Thought Leadership March 14th 2017

From CRM to Big Data: Top mistakes CIOs & CMOs should avoid from now on!

Big Data from buzz Word to key interest

During the last year Big Data has evolved from a buzzword to an important concern in executive’s strategic orientation. The traction is real, Big Data is hype, and everybody wants to use it, even though they often haven’t a clue on how to start. I once heard in a Big Data convention, “Big Data is like teenager sex, everybody claims he does, while no one did”.  According to this year’s GE Global Innovation Barometer “70% of businesses see big data as critical to optimize business efficiency, 61% believe it will be a real challenge to implement. For those who utilize it, 69% see added value for the innovation process”. According to Jean Baptiste Bouzige CEO Founder of Ekimetrics - a niche strategic consultancy firm, that advises Fortune 500 companies in their Marketing optimization and Data Strategy- “fields such as analytics and data science are rapidly growing in budget and interest within established firms, it is promising, as long as business executives don’t repeat the same mistakes they have been doing since the CRM breakthrough in the early 2000”.

“No matter the noise in Data, as long as we have volume and automation”

It is true, that many people from the data industry have forgotten, the “CRMania” that started a decade ago, according to a recent study from Gartner, the CRM market is still growing and will reach a $24 billion record this year. This level of investment is huge and companies obviously want to get as much value as possible out of their systems, however “a CRM strategy can’t be based on a software integration or customization […] CRM means customer relationship management, too bad that it became a generic term to designate systems in charge of targeting campaigns, implying a general confusion between methodologies, databases and tools” considers Bouzige. People seem to repeat the same mistakes they did in CRM with Big Data, while focusing on tools and software instead of understanding the needs of executives and broader business teams to deliver accurate and actionable analysis. Today there are many technical options on the table, these include Hadoop, NoSQL systems, data lakes and graph databases, it is for sure very important to understand them, although the core objective is to architect information systems for scalability in order to capture meaningful intelligence, even from unstructured information. It seems easy to peek the “best tools” or the most “popular ones” when you are well informed, it’s much more difficult to implement them efficiently and react properly when they are not delivering the way they are supposed to. “A major industry leader was not understanding why the best tool on the market was not able to perform well” tells us Bouzige, “we found out after investigating the process, that the datasets behind the tool were polluted with bad data quality”. High-tech, banking, insurance, telecommunications, pharmaceutical, consumer goods, IT manufacturing and IT services vertical industries are the largest spenders on CRM, because they have a large volume of Data and need automation. “The whole point is to bullet proof the process by understanding all datasets for a better business accuracy, the automation comes afterwards, too many firms rush themselves into automation before getting this first step properly done” regrets Bouzige, “I heard too many CIOs say: no matter the noise in Data, as long as we have volume and automation.”

 

“The numbers speak for themselves”

One of the big advancements implied by the CRM industry innovation was the scoring system, Bouzige explained to us that “a score is a way of calculating a probability to purchase a product. Scores are efficient to reduce the target size for a CRM campaign”.

Thought Leadership July 10th 2019

McDonald's France : business strategy, behavioural analysis & big data

Whether I’m in class or talking to friends, it is often difficult to explain to them what the meta-data in their daily lives can do. It is easy to imagine how big data is used in scientific calculations of meteorological models or for managing transport, but it is harder to see how it can be used in our daily lives.
Thought Leadership June 19th 2019

How to combine business approaches, advanced statistics, and technology?

It is sometimes difficult to create a link between highly sophisticated statistical approaches and business reality. The transformation of data into value is an art that needs to combine three different pillars: business understanding, advanced statistics and technology. This triangular approach needs to be adopted by all machine learning projects.