The proportion of records in the positive list or the house list that display a given indicator.
A segmentation tool in the Predictive Insights platform that allows users to build segments based on models and scores, new and/or existing records, as well as specific indicators. Note that new records are defined as ones that were not introduced to the models in question.
A tab in the Predictive Insights platform that allows users to review the full DNA profile of the positive list and house list that makeup the underlying model. The DNA is comprised of thousands of indicators, which could be selected by users for enrichment (record and list append). The functionality of the tab also allows users to download the list of indicators presented on screen, as well as change the tile view to a list view.
A tab in the Predictive Insights platform that presents the Ideal Customer Profile. The tab is comprised of several screens; The indicators screen displays a set of a few tens of indicators recommended by the model as prominent ones. The other screens display the firmographic characteristics of the positive records used in the model.
Lift (on enrichment screen)
In the context of a single indicator value, the lift is the ratio between the coverage over the positive list and the coverage over the house list.
Lift (on prioritization tab)
The multiplier over the baseline conversion rate. It is calculated for each rank by dividing the proportion of the positive records in that rank (positive records/(positive records + house list records)) with the overall proportion of positive records across all ranks. By multiplying the lift by the baseline conversion rate, users can assess the projected conversion rate per rank.
The object in the Predictive Insights platform that represents a single- product model or a multi-product model. The object stores the model information itself, as well as additional components such as indicator selections, integration configurations etc.
The House List is the universe of potential targets (prospects), in your systems, for this model. Examples:
If you are trying to optimize the Marketing funnel, then the relevant universe consists of all of the leads in your marketing platforms.
If you are trying to optimize the BDR performance, then the relevant universe consists of your MQLs that are passed on to BDRs.
The positive set is a subset of the total universe, where the desired outcome has occurred. For example, if we are optimizing for closed-won, we take the closed-won records from the CRM.
A tab in the Predictive Insights platform that allows users to review that score distribution of the model records and the per-rank lift over the baseline conversion rate. In addition, users can use the record density bar to adjust the score thresholds, which change the definition of each rank.
Rank (on prioritization tab)
The grouping of the model scores into 4 or 5 groups – A, B, C, D and F (if configured), where each rank represents a different score band. The rank definitions can be adjusted through the Prioritization tab.
Score (on prioritization tab)
The numeric representation of the model-based propensity to convert. The higher the score, the higher the propensity of the records assigned with that score to convert.