Scientific Customer Gardening stands for a systematic, data driven approach to
- constant growth at a strategic level; and
- sales success at an operational level.
It comes into existence with the synchronization of CX (Customer Experience) and CRM (Customer Relationship Management) data. This leads to the integration of subjective and objective customer data – on the one side – and the integration of behavioural and perception related data, on the other side.
We may also look at it like this: The integration of CX and CRM offers, for the first time, the possibility of connecting subjective customer data (the source of value) with behavioural data (purchasing). For the first time, we are able to design an integrated and consistent logic of how we create value for customers. This means, for the first time we have tools in our hands to measure, manage and monitor value creation as a driver of business success.
The pain of birth
So far, so good. However the existence of data does not yet represent the solution to the problem. The resulting richness of data (BigData) carries enough reasons for possible confusion. But then, in setting up rules and targets, it is all about ownership, responsibility, and positioning. Alongside, possibilities have to be checked, and purposes defined. At the end, things can go right if there is a consistent plan, which starts simply, and there is learning from the findings. Without a plan, ‘organic’ structures take over with an unknown course.
Once the plan finds acceptance, there are technical issues of integration. Pre-processing, processing and delivery all need to be taken to common ground. Now we can evaluate, what we have and how well it drives insight into the existing (subjective customer) reality.
(From this point of view, the subjective data environment is the leading line of analysis, and objective data helps to create solid models – there will also be user requirements for other CRM instruments, regarding subjective data to complement their specific analyses)
At this point, assuming that business success is the target, we will be able to (and will) find the omissions in our resulting data field. We will probably also find irrelevant and overlapping information. This all leads to a renewed definition of our ‘success mechanics’ data field that needs to be filled with data.
In B2B, we will be quite successful at finding good soil by looking at ‘purpose’. However, the new technology to systematically create value comes to its limits when the conditions for success need to be considered; the Deep Purpose level. Deep Purpose reveals the individual measure to judge over the quality of a service (Read more in The Mystery of Deep Purpose)
This is where Scientific Customer Gardening shows its strengths
Scientific Customer Gardening uses the analogy of Scientific Gardening, a technique to create the optimal growth conditions for plants. Scientific Gardening involves computer monitoring and adjustment, uses sensor technologies, and can optimize growth conditions broken down to the single plant. Scientific Customer Gardening uses the same approach to business growth, broken down to the individual customer. In both cases, not only plant growth is achieved, but also the ever increasing performance of algorithms for the underlying learning logic. (Find more about the method of Scientific Customer Gardening here:
In the reality of a customer relationship, Customer Gardening runs under a program that is agreed upon with each single customer. It has an attractive purpose, like ‘win-win optimizer to find the best value for you and us’. It can have an agreed target like ‘let’s plant 1,000,000 trees, and for every interaction in the ‘customer garden’, 100 will be planted.
This creates a base for two important achievements: firstly we can discover information about white spots in our data, and secondly we increase the robustness of the relationship with the customer, who becomes something like a ‘co-owner’ of our achievement.
The Scientific Customer Garden creates results that are to be applied with all other customers. For that reason, a central element of the Scientific Customer Garden is the classifier. It is all about classification for intervention.
The Scientific Customer Garden creates results that are to be applied with all other customers. For that reason, a central element of the Scientific Customer Garden is the classifier.
It is all about classification for intervention
In B2B, the reality is that there are only a few customer cases (hundreds or thousands, instead of millions). [MH1] This has always been a challenge for any kind of statistics. The solution is to classify the cases. A consistent and adaptable classification logic is the backbone of any systematic customer intervention system. The strength of classification is that its results are observable one by one (and not only as an average). The errors in classification can easily be identified, and fed into a learning logic to update it and keep it elastic.
The classification system is also at the core of a governance system, another pre-condition for success. A governance system provides orientation to all people involved, helps make decisions at a day-to-day level, and to interpret the results and findings.
The results come in the form of rules, patterns, predictions, interventions, actions and alarms. Attention! Also results often enough cause more confusion than clarity. Here a governance system is required. Simplicity rules!
Feeding back into CRM
As CX data can be improved by CRM data, CRM data can improve through CX data and customer analysis. The most noble result of a comprehensive Customer Gardening data algorithm is the value creation logic. In the context of interaction cost, production cost, and ‘value levers’, new business models can be systematically developed and tested. The success of these new business models can later be fine-tuned, looking into the working mechanics at a day-to-day level.
One of the big advantages of customer gardening is that it creates (by agreement) a network of engaged people on both sides, who will serve to quickly dive into the customer reality. Another advantage is for the developer of a single product that rarely receives mentions in executive interviews.