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5 Tips for Lead Scoring by Economic Development Corporations


What is a lead? The answer depends upon whom you are asking. In the case of economic development efforts for cities, towns and municipalities, a lead is usually defined as a company which could potentially consider re-locating, expanding into, or otherwise setting up shop in the area.

Economic development corporations (EDCs) large and small have limited resources (i.e., time and money) with which to reach out to prospective leads. Whether you have a marketing staff of 15 or 2, there is only so much time that can be devoted to talking to prospective businesses each month.

And, whether you have an annual marketing budget of $1 million or $20,000, there is a limit to the size and scope of marketing campaigns, advertising placements, and in-person visits you can afford. Couple that with the fact that there are tens of thousands of possible prospects in the market and you realize that you just cannot meaningfully reach out to everybody. You clearly need to narrow down the field.

The Solution: Lead Prioritization

That leaves you with two choices: you can either take a passive, “random walk” approach to identifying and reaching out to leads – whereby you spend your time and resources pursuing those leads that happen to cross your path.

Or, you can take a systematic approach to identifying and prioritizing your leads in terms of those who are most likely to respond to what your city or town has to offer.

The latter option is called lead prioritization – and it is the option that is most likely to bear fruit.

When faced with the idea of prioritizing your leads, you again have choice. You can take a simplistic approach of choosing companies located within a given geography (such as California, Cleveland, the Northeast, etc.) or by leveraging another one-dimensional variable.

Or, you can score your leads by assigning an overall score to each lead based upon its unique set of characteristics. The second option – called lead scoring – is the most precise and, again, will bear the most fruit.

5 Tips for Lead Scoring for EDCs

If you are considering prioritizing your leads through lead scoring, here are 5 tips that will help ensure you get it right:

1. Start with a List that is Complete:

Make sure that the list you start with (or purchase) is as complete as possible. This means that it contains all of the basic company information (e.g., Company Name, Phone Number, Address, City, State, ZIP, etc.), as well as contact person information, if available (e.g., Contact Name, Contact Title, Contact Phone Number, etc).

2. Start With a List that is Clean:

A clean lead list is one that has values “filled in” for each data type. Meaning: the Company field should have a company name for each record/row in the list, rather than a blank spot. A clean list is also one that has the right data in the right fields.

For example, if a phone number appears in the Contact Name field, that would need to be resolved. And, erroneous characters need to have been removed from the data. For example, having the following characters appearing in various places in your lead list data might introduce problems later: /, , *, ~,. Better to remove them.

3. Append Additional Company Data to Your Original List:

Data appending refers to the act of adding more fields (i.e., types of data) to your existing list. When it comes to lead scoring, desirable appended data items might include: industry code, annual sales revenue, employee size, year established, rents vs. owns, facility square footage, and location type (i.e., whether the particular location is a subsidiary, branch, headquarter or single location).

4. Assign a Score to Each Possible Value in Each Field:

This is best explained with an example. Let’s say you have appended industry code, annual sales revenue, and location type to your original list. Each of those fields will have a range of possible values. There are hundreds of niche industry codes, for example.

Meanwhile, annual sales revenue is displayed in terms of ranges (e.g., under $500,000, $500,000 to $1,000,000, etc.).

You will need to assign the appropriate score to each possible value within each data type. For example, maybe electrical engineering companies would be assigned a score of 10 while mechanical engineering companies would be assigned a score of 8.

5. Combine the Individual Scores Into a Single Score for Each Lead:

Finally, in order to make sense of all of those scores such that you can prioritize your leads, you will need to combine the individual data field scores for each lead (i.e., each record/row) into a single score. This is accomplished by developing a formula that combined the scores together. Usually, the formula will allow you to weight each individual data value score.

For example, you might have evidence that industry type is the most important, so maybe it gets a weighted of 0.5, while revenue range only gets a weight of 0.3 and location type gets a weight of 0.2.

Once you have combined each individual data value score into a single score for each lead, it is then very easy to sort your leads by this score. The higher the score, the more time, attention and resources you should spend on trying to build a dialogue with that lead.

Maybe the top 20% of your leads deserve a LinkedIn InMail contact, a phone call, or letter. Meawhile, maybe you e-mail (from a CAN-SPAM-compliant list) 100% of your leads. In other words: differentiate your marketing outreach based upon groups of lead scores.

Bonus tip: once you have scored and prioritized your lead list, be sure to make sure it passes the “common sense” test. In other words, if among your top 50 best-scoring leads there are several leads that do not ring true as being great opportunities, examine why that may be.

This could be a sign that you will need to revise your scoring model in order to weed out undesirable companies. If so, do so, then re-calculate your lead scores. Happy hunting!

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