The holiday season is the perfect time to organise your paper and the house. My growing son Emilien needed a new bed and I drove last week with him into this giant and well-known Swedish retailer. We selected the bed and proceeded to get the different parts in the corresponding alley. I realised a bedspring was missing and asked an employee who gave me the exact alley number and position. At the cashier, I was told that another piece was missing. A metal bar that should go under the bed. Astonished I asked how she knew that and she showed me her computer screen with the Swedish name of the part displayed with a big question mark. I quickly sent my daughter to get one.

It was for me another proof on how the algorithm could support the humans like a cashier and the customer. lately I read an article on a survey this retailer made on how to use Artificial intelligence (AI) in the future. They mainly asked themselves if the shoppers wanted a virtual assistant and the features that it should have. This huge retailer has the capacity to embrace this new technology trend and I am sure that he will (if not yet done) optimize the position of his items on the selling path we are forced to take to increase its sales. They have at their disposal a tremendous amount of data that can already guide them in this strategy. So, why this retailer which has the means to do it has not started embracing this trend yet? The fundamental question is not yet asked. How (and why) do I apply those new technologies in my industry? It seems easy for Google, Amazon, Facebook, and other big ICT companies to embrace those new trends like advanced analytics. If B2C companies are finally adopting those technologies then why are B2B organisations still far behind? I believe that the main reason is to be found in the power of their marketing and the readiness of their data. Finding the right question is the fundamental step in the advanced analytics strategy and in B2C industries, strategies like price definition or sales advice have helped define the key target of data analytics models. Insurance, Banks, marketers had already their strategies in mind when defining which data from the consumers to catch whereas in the B2B industry we start analysing what we could do with the data at our disposal.

besides, the legal aspect of personal data is quite a grey area on sales platforms which give an advantage to generate data on shopping behaviours. The legal aspect is more restrictive in our B2B organisations. Let’s assume for example that you would like to use advanced analytics to optimize the productivity of your employee by reorganising the working space in the building. You would then need the badging time of your employees, their habits in terms of meetings, who they work with more…. You would probably be immediately stopped by the work Council even if the information could be anonymised and lead to the survival of your organisation.

On the other hand, B2C organisations like Amazon, eBay, Google, Facebook, analysed the habits of their users to orient them to a targeted product and increase their chance of “sales”

What could accelerate adoption in our B2B industries?

1. In an advanced analytics strategy, the decision maker cannot be the CIO since traditional IT is far away from the operations of your organisation. Business unit executives must define their strategies and the role of the CIO is to give the data accordingly.
2. Business executives must be trained on the potential of advanced analytics. Not by waiting a proof of concept (POC) in their industry which could take time but by being thought /coached on the emerging business case in B2B. It will help them understand the concept and be creative on the usage of these dormant data.
3. Stop data silos. Find a good data engineer that connect all the data silos in one single data pool. Avoid the war of data between the different business units. Data = power and most of the time BU want to keep their data.
4. Be agile and build a quick MVP to show quickly a business case. Do not invest massively before having done that. Show to the business leaders the capability, explain them that it is a POC. Most of them are not familiar with this notion of partially finished product in B2B industries.
5. hire the good Data scientists and prevent them falling into their natural obsession of admiring technologies. We do not sell technologies. Technologies must assist to sell products. -> Products and services first!
6. Find the expert that can translate the business needs to the data scientists. This will be the most difficult part. Find an expert who can take the time to define a strategy (big picture) for the company and translate into words that can be understood by data scientists. The alchemist who transmute data into a gold mine is a scares resource to find but is key in this journey through this sea of new technologies.

For the story, when assembling the bed, I realised that the part the computer suggested we should purchase had no purpose for this bed.

It shows that even in B2C industries there are still errors and fine tuning remains necessary but this should not stop us in our journey.

Picture: Jaume Plensa