All posts by babettep

How Deutsche Bank Crowdstorms the Future of Banking with Jovoto

Artificially Enhanced Banking

Deutsche Bank is Germany’s largest bank and has big markets in all continents. It provides wide-ranging financial services and like all financial companies is increasingly using online technology to provide these. Deutsche Bank believed they could use Artificial Intelligence (AI) to improve their business, but did not know how and were spending a lot of money researching this. Deutsche Bank therefore chose to collaborate with Jovoto, a company providing innovation platforms, to establish a co-creation project that got the public to provide it with ideas about AI. (Deutsche Bank, 2017)

Jovoto helps organizations to innovate. They set-up and manage online spaces that gather ideas about different questions posed by organizations. By doing this, Jovoto allows brands and NGOs to carry out a ‘co-creation process’ – to brainstorm at scale and to work out design and innovation challenges with more than 80 000 creative professionals. Jovoto call this ‘crowdstorming’, essentially a form of co-creation where the public and a company collaborate to generate ideas.  (Jovoto, 2017)

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How did Deutsche Bank through Jovoto carry out co-creation?

Deutsche Bank got Jovoto to create an innovation competition, challenging the public to submit ideas and offering rewards for good ideas (key resource and process). Jovoto posted the challenge ‘share your vision of how Artificial Intelligence can help Deutsche bank reinvent its customer service experience’ and promised the best idea would win awards and cash prizes – see video below for a glimpse of the challenge.



Jovoto managed this competition and vetted who could contribute ideas, making sure the input was only from professionals. Jovoto determined the institutional environment of the platform. They make clear that all users keep the copyright of their ideas.

This competition offered ‘joint profitability’, providing gains for the company and public. For Deutsche Bank, it was a way to get new ideas about AI without having to invest in Research & Development (R&D). For professionals, the competition offered an opportunity to collaborate with a multinational (which boost their reputation) and possibly to win money (customer value proposition). In reality Deutsche Bank benefits more and so it had to make the competition prize attractive to motivate people to participate.

From the information available, it is not possible to tell how many ideas in total were submitted. Deutsche Bank said they had acquired 25 good ideas, but did not publish what these ideas were. As such, it is difficult to evaluate how successful this project was.


In general, the project seems to have worked well for Deutsche Bank. Deutsche Bank clearly think it has been a success, because they have since done two more competitions with Jovoto. It should be noted that even though these projects may save on R&D costs, Deutsche Bank did have to spend money to carry them out.

Based on Deutsche Banks’ case, other companies should also consider using Jovoto to set up co-creation schemes. It allows companies to generate new insights from a wide range of experts around the world. The submitted ideas are in general of high quality (feasibility requirement is met) and therefore are of great value for the company. Using conventional R&D methods (which are more expensive) companies are unable to get so many inputs of ideas or such high-quality ideas.


Sources (2017). Available at: Accessed on 09/03/2017 (2017) Acessed on 09/03/2017


DHL: Reinventing the role of the customer

Don’t limit co-creation to just problem solving or new product definition; use it to define new markets to grow into- Tony Atti

DHL, the global market leader in logistics, is part of the world’s biggest mail and logistics services company Deutsche Post. DHL provide an excellent example of a company which are using co-creation (where the company and customers collaborate) to generate new business ideas. This is particularly evident in their recent Parcelcopter project. This short  video shows the essence of the Parcelcopter project. (DHL, 2017)


ustomer Co-Creation: what is it?

The term co-creation was coined by Prahalad and Ramaswamy (2004) as ‘the joint creation of value by the company and the customer; allowing the customer to co-construct the service experience to suit their context’. Basically, co-creation means the company and customer work together to, in this case, produce new ideas. This can have a number of benefits for a company, offering a new way to innovate and also boosting customer satisfaction. The more the customer feels that the company is listening to them, embracing and delivering their specific needs, the more the customer wants to be associated/engaged with this particular company.

How does DHL apply Customer Co-Creation?

DHL has used co-creation to improve supply chain logistics with its Parcelcopter project. DHL noticed that their customers wanted to help in rethinking about how to improve their supply chains and thus improve their business performance (customer value proposition). DHL set up some innovation centres and invited their customers to come and interact with DHL employees to share ideas with each other (key resource and process). From this collaboration, a number of new ideas have emerged, including the Parcelcopter. The Parcelcopter (see video for further explanation) is an idea to use drones for delivery, which could improve DHL services. (DHL, 2017)

How well has Customer Co-Creation worked for DHL?

