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'Big data' has become a big buzzword in the tech community over the past year, and for good reason: technology has made it possible for companies to collect massive amounts of information and analyze it in ways that were never before possible.

To Reid Hoffman, the founder of LinkedIn, big data is so important that it can provide a significant competitive advantage. How significant a competitive advantage? Hoffman believes big data gives a big edge to a company that produces big skepticism: Groupon.

Hoffman, widely considered to be one of the sharpest figures in Silicon Valley, told Fast Company:

As the activity in this space gets denser, it becomes important for [deals companies] to maintain their value proposition, both for the merchant and the consumer, and to be able to match the right two.

The ability to do that kind of matching, off the data, is the kind of thing that has a robust, at-scale, defensible value proposition and makes it harder for other people to offer products that are as good.

This statement seems reasonable. Groupon has collected a lot of data about the deals its customers buy, so it's not a stretch to believe that the company should be able to more easily discern what they're looking for going forward.

There's just one problem: data is not always easily applied to product. In this case of Groupon, the problem isn't necessarily determining what kinds of deals particular groups of customers want; increasingly the problem will be delivering those deals to them. There are three big reasons why:

  • Competition. Groupon may be the leader in the daily deals space, but it's got plenty of competition. Two of the ways competitors have made inroads are by charging a lower commission to merchants and paying them faster.

    Because there's little differentiation in the way deals are offered to consumers, it's logical for merchants to be price sensitive. More players chasing a finite number of deals and offering better terms to get them means that Groupon won't always be able to get the deals it and its users want.

  • Fatigue. How many discount spa treatments or photography sessions do you really want? Deal fatigue is probably one of the top reasons why the majority of Groupon's customers aren't regular buyers.
  • Reputation. There may be room for the daily deal model, but the reputation of the daily deal and Groupon in particular have taken a beating. Thanks to more than a few horror stories, more and more businesses are questioning the wisdom of offering daily deals. This more than anything else will make it difficult for Groupon to acquire fresh, attractive deals that it can offer its users.


The implication of the above is clear: even if Groupon can work big data magic to determine the type of deals some of its subscribers are more likely to purchase, it still may not be able to deliver them. That, for obvious reasons, is a big problem.

Which highlights an important point about big data: companies that collect the right data ('big' or not) and analyze it are probably more likely to make important discoveries that can help their businesses. But that data has to be actionable in a meaningful way. With this in mind, it's crucial for businesses to not fall victim to big data mania.

Data in and of itself is not a panacea, it's an asset of varying value. How that asset is put to use in the real world determines whether that value is high or low.

Patricio Robles

Published 22 November, 2011 by Patricio Robles

Patricio Robles is a tech reporter at Econsultancy. Follow him on Twitter.

2392 more posts from this author

Comments (2)

Lee Cash

Lee Cash, Senior Business Development Manager at Qubit

Hi Patricio.

Great article. Thanks.

I'd like to pick up on the statement "data is not always easily applied to product"...

You're absolutely right of course, as reporting platforms have previously concentrated on the media channel, or placement, rather than the details of the products purchased.

We're now seeing huge demand from clients who want to integrate their product level data with their ecommerce metrics to understand the relationship between the sales channel and the actual products sold through them.

A great example of this can be found in travel, whereby the client really needs to know which channels and partners generate sales for which destinations. This allows them to optimise these channels and partnerships accordingly, without eroding margin unnecessarily.

In retail, associations between product attributes such as categories, brands, specifications etc can provide huge insights into what the user is looking for.

I think the term "Smart Data" might be a more appropiate one than "Big Data" in the long run.

almost 5 years ago

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Neil Mason, Director of Professional Services at Global Dawn

Hi Patricio

I agree with a lot of the sentiment in your article. My thought about the Groupon scenario that you use is that in the more competitive environment that you describe is that Groupon would start to use their data differently. Their focus would shift from just understanding what deals people want to understanding what deals are worth fighting for based on their understanding of their customers preferences and the response levels they get from different promotion offers.

Ultimately smart use of their data should allow them to be more forensic and targeted in the types of deal they may want to acquire to be more relevant to their customer base.

almost 5 years ago

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