Revenue Science Has A Compound Effect
Dealtale fuses the data industry's two most powerful methods—causal machine learning and prescriptive analytics. When these two elements are combined, the results are explosive.
Traditional analytics and machine learning solutions aren’t designed to help marketers make better decisions because they are only:
Giving an understanding of what happened in the past.
-or-
Estimating what will happen in the future.
Prescriptive analytics, on the other hand, give marketers a precise understanding of how to improve their strategies in real-time.
Predictive technology assumes that marketing activities in the future will be similar to marketing activities in the past–which is why it lacks an element of improvement.
That’s why Dealtale focuses on outcomes instead.
Outcomes estimate how a KPI will change, based on adjustments to the marketing strategy.
For example, say you’re building a campaign to manage churn. You don’t want to just predict how many customers will opt out of your services – you want to reduce turnover and improve the outcome.
Dealtale helps you understand which actions will achieve those better outcomes through prescriptive analytics.
Predictive technology assumes that marketing activities in the future will be similar to marketing activities in the past–which is why it lacks an element of improvement.
That’s why Dealtale focuses on outcomes instead.
Outcomes estimate how a KPI will change, based on adjustments to the marketing strategy.
For example, say you’re building a campaign to manage churn. You don’t want to just predict how many customers will opt out of your services – you want to reduce turnover and improve the outcome.
Dealtale helps you understand which actions will achieve those better outcomes through prescriptive analytics.
Correlation involves two or more sets of data that appear to be related, but may or may not have any impact on one another. Unfortunately, many marketers are leaning on correlated data when they are reporting on past campaigns and making decisions for the future. Because their insights are not accurate, they have trouble recreating success–draining revenue from their funnel in the process.
Instead of correlations, marketers can achieve greater success looking at causality–the cause and effect of campaign outcomes. By understanding the root cause of customer behavior, you can take the right actions to maximize every single activation and achieve the best results. Dealtale’s technology is based in causal data science, helping marketers to scale this type of specificity across their entire marketing funnel.
Dealtale’s causal machine learning is designed to establish a relationship between cause and effect, based on all relevant data.
And if causal machine learning is the WHY of outcomes, predictive analytics is the HOW of improvement.
Once the effects of actions have been estimated, a prescriptive model is used to determine what the ideal course of action is for a particular customer scenario.
Maybe this is tailoring content to an individual prospect as they move through the funnel–or maybe it’s matching the right account executive to the right customer.
Dealtale’s causal machine learning is designed to establish a relationship between cause and effect, based on all relevant data.
And if causal machine learning is the WHY of outcomes, predictive analytics is the HOW of improvement.
Once the effects of actions have been estimated, a prescriptive model is used to determine what the ideal course of action is for a particular customer scenario.
Maybe this is tailoring content to an individual prospect as they move through the funnel–or maybe it’s matching the right account executive to the right customer.