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Entrepreneurs: CB Insights

Thursday, March 31st, 2016

CB Insights

Founders, closely follow those they wish to emulate, “names” they trust, peers, competitors, etc., mostly from the viewpoint of the media or their own self-generated content (blogs, articles, etc.)

In other words, content developed for either the entrepreneurial community or general public.

Whereas CB Insights was created to provide information to industry.

So in 2010, they launched CB Insights to use data, algorithms and predictive analytics to help customers answer questions about “what’s next?”

  • What company is our next customer? Investment? Acquisition?
  • What’s the next big industry we should position ourselves in?
  • What are our competitors up to and what is likely their next move?

And while it’s doubtful you could afford a subscription, or that it even would pay to have one, its newsletter is a goldmine of information — plus it’s well-written and an enjoyable read.

I reached out to Anand Sanwal, CB Insights’ CEO / Co-Founder / Customer Service with the following questions.

Your About page states that CB is revenue-funded. Why did you make the decision not to seek funding?

We were revenue-funded for our first 5.75 years but did take $10M of funding in November 2015. More on that here

What special challenges did you find and how did you overcome them?

In the beginning, it was figuring out how to get our name out there since nobody had ever heard of us.  We started doing data-driven content to stand out and this worked.  It’s been our secret weapon.

Other than that, there are the perennial challenges of recruiting, building a great product and selling. These challenges are not unique to us. They just keep changing as the organization grows.

Regarding Lesson 1 of the CB Insights Quantitative Venture Capital Class, are there more lessons? If yes, are they also free and is there a tag/link that accesses them all?

There are several. They are here, here and here(Be sure to use these links! Ed.)

How useful is your content to entrepreneurs?

Entrepreneurs waste inordinate amounts of time doing diligence on investors and markets, and so it’s very useful to them. Knowing who the most active investors are in a space or who has the highest follow-on rate saves them a lot of time.  

The alternative is Googling around doing lots of data janitor work.  Based on feedback/emails we get in response to our newsletter, founders have been very appreciate of us cutting through the noise with data.

Is there a best way for them to utilize it?

We’re an institutionally oriented product with a nearly $40,000 per year average price so the best way for them to use us is to subscribe to our free newsletter, follow us on Twitter (@asanwal)  and read our research blog. 

Our target customer is not founders/entrepreneurs.

Any other comments or advice that you think would be useful to founders?

I read a great quote (not sure who said this) to “never take advice from someone who doesn’t have to live with the consequences” so take this as my disclaimer.  

Everyone’s situation is different and so there are no absolutes.

But if I had to offer any advice to founders, it is to sell, sell, sell. We did it to some extent, but I wish we’d done it more aggressively because the best type of funding is from customers.  It shows your product is something they want and is the ultimate validation of what you’re doing.  Too many folks mistake raising money from investors and giving away equity as validation. It may not be.

I highly recommend CB Insights; what you’ll learn will provide high ROI for the time you spend. –Miki

KG @ The Data Alchemy Conference

Tuesday, May 26th, 2015

kg_charles-harris

The Data Alchemy Conference that I recently attended was well worth going to. In contrast with a lot of these types of conferences, it was an interesting view of how to use predictive and other technologies to improve business outcomes, i.e. not the more common type of technology or data scientist oriented conference here in Silicon Valley.

One of the factors that was attractive was the way that vendors used case studies and best practices to elucidate some of the advantages and complexities of big data and analytics. People from companies such as PayPal, IBM, HP, SAP, Silicon Valley Data Science and others were speakers. There were also lots of industry practitioners in the audience.

The emergence of predictive analytics as a core tool in planning and monitoring in organizations is a relatively recent phenomenon, being less than 10 years old. Now, companies like SAS have been around for a long time, but it is only when IBM acquired SPSS in 2009 and applied their significant marketing engine behind predictive analytics that this market started to take off. 

Of course, it had been used with regards to risk analytics in insurance, churn analysis in telecoms companies and credit worthiness analysis in FICO scores, etc.

Since then we’re seeing predictive analytics being incorporated in many different areas in enterprises based on the growing amount of data and the increasing need to make decisions based on data.

This comes partly from increasing complexity in the business world, greater binary behavior (1 major company in each market that is 10x larger than #2), speed of growth and decline of companies, and decreased cycle times.

One of the most interesting talks was by Jenny Dearborn, Chief Learning Officer at  SAP, who spoke of the way they’re using predictive analytics with regards to employee turnover and onboarding. By using big data analytics on structured and unstructured data, it is possible to understand employee sentiment, training needs and likelihood of staying at the company.

A major challenge to analytics is data quality, what in common vernacular is termed bad or dirty data. Theresa Kushner, VP Enterprise Information Management at VM Ware mentioned that 1/3 of her staff were focused on data quality and cleansing.

It seems as if data quality is an even more important issue than being able to apply advanced algorithms to the data, and that by just ensuring that data is clean we can make better decisions that reduces the need for advanced algorithms in many situations.

In short, it was interesting to see how analytics is being advanced within organizations and getting a practical view of what challenges are faced from a business perspective.

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