Automated mass decisions with the Blue Yonder platform

IN Machine Learning — 22 May, 2014

From San Francisco to Karlsruhe, Germany: Lars Trieloff has been responsible for product management at Blue Yonder for some weeks now. The Blue Yonder blog spoke with Trieloff, a software expert, about the US and European market, the predictive analytics platform, and about Blue Yonder's product strategy.

Digital data flowing

You have been responsible for product management at Blue Yonder since April of this year. Before that, you were product manager of the Adobe Marketing Cloud in San Francisco. What do you like about your new task, and why is it bringing you back to Germany?

 Trieloff: When Jan Karstens, CTO at Blue Yonder, asked me, I was fascinated. A German company that solves customer problems based on a unique technology that goes far beyond what is being tried with big data analytics in Silicon Valley. After my time in San Francisco, the prospect of working again in an agile and very fast growing firm was very enticing to me. And after I met the entire team, my decision was made.

How in your view do the US and European markets for big data analytics differ? How would you characterize the DACH region, or: Germany, Austria, and Switzerland, as it is referred to in Germany?

Trieloff: The German and middle-European market for enterprise software generally is considerably more conservative than the American market. Customers buy less based on the innovation potential of the technology. They base their decision on the influence it will have on their daily business. In the big data market, which is still very technology-driven, that can seem like the German market is three years behind the US one. But from my visits with customers, I came away with the view that the first impression can be deceptive. When solutions like the one that Blue Yonder provides help customers reduce their costs and increase sales and margins, European customers are anything but conservative. Big data is merely a foundation. It depends on how enterprises intelligently use their data and how they use it as a foundation for automating processes.

The predictive analytics software from Blue Yonder is part of a SaaS model. How is that comparable to the Adobe Marketing Cloud? What experiences can you bring with you from that area to product management at Blue Yonder?

Trieloff: There are a whole series of parallels. Both products make it possible for customers to profit from the SaaS model and to use the software fast and without a big IT investment and effort. Both products use data to automate enterprise decision-making. Both have a platform at their core, over which a deep integration between products can be achieved. However, an important difference is in the way that data is captured and how decisions are made. In the marketing cloud, data collection and content delivery are centrally controlled. At Blue Yonder, we integrate ourselves into the core business processes of our customers and can thus control decisions at an entirely different depth.

This past year, with Forward Demand, Blue Yonder has already introduced into the market a simple solution customized for sales planning. What others solutions will follow, and when will they come out?

 Trieloff: We are working with our customers in various industries to find out which of the commercial, but project-specific solutions we have built so far would be a good fit for an expanded market presence using a standard SaaS product like Forward Demand. In addition, we are currently working hard on our platform strategy and the continuing development of Forward Demand.

A substantial strength of the Blue Yonder software is the automation of mass decisions on the basis of forecasts. Enterprises thus make the move to becoming predictive enterprises. You have set the goal of expanding the Blue Yonder platform so that it is an even stronger platform for predictive enterprises. Please explain the concept and the next steps to us.

 Trieloff: In every enterprise and on a daily basis, thousands of decisions are made: about customer-, vendor-, and employee relationships, about prices, risks, and marketing offers.  This mostly happens below consciousness and on the basis of simple rules. An enormous potential lies in these subconscious or "unoptimized" decisions. Today, employees are mainly responsible for that. But the size of data sets in future will increase exponentially and it will become harder and harder for people to analyze the enormous data volumes in real time. Well grounded and rational decisions in just seconds are thus almost impossible to make. That is the ideal job for computers that can learn.

 We want to bring that treasure to the surface with the Blue Yonder platform. The rules of the game of business are stored so that the platform can learn how different influence factors are related. Our data scientists are experts in that. Afterwards, software developers can include the predictive applications in the business processes. Blue Yonder runs these applications 24/7.

What concrete use does the Blue Yonder platform offer to the mid-sized market?  Which industries particularly profit from the solution? Outline some applications for us.

 Trieloff: An application that I find particularly exciting is dynamic pricing. Pricing is the most important and simplest "knob" that can be turned for profitability. A price change of one percent results in a more than ten percent change in profitability. For the "hidden champions" in the German mid-sized range of companies, that is particularly important, because they offer a spectrum of products that are not interchangeable, for example, in electronics, in climate technology, and in the construction sector, and that have a long data history. The ability of customers to pay can thus be ascertained.

What does big data analytics mean to you personally?

 Trieloff: I am personally fascinated by data and try to gather as much data as I can in the context of the quantified self: what music I listen to, which restaurants I visit, how often I jog, fly, ride my bike, and how much time I spend on the computer and so forth. Thus I found out for example that it is 18 percent more likely that I will listen to music if it rains on a certain day. And that also means that my headphones are going to enjoy the move from San Francisco to Germany!                                                     

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