Strategic Partnerships and Lead Generation Shortcuts for Entrepreneurs

Date Updated: Friday, October 5, 2018

About the Author

Chun Ming leads a team of engineers as the technical program manager at Microsoft’s AI and Research group. He has also worked with Microsoft Research (MSR) scientists to coauthor a web search paper, which was accepted by an international computer science conference in Italy (Sigir 2016). In addition, he founded a growth hack project at Microsoft around a patent he authored as the solo inventor titled “Intelligent Tabular Big Data Presentation in Search Environment,” which inspired and informed this article.

Case Study: Customer Lead Generation with Dun & Bradstreet

An increasing number of enterprises want to monetize their big data by opening up public access via an interface known as the Application Programmable Interface (API). As one Amazon book reviewer put it: "APIs enable enterprises to publicize their business. An enterprise without an API in 2016 is like one without a website in 2006." For example, the US S&P 500 enterprise Dun&Bradstreet (D&B) calculates business credit scores for millions of companies worldwide and has collected data containing names, roles and contact information of decision-makers working at those companies. This company contacts data is useful for startups doing customer lead generation. Take for instance what Dorin Rosenshine, a Microsoft Accelerator alumni and CEO of startup Outleads had to say: “I need to find the correct person at each company, which I planned to do through LinkedIn. With Microsoft Accelerator’s help, I discovered D&B’s company contacts data. I was able to shorten the lead generation process and that is a huge help! Time savings-wise, I estimate 15-30 minutes for lead generation per company, so about 30 hours for 100 companies.” There are many hidden opportunities for startups to leverage such commercial data, but the challenge for enterprises owning commercial proprietary data—like D&B—is that their data have limited discoverability on the Internet. Hence, monetization is not maximized.

Graph showing upward trajectory Accelerating growth in public APIs (source: ProgrammableWeb)

On the other hand, Bing users are searching for queries with tabular data download and Application Programmable Interface (API) intent. Such results are best represented in a table format, therefore, the classic way to display organic URL results is not optimized for users to search for tabular data sources. For example, if you are a startup owner and you want to find a list of prospective business contacts from the investment banking and financial services company Citigroup, it takes time for you to find, generate and convert prospective customers into buyers.

Value Proposition

The opportunity is to marry the supply of commercial data with the demand for such data expressed as Bing queries. For end users who are dissatisfied with search results when searching for tabular data and 3rd-party companies who want to make more money from their data, the proposal is a big data search model to help enterprises monetize their data on Bing by showing big data results in a table format with the most relevant data rows and columns per query. In the D&B example, D&B can now advertise their datasets on Bing for startups to generate customer leads. For example, to find the names and contacts of key corporate decision makers at the retail fashion company Gap, all you have to do now is search on Bing with the query: “Gap corporate relations contact”. Beyond the D&B example, enterprises (i.e. big data owners) that do not traditionally sell their data to make money can now explore their underutilized data assets as new monetization opportunities since data that is seen as worthless for one company can be a new revenue generating opportunity for other businesses (i.e. big data consumers). This relationship between big data owners and consumers is a big data strategic partnership.

Relationship between enterprise big data providers leveraging Bing search to increase discoverability of their data to startup data consumers for company contacts lead generation.

The Jay Abraham Motivation

Chun Ming with Jay Abraham in Los Angeles Chun Ming with Jay Abraham in Los Angeles

This big data strategic partnership concept was inspired by an American businessman named Jay Abraham, founder and CEO of the Jay Abraham Group. He uncovers underutilized relationships, underperforming activities, and hidden assets among businesses for marketing and strategic partnerships--making about $2 billion for himself and over 10,000 clients in 400 industries worldwide. Some of his students include Daymond John, judge of the TV show Shark Tank, as well as Tim Ferriss, entrepreneur and author of the 4-Hour Workweek.

This is an important insight for startups because in Jay's words, most entrepreneurs have been trained to do everything alone: build your brand by yourself and grow in slow, incremental, linear steps. For example, after interviewing startups, I found that a customer lead generation strategy startups use is:

  1. Find name of a person (e.g. John Brown) on LinkedIn working at the company (e.g. Contoso) the startup is interested in
  2. Programmatically generate permutations of first and last names to guess email addresses owned by the person (e.g. johnbrown@contoso.com, jbrown@contoso.com, johnb@contoso.com etc.)
  3. Mass send emails

While this is smart and hacky, it is costly and inefficient as most emails will bounce! On the other hand, D&B has the complementary solution to the startup customer lead generation problem because they have collected contact names, emails, and phone numbers as a result of their research in business credit reporting. In general, for every problem you face in your startup, it is possible that there is another company that has the polar opposite of your problem--someone that has:

  • Existing customer relationships they are not maximizing
  • A sales force they are not optimizing
  • Facilities that they are not using
  • And so forth

