Out-of-the-box ML models fail to deliver the accurate results.
Predict with custom built ML models.
Machine learning projects are notoriously resource-intensive. Even if you have a good model, it can be hard to feed enough high-quality data. Once you figure that out, it can be even more time-consuming and difficult to activate those insights, so they’re actually benefiting your customers.
Some platforms address that offering out-of-the-box models claiming that they can “predict any action”. However, the reality is different, pre-packaged models rarely give accurate predictions. Every business has different use cases and distinct customer profiles - there is a significant data variation even within specific industries. Out-of-the-box ML models are not capable of accurately standardizing widely distributed variables.
With Intempt Open CDP, you can build custom ML models that accurately fit your business case and predict actions based on your unique customer profile.
One-size-doesn’t-fit-all, but here are a few teasers of what models you can build
Predict near-future repeat purchasing behavior based on past transactions as well as email and browsing behavior. Aim your marketing activity at those with a high purchase probability.
Trigger relevant marketing messages and/or prioritize your sales team’s outreach efforts when a prospect reaches a certain threshold in your lead scoring model.
Predict the expected revenue or margin associated with a particular customer in the next 12 months. Easily and accurately identify your VIP customers and create targeted campaigns.
Identify at-risk customers, particularly those who are becoming less likely to buy over time. Create win-back campaigns focused specifically on customers you are most likely to lose
Use the power of machine learning to predict any likelihood or behavior
Predict which customers will behave in certain ways, and create target campaigns accordingly.
Drive better CX with predictive attributes
Likely to buy, likely to churn, likely to engage on email. Leverage ML attributes to drive the right experience.
Adapt to changing conditions
ML models are continuously recalculated so you are always predicting user actions based on most recent data.
Advance your campaigns by anticipating customers actions and engaging before they churn or leave your site.
Discover lookalike audiences
Determine your ideal customer persona attribute and use predictive scores to create lookalike audience segments to activate.
Choose the best channel to engage
Predict which engagement channel user will most likely respond to.
Unlike most ML-powered customer insight tools, Intempt Predictive Models does not just generate a number that you have to trust on faith.
Get full visibility on your data
Create your own bespoke ML model to give full visibility into data used, attribute weighting and more.
Build models that fully align with your goals
Configure your model based on your own goals and then use the scores to drive action all in the same platform.
A simple, easy-to-understand UI that enables anyone on the team to upload your custom ML model to predict customer behavior. Activate a model directly driving CX actions in under few minutes.
Activate ML models with any integration
The Predict score becomes another data point in the customer profile at the end of every visit. The score is available for use in real time to trigger actions in our downstream integrations.
Predictive models use the power of machine learning to predict the likelihood of any behavior you’re tracking in Intempt Platform.
Generate attribute values for your customers based on the trained ML model. Use the attributes to create predictive segments to engage your customers.
Intempt ML models are custom made and are fully tailored to the specific business model and use case.
User predicitve data to get deeper insights into future ROI combining behavioral, transactional, and demographic data.
Analyze your seed audience, identify predictive attributes and group customers who are similar to your target audience.
Leverage predictive attributes and segments with well-documented REST APIs, server and client-side SDKs.
Steps to get started with predictive modeling
Exports data to your preferred storage source
Ingests and processes the model
Creates predictive attributes
Updates predictive values using newly arrived data
Gather the dataset for your selected ML model
Bring data scientist to build and train the ML model
Upload the packaged ML model via Intempt console
Select the the required dataset mapping
A truly unified customer experience requires integration across the entire technology stack - including eCommerce platforms, Ad networks, Messaging services, CRM and customer experience channels.
Explore how our vendor integrations can improve your workflow to acquire, convert and grow througout the customer journey.
Refine the selection by filtering the use cases by industry, role and connectors.