In the beginning, the co-creation concept was received with skepticism, both internally and externally. Customers thought it was a clever marketing/sales technique. The company was forced to take a strong look at its own structure and processes. However, the result has been well worth it. With this structure, customers as well as DHL benefit from the co-creation in distinctive ways. Co-creation aims to improve customer satisfaction/engagement. The customer is better served in this structure. It also aims to produce new ideas whilst lowering research and development costs for the company. There is some evidence suggesting that customer satisfaction improved after they begun using co-creation. According to Forbes, DHL’s co-creation efforts resulted in customer satisfaction scores rising to over 80 percent and a higher level of customer retention (joint profitability). DHL’s co-creation scheme has also helped them to produce a lot of other new ideas beside the Parcelcopter. It is unclear whether it has also allowed them to reduce research and development costs. The institutional environment, in this case,  is less relevant because of the used co-creation platform (customers only interact with DHL employees to share ideas). The feasibility requirement is met, the co-creation concept is implemented and proved to be successful.

Implementing a customer co-creation practice as part of their broader innovation research work is shown as a successful idea. It could also be a useful consideration for other companies to follow the path of DHL.



Prahalad, C. K., & Ramaswamy, V. (2004). Co-creation experiences: The next practice in value creation. Journal of interactive marketing18(3), 5-14. (2017). Available at: Accessed on 02/03/2017

Know Yourself And Know Your Enemy

‘If you know yourself and know your enemy, you need not fear the result of a hundred battles’ (Sun Tzu, Art of War)

Online retailers currently implement and leverage a variety of sales support tools. This article provides insight in consumer (users) behaviour on those e-commerce websites (Adomavicius and Tuzhilin 2005). It looks at different factors that might affect consumer behaviour (specifically consumers’ decisions whether or not to buy a product) (Hennig-Thurau et al. 2012). In this research the effect of two things on sales of a product is examined. One is recommendation systems (generated by the firm) and the other is online review systems (generated by customers). Previous literature has primarily focused on both loose effects; this study particularly looks at how these two factors might combine together to affect consumers’ behaviour.

Recommendation systems are where each product will give you recommendations to other (similar) products (Oestreicher-Singer and Sundararajan 2012). This study argues that we should see recommendations systems as networks with a number of different products linked to each other. This study seeks to analyse the whole network of related product referrals. The author predicts that the position of all the different products within a network might affect the sales of a product. Any product near to the centre of the recommendation network will get more attention. So, if the Product A is near to the centre of the network it will get more sales. If the competing product (or normally products) is near to the centre these will get more attention, taking attention away from the Product A, and so Product A will get less sales.
Online review systems (eWOM: electronic word-of-mouth) are where users of this site write a review of a product. This study observes when the consumers is shopping on the site, they can see both reviews of the product they are considering and also all reviews of other recommended products. The author predicts that the fact that they can see these other reviews creates competition, which makes the customer less likely to buy the particularly product.

The empirical analysis are performed with collected data from on 1.740 randomly selected books within four categories (programming, business, health and guide books) over a period from two years. This empirical analysis yields three major findings. The research has shown that recommendation systems intensify the competition between products. The authors state that the products which are linked to recommendation systems generate more sales if they have a central place in the referral recommendation network. There have been extensive findings that these sales gains are impeded by improvements in the reviews (eWOM) of competing products. This indicates that a positive eWOM received by a competing book worsen the rank of the focal book.

The main limitation of this study is that they used data at aggregate level, and not at individual consumer level. Therefore, the collected data does not track the actual activity of reviews/recommendations. This is a missing link for virtually all eWOM studies. The solution to this limitation could may be collecting click-stream data to better connect behavior and action.



Adomavicius, G., and Tuzhilin, A. 2005. “Towards the Next Generation of Recommender Systems: A Survey of the State-of- the-Art and Possible Extensions,” IEEE Transactions on Knowl- edge and Data Engineering (17:6), pp. 734-749.

Hennig-Thurau, T., Marchand, A., and Marx, P. 2012. “Can Auto- mated Group Recommender Systems Help Consumers Make Better Choices?,” Journal of Marketing (76:5), pp. 89-109.

Oestreicher-Singer, G., and Sundararajan, A. 2012. “Recommen- dation Networks and the Long Tail of Electronic Commerce,” MIS Quarterly (36:1), pp. 65-83