Intent & Prediction

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Digital Marketing Solutions

Dig into consumer intent and uncover critical timing. Consumer prediction on the next level.

A data-driven approach that provides businesses with insights into consumer behavior and buying trends. Using advanced predictive analytics, analyze customer data and predict customer actions. This innovation enables businesses to better understand consumer preferences, create targeted marketing campaigns, and develop strategies to increase customer engagement and loyalty. Gain new insights into the customer journey and strengthen consumer-facing strategies.

Data-driven decisions based on diverse datasets and AI intent and prediction tools. At your fingertips.

Intent & Prediction

Explore & learn how Intent & Prediction can bring value to your organization

Unearth hidden consumer needs and preferences and predict future buying behavior.

Predict purchase intent, measure interest, focus organizational resources

Purchase intent can be predicted by examining consumer spending patterns and behaviors. By considering the actions of an individual, or organization as a whole, looking at what products they are buying or how frequently they purchase, what things they are looking at online, evaluating their attitudes on social media, and examining a number of other factors, you can create reasonable expectations for behavioral next actions. With enough information, marketers and leaders and determine which offerings might appeal to particular consumers and drive them to make a purchase.

With the right tools and the right data sources, you can accurately assess where a potential customer is in their purchase process.

Measurement of interest in certain products or services could include evaluating site traffic and data analytics (number of visits, time spent on page), captured data from customer surveys or polls, reviews/testimonials posted on sites like Yelp or Amazon, email open rates for promotional campaigns related (or unrelated) to the product offering etc. Focusing resources appropriately would require analysis of these KPIs (key performance indicators) combined with other factors that may affect consumer behavior such as seasonality trends i.e., when specific items tend to sell more due higher demand during certain times period per year. For example focusing efforts around holiday seasons for seasonal items e-commerce retailers may find successful outcome compared to regular months.

Intent and prediction for digital marketing

Digital marketing is an increasingly important component of a business’s overall advertising strategy. The goal of digital marketing is to use various online channels (including search engines, social media, email and websites) to engage with potential customers in order to build brand awareness and drive sales conversions.

By taking advantage of tracking tools such as analytics software or social listening platforms, businesses can create individualized campaigns that target specific users based on their geographic location and interests. Organizations and companies are incorporating artificial intelligence into strategies by leveraging machine learning techniques such as predictive analysis in order to optimize their messages for greater impact across all platforms. Leveraging predictive analytics tools, marketers can analyze large datasets to anticipate consumer behavior in order to tailor highly individualized experiences for each person they engage with. For leaders, the intelligence offered by intent data and prediction tools let’s your company focus on creating more meaningful connections.

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Resources for learning more about Intent & Prediction and how to make this work for your organization

For those looking to learn more about intent and prediction, we recommend checking out the following resources:

For those interested in learning more about Intent & Prediction, we recommend checking out the following videos, articles, and books:

Videos about Intent & Prediction

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Articles about Intent & Prediction

This article introduces methods to infer analyst intent behind selections in data visualizations, such as scatterplots. A prototype system is presented that can auto-complete partial selections and capture pattern-based intents. Evaluation in a crowd-sourced study shows the accuracy of these methods and their potential for improving accuracy and reproducibility of analysis workflows. [Predicting Intent Behind Selections in Scatterplot Visualizations, (online access, PDF download) hosted at the University of Utah]
This (seemingly unrelated) article examines the relative ease of application and predictive performance of factual questions and attitudinal questions in the context of predicting willingness to donate blood. Results suggest that factual questions are easier to apply in the field than attitudinal questions, while both approaches have equivalent predictive performance. We find there to exist a broad applicability to research findings. [Predicting behaviour: comparing the performance of factual versus attitudinal approaches, (online access) hosted at the International Journal of Social Research Methodology]
This study examined the effect of combining a motivational intervention based on Protection Motivation Theory with a volitional intervention based on Implementation Intentions in order to increase subsequent exercise behavior. Results showed that while the motivational intervention increased threat and coping appraisal and intentions to engage in exercise, the combined PMT/implementation intention intervention had a dramatic effect on subsequent exercise behavior. [Combining motivational and volitional interventions to promote exercise participation: protection motivation theory and implementation intentions, (online access at PubMed) hosted by the National Library of Medicine, NIH.gov]
This interesting paper discusses “a system for modeling human task-level intent through the use of Markov Decision Processes” for the purposes of “a robot to estimate a human (or robotic) companion’s current action(s) and goal priorities, and then use this information to predict their intended future action sequence”… [Human Intent Prediction Using Markov Decision Processes, PDF (download) hosted at University of Michigan]
Catharine L. R. McGhan of the above U. Michigan paper; the research goes on with further intent prediction data modeling and makes direct comparison to broader applications of said models. [Safe and Efficient Robot Action Choice Using Human Intent Prediction in Physically-Shared Space Environments, PDF (download) hosted at University of Michigan]
This article examines the relationship between stated intentions and actual behavior in survey responses, with a focus on binary outcomes. It discusses the limitations of intentions data and suggests that individual differences should not be assumed to “average out” at an aggregate level. [The Use of Intentions Data to Predict Behavior: A Best-Case Analysis, (online access) hosted at Taylor & Francis]
This paper, Intent Mining: A Social and Semantic Enhanced Topic Model for Operation-Friendly Digital Marketing, introduces a social and semantic enhanced topic model (S2TM) for mining users’ intents from user-item visits. It utilizes graph neural network atop of a social network to optimize the user-intent estimates, as well as skip-gram word embedding approach by linking item semantics to pre-trained word embeddings to optimize intent-item estimates. An efficient stochastic variational inference algorithm helps with learning latent variables and parameters, which is then validated through real world experiments in terms of perplexities, coherence scores and click through rates from Alipay marketing campaigns. (online access PDF at Ieee.org)