The Next Web Europe 2016: What is perhaps the most important digital economy conference in Europe has undertaken substantial growth this year. Three times more visitors than in the previous year, a more expansive conference venue, more stages, more workshops, more exhibitors and of course all the most important speakers and pioneers of the digital economy. We were at The Next Web Europe 2016 and have summarised our impressions for you.

How Do You Detect Trends?

The Next Web Europe 2016 was all about digital trends. But how do we detect these trends? How do you become an innovation driver? These questions were answered by speaker David Mattin, Head of Trends and Insights at TrendWatching. Companies nowadays are facing a true flood of new technologies and products. The market is getting more complex and at a first glimpse more confusing. Many companies are burdened with unanswered questions. What do my customers want? Which innovations will impressed them tomorrow? In order to find this out, companies use different methods which according to David Mattin in most cases don’t work properly. Simply ask the customers? According to the famous quote by Steve Jobs, customers do not know what they want before we show them. Even Henry Ford said if he had asked people prior to introducing the automobile, what they wished for, they would have answered, “faster carriages”. Field observations? These are lengthy and expensive. Analysis of customer data? Even progressive and predictive analytic technologies only deliver results within a limited scenario since they can only work based on historical data. If you use data analysis to find out that customers who purchase product A probably also purchase product B, you still don’t know how a potentially innovative product C which customers could be interested in in the future could look like.
David Mattin’s approach seems contradictive at first sight.. If you wish to understand tomorrow’s customers, you will need to turn away from them and instead observe new products and technologies in the market. To understand this, you should move back one step and ask the question what actually is a customer-relevant trend and how it comes about. Every customer has specific requirements (security, entertainment, etc.) which generally remain relatively constant over a longer period of time. The world around them, however, continually changes, e.g. through new /en/en/en/en/en/products/technologies entering the market. Some of these products are able to satisfy customer requirements in a new way. This changes customers’ expectations, initially towards the products which address the same requirement. These expectations, however, expand onto other product types, markets, areas of life etc. As soon as this expansion happens, we can call it a trend.
As an example, David Mattin mentions the transportation network Uber. Via a few clicks, Uber allows you to book a driver who’s usually with you in less time than a taxi. Payment takes place automatically via the app. Uber has therefore significantly influenced the expectations of customers regarding usability and speed of the service provider. Customers nowadays expect that they can request a service with little effort and that this service is provided quickly. Providers from other sectors orient themselves on these expectations and offer, e.g. products such as one-click-buttons via which customers can place orders and preferably have their products delivered the same day. The example of Uber is also relevant for digital marketing. It underlines the relevance of real-time communication, i.e. real-time marketing automation. Customers nowadays expect that marketing and service communication provides them the right information in their current user context in real time – such as Uber.
It is secondary for the development of trends whether the innovative products are actually used a lot or are commercially successful. The simple fact that customers know them and know how they work or how they can satisfy their needs with them, already changes expectations. We can derive an important conclusion here (not only) for digital dialogue marketing. It is worth investing in innovative communication concepts and relevant technologies now in order to be ready for customer expectations in the future.
So, what should companies do to be able to respond to trends in time? They should carry out continuous monitoring to identify new and innovative products in the market at an early stage. With each of these products you should ask yourself whether it satisfies a customer need in an innovative way and therefore influences the expectations of customers. If the answer is affirmative, you need to check if other products already exist which address these changed expectations in order to be able to judge whether a trend has already appeared.

How to Be Successful in a Digital World

A trend has been identified but how should you react? Which innovative business models are possible in the digital world? And how can companies implement them successfully? Rashik Parmar, Lead Cloud Advisor at IBM identified five patterns according to which innovative business models can be created.

  1. The networking of products to obtain and use data (Internet of Things).
  2. The development of unique services based on data.
  3. The combination of data within one industry sector or across several sectors.
  4. The trade with data.
  5. The digitalisation of (offline) goods.

This leads to the next question: Which (personal) abilities are required to successfully implement innovative digital business models? Rashik Parmar identified five competences, i.e. five types of people who are equipped with these competences.

  1. Gatherers generate data, open up new data sources and maintain the overview over existing data.
  2. Visionaries recognise which benefit can be drawn from this data and create the corresponding concepts.
  3. Theorists develop algorithms which “change” data into benefits.
  4. Engineers build the system where these algorithms are embedded, i.e. the software platform.
  5. Righteous ensure that data protection and legal compliance are guaranteed.


Individualised Communication Through Artificial Intelligence (AI)

In Silicon Valley, artificial intelligence (AI) is often celebrated as a saviour. Some critics, however, predict a threatening future scenario with AI where machines go against humans. According to Werner Vogels, CTO at Amazon, both is exaggerated. To him, AI is just computer science. Algorithms meet data, analyse this and auto-optimise based on the results. Werner Vogels views three development steps in the use of data.

  1. The analysis of historical data to detect interrelations and create an understanding of previous events.
  2. The real-time monitoring of data to (automatically) respond to changes immediately.
  3. The prediction of future events based on correlations in historical data (predictive analytics).

AI becomes particularly interesting for marketing when it is used for the individualisation of communication, e.g. for product recommendations in digital dialogue measures. There are different reasons which explain the individualisation in marketing. As one of the reasons, Vogels states that individualisation reduces the options for action. Customers feel overwhelmed by the large number of options for action, i.e. in this marketing context, a large number of products. They are not really happy with their purchase decision because they feel that the purchased product was not the best choice. Individualised communication shows a customer only a small selection of products which are particularly relevant to him. Vogels names three different methods to individualise product offers in the marketing communication.

  1. Matching products: Here, the customer is recommended products which fit to other products he has previously purchased. This includes classic cross and upsell recommendations. The analysis shows which products are most frequently bought together with the products purchased by the customer. In this type of individualisation, the concept of social proof is very important. Customers prefer to buy products which were bought by other customers, too. “Customers who purchased product A, also purchased the following products”, “Often purchased together”.
  2. Personal recommendation: Based on its personal data, the customer receives individualised recommendations Based on the purchase history, favourite brands or product categories can be identified. Other relevant customer data would be, e.g. socio-demography, place of residence, location/context of use or click stream data from digital dialogue channels.
  3. Trends: Here, the communication is not really individualised. Instead, the aggregated data of all customers is used to make suitable recommendations. “Our best seller from last month”, “The most popular products from category X”, “On top of our customers’ wish list”