your marketing platform or marketers make decisions, by performing binary or multi-class predictions and classifications for example or sanity checking their existing predictions/classifications for example:
1. Predict whether a person should be targeted during next marketing campaign of a new service or product? Butterfly AI can help marketers or their AI-powered marketing platforms to target the right person that will mostly likely purchase the new product or subscribe to a new service or improve the success of their current targeting technology (a basic baseline example input data set is the “Bank Marketing Data Set” presented in https://archive.ics.uci.edu/ml/datasets/bank+marketing). Having collected and verified data features (attributes) of thousands of past customers during past marketing campaigns including the number of customers out of N (e.g. N= 10) closest customers to the target customer (a single distance metric based on the attributes described below, for example income, age, house value differences, etc.) who have responded positively to the most similar marketing campaign in past, customer’s presence in social media channels, their responses (click history) to past marketing emails, their responses (click history) and reactions to past marketing surveys, their responses, and reactions to past marketing banners on social media channels, their search history relevant to the new product or service, habits, likes, presence intensity, posts. shopping history, rating they have given in past to the most similar service or product (‘1 star’, ‘2 stars’,...,’5 stars’, ‘unknown’), income, credit rating, postcode, city, country, house ownership (‘no’ or ‘yes’), house value, car, usual channel type (mobile, laptop, desktop, iPad, etc.), whether the person is already a customer (‘no’ or ‘yes’), whether the customer has already purchased the previous version of the future product or service, age, gender, employment ('admin', 'blue-collar', 'entrepreneur', 'housemaid', 'management', 'retired', 'self-employed', 'services', 'student', 'technician', 'unemployed', 'unknown'), marital status ('divorced', 'married', 'single', 'unknown’), education level ('high school', 'professional course', 'university', 'unknown', etc.), has credit in default? ('no', 'yes', 'unknown'), has mortgage? ('no', 'yes', 'unknown'), has personal loan? ('no', 'yes', 'unknown'), contact communication type during that past campaign ('cellular', 'telephone', ‘email’, ‘in-person’, ‘social media chat’, etc.), last contact month of year, last contact day of the week, last contact duration in seconds, number of contacts performed during that past campaign and for that client, number of days that passed by after the client was last contacted from a previous campaign, number of contacts performed before this campaign and for this client, outcome of the previous marketing campaign ('failure', 'non-existent', 'success'), employment variation rate - quarterly indicator within customer’s neighbourhood, consumer price index - monthly indicator, consumer confidence index - monthly indicator, Euribor 3 month rate - daily indicator, if an employer number of employees, and any other relevant data attributes, and for all those past marketing campaigns having verified and correctly labelled whether the customer purchased the product or subscribed to the service as a result of that targeting campaign (the target of the prediction), predict and decide whether a potential new customer or an existing customer should be targeted during the next marketing campaign (also provide the probability and certainty of the prediction that they will purchase the product or service).
2. Predict if customer is going to click on an online product advertisement on a webpage or social media site: Butterfly AI can help marketers or marketing platform determine whether a customer is going to click on a product page or banner and to determine the probability of the click. Knowing the key features of thousands of customers’ recent online journeys including product ID, web page ID on which the product has been displayed, social media page ID, whether the product banner displayed on a web page or social media site that customer frequently visits (yes or no), the frequency of visit to that web page by customer, the category of that product, has the customer clicked on similar products from that category of product in past (yes or no) (similar products may be extracted from a knowledge graph), the number of products customer purchased from that category of product within past 6 months, the durability of the product, gender, age, income, employment category, city of location’s development index, customer activity depth score with that website or social media site, product brand or manufacturer, the number of same brand products purchased by customer in past 6 months, is current product on offer (e.g. offers a discount), whether the product is a seasonal product and we are currently in that season (e.g. Christmas, summer beach season), whether the most similar products purchased by customer were on offer (yes or no), search history of customer (frequency of search for that product or its features) within last week, the whereabouts of customer on website right now, the product banner format similarity score (including colours used, frame size, text used, et.) of the past similar products clicked and purchased by customer to the current target product advertisement banner, whether the top ten most similar customers to the target customer in terms on online behaviour (similarity for example in terms of age, gender, demographics, brand of products purchased in past. etc.) have clicked on that product advertisement banner during past couple of days (the similar customers for example may be determined from a knowledge graph), the frequency of visit to that web page by the top ten most similar customers, whether the top ten most similar customers to the target customer in terms on online behaviour have searched for that category of product during past couple of days. whether the product is an accessory to another product that customer purchased just now, and any other relevant data features (including potentially some of the data features described in previous section) and having recorded, verified and labelled whether during those past journeys customers clicked on the product page or not (the label and target of the prediction), predict whether a target customer during a new online journey will click on the target product advertisement or page (and provide the probability of click), provided that those features (listed above) are available for the current targeted customer.
3. Classify the customers according to the most successful marketing channel they will highly likely respond to: Considering the customers’ data features and behaviour during thousands of past marketing campaigns including social media activities, habits, likes and presence, posts, shopping history, income, credit rating, interests, location, house value, car, social networking profile, mobile type, what type of marketing channel the most similar customers to that specific customer have been engaged to most, during past couple of days (similarity for example in terms of age, gender, demographics, brand of products purchased in past, the similar customers may be determined from a knowledge graph) and any other relevant data features (potentially some of the data features described in the previous two sections) and having recorded, established and labelled which one of the marketing channels, A (Direct Contact), B (Email Marketing), C (LinkedIn Approach), D (Facebook targeting) the customers during those past marketing campaigns have responded to, predict and classify an existing or new customer into the marketing channel A (Direct Contact), B (Email Marketing), C (LinkedIn Approach), D (Facebook targeting) segment (and also present the probability of success) during an upcoming marketing campaign.
and any other binary or multi-class marketing-related classification or prediction use cases.