How much is that doggy in the window

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How much is that doggy in the window?  Depends – what time of the day is it?  Who are you? Are you using a Mac or Windows device?  CAVEAT EMPTOR !!

Having grown up in the bazaars of India and having been schooled by my grandmother on how to negotiate – I could never understand why she always told me to not dress fancy when I went shopping.  Now I know why.  The merchants would size up the customer based on whether they drove up in a car, how they were dressed etc. and set the price.  A time honored and age old way of doing business.  It’s extremely ironic to note that we have come full circle and what’s even more ironic is that technology has taken us back to the future(no Michael Fox references – I promise).

Let me illustrate how it works and then we can explore the phenomena.  Here is the price history of a set of DVD’s in a short window of time:

October                                          $100
Week before Black Friday             $70
Black Friday                                    $90
Day after Black Friday                    $134
After that                                        $84

These are the prices from the same website!  That means that you would have paid almost double (70 vs 134) for the same exact product, from the same exact website.  Let that sink in for a minute.  Or take the example of Orbitz steering customers to higher priced options if you are a Mac user(about 12% premium) because they have determined that Mac users can afford and spend more.

This concept of dynamic pricing is nothing new.  Airlines have been managing their capacity risk using it for quite a while.  I’m sure all of us remember flying next to someone who paid a tiny fraction of what you did.  Utilities have been using this same concept for peak and off peak rates etc.  But technology has made it possible to apply this at a retail level also.  Just like a car salesman assesses the customer and the price of the car, so do retailers – especially online.

The price you pay depends on many factors.  The seller takes into account what the competitors are doing and reacts quickly.  They try to assess how the customers are responding to various price levels.  They are also looking for the imbalance between demand and supply and factoring that in.  This is all done by fancy pricing bots that use sophisticated analytics.  No need to fret – you can fight their bot(software robot) with your bot that will alert you to when the price drops so you can jump in and buy.  This all seems pretty benign.  Do remember, sometimes these changes are taking place every 15 minutes.  Brave new world will involve your bot against the sellers bot and may the best bot win!!

Now let’s take it a step further.  What if the bots were pricing based on who you are, where you live, what you bought before, how much you paid, are you using a Mac or Windows device?  It’s no longer an if – those types of micro targeted pricing strategies are already here. The amount of data collected on you is incredible and retailers have figured out how to exploit that data.  Your past buying habits determine the price you pay.  It’s called Predictive Analytics.

Sooo, how much is that doggy in the window?  If you listened to my grandmother, you would get the biggest, baddest bot and go to battle.  The other thing you might want to do is to get rid of your Apple devices, make sure you protect your privacy on the net and sow confusion in their algorithms by looking and acting less affluent online.   What happens if this phenomena moves from the B2C world and into the B2B world?  Even in the B2C world, how do you develop margin based  “should “ cost models if you don’t have any kind of predictable pricing models.

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Dalip Raheja is President and CEO of The Mpower Group (TMG). Dalip has over 30 years of experience managing large organizations and change initiatives. He has worked across the spectrums of supply chain management, strategic sourcing, and management consulting.

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