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Disparate data sources and marketing applications are leading to inconsistent and inaccurate data making it difficult for marketers to keep up with lead requirements and demonstrate marketing’s contribution to revenue. Marketing teams need to rationalize program spend, investment in applications, and demonstrate marketing’s contributions to the sales funnel. While data normalization is nothing new, automating the process amidst disparate applications, large volumes of data, and varying data sources can be very taxing for any sales and marketing operations team. To ensure success, assign a leader, document your existing processes, develop business requirements with buy-in from sales and marketing, and schedule recurring business reviews to identify areas for improvement.

Disparate data sources and marketing applications are leading to inconsistent and inaccurate data making it difficult for marketers to keep up with lead requirements and demonstrate marketing’s contribution to revenue. Marketing teams need to rationalize program spend, investment in marketing applications, and demonstrate marketing’s contributions to the sales funnel. While data normalization is nothing new, automating the process amidst disparate applications, large volumes of data, and varying data sources can be very taxing for any sales and marketing operations team. * Business requirements and objectives should be defined jointly by sales & marketing with buy-in from sales and marketing leadership.

Rather, it does so slowly and almost imperceptibly: a couple of transposed characters here and there, a few missing email addresses, contact information that goes out of date, and duplicates. Before long, your ability to get your message out, contact key clients and prospects, or fill the top of the sales funnel becomes severely compromised. Just as the leaky pipe is hidden behind a wall, the true scope of the dirty data problem can remain undetected for years, until it is exposed. When you consider that bad data costs U. S companies alone up to 27 percent of their revenue in the form of wasted resources, lost productivity, cancelled orders, and poor customer retention, it would seem that a continuous data maintenance program should be at the top of the budget agenda.

the average rate at which a (MQL) converts to a Sales Qualified Lead (SQL) This model only accounts for the revenue lost by ignoring the contacts that decay and presuming that none of them can be recovered. In this model, we’ll estimate the potential revenue that could be recovered if the company implemented a strategy to mitigate the effects of contact decay. * 30 – number of recovered SQLs that are worked to Closed Won status Of course, this is only one example where bad data costs your business, others are: labor costs, customer satisfaction costs, billing and collections, reputation, and others.