Intent data is the behavioral and transactional information collected from individual B2B .Intent marketing processes the data to predict user intent to buy, process, or to adopt a specific service or product. The user can be an anonymous visitor or a lead, a prospect or an existing customer.
There are several benefits in predicting user intent, especially in a B2B environment:
- It increases your ROI by increaseby removing ‘the absence of awareness stage creation in promoting a service or product.
- Asit is the cornerstone of predictive lead scoring and account based marketing, it increases prospect conversion to customers and assures better customer engagement and after sales service.
But the question is, how this is achieved.
There are several sources of data you can be use for retrieving user intent data. These may include:
Internal data, or 1st party data, comes owned your own data sources. , including your company’s web site, past transactions, or any financial information coming from an integrated CRM or ERP platforms. They may be structured or unstructured such as behavioral information coming from social media etc.
You need a powerful computer to effectively handle your 1stparty data but, depending on the model, you can still find insights in them According to some recentstudies, 48% of marketerssurveyed that their 1st party data was important to their operations, while the remaining 51% considers both 1st and 3rd party important.
You acquire your external, or 3rd party, data from data providers. While they only provideless-valuable “generalized” information many marketers find this data useful as well.
Regardless of the type of data, you can use user intent data to predict the outcomes of your campaigns, processes, and operations.
Machine learning is a subset of artificial intelligence and computer science, where systems “learn” from data to make decisions and predictions It enables computers to improve performance and make data-driven decisions without being explicitly programmed.
Machine learning, along with statistics, is a part of data science. Its algorithms and predictions are only as good as the dataset used to build and evaluate its predictive model and parameters. Furthermore, within data analytics and computational statistics machine learning is a method fordesigning and building predictive models and algorithms These analytical models, known as predictive analysis, allow researchers, data analysts, and scientists to produce reliable, repeatable decisions and results.
In a programmed prediction method , you need to implement a decision path based on your business rules resulting to a series of complex if,then, else clauses. In machine learning however, someone may start with series of data for example produce outcomes, and then apply a pattern-driven model to the data to predict the output of a anotherdataset based on the model/pattern.
Machine learning methods can be categorized as supervised or unsupervised.
Supervised machine learning
Although not explicitly implied above, in supervised machine learning, you might have is a series of example or historical data for a given outcome wantsthat you use to predict the outcome of another dataset. You can do this by separating thedata you to serve as your model, known as training data, from the data you want evaluated (evaluation data).
Since there always is an outcome, this will result to a comparison between the predicted and the actual.
An example of supervised machine learning is testing prices against man days to predict the price fora new service. This is achieved by applying a model that best fits to existing data and reevaluating itwhen new data comesarrives. Supervised machine modelslearning models may include regressions (linear, polynomial, logistics) and classifications.
Unsupervised machine learning
Withunsupervised machine learning, you still have a training set of example or historical data, but there is no specific desired output Unsupervised machine learning is the most common category and is mainly used in pattern detection and descriptive modelling.
A typical example of unsupervised machine learning is having several customer product ratings that you want to use to predict what a new customer may buy. Unsupervised machine training models may include clustering, association discovery and anomaly detections.
Reinforcement machine learning
Reinforcement learning allows machines and software to automatically identify the ideal behavior within a specific context, having as objective to maximize performance. In reinforcement machine learning, a machine / software (the agent) takes an action that has the bigger probability to return an award from the environment. After several irritations, machines “learn” to avoid actions that didn't result to award and to reach the optimum performance:
Reinforcement machine learning does not require historical data . Common algorithms are neuro networks.
Machine learning process
Regardless of the model type (for supervised / unsupervised) usedyou use, there are several steps you must take , when implementing a machine learning model.
Data gathering and preparation:
- Collecting intent data (either 1st or 3rd party data),
- Noise separation and keeping only valuable and exploitable ones.
- Data consolidation by removing duplicates coming from different systems In the case of supervised learning, for assuring better reliability after data consolidation, data will be distinguished to training and evaluation data:
- Those that will be used for building the model (training data)
- Those that will be used for evaluating the model (evaluation data)
After choosing and building a model, you should evaluate the model and only deploy it if successfull.
As described above, the model definition depends on the used dataset size bigger datasets are more reliable This is a basic problem of the machine learning method, the so called “cold start”.
Choosing a model
Choosing the model type and applying model parameters beenare the most critical points in any machine learning method. Intent data sets are often large with numerous exploitable attributes, and -depending on the case- they may form a multifactorial system of equations that cannot be accessed using if..-then..-else clauses. Thereforem you must define your machine learning models to minimize the possibleerrors for a specific dataset.
How it works? A (very) simplified example
In order to illustrate the above, consider the following example: a customer asks a delivery company to estimate the time required to delivery something from a specific store to a specified delivery location.
Conventionally, time required should be proportional to location distance from the store. Proportional means that it should follow an algorithm of type:
Here,y is the time required and x is the distance. Values of a,b may be defined according to speed limits, working hours etc.
Often in machine learning - and this is the case of supervised machine learning- you may apply a linear regression model. For a specific store, historical deliveries may produce a distribution like:
Linear regression is also described by y=ax+b. But in this case:
- Values of a,b are defined under the condition of what are the values that produce the minimum of the sum of the square of the vertical distance between delivery times (the red points) and the line.
- Values of a,b are continuously adjusted, according to successive deliveries delivery time.
User intent and machine learning
Predicting user intent may not be as simple as the above example:
- - There are several customer features that should be taken into account: what are the geographical data? Is there any similarity with other customers? What is the customer group?
- - There are several product features that may be taken into account. What are other products that a specific is related? What are the competitive one? How a product group is ranked to a customer segment profile?
- - There are several outcomes that may be of interest: is the customer financially reliable? What is the ROI? What is the lead scoring etc.?
All these, result to multifactorial equation systems that can be solved only computationally. Big data analysis, computer hardware evolution and cloud architecture have resulted to make user intent predictive analysis feasible and efficient. Today cloud architecture with API integrated platforms give the opportunity to even small sized organizations to exploit such features.
A most common example used in predicting user intent is recommender systems. Recommender systems or engines is a subclass of information filtering system that aims to predictspredict the preference of a user into an item. It is seen as an intelligent and sophisticated salesman who knows the customer behavior and can make intelligent decisions about what recommendations should benefit the customer most. Reliable recommendations can result to more effective personalized content, advertising thus increasing lead conversion rate.
There are several types recommendation engines depending on the algorithm applied for filtering such as collaborative filtering, content-based filtering, demographic filtering etc with the first two (and the combination of them) being the most widely used. From a machine learning point of view and regardless of type, recommender systems are based on unsupervised machine learning models.
In collaborative filtering recommendations are based on product ratings, derived either from explicit data such as user ratings or from implicit data such as web site user activity or social data. In content based filtering, the system makes suggestions based on the user profile related to product features vector. User profile may be based on customer segmentations or groupings
Recommender systems are currently used in several industries :
- Customer who bought this also bought.(www.amazon.com)
- Up next (Recommended videos).(www.youtube.com )
- Other movies you may enjoy.(www.netflix.com )
- People you may know... (www.linkedIn.com )
Furthermore someone can reveal recommender engines through all of the customer journey:
- Personalized content
- Personalized ads
- Voice recognition (NLP), chatbots
According to studies 35% of Amazon total sales and 78% of Netflix total watches are based on recommendations and these values are continuously increasing.
Thanks to latest technology achievements, machine learning techniques have become a building block in automated marketing based on user intent. It can derive more accurately and efficiently user intent throughout the customer journey leading to better customer engagement.