Big Data + SaaS = Better Marketing

Paul Ferrill

Updated · Mar 26, 2013

Web applications which aggregate vast quantities of publicly available data and turn it into useful information for customers — for a fee — are a natural progression of software-as-a-service. These Big Data applications — or BDAs, as some are calling them — are already starting to appear.

Before taking a closer look at BDAs, let’s define what we mean by Big Data.

“Big Data is based on three Vs – Volume, Variety and Velocity,” says Yvonne Genovese, an analyst at Gartner. “Volume means there is a lot of it. Variety means that there might be text, video, pictures, audio, not just numbers that fit into the columns of spreadsheets. And velocity means it is being generated very rapidly.”

Big Data Use Cases

There are plenty of use cases for Big Data analysis when the data is proprietary, generated and owned by a particular organization. “In the music business, companies have album art, sleeve notes, music and so on. This is Big Data. Or project management companies probably have vast amounts of paper and digital records of their projects,” says Genovese. “It would be very useful for these companies to be able to analyze it and get nuggets of info from it.”

Other use cases for Big Data include drug research, credit card fraud detection, sports performance management and even crime prediction (A California-based company called PredPol analyzes police crime data to predict where and when crimes are likely to be committed, so that police officers can be dispatched there before the crime takes place.) Some dating sites and job-finding services (another type of match making) are also known to use Big Data analysis.

Big Marketing Value for SMBs

But there’s another important use case that needs mentioning, and that’s marketing. There are some special considerations when Big Data is used for marketing purposes, according to Joelle Kaufman, marketing director for Bloomreach, a company that provides BDAs for marketing. “You need to take a decision quickly and repeatedly, informed by massive amounts of data. In marketing, the most recent data is often the most relevant.”

Very large organizations generate massive amounts of data about their products which they can analyze on their own Big Data platforms for marketing purpose – for example, to create sales promotions or customer retention programs. But smaller companies don’t generate the same volumes of data, and even if they did, the cost of the computing resources required to process it “quickly and repeatedly,” along with the cost of sufficient storage resources, would likely be prohibitive.

That’s where BDAs come in. They are uniquely placed to offer Big Data analysis-as-a-service for marketing purposes because they can offer three key things:

  • Computing resources
  • Data
  • Analysis algorithms

Let’s look at those in turn. First, they can provide the computing resources required for Big Data analysis at a fairly low cost by harnessing the power of storage and processing in the cloud.

More importantly, they can aggregate and store relevant Big Data. The reason that BDAs are so well suited to the marketing use case is that the type of data that’s relevant for marketing purposes includes things like consumer sentiment, consumer comments, and information about the types of words that consumers are using to search for things online. That’s data that is freely available, on blogs, on Facebook, on Twitter, in Google, or from countless other sources on the Internet.

This Big Data is generated so quickly and in so many places, that it is not practical for most companies to attempt to collect it for their own use. But because BDAs can pool data and make it available to a large number of customers, the costs are shared, the economics change, and collection (and storage) becomes perfectly practical.

Finally, there are the algorithms and other analysis techniques which BDAs use to process Big Data to produce business information and insights. “The data is available, but creating the algorithms is hard, so the value of BDAs is in combining public data with proprietary algorithms,” says David Feinleib, managing director of technology consultancy The Big Data Group.

Some Examples of Marketing BDAs

Examples of marketing BDAs include social media monitoring, analytics and listening services like’s Marketing Cloud or BrandsEye. These BDAs have the ability to collect social media data in close to real-time and run in it through their algorithms to produce insights such as near-instant feedback on the effectiveness of new marketing campaigns or alerts about emerging problems with products.

A site like LinkedIn can also be thought of as a BDA. That’s because the data it depends on is not generated by the site itself, but by its members all around the world. It is then aggregated, processed and presented on the site. Its algorithms enable people to network and market themselves and their services.

“LinkedIn is really a kind of contact manager BDA,” says Bloomreach’s Kaufman.  “A traditional contact manger soon gets out of date as people move jobs, but now you don’t need that sort of contact manager.  With LinkedIn, people update their own information as they have an interest in doing so.”

Other BDAs offer more unusual services. Bloomreach, for example, collects and analyzes Internet search queries. “We use Big Data on the Web to find out how consumers express their intent, and we match it with what customers have on their sites,” explains Kaufman. “If we understand consumers’ intent, and what our customers have, we can bridge that and bring them together.”

Bloomreach does this by modifying its customers’ websites automatically to help ensure that they get found by people searching for the products they sell, regardless of the search terms people use when looking for such products. Because terms can vary according to current fashion, or even what may have been mentioned on Oprah the night before, up-to-the-minute data must be analyzed frequently to ensure that this bridge between what customers want and what websites offer is effective, Kaufman says. In effect, his company’s app is a search engine optimization (SEO) BDA.

BDA vs. SaaS: It’s the Data, Stupid

So how do BDAs differ from more traditional software-as-a-service (SaaS) companies? “For SaaS companies, the main assets are their software and their customer base,” says Feinleib. “By contrast with BDAs, the data itself is one of their assets, and the more they collect, the better their algorithms are likely to be.”

Bloomreach differs from most SaaS companies  in that it charges by results — usually incremental customer sales – and not a monthly fee based on usage.

So are all SaaS operations likely to turn in to BDAs in the future? Not all of them, Kaufman says. “Lots of decisions don’t need to be taken ‘quickly and repeatedly, informed by massive amounts of data.’ Decisions that need reflection and creativity won’t be supported by BDAs,” she says.

“But,” she adds, “everything that does, will be.”

Paul Rubens has been covering IT security for over 20 years. In that time he has written for leading UK and international publications including The Economist, The Times, Financial Times, the BBC, Computing and ServerWatch.

Paul Ferrill
Paul Ferrill

Paul Ferrill has been writing for over 15 years about computers and network technology. He holds a BS in Electrical Engineering as well as a MS in Electrical Engineering. He is a regular contributor to the computer trade press. He has a specialization in complex data analysis and storage. He has written hundreds of articles and two books for various outlets over the years. His articles have appeared in Enterprise Apps Today and InfoWorld, Network World, PC Magazine, Forbes, and many other publications.

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