Tag Archives: Social Media Analytics

Part Deux – 4 ways your analytics can deceive you

Last week I kicked off a series of posts about how analytics can deceive you.  Today I continue this thread with some concrete, public examples of how organizations have “missed the boat”

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We have seen four trends in our prospects and clients which have led to (in some cases) disastrous results.

The first is related to data missing from an analysis. To make informed decisions, you need to synthesize all the relevant information into your decisions. Companies have made vast improvements in terms of incorporating internal data but there is at risk of getting blindsided by information that can only be found in external data – like weather, Dun and Bradstreet, economic indicators or social media.

In April of this year, a major US airline had an unfortunate incident where a passenger was physically removed from a plane. The company could see the metrics related to social media activity but by the way they were reacting, it became clear they were missing information around social sentiment. The initial incident was rough enough but made worse as the CEO made a cold, victim blaming speech. The airline lost a BILLION dollars in market cap in under 8 hours – not because of the incident itself but as a reaction to the CEO’s statement blaming the passenger.  If only they had been more attuned with the sentiment of their long time customers, they could have reacted faster and headed off the stock slide. The cost of not knowing.  (See more examples of social media fails here)

The second is related to incorrect data. Excel remains the BI tool of choice for many business users and analysts and there are no shortage of stories where a transposed number, missing decimal, or issue with a minus sign wreaked havoc.  In fact, a Forbes article suggests that “excel might be the most dangerous software on the planet.”

Today, we see manual checks built into processes that involve manual entry. However, the bigger problem lies in places where companies have outgrown legacy systems and use complex Excel models to perform calculations and transform numbers as part of a workflow. The very nature of excel is that the calculation lives in each cell and no mechanism can ensure accuracy. A complex workbook can have thousands of calculations. A MarketWatch article entitled 88% of Spreadsheet Have Errors, cautions that “Spreadsheets, even careful development, contain errors in 1% or more of all formula cells.”

This is what happened to a large US based financial services company. They had outgrown their accounting system and inserted a complex Excel model into a process that exported all open positions into Excel so they could be priced at current market rates and the values were returned back into a work stream. Unbeknownst to them at the time, there was an error in one of the calculations that led to Fannie Mae overstating revenue by more than $1B. When they announced the correction, their stock dropped $2.25 per share. The cost of not knowing.

These are two examples of common problems many companies face – MISSING DATA, or not including all relevant data points in an analysis and INCORRECT DATA, just blatantly having the wrong information at hand.  Both of these have have led to significant meltdowns for their respective organizations.  The internet is littered with stories similar to these.

Check them out Google:

  • Social Media fails
  • why excel is bad for data analysis

Would you have married your spouse if you only knew his or her address?

Marketers claim to know their customers because they have captured demographic and historical transaction information about them but would you have married your spouse based on age, height, address and the fact that they bought shoes once?  I think not.  Why should marketers think this enough to truly address the needs of today’s customers?

Many marketers are doing a very good job an incorporating traditional data critical to being able to target marketing efforts.

Data such as:

  • Self-declared demographic information
  • Marketing inquiries
  • Sales leads
  • Orders, payment history

This information is a great place to start.  There is no doubt that applying advanced analytics help marketers find patterns and trends while also predicting what is likely to happen next.  

Think about being on your first date with you spouse.  The conversation starts with the basics:

First Date

  • Where are you from?
  • Where do you live now?
  • Where did you study?

But quickly moves to:

  • What is your opinion of the President?
  • What do you do for fun?
  • What do you really dislike?

Now think about applying this in a business context.  As a marketer, I would like nothing more than to put an offer in front of customers and prospects that would not only speak to their business need but would also speak to personal likes.  How can you do this?

digital thumb print

Every interaction is an opportunity to get to know your buyers better.

Traditional interaction data from a marketing automation system such as Unica, Eloqua, Marketo or Neolane is a valuable source of information about  buyers’ behavior.  By capturing all interactions, regardless of channel, allows marketing organizations to apply predictive models to predict which customers are likely to respond to marketing offers via which channel and how frequently.    Using predictive models as part of a broader  customer analytics initiative helps marketing organizations identify which buyers to target and personalize offers for cross and up-sell opportunities

Great, we now know which offers my buyers will likely respond to and how frequently!  Now, go back to my first example – the date.  Getting to know your buyers personal preferences allows you to gain a deeper understanding of customer attitudes, preferences and opinions to make them part of the decision making process. Think about collecting customer opinions, attitudes and interest via surveys or data collection.  Use the interaction opportunity to capture a hobby or other personal activity.  Then apply this in your marketing activities.  If your buyer likes golf, find a way to incorporate it into your outreach (e.g. my marketing team has used direct mail/dimensional mailers giving away a free driver!).  

Social Media is another data source which can provide tremendous insight into customer opinions, both positive and negative.  Apply social media analytics to get the real opinion of your products to ultimately engage brand advocates and detractors in a conversation. CI Social media analytics allows organizations to capture consumer data from social media to understand attitudes, opinions and trends.

They key here is not to look at each of the pieces of data as stand alone pieces of information.  It is about combining them to get a “thumb print” which identifies the uniqueness of an individual.

Please feel free to connect with me on Twitter to discuss further @BrendanRGrady