The German book publishing industry is gradually awakening to the potential of data and AI, with curiosity and a desire to understand the technology and its possibilities emerging among both management and employees. The industry has been receptive to data-driven approaches, with many individuals having some experience in working with data. However, there is uncertainty about which AI applications to embrace, as the industry lacks literature-specific AI and data offerings. Selecting the right solutions requires a blend of technical AI expertise and business acumen. The transition takes time, as the industry has a habit of slow 6-month “sprints”. Adoption, however, happens, with publishers going through unique (emotional) phases in the process.
We founded Lit-X in June 2023, engaging with over 50 of Germany's foremost publishing houses and conversing with more than 150 domain experts over 300 hours. The objective was to dissect the industry's needs, internal dilemmas, and operational processes. Out of these extensive interactions emerged "Success Analytics," our current core product. “Success Analytics” aggregates the most relevant international literary data, facilitating data-driven decision-making and practical AI assistance. Applications range from international trend scouting in program management and strategic life-cycle pricing for sales to ad-spend optimization in marketing.
Curious, passionate, and ready
The German publishing sector is awakening to the undeniable potential of data and AI. It can be observed that there is an innate curiosity about the topic, that does not just pertain to senior management, but to each and every employee we met. The oft-assumed protective stance against innovation and new technology within the industry has been gradually dissipating. And while it has not gone completely yet, there is a new wind blowing. It’s actually heart-warming to see how the people in this industry, who really love what they do, uses their enthusiasm to embrace a new way of doing things and actively start to create the next, lovable, version of the industry.
This curiosity takes on multifaceted dimensions: people want to understand the technology, they also want to managing risks, and some forward-thinking individuals think about how to harness the new possibilities.
More adept than given credit for
Before our discussions, and even during them, we were repeatedly warned by recognized industry experts that the industry's ability to work with data, let alone AI, was lacking. Instead, we should expect a defensive stance to be taken as soon as data tables or data visualizations were discussed. We had to be particularly careful to present our content in a very, very simple, easily understandable, and non-intimidating manner.
These well-intentioned warnings turned out to be unnecessary. In none of our conversations did we encounter resistance. On the contrary, we met incredibly dedicated, interested, and curious individuals. Even when there was little experience in working with data, questions were asked, time was taken, and effort was made to develop these skills.
Interestingly, most of our conversation partners also had some experience in working with data. The industry is actually quite accustomed to manually piecing together data, bit by bit, to help make decisions. As Lit-X, we were able to build on this by replacing the manual patchwork with a comprehensive, market-wide database. We simplified data interpretation with professional visualizations, processed and made accessible large volumes of data through AI, and provided AI-driven insights for the expanded breadth and depth of data.
Unsure about what’s wanted and needed
While there is a basic understanding of AI, data, and the new possibilities that come with them amongst book publishers, this knowledge is not yet sufficient to form a clear opinion about what is actually needed or wanted.
Given the vast amount of AI applications and offerings, there is no way for book publishers to from an educated transparency about the market.
Should they go for AI chatbots for support, even AI-generated books, or perhaps AI-generated cover images (as "it's all about the cover in the end")? AI can also assist in smart and structured metadata management and in creating and optimizing marketing texts. Yet other applications, e.g., Lit-X Success Analytics, use AI for discovering and validating new authors and titles, implementing strategic life-cycle pricing, simplifying influencer selection, and optimizing ad-spend allocation.
The list of possibilities is extensive, despite the notable fact, that there are still very few literature-specific AI and data offerings.
To select from this bouquet of options the right high-impact ones for each individual publishing house, more and different expertise is required. The challenge lies in finding the rare combination of technical expertise regarding AI plus business/domain expertise regarding impact and integration.
This difficulty became evident in many of our discussions, making it necessary for us to fill the expertise gaps where we could. We provided Data Science and Business experience and insights, navigating the AI landscape and then, in an actually altruistic manner (promised), helped to prioritize.
In prioritization, business impact takes usually precedence as a criterion. For example, data and AI-supported trend scouting can alter a publisher's title portfolio, leading to more bestsellers, more titles with a positive return on investment, fewer break-even titles, and fewer titles with a negative return on investment. It ultimately becomes a classic return on investment calculation.
Following business impact, ease of implementation is the next most important selection criterion. External tools like Lit-X Success Analytics, for instance, can be implemented with no internal IT integration effort.
Despite all educated selection processes, at the end of the day it all comes down to an organization's willingness to go for AI and data. Is the leadership truly convinced that AI and data are necessary steps to ensure the company's future viability?
It takes the book publishing industry time to warm up to AI and data
The German book publishing industry is accustomed to slow publishing cycles, adhering to a particular rhythm. This rhythm stands in stark contrast to the rapidly evolving world of AI. While publishing operates on six-month cycles, planning for spring and autumn programs, the AI world witnesses weekly additions of remarkable new technological advancements. Mentioning examples here would be futile, by the time you read this they would be outdated.
As we help publishers getting used to this disparity in the perception of time, we observed them going through various (emotional) phases:
- Curiosity: "What can you do?" and "How does this work?"
- Skepticism: "What if this goes wrong?" and "But I still need to do this, right?"
- Love for details: "Okay, if this works, I also need to consider..." or "Maybe we should look into whether..."
- Data Quality: "The price is different from what I see here – oh, it's from yesterday? I see..."
- Admitting the industry is used to poor data quality standards: "In-house, we have three different genre structures.", "Genre categorizations are somewhat arbitrary; we strategically adjust them, sometimes Audible just changes them."
- Understanding: "So, if I filter this and interpret the data this way, it means..."
- Excitement: "This is fascinating! I can use it this way...", "Great, I'll use the tool for this, and then I'll check here again..."
Each of these phases is crucial for an actual data and AI adoption and cannot be rushed or skipped. Skipping phases only leaves conflicts unresolved, forcing everybody to get back to them over and over again.
In conclusion, German book publishers should invest in building data & AI capabilities and knowledge. This in order to firstly get the own adoption agenda right, and secondly to enable a well-informed and sustainable implementation of suitable solutions. Since the industry is slow in adoption any one publisher distinguishing themselves with these investments, will likely excel over the others in the mid-term.
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