Accurately determining your brand’s attribution model can be lumped into two categories of models: rules-based and algorithmic machine learning. A rules-based model is humanly deducted, and therefore could be labeled as fairly subjective. These more traditional attribution models are based on commonly understood assumptions about first and last touch, equal touch, time decay attribution, and other scenarios. For example, it was the last exposure that drove the person’s purchase decision or the first and last touches are given equal weight. Rules-based marketing attribution is known as the simple attribution approach. These models assign values to each consumer touch point that is established by a predefined formula. It assigns 100% credit to only one touch point in the purchase journey, usually the first or last one. To improve on this subjective process, in case of perceptions being misaligned, multi-touch attribution modeling that relies on the analytics maturity scale is more descriptive and involves a lower consideration process while leaning on machine learning. By replacing a single touchpoint attribution — last interaction and first interaction — to multi-touchpoint models, it is easier to assign credit to various touchpoints based on existing rules.