Rich data and consumer insights strengthens competitiveness through improved campaigns and better aligned offerings.

We assume you already have a capable analytics environment and would love to work with your experts to make consumer insights more operational and make your data richer. Together, we can make your customer journey analytics prosperous.

For near time improved campaigns and your future marketing automation excellence, here are the seven fundamental algorithms your analytics’ provider need to define

  • tp (I, cb, tm)
  • cpp (I, rd, mr, tp)
  • cbp (I, rd, mr, tp)
  • tsr (rd, mr)
  • ssr (rd)
  • tsb (I, cpp, tsr)
  • ssb (I, cbp, ssr)

The two most critical algorithms are

  • cpp (I, rd, mr, tp)
  • cbp (I, rd, mr, tp)

These two algorithms are the most cost efficient way to create precise powerful individual customer profiles for all your customers. Stable personality profiles as well as agile behavioural profiles on every single customer.

During the coming 4 weeks, we will in weekly blogs go through above algorithms here on Previsions’ blog. We will start with 2 blogs on the critical input variables to these algorithms, or more precisely input variable sets. This will be followed by one blog on individual customer profiles. The fourth and last blog will be on the future of segmentation.

Before we go into today’s variable blog. Let’s set the scene. The world is going digital. Human intelligence and artificial intelligence will work hand in hand. The fundamentals of marketing such as segmentation, targeting and positioning will prevail, but execution will fundamentally change. Future segmentation needs to work in the cloud as well as on the street. It needs to support human as well as artificial decision making. It needs to be agile as well as stable i.e. a solution that is clear and concise for corporate strategy while also empowering continuous individual customer relations. A solution that builds trust and puts the customer at focus. Segmentation as we know it is dead, long live segmentation.

Rich data coral reef

Today, we will share insights on “rd”, the most unique variable, and a very critical input variable in four of the 7 algorithms. “rd” stands for “rich data”, and it’s like a coral reef of your big data ocean.

Coral reefs, often called “rainforests of the sea”, form some of the most diverse ecosystems on Earth. They occupy a fraction the world’s ocean surface, yet they provide a home for a large part of marine life, Coral reefs flourish even though they are surrounded by ocean waters that provide few nutrients. The economic value of coral reefs is immense. However, coral reefs are fragile ecosystems.

The logic above can be applied also on big data. Enormous big data oceans consist of huge volumes of transactional data, but very few of these data provide any wealth in the meaning of business value. The big data oceans lack nutrition, and to nurture the whole ocean becomes immensely expensive with tiny financial return.

Nature gradually built the coral reefs. In a similar way, business need to build “coral reefs” that nurture richer data. While nature took long time to form the coral framework there are luckily fast ways to build big data ocean coral reefs. Another great thing with big data coral reefs is that their richness can be extended to the whole big data ocean. To every single current customer and as well prospective customers. They become the nutrition factory for prosperity of your whole business.

A rich data coral reef can carry unlimited amount of data variables and volumes. However, a successful implementation of a rich data coral reef in your existing big data ocean is built on three data principles for securing a sustainable “calcium carbonate” structure that hold it together:

  • Individuality
  • Width
  • Relevance

These are the corner stones of what builds a rich data coral reef. Rich data are always glued to an individual, while the rich data structure is always built on a wide holistic view of what influence consumption and loyalty. Useful rich data are always connected to a refinement and reduction of what is the most relevant data over time.

“Enormous big data oceans consist of huge volumes of transactional data, but very few of these data provide any wealth in the meaning of business value. The big data oceans lack nutrition, and to nurture the whole ocean becomes immensely expensive with tiny financial return.”


A big data ocean coral reef is built from carefully selected individuals of your customer base. The success is dependent on that we respect that customers are individuals, not segments, target groups or any other form of grouping. The customers are individuals and need to be treated as such. Thanks to increased automation, business can treat people more and more on individual bases. While humans are limited in how many different groups of customers we can handle are computer based machine learning capabel to handle individual customers precisely. Businesses therefore need to know and encourage customers as individuals. Even the best segmentation model violently force people into groups they have little in common with. By this, we destroy automation critical customer insights. When we historically designed a segmentation model we created a few distinct boxes. We then expected that all our customers fit in one of these boxes. For a minority of customers this fit was good. Most customers however have profiles that diverge quite significantly from the profile of all these poxes. Simply because people are more diverse than the number of segments we have created and because staff and businesses historic limitations we have for good reason oversimplified reality. At the same time when we put an individual in one of these boxes a lot of valuable information about this individual gets lost. Information that machine learning is able to use. For foreseeable future, some business activities will be handled by humans and will still need segmentation, but entering the world of automation and applied artificial intelligence, the norm should be that people are individuals and grouping them into segments are exemptions when really needed.


Humans are a complex species. Any ambitious business aim to grasp what builds desires, needs and demands. In a B2C business consumer desires, needs and demands are built from an intrinsic mix of the customers’ personality and context. Personality and context is captured in five categories; Values, attitudes, life stage, needs and behaviour. To create a successful rich data approach, every single individual that creates the coral reef structure needs to be profiled from all these five perspectives. When taking a snap shot of the market, these categories seem to be independent. However, they are not isolated over time. For example, behavioural experiences form new attitudes and these new attitudes might even (over years) change the values held dear by an individual.

A customer’s personality comes from values and attitudes. The values (psychographics) are individual deeply held norms, personality and guidelines. These are shaped over many years and mainly during childhood. Values stay stable over long time. They stay as core influencers in the customer journey, particularly for achieving strong advocating customer relations. Attitudes are more our direct personal views of contemporary life phenomena. Attitudes are formed by the close encounter between our values and the current context.

The context influence individuals a lot. When we use demographics, we give a simplified description of an individual’s context. Yes, demographics has little about our personality to do. When talk about youth, white colour, blue colour, literacy etc. we talk about strong contextual influencers, but little about personalities as such. Our experience say that categorising demographics from a life stage perspective gives the strongest business relevance. Another important contextual component are needs, or more precisely specific needs, connected to for example work role, hobby or special abilities. Last, but not least, our desires, needs and demands are also influenced by our current behaviour and behavioural experiences.

To achieve a rich data coral reef individual values, attitudes, life stage, needs and behaviours all have to be equally embraced. Every single individual that builds the coral reef structure needs to be profiled from all these five perspectives.


A rich data coral reef can carry unlimited amount of data variables and volumes. However, what builds the “calcium carbonate” structures must be very strict in relevance over time. Data that change too fast or is only interesting for the moment can never be part of the structure. Another aspect is that relevance often comes from relations or correlations between data rather than in the “raw” data itself. Relevance also need to be seen in contextual environment. For example, society are full of formal and informal legacy classifications of people. If such a rigid classification is critical to your business this must be reflected in your rich data coral reef. Take cars as an example, it’s a given that people reaching a specific driving license age has a fundamental impact for your business.

Rich data summary

In summary, Rich Data is important in the success of your analytics. Big data without aggregated market research is like cooking food without spices, it maybe keeps you alive but your brand will not flourish nor prosper. Customer personality profiling is about the spices. Adding 10 KB market research data to 100 TB big data can make billions of dollars’ bigger difference than another 100 TB big data.

Personality prediction

We hope we have made it clear what we mean with rich data. The beauty is that the value of your rich data coral reef can be extended to the whole big data ocean. The customer profiles identified in the coral reef can be expanded and predicted for every single customer and prospect. This way we create a very operational, powerful and adoptable customer analytics solution. More about this in coming blogs.

Segmentation as we know it is dead, long live segmentation!

Henrik Pålsson
Chief Visionary Officer
Previsions AB

[email protected]