The Customer Data Iceberg

Most big companies have a well-established contact mechanism, usually with some sort of contact center and probably an online  option too.  Some have implemented web chat or forums, all in the name  of re-establishing the lost bond between customers and companies.  Unfortunately, that strategy is still largely one way.  Despite the fact it looks like a conversation is happening, it’s not, because all those invaluable customer responses are dropped into a big data black hole once the interaction is concluded.  There is rarely an  opportunity to look at this data again and identify trends or recurring themes.  And almost mythically rare is the ability to do this in real time.

But let’s take a look at what metrics these companies can currently deliver on:

  • How many people have contacted you
  • How many of those emailed, called or web chatted with you
  • How much those contacts cost you in overall terms (cost of running the contact center)
  • Whether that’s gone up or down over time
  • Possibly even the categories each contact selected through IVR or online menu options you’ve pre-set.

Even bearing in mind the black hole, this sounds quite comprehensive, doesn’t it?  Those companies seem to be doing a good job of managing all the customer contacts and everyone’s happy.  Or are they? The advent of data mining tools allows companies to dig much deeper and find out what’s really going on, right down to an individual conversation if necessary, revealing the real mass of the iceberg under the water.

Things like:

  • Which customers contact you
  • When they contact you
  • Why they contacted you, in their own words.  Are they praising, complaining, requesting help?
  • If the customer arrives unhappy, do they leave happy?  And vice versa!
  • How often have they contacted you – more than once for the same issue?
  • Do they need multiple interactions with your staff for issue resolution
  • Is each person asking the same question more than once; or are multiple people asking the same thing?
  • How valuable are the most prolific contacters?
  • Do you hear from your most valuable customers?
  • Is there a sales opportunity in the contact?  Or an opportunity to build loyalty/advocacy?
  • Do you actually answer their question?  Can you exceed their expectations?
  • What channels do your customers prefer to use, and does this change during the course of a day?
  • What devices are they choosing to contact you on?
  • Do you service that channel proportionately?
  • Etc.

As you can see, the mass of information you didn’t know you could know offers a game-changing level of detail.  It’s only since the tools became available to understand the largely unstructured, messy types of data that are now being generated in their exabytes weekly, that we can take advantage of the richness it contains.  Most data analysis tools are still trudging on with specially structured, precisely formatted data, with no hope of understanding thousands of social media mentions, email conversations or other real-time data that is now available to brand owners.  This is where the real conversations and bonding will happen, not in the sterile one-way world of fixed-data processing.  Modern NLI software can interpret this unstructured data in ways not previously possible, taking real-life language inputs,  and understanding its real meaning and sentiment, and ensuring that old linguistic conventions are not applied to the rapidly evolving modern usage of our vocabulary.


Comparing old data analysis methods to new is like night and day.  Everyone likes a nice moonlit night, but the brightness of the sun is incomparable.  How does your data analysis compare on that solar scale?

Andy, who lives with his family in the UK, is Chief Marketing & Strategy Officer at Artificial Solutions. A regular speaker at industry conferences and events, Andy delivers insight on the rise of AI, the challenges businesses face and the future of intelligent conversational applications.

Leave a Reply

Your email address will not be published. Required fields are marked *