We all know that bad data is, well, a bad thing. But few have stopped to take the time to quantify its economic impact, and the potential revenue upside in creating a regular data hygiene program. These are simple models to follow and will provide a good “back of the envelope” view of the revenue lost or gained.
For both scenarios we will use the following assumptions:
- 100,000 – number of records in the contact database
- 30% – the industry average annual rate at which customer data decays
- 20% – the average rate at which a contact converts to a Marketing Qualified Lead (MQL)
- 10% – the average rate at which a (MQL) converts to a Sales Qualified Lead (SQL)
- 25% – the rate at which an SQL converts to Closed Won
- $50,000 – the Annual Contract Value (ACV) of the product being sold
Lost Revenue Model
This model only accounts for the revenue lost by ignoring the contacts that decay and presuming that none of them can be recovered.
- 30,000 – the number of contact records lost to decay
- 6,000 – number of potential MQLs lost to decay
- 600 – number of potential SQLs lost to decay
- 150 – number of closed opportunities lost by default owing to decay
- $7,500,000 – in annual lost revenue due to decay
Recovered Revenue Model
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. This is normally an intentional process of “following” a known contact to their new job or position and/or identifying his replacement in order to continue or restart the sales campaign.
In addition to the assumptions outlined above, we add the following:
- 20% – the number of “lost” contacts recovered and returned to Sales
This results in the following recovery scenario:
- 30,000 – number of contact records originally lost to decay
- 6,000 – number of records recovered
- 1,200 – number of MQLs that are recovered
- 120 – number of SQLs recovered
- 30 – number of recovered SQLs that are worked to Closed Won status
- $1,500,000 – in annual revenue recovered
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. Implementing a contact data maintenance plan will provide improved outcomes for your sales team and increase your bottom line.