Knowing how to identify such underutilized relationships, underperforming activities, and hidden assets enables you to grow exponentially. Some tips on how to structure such strategic partnerships with prospective partners:

1. Pre-emptively address questions that you prospect will be thinking when you propose a strategic partnership:

    • "What have you done in the past?" Show what you have done previously to prove your credibility and authority.
    • "How do I know you can deal with my problem?" Tell the prospect why you can deal with his problem based on your past experiences.
    • "How will you solve my problem?" Have a specific plan covering how you will solve his or her problem.
    • "When will you solve my problem?" Plan timelines proposing when you will solve the prospect’s problem

2. Show that you have their best interest in mind. Be willing to do small, safe experiments to pre-validate the idea before committing to it. If it's not successful, you lose and they have little or no risk/opportunity costs. If it succeeds, you both win. In the D&B tabular big data search context, experimental flights were done without D&B spending on advertising to validate the idea. To further reduce downside risks, legal resources were invested to file a patent for the tabular big data search prototype so as to protect intellectual property.

3. You have to articulate the problem/opportunity your prospects have better than they can describe it themselves because many people don't actually know what they want or have. In the big data strategic partnership context, companies have sunk costs, time and processes into their business data. When they are done with it, it has little value to them, but it could have enormous value to other people. D&B years ago got into the data-based management business (i.e. renting names, contact information) because it was a byproduct of all the research they had done in business credit reporting.

In summary, says Jay: “No one has infinite pockets, infinite skills, infinite assets or infinite access. But you can g­­­­et all of that if you know how to structure strategic partnerships.”

Other Case Studies: Online Retail with Macy’s, Nordstrom and Neiman Marcus Data

Beyond D&B’s Business to Business (B2B) customer lead generation scenario, another example is in online retail shopping with big data providers like Macys, Nordstrom and Neiman Marcus to generate more sales. There are many search queries where Bing users are searching for products in the fashion/clothing/apparel industry, and department stores like Macys have related big data that enables new online shopping experiences on Bing. ­­­

 

Search query on Michael Kors handbags brings up data provider, Macy's results. Search query on Burberry shoes brings up data provider, Nordstrom's results. Search query on Armani shirt brings up data provider, Neiman Marcus's results.

Call to Action

For startups who are dissatisfied with the time and effort needed in customer lead gen to find the job title, email and phone numbers of decision makers at other companies, we’re testing a solution just for you. We’re running a patented next-gen lead generation pilot with Bing, designed to help you save time when searching for relevant contacts at other companies.

1. Click on this link, which uses the investment banking and financial services company Citigroup, as an example, or input any other query into Bing using one of the formats below to view the D&B Bing answer, where “CompanyName” is the name of the company you want to get B2B leads from:

     a. CompanyName company contact

     b. CompanyName business contact

     c. CompanyName business development contact

     d. CompanyName biz dev contact

     e. CompanyName corporate relations contact

2. You should see a preview of the lead generation result, like the snapshot below. If you are unable to see the D&B Bing answer, it maybe because there are already other Bing answer types (e.g. map preview, customer service phone number) that are overwriting this D&B answer. To work around this, try other variants of the input query formats (items ‘a’ through ‘e’)

3. You can also append a “State” or “City” at the end of your query to get location specific B2B contacts. The example below uses the state of California (“ca”) in the query for Citigroup.

4. To respect privacy, personal identifiable data is protected on the search results page. To access the data, you must select “Click to Access” in the results table, sign into the Azure data marketplace, and agree to terms and conditions of Azure usage in order to see contact info details in the “Company Contacts” and “Contact Details” dataset. You can enjoy the following free trials:

5. Next, click on the ‘use’ button under the “Company Contacts” dataset list.

6. Input the CompanyName, City and State to see the ContactName and JobTitle of the leads you want to find. To see contact details, sign up for the separate dataset called “Contact Details” in step 4 and repeat step 5.

Conclusion

The steps above were motivated by helping startups to save time and money in customer lead generation. Below are more examples of known queries that have lead generation results as of Oct 6th 2016:

  • apple biz dev contact
  • wynn biz dev contact WA
  • citigroup corporate relations contact
  • citigroup corporate relations contact CA
  • bank of america corporate relations contact
  • chipotle business development contact ca
  • att business development contact seattle
  • whole foods business development contact
  • walgreens business development contact wa
  • verizon company contact
  • tiffany corporate relations contact
  • starbucks corporate relations contact seattle
  • pepsico corporate relations contact

Depending on the search query demand, more contacts from other companies will be included in the next iteration of this search feature. Also, look out for content from Jay in a follow-up blog post on how to maximize your email and phone conversions given the names, emails and phone numbers you have access to from D&B!

 

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