How The World Champion Red Sox Can Help You Improve Your Franchise Recruitment

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The excitement has dwindled, and most are no longer thinking about the baseball playoffs. Unless, of course, you’re me – a devout Red Sox fan. I enjoy any excuse to talk about my favorite team, but this one is a good one.  The Red Sox is a team totally transformed by using data and statistics to go from worst to first. In 2013, Boston Red Sox General Manager, Ben Cherrigan (user of the “MoneyBall” approach to baseball) found a way to get better results and rebuild a winning baseball team.

Can you implement the same techniques as the Red Sox to find better franchise candidates?  Yes! Do you have a limited budget for franchise recruitment?  Are you looking for ways to get better results with fewer resources? Then take a cue from some of baseballs most successful General Mangers by implanting three key “MoneyBall” tactics that can help you get better short- term and long-term results.

1.  Use Past History To Figure Out What Works. Baseballs most successful teams use data and statistics to help them understand that successful players get on base.  Study your most successful franchisees and identify, statically, what kinds of demographic, lifestyle, and other characteristics make them superstars.  In addition, what channels did they use to find you? Also look at your bottom franchisees to learn what doesn’t work. It’s as important to identify what works as well as what doesn’t work.

2.  Now That You Know What Works, Shift Your Dollars There.  Always focus your dollars on what is the most effective. If you are generating the right franchise candidates using SEO linked to a content-driven landing page, shift your resources in that area.  If posting on LinkedIn works, test paid advertising to help layer your message.

3.  Stop Funding What Doesn’t Work. If sound evidence shows that a certain investment is failing, repurpose the dollars to something that does work.  The Red Sox lost 69 games in 2012.  Cherrigan had to first analyze what was not working before he could design a World Series championship plan moving forward.

Embracing “MoneyBall” for franchise recruitment will provide you better information that could be your first step to designing a successful, results-oriented campaign that’s as big a winner as the Red Sox. Go, Sox!

Seven Highly Effective Ways To Use E-mail To Generate New Prospects

After running hundreds of e-mail campaigns for my clients, I’ve learned a few best practices. Note that these best practices focus on two key areas:  List and Content. A quality database combined with the right message will produce the best results possible.

1.  Identify your target audience.

Like with all marketing campaigns, make sure you have properly outlined whom you plan to target. Then you can talk with your data provider about what sources they have that will match your ideal prospect profile.

2.  Source your list by SIC and NAICS codes.

If you are targeting individuals who own and or work at a business, start with identifying Standard Industrial Classification (SIC) codes of the industries you are targeting.  SIC codes have been assigned by the US government to classify businesses.  In addition to SIC codes, the North American Identification Classification System (NAICS) has also been developed to provide more specific categories of industry.  Always be sure to cross-reference both systems when using this approach to build your initial target list.

3.  Segment by geography.

Focus on growth areas with geographic markets that have a higher qualified inquiries segment and/or markets that make the most sense from an operational prospective.

4.  Segment by firmographics and demographics.

What are the key firmographics and demographics of your target profile? Here are some sample selects that are available to you.  Note that this information can tie back to the SIC codes I noted earlier.

• Company

• All Address Information

• Latitude

• Longitude

• Telephone

• Fax

• URL

• SIC 1

• SIC 2

• SIC 3

• NAICS Code

• Sales Volume

• Number of Employees

• Time in Business

• Square Footage

• Number of PCs

• Name

• Ethnicity of Contact

• Gender of Contact

• E-mail Address

• Title

5. Test the data.

Data compilers use multiple sources to identify and verify information.  Some data is complied through online information like job boards, discussion groups, and feedback forms. Some data companies use “old-fashioned” outbound telemarketing to verify sales figures and e-mail addresses. It’s always a good idea to test in order to verify your data.  That means you should always try to test several lists, try A/B splits, and measure results to determine what works best. It’s also a good idea to establish a relationship with two or three data firms.  They are experts in what is a very dynamic industry, and they can help educate you on the ins and outs of data compiling, keeping a clean list, ensuring that you are spam compliant, segmentation, and targeting.

