Due to high availability of customer datasets and the complexity involved in using these datasets productively, there has been a great need to streamline the usage of this data. One of the common solution to this is ‘marketing automation’.
But, somehow, traditional marketing automation has often not given the expected rewards. There are various reasons for this ineffectiveness, ranging from ‘inadequacy of continuous lead generation’, ‘less intelligent data handling’ to ‘worsening the customer’s experience due to bombardment of irrelevant information’. Out of these reasons, less intelligent data handling by market automation tool contributes greatly to the inadequate results of market automation.
The recent surge of data analytics abilities and their usage in commercial applications has brought in options of adding intelligence to marketing. ‘Predictive lead scoring’ really hits the nail when it brings in the smartness of mapping the offerings with the right set of customers by utilizing data from web, social media, CRMs and other external sources. It is able to score the leads based on how much is the probability of a particular lead/customer buying a product. Scoring will let the most relevant advertisements and offers reach every customer, improving the lead conversion rate, enhancing the customer experience and leading to a faster sales cycle.
Predictive scoring, if integrated into traditional marketing automation system, adds great value to it by making the lead conversion probabilities higher. The leaders in the field, like Amazon, besides many followers, have already reaped benefits of it. Not only, its benefit has been proven in B2C industries, it rather has established its utility in B2B industry as well. The good part is that it is no more a daunting task as it could have been if developed by an in-house team from scratch. There are players like Lattice Engines, Versium, Mintigo who offer out-of-the-box products for this purpose. These products are quite state of the art and not only use data which the marketer herself has, rather it adds the majority of data from external sources which would make a huge base to apply intelligence on, leading to highly accurate results.