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5 Questions with Sebastian Klumpp

06 July 2022
Ida Lorenz
Digital expert Sebastian Klumpp is CEO at XPLN. His previous professional stations in retail, among others at the Klingel Group, form the foundation for his dedication to e-commerce, marketplace business and online marketing. He founded his first start-up in 2006. Digital innovations and the strategic growth of companies are his passion.
Sebastian Klumpp
From your point of view, what are the current trends in digitization strategies in companies?

One absolute trend is the topic of D2C and the associated strategies and measures. Since the middle of 2020 at the latest, we have been seeing increased digitization measures among well-known brand manufacturers, especially in setting up their own e-commerce units and, of course, further digital initiatives going along with this. 
Manufacturers have understood more than ever that they need to take the business into their own hands, also in order to be able to build on valuable customer data and let these insights flow into product development and service. This has led to many measures that we already saw in the retail sector a few years ago.

Specifically, sales via marketplaces and platforms, but above all also the topic of product data (attributes, images, texts, prices, shipping costs), which is necessary in this context, is becoming very important. Massive investments are being made in expanding the quality of product data and analyzing it accordingly in real time.

What are the requirements for the success of 'Data & Analytics' initiatives in the company?
  1. Know where data is/is to be obtained
  2. Willingness to share (change)
  3. Data quality and structure
  4. Existing evaluability and executability

In the end, experience shows that, on the one hand, there needs to be a democratization of data, departments need to provide easy access to their data across the board, and that data quality needs to get the strongest focus. The rule is: shit in, shit out. It applies to both internal and external data. Our customers make authoritative decisions based on the data we provide. Pricing, product, assortment and even partnership decisions must be based on reliable, verified data. This is not an area to skimp on, the impact of incorrect or inaccurate data is enormous. For example, once a wrong decision has been made, the market recovers only with great difficulty, sometimes not at all. We follow this especially in the area of incorrect prices. A relevant market player is able to shoot a product out of the market in terms of price. All others will follow. Consumers then find it difficult to understand revisions upwards. 

But I would also like to add that blindly collecting data is like throwing money away. Data must not only be collected and structured - checked for data quality - but above all it must be able to be analyzed so that measures can be derived from it. We often experience that data is obtained from us in large quantities, but in the end cannot be evaluated internally. This is where we come in - also in the parsionate Group - with Data Science and Analytics Services.

What are the three most important benefits that companies can achieve with 'Data & Analytics' tools?
  • Transparency 
  • Speed
  • Data driven decisions

We create transparency in e-commerce with our data and our analytics tools. Many of our customers are faced with a black box of commerce. Decisions about customer segments, marketing efforts, prices and assortments, and product portfolios are made on outdated market research methods that simply no longer reflect the dynamics of the market. I can't conduct a survey of X consumer and only a few times a year. Qualitatively, this often makes sense, but quantitatively and in terms of dynamics, rather not. 

The data is there, in real time and on an immense scale. But as described before, the sole possession is not the solution, the data must be transformed into insights by means of data science measures (machine learning) and analytics. The art is to obtain the data in high frequency and to get the best out of the rough diamond. 

How are 'Data & Analytics' technologies changing the way we work?

First of all, it is important not only to have tools and data, but also to use them. As so often, there are many initiatives where tools and data are purchased but end up gathering dust in the closet because they are not applicable in everyday life or there are other priorities.

The question we ask ourselves again and again together with customers is how do we ensure that these are also used across departments? Not only software is needed here, but also support to ensure that it is used and that the right measures are initiated and implemented. Analytics only makes sense if, in the end, issues are identified and, above all, solved. 

In concrete terms: Analyses are needed for every area of the company, which are prepared in such a comprehensible and department-specific manner that insights and measures are made possible.

We recently empowered a team of 40 category managers (the people who decide on assortments and buy in retail) in a one-year project from manual, daily searching for current e-commerce data to fully automated decision-making. And that's not just software-usefulness, but above all the challenge to show with analytics that you can trust data and the machine that fully automates the former manual work!

What would you recommend to companies that are at the beginning of strategic data management projects?

Always keep the end-to-end process in mind, keep the customer in focus, and live agile and not just preach it. This means launching and testing real MVPs and accepting rapid failure. Data projects are not a blueprint, especially since every company has different data sets and qualities. But that's why it often happens that projects don't immediately deliver the benefits you expect, it also takes time to bring the organization along. 

And as always: do your homework. Hiring the data scientist without clarifying the data situation (existence, knowledge, where, and data quality/stringency, etc.) always leads to prompt termination and a bad mood in the company.

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