Given the variety of options for both the model (approach) and the technology to support scoring, we felt it was important to identify and define the options. From our observations, today’s perspective and guidance is focuses too heavily on the technology (e.g. marketing automation vs. predictive), without a broader consideration for what approach makes the most sense for an organization. The types of scoring models include:
- Manual. Cherry-picking; no standardized methodology or process – not a recommended approach.
- Assumption-driven, rules-based. Attributes and weighting developed according to assumptions vs. informed by data – not a recommended approach.
- Data-driven, rules-based. Uses statistics to identify and prioritize attributes and behaviors that are configured into the model. When the scoring threshold is reached, the prospect is handed off for followup – a recommended approach.
- Predictive prospect prioritization. Prioritization models work like lookalike models – they mathematically compare known prospects and accounts to an ideal and prioritize accordingly. Followup is determined by statistical processes.
- Hybrid. Combines data-driven, rules-based and predictive prospect prioritization to determine hand-off and prioritize followup. Hybrid approaches score the account via predictive and deliver the contact via marketing automation platform (MAP) behavior.
I've completed abandoned lead scoring as a viable tool.
RYZZ: it’s a better approach to MarTech for B2B Marketers.
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