Prospecting SMEs within a data-driven model
Demyst has helped several data-driven firms crack the code on how to find and rank the biggest and newest businesses within a particular geography, to identify which companies might be ripe for lending and insurance products.
We know of a company that started from scratch to accomplish a more data-driven approach to optimizing the SME target population. They first took a reference set of businesses from their market analysis and one of their external data providers. In this case, phone verification from a provider of business data in the US and Canada (the provider has a massive call center in the Midwest and makes approx. 300,000 calls to businesses every week!). To help focus the campaign, they selected a state in the US known for the fastest growing populations and by proxy a lot of new companies popping up too.
With this list, they created a dataset with commonly used fields for prospecting: Company Name, Filing Type such as Inc. or LLC, Address, Director/Officer First and Last Name, NAICs Code, SIC Code, Estimated Revenue Range, Estimated Number of Employees. The usual stuff we think of when we are trying to codify SMEs.
Because they were interested in newly formed companies that might need new financing, a new credit card, or insurance products, they cut the dataset down to the smaller set of SMEs that were contacted by the external data provider during the previous 60 days.
As anticipated, the dataset needed even more refining.
They had a relationship with one external data provider that had revenue and headcount estimates for the companies on the list. Yet there was a low level of confidence in the figures. Further analysis also concluded that most firms did not have good estimates, to begin with — more information was required.
Through a quick market review, they found another provider that had some of the additional data they required. Unfortunately, this meant they needed to strike up new commercial relationships with those vendors, causing additional cost and delays in bringing the capability live (it took a while to engage, align InfoSec and compliance, and commercially contract with the new vendors).
After some time, Demyst was called on to review the data sources, and data usage within the prospecting function as match rates and successful conversions were decreasing.
With our knowledge of the External Data Marketplace, we identified ways to increase match rates, reduce costs, improve risk and compliance, providing a more agile set of sources and attributes that helped increase the quality of prospects and positive-success rates.
We agree with the World Bank and believe SMEs are a vital growth opportunity for any lending or insurance company. Refining a vast ecosystem of potential targets to those that are highly likely to buy services improves the hit-rate for any advisor or agent. And greater hit-rates from the deeper understanding of SME business situations improve affinity. It enhances the likelihood of additional sales as you both move forward on your respective growth journeys.
Trying to do this piecemeal or through singular or multiple single data source relationships is both costly and time-consuming.
By making use of a single platform that provides frictionless access to 500+ data sources and 100,000+ attributes, you can activate this data-driven capability in a matter of weeks under a single commercial agreement. And this will be secure, compliant, and agile to help you expand your usage of external data into other critical growth areas.