Key concepts for AI and Machine Learning in BlueConic
Top AI marketing terms and definitions to help you learn about artificial intelligence, machine learning, and working with the BlueConic AI Workbench.
AI A/B testing is an experimentation process where two or more versions of a variable (such as web page content, offers, emails, etc.) are shown to different segments of website visitors at the same time to determine which version performs the best. In A/B testing, A refers to the ‘control’ or original testing variable and B refers to the ‘variation’ or new version of the original testing variable. BlueConic offers an Advanced A/B testing notebook that lets you select a BlueConic dialogue and examine how variants of this dialogue would perform against it.
An algorithm is a process or set of steps to follow to solve a problem. For AI and machine learning for marketing, there are a number of different algorithms for solving complex problems -- your choice depends on the question at hand (are you making predictions? finding patterns? looking for connections among bits of data?) and the type of data you have available.
Artificial intelligence (AI)
Artificial Intelligence, or AI, refers to a computer's ability to process information, find patterns, make decisions, and even predict future outcomes – in essence, to function similarly to a human brain. Marketing teams can use AI to process enormous amounts of customer data to deliver customized user experiences and to predict customer needs and behaviors.
CDP use cases with AI and machine learning
With the BlueConic AI Workbench, you can apply the power of machine learning to your first-party customer data. You can use AI Workbench to run machine learning models with data from BlueConic profiles, connections, timeline events, and listeners. Learn more about use cases with AI and machine learning.
BlueConic uses collaborative filtering to drive its content and product recommendation systems. Collaborative filtering sifts through vast amounts of data to find patterns among similar users, in order to predict the best matches or items for users who exhibit similar habits, behaviors, or choices.
Customer lifetime value (CLV)
Customer lifetime value (CLV) is a metric that represents the total net profit a company makes from any given customer. CLV is calculated as the average order value multiplied by the average frequency rate of purchase. Using CLV metrics can be a valuable tool to identify, target, and grow customer transactions. BlueConic provides a prebuilt AI Workbench model you can use to calculate CLV for a segment of customer profiles.
BlueConic offers out-of-the-box AI models to calculate customer scoring for CLV and RFM. Use customer scoring to create customer segments based on engagement scores, cart totals, order value, engagement scores, and other numeric data. Learn more about using AI modeling for customer scoring in AI Workbench.
Data processing for AI
Before data can be used for training a model, it is processed. For example, numbers are normalized to increase the performance of the machine learning algorithm. One cool thing about doing this with BlueConic’s AI Workbench is that data scientists don’t have to spend nearly as much time processing the data as they would normally have because a lot of the data is already transformed. BlueConic gives you a single view of the customer so you don’t need to stitch data from different files together to try and create that view.
Since you can also use the full power of the Python programming ecosystem with AI Workbench, complex transformations are easy to apply to BlueConic profile data. There are instances where you will still have to spend time transforming data – like converting a date to the number of days since that date because that’s more complex.
Deterministic matching in BlueConic is the process of identifying and merging two distinct records of the same customer where an exact match is found on a unique identifier, like customer ID, Facebook ID, or email address.
These identifiers often come from a user that has authenticated (i.e. filled out a form or logged in) or from a system that generates a unique ID. The key here is that you are looking for an exact match on the first-party data you have the most confidence in, plus the ability to use any combination of identifiers to match on. Learn more about profile merging via deterministic matching.
Fuzzy or probabilisitic matching
BlueConic provides a Probabilistic (or fuzzy) matching notebook in AI Workbench that you can use to find “fuzzy” matches in your customer profile database. Fuzzy matches are profiles that likely belong to the same person even though not all fields in these profiles have the exact same values.
A simple, but common, example of a fuzzy match is where two profiles have identical first names and surnames, but their phone numbers differ by one digit, possibly because of a typo. The notebook determines a match by finding common typos, misspellings, and the deliberate replacement of a character or digit by a person with multiple profiles. This happens in a probabilistic way, instead of through exact matching. It is important to note that the notebook also detects exact matches for the profile properties it examines.
The Probabilistic matching notebook uses the symspell algorithm to find near matches and measures similarity between values based on the Damerau-Levenshtein measure of edit-distance. Contact your Customer Success Manager to learn more about using this notebook.
Generative AI is a subset of artificial intelligence that focuses on creating new and original content rather than just analyzing or processing existing data. It employs deep learning techniques and neural networks to learn patterns and structures from large datasets, allowing it to generate human-like text, images, music, or other forms of content. Generative AI has applications in various fields, including natural language generation, image synthesis, and content generation for chatbots and virtual assistants. Examples of generative AI tools include ChatGPT, Bing Chat, Google Bard, and Watson Assistant.
In BlueConic, the AI Workbench uses the Jupyter notebook environment for creating and running machine learning models against BlueConic data.
Learn more about Jupyter notebooks.
AI Workbench notebooks are connected to a Python3 kernel through the Jupyter UI. A kernel executes the notebook code and returns results. The kernel and the notebook remain active while you are actively working in them. The kernel will be automatically terminated after 36 hours of inactivity, or when a BlueConic user manually terminates them. A kernel will be automatically terminated when a notebook is opened without running one or more cells (once you navigate to another notebook). Learn more: Scheduling and running AI Workbench notebooks
Large language model
This is a type of artificial intelligence model designed to understand and generate human language. These models are based on deep learning techniques and are trained on vast amounts of textual data, allowing them to learn complex language patterns, grammar, context, and semantic relationships. The larger the model, the more capable it becomes in understanding and generating human-like text.
