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Data Analytics Knowing today what existing customers will want tomorrow

A guest post by Nils Niehörster* | Translated by AI 3 min Reading Time

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Anyone who always knows which customers have the greatest growth potential and what the Next Best Offer for them is has a clear advantage over competitors. We show how companies can achieve data-driven sales to existing customers in three steps.

Optimizing sales to existing customers with Data Analytics – here's how it works.(Image: freely licensed / Unsplash)
Optimizing sales to existing customers with Data Analytics – here's how it works.
(Image: freely licensed / Unsplash)

Acquiring a new customer is many times more expensive than retaining an existing one. Generations of marketing and sales experts have grown up with this rule of thumb, and yet it still holds true today without dispute. It is known that a large portion of revenue comes from transactions with long-standing customers and that acquiring new customers incurs higher expenses. In contrast, however, the paths leading to successful existing customer sales often remain hidden. Modern data analytics solutions can point the way.

The three most important steps towards a data-supported, targeted, and loss-free development of existing customers are listed here:

  1. Identify and reproduce patterns of success

  2. Recognize growth potentials

  3. Create an overview of global customer structures

1. Identify success patterns and reproduce them for existing customer development

Finding success patterns in one's own data, reproducing success with other customers, and thereby making them happy: what sounds simple has not been so far. Identifying the sales strategies that truly lead to the greatest successes from the sheer mass of data exceeds the capacities of even specialized sales professionals. Recognizing the customers who bear the greatest similarity to the previously considered success example and at the same time possess unexploited potential is an even greater challenge.

With the help of powerful Data Analytics software, it is easy today to identify both success patterns and suitable target customers. With artificial intelligence, data sets of any size can be easily sifted through. Thanks to this technology, sales successes can be transferred to other existing customers faster, more reliably, and thus more profitably.

2. Existing customer development: Recognize growth potentials

Which customer offers the greatest growth potential? Where can untapped cross- and upselling potentials be found? With the help of powerful data analytics solutions, growth potentials in the existing customer segment become transparent at the push of a button. In the context of established SAP standard applications, the solution combines all available market and customer data into a Single Source of Truth and refines it through lean machine learning processes.

From the mass of data, those companies are identified that show the greatest similarity. From the knowledge of which products