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Data Analytics’ splendid isolation from the customer reality.

by Michael Maoz  |  August 3, 2014  |  4 Comments

Empirical data, AKA ‘stuff that happens to us as ordinary consumers’ is a decent way to capture the state of BI, Data Analytics and Predictive Analytics across industries and continents. In the past six months our team members have been on business trips and vacation on five continents and 18 countries and 12 US States. Some of us added a credit card, had a child prepare to leave home, rented cars, bought furniture, sporting equipment, electronics gear, entered the market for a new car, and had changes to our medical insurance. From hundreds of transactions there was zero perceived contribution from BI, Data Analytics and Predictive Analytics tools in any part of any process.

Some examples: While adding a Credit Card, one of us shifted about $3,600 in monthly spending from one card to another. This is after 12 years of consistent, and very frequent, use of the charge facility.  After eight months the original credit card company has failed to acknowledge in any way the 85% drop in monthly charges. Neither the retailer whose compromised security system, nor the Credit Card provider who failed to inform or advise in time and caused the move to another Credit Card made a move, sent a notification, attempted to explain/reach out/persuade in any way.

One of us reserved a car for a Sunday morning in Paris this July from a global Car Rental company, let us give them the name der Schmerz. Despite being high on their Loyalty Program and a 20 year customer, and despite confirmations by phone to their global office, they cancelled the reservation because we were not at their downtown site within two hours of the window allowed. A long, long story, but after renting at least 100 times previously, and despite a perfect record of picking up cars, there was no notification, no reminder of the 2 hour rule, no mention of the rule in the phone conversations or in the reservation reminder email. And despite having cars available would only provide a car at an 80% premium over the quoted rate. And the customer was to blame for being ‘late’ though they were unawares.

The automobile example is equally interesting: owning five cars from the same manufacturer over 15 years, we are again on the market. Multiple generic promotions are mailed to the house. None recognize what might be of interest NEXT. No pattern detected, no hints as to what might the next best approach: Lease? Purchase? Finance purchase? Features? Timing? Gornisht.

Whether it is financial advice for a student, retirement savings for an adult, government services, telephone/internet advice on best bundle, consumer electronics, pick an area and you will perceive as a consumer an absolute lack of understanding of customer intent. No traction, no engagement, no anticipation, no concierged service that rings of: “We are looking out for you!”

The reasons are legion – Marketing is interested in the ‘top of the funnel’ and the job of customer support is keeping you happy though you have a problem. Sales wants new money. BI wants to understand what is happening. But whose job is it to provide the actual mechanism to anticipate the customer’s needs, value, and corporate real-time response? We need a Dalai Lama of Business Execution, someone to enforce the coda: If you want your customer to be happy, practice compassion. If you want to be happy, practice compassion. I know his Holiness will excuse the mangling of ideas, but the message is: who is in the line of fire to guarantee that customers are treated correctly? That all of the rhetoric about customer experience and customer blah blah blah finally comes together in a systematic set of processes of mutual value exchange? (in simple words: the customer hands over their money because they really perceive the value at each step in the CRM process.)

The interlocking corporate team members from across BI, CIO, and Lines of Business either hang together or the customer relationships hang separately.

Do you know of any companies (and do NOT say Amazon or Nordstrom or Sephora again, please) that can carry out genuine, predictive and proactive analysis on behalf of the customer to offer them germane offers and services that engender trust? Let’s hear about them – name names! And tell us who is running the initiative!

Category: analytics-for-social-crm  applications  business-intelligence  cio  crm  innovation-and-customer-experience  intent-driven-enterprise  it-governance  leadership  strategic-planning  

Michael Maoz
VP Distinguished Analyst
13 years at Gartner
26 years IT industry

Michael Maoz is a research vice president and distinguished analyst in Gartner Research. His research focuses on CRM and customer-centric Web strategies. Mr. Maoz is the research leader for both the customer service and support strategies area and customer-centric Web… Read Full Bio

Thoughts on Data Analytics’ splendid isolation from the customer reality.

  1. […] Source: Data Analytics' splendid isolation from the customer reality. […]

  2. […] The reasons are legion – Marketing is interested in the ‘top of the funnel’ and customer support and keeping you happy though you have a problem. Sales wants new money. …read more […]

  3. I agree with Mr. Maoz. At Skytree, we believe Machine Learning is the best way to gain insight into customer behavior. Companies are moving in that direction but the technology is so new and disruptive that it takes time.

  4. Jim Acker says:

    This is one of the best posts I’ve encountered for some time on LinkedIn and on the subject of WHY we should be doing big data analytics. This is not rocket science and while I make a living here, I won’t cheapen the quality if this post with self-promotion.

    I will only say that my own travel and life experience as a consumer exactly parallels Michael’s experience and I think our industry has done much to delay the solution. All the hype around customer analytics and the supporting big data technology has been made by us to seem overly complex and our “transformational” value propositions simply reinforce caution and delay in investment.

    Recognizing my patterns with no social media or other BS and simply rewarding my loyalty and taking the simplistic steps to offer me things with some correlation to my established patterns would be a giant leap forward. Google applies very simple algorithms to incomprehensible volumes of very dirty data. It is still about analyzing ALL the data for the most basic insights and skipping all the data management overhead we have applied to operational data systems. This isn’t calculating weekly payroll. It’s reasonably simple to do and given the state of the art, even failure isn’t costly.

    Kudos to Michael for pointing it out.

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