Machine learning is a branch of AI that uses algorithms and models trained on thousands or millions of pieces of data to help businesses to make better decisions or predictions. BlueConic uses machine learning marketing algorithms to determine optimal product and content recommendations.
In AI and machine learning, a model is a data structure that represents a real-world process (for example, the relation between visits, page views, and propensity to buy). You’re basically codifying your hypothesis of how (a small part of) the world works. Let’s say, for example, that your hypothesis is ‘the number of page views and visits of a customer determines how likely that a customer is to buy something.’ In that case, the relationship between page views, visits, and whether the customer bought something is the model.
AI marketing models are at the heart of an AI and machine learning workflow for marketing teams:
- Building AI marketing models: In AI Workbench, data scientists build a custom model or import one from a Python library. Using the embedded Jupyter notebook environment, teams can build their own models, or tweak any existing models within the BlueConic library. If you're not familiar with Python, you can also use the built-in example models BlueConic provides.
- Training AI marketing models: Typically, you train a machine learning model by giving it an initial set of representative data to train the model to recognize patterns, calculate scores, etc. Next, you test and validate the model, and then apply it to larger datasets to test your results.
- Optimizing AI marketing models: A key step in the workflow is to optimize your AI marketing model once you've seen how it runs with real customer data. Using Jupyter within BlueConic gives you access to unified customer data – which updates as the customer’s attributes change. Your model can access to the most up-to-date data because it’s pulling right from BlueConic's persistent, dynamic profiles. Use the insights from your AI or machine learning model to create smarter segments based on the model's output, for example, prediction scores.
- Deploying AI marketing models: Deploying a model means you’re sending the outcome of the model to your external marketing systems. Because models are being run in BlueConic, you can deploy the model across all your marketing system because BlueConic is already connected to all your activation channels. With AI Workbench, you can attach scores as profile properties to activate segments in real time.
- Scheduling AI marketing models: To schedule an AI or machine learning model in BlueConic means you can determine the time period in which the model is run. Currently, it’s difficult for data scientists to ensure predictions are kept up-to-date because their models operate on static data. With AI Workbench, marketing teams can schedule a BlueConic AI Workbench notebook, which automatically ensures they can use the latest machine learning predictions in their customer segments and marketing applications.
Generative AI models are typically based on neural networks. These networks consist of interconnected nodes (neurons) arranged in layers, and they process data in a way that simulates the functioning of the human brain, allowing the model to learn and make predictions.
Notebooks in BlueConic are AI models that are written in Python and built in Jupyter and can be used to perform analysis, gain new insights, and further enrich profile data. in AI Workbench. Notebooks can be prebuilt within the platform and used without writing any code or customer built.
AI Workbench provides prebuilt AI models and examples for marketing teams to gain insights into their BlueConic data in real time. Data scientists can also write custom Python code in Jupyter notebooks, which defines parameters and what type of value they accept (string, integer, BlueConic profile property, BlueConic segment, etc.).
Marketing teams use the AI Workbench UI to supply values for the parameters of the machine learning models and analytics – without ever having to write any code. For example, to run an AI model that predicts customer engagement levels, you might feed order history and recency into the model as inputs. The profile properties 'order history' and 'recency' and the resulting output are parameters in AI Workbench. Marketing teams can easily update these values in the Parameters tab, without having to know Python.
Predictive analytics for marketing
Predictive analytics models examine customer data to make predictions about future customer behavior or events. For example, you can use AI Workbench to calculate a customer's lifetime value based on a customer's past spending behavior. You can also calculate scores that measure recency, frequency, and monetary value to create better customer segments to target customers based on past behavior or spending. With the Advanced A/B testing notebook, marketing teams can apply AI marketing to their A/B tests to find the optimal content variants.
The initial input or instruction provided to the AI model to initiate the process of generating output. The prompt is typically a piece of text or a specific format that serves as a starting point for the AI model to understand the desired task or context. For instance, in the case of a language model, the prompt could be a sentence or a few keywords that instruct the model on what kind of text to generate. Depending on the complexity of the AI model, prompts can vary in length and specificity.
Propensity to churn
Propensity to churn is a metric that estimates the probability of a customer to terminate their relationship with a brand. Using propensity to churn metrics can be valuable to identify at-risk customers and keep them from discontinuing their service. BlueConic provides two prebuilt AI Workbench notebooks relating to customer churn: predict propensity to churn and analyze propensity to churn.
Python is a widely-used, object-oriented programming language. The BlueConic Python package is the cornerstone of building custom notebooks in AI Workbench. This is how data scientists can create their own AI models, by using Python in the Jupyter notebook environment. Using it will open up endless possibilities to analyze and optimize your BlueConic profile data. See the list of Python dependencies for AI Workbench developers.
Recency, frequency, monetary value (RFM)
Recency, frequency, monetary value (RFM) is a metric that identifies loyal customers by analyzing how recently they made a purchase, how often they make purchases regularly, and how much they spend on average. RFM analysis numerically ranks all customers in each of these three categories on a scale of 1 to 5. BlueConic provides a prebuilt RFM notebook in AI Workbench that performs this analysis and scoring. With RFM scores stored in customer profiles, you can create segments like "Frequent Buyers," "Recent Customers," or "Top Spenders in the Month before Holidays." This will allow you to market to specific types of customers based on their purchasing behavior.