Cultivate your own growth algorithms bridging CX and CRM

Scientific Customer Gardening is a systematic, data-driven approach to ensure

  • constant growth at a strategic level
  • and sales success at an operational level

It becomes relevant when CX (Customer Experience) and CRM (Customer Relationship Management) data are integrated. This aligns subjective and objective customer data, on the one side, and behavioural and perception-related data, on the other.

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 that, for the first time, we have tools 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 this data does not yet solve the problem. Big Data can also create possible confusion. Therefore rules and targets have to be established. The possibilities have to be checked and purposes defined. In the end we can learn a lot from the data by following a consistent plan.

Once the plan finds acceptance, then the data is prepared and processed. After that we can evaluate what we have and how well it drives insight into customer perceptions.

Finally! Building the bridge between subjective and objective data., between CX and CRM

(The subjective data environment drives the analysis and the objective data helps in developing solid models – there will also be user requirements for other CRM instruments in terms of handling subjective data to complement specific analyses).

Assuming that maximizing revenue is the objective, we will be able to find the omissions in our resulting dataset. We will probably also find irrelevant and overlapping information. This all helps us to fine tune our data structure.

In the B2B world, 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. It takes advantage of the two sides of scientific gardening, to optimize the growth conditions and to train an artificial intelligence to adapt quickly to changing data context. (Find more about the method of Scientific Customer Gardening here):

Customer Gardening runs under a program that is agreed upon by each customer. It’s purpose is to find a ‘win-win’ solution i.e. to find the best value for all sides. Customer Gardening is based on a program that is agreed by each customer. It’s purpose is to find a ‘win-win’ solution i.e. to find the best value for all sides. The secret is to agree on a common goal and to go for it.

This creates the basis for two important achievements: firstly we can discover information about blank areas 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 applied to all other customers. For that reason, a central element of the Scientific Customer Garden is the classification.

It is all about classification for intervention

In the B2B world, there are far fewer customers than in the B2C world. This has always been a challenge for generating sufficient data. 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 the 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 flexible.

The classification system is also at the core of a governance system, another pre-condition for success. A governance system provides an overview for everyone involved and helps to make decisions on 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. Results often cause more confusion than clarity. Therefore a governance system is required. Simplicity rules!

Feeding back into CRM

As CX data can be improved by CRM data, CRM data can be improved through CX data and customer analysis. The most notable result of a comprehensive Customer Gardening data algorithm is the value creation logic. When taking into account 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 by looking at the working mechanics on a day-to-day level.

One of the big advantages of customer gardening is that it creates a network of engaged people on both sides, who will quickly dive into the customer reality. Vendors of single products that rarely receives mentions in executive interviews also benefit.

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