The State of Machine Learning and Predictive Analytics for Small Business

Daniel Faggella

Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.

The State of Machine Learning and Predictive Analytics for Small Business


Even folks without a remote interest in artificial intelligence understand that it’s starting to surround them. The easy examples can be conjured by just about anyone walking the street: Siri, Amazon’s recommendations, Pandora’s playlists, Facebook’s face-tagging and newsfeed, and Google’s search results – these are the easy examples.

However, most of the commonplace applications of machine learning seem to be technologies used on us – technology techniques that happen to us. “AI” isn’t something that mom and pop shops do, and outside of fresh-out-of-Stanford startups, machine learning is not something that we associate with “small business.”

Since getting the lay of the land is what we do around here, I figured I’d dive into the reputable research in this domain and answer a critical question: “Can and are small businesses using machine learning?”

Below is a collection of the best resources that I could dig up, and addition to the major lessons gleaned therefrom. At the bottom of the article I sum up some of the common themes from the sources mentioned, and as always, I encourage you to come to your own conclusions.

Neilson Small Business Big Data Study

Neilson is among the largest market research firm in the world. In this 2014 Neilson study, the company interviewed thousands of small businesses in the US and found a mixed bag of results in terms of sentiment / assumptions about predictive analytics:

In looking at responses from a recent Nielsen poll of 2,000 small businesses in the U.S., 41 percent think conducting market research is too costly, and 42 percent say they just don’t have the time. And even more surprisingly, 35 percent went so far as to say they’ve never even considered it.

The article mentions potential market forces that might be driving small business toward data science, including the advent of much for tailored and personalized marketing and products from bigger players (made possible by their own big data investments and initiatives).

The infographic list in the article (which is world a look) identifies “Gather data about ideal customers” as the top reason that this sample of small businesses conduct market research.

IDC Worldwide Big Data and Analytics Predictions

International Data Corporation ranks among the largest IT market research firms, and was founded in 1964. In their recent analytics predictions press release, IDC leads by stating the following:

Visual data discovery tools will be growing 2.5x faster than rest of the BI market; investing in this enabler of end-user self service will become a requirement for all enterprises by 2018

While the majority of IDC’s clientele are not small businesses, the trend seems to coincide with a general trend towards accessibility of analytics tools. This trend seems to serve small businesses for two important reasons.

First, as IDC points out in their report, there is and will continue to be a large gap between demand and supply of data science talent (IDC predicts that the demand will exceed talent supply by 5x in the year 2018). Harvard Business Review has seemingly dubbed data science correctly as the “sexiest job of the 21st century”,  though some sources seem to suggest that SMBs are more likely to use consultancies, crowdsourcing, and cloud software, rather than hire teams of six-figure data scientists. It’s possible that this shortage of talent could stave off an overwhelming data advantage of larger corporations with larger budgets for big data departments.

Second, many small businesses do not have the budget to allocate to high-paying data science positions, and will instead need to rely on easier to use tools for existing staff in marketing, IT, and management.

Another IDC trend that seems to bode well for SMBs is the trend towards cloud-based solutions. IDC predicts that cloud-based big data and analytics solutions will grow three times faster than their on-premise counterparts – likely lightening the load on SMBs IT staff, and on company budget.

Predictive Analytics Providers for SMBs

The last two years alone have seen a significant uptick in data science boutique consultancies and predictive analytics platforms and applications. Below we’ve listed some of the most popular and reputable solutions that apply to the small business environment. Note that we’ve linked directly to the SMB landing page so that you can read the companies’ copy in their own words (revealing some of the apparent value propositions that predictive analytics companies are using to attract small business clients):

  • IBM Watson – Easily the best known player in the predictive analytics field (due in large part to the press the it’s Jeopardy! victory, and advertising), IBM is positioning itself to help small businesses. Here’s an interesting YouTube video featuring an IBM marketing message geared specifically to small businesses without data science talent
  • SAS – Another giant of the analytics world (though not nearly the size of IBM), SAS now offers an option for SMBs
  • Canopy Labs – “…customer analytics platform, uses customer behavior, sales trends and predictive behavioral models to extract valuable information for future marketing campaigns…”
  • Qualtrics – Touting itself “the world’s leading enterprise survey platform,” Qualtrics aims to go beyond Google Forms and SurveyMonkey in drawing deep insight from a wide variety of survey and qualitative tools
  • InsightSquared – Focuses on predictive analytics for sales, including pipeline management, sales forecasting, SaaS metrics, and more

By no means is this an exhaustive list, but it’s a good start in exploring established and up-and-coming providers of predictive analytics.


Tools like IBM Watson are aiming to improve the accessibility of machine learning solutions, but most still require quite a bespoke setup period and unique skill set – both of which might fare in favor of large companies with existing data science teams.

That being said, more and more younger companies and startups are leveraging the power of data science, and the massive shortage for machine learning talent may give smaller companies a good chance at catching up to bigger players, and may give machine learning / analytics providers a hungry market of “non-data-scientist” clientele eager to expand the technology’s potential for their smaller company.

Notes: For a good list of potential machine learning use cases for smaller businesses, this article on Forbes is a nice start. For overarching industry trends and predictions, we recommend founders and CXOs read Gartner’s “Trends and Predictions” blog. This well-referenced article in PCMag provides an interesting set of coming analytics trends for 2016.

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