6.  How to write a successful e-mail campaign.

Here are some tips for writing a successful e-mail campaign.  If you don’t want to try this yourself or have tried something like this with lousy results, contact me and I’ll help.

1.  Keep initial contact brief.  Since you are writing to cold prospects, keep your first communication short and simple.  Once you start engaging with the prospect, you can get more fancy.

2.  Make it personal.  Use personalized text e-mails that come from the salesperson. State clearly why you are trying to communicate with them. Make it easy to read.

3.  Ask a question.  I’ve had good luck with asking a simple question in the subject line.

4.  Test html vs. text.  Although I have found that text beats html, it’s always a good idea to test it for yourself.  Set up a simple A/B test with analyzing opens, clicks, leads, opportunities, and opportunities won.

7.  How and when to deploy the campaign.

I’ve tested sending e-mails during all parts of the day and found that early morning and late afternoon work best.  Regarding deployment, it is very good idea to build a relationship with your data provider.  In most cases, they will provide you the data, deploy the e-mail, and report the results.  Most importantly, they will help you understand what works and what doesn’t so you can adjust the campaign and improve results moving forward.

Would an Algorithm That Forecasts the Value of a Prospect Help You in Your Efforts to Accelerate Deal Flow?

I took my young son to his first baseball game recently. A major right of passage complete with hot dogs, peanuts, and ice cream. As I sat beside my son teaching him how to keep score, we launched into a conversation about how players are selected to build a team. No, it’s not like getting picked for dodge ball. It’s a lot more complicated than that. As a matter of fact, over the last ten years, most major league teams are using statistical analysis to place a value on major league baseball players. For an entertaining example of this, catch the movie from a few years ago called Money Ball, which stars Brad Pitt. So, what’s that got to do with lead generation? Well, it got me thinking…mining for qualified, quality prospects who are interested in investing or buying a small business isn’t too unlike major league player selection.

Can you predict the way people will behave based on statistical analysis? Yes. One thing we know for sure, big consumer brands use predictive modeling to accelerate the growth of their business. Companies like Proctor & Gamble analyze credit card purchases to help determine who buys what and how much. Grocery store chains do it. All retailers do it. Is it possible to create a database that indicates who is likely to invest or buy a business? Could there be a “Moneyball” algorithm for buying businesses? Yes!

If you have followed my blog, The One-Minute Lead Generator, you know that I enjoy focusing on this subject.  And if you have read my book, The 30-Minute Lead Generator, you know that I have an entire chapter that focuses on predictive modeling.  (If you haven’t read the book, I’ll send you a free sample chapter.  Just click on the link below.)

Let me return now to my original question I asked in this post: Would an algorithm that forecasts the value and performance of a prospect help you in your efforts to accelerate deal flow? You bet it would, and I can show you how. Click on the link below to read more on this subject.

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White Paper: Can Predictive Modeling Help Identify Potential Franchisees?

When I first started working and consulting with franchisors, I was a little bit surprised by how little statistical modeling was used to help identify potential franchisees.  I had used the statistical modeling approach throughout my career. For example, when I was the Vice-President of Sales at Home Shopping Network, we regularly used past behavior to help predict what people might buy in the future. In a marketing position previous to Home Shopping Network, I used modeling to help improve conversion rates of catalog mailings.  I have always had pretty good success using statistical modeling to help guide my decision-making.  So, I wondered why not apply this same approach to help identify potential franchisees?

I decided to write this White Paper to give you valuable insight into my experience designing predictive models to help create databases of potential franchise prospects. Did I lose you? Don’t panic, there is no math involved. In this White Paper I share a few stories regarding how math was used to help predict who would purchase a franchise.  I also touch on the trials and tribulations of developing what has been rarely done in franchising – using statistics to identify past behavior and using that information to help predict the future.

For your free download, just click on the link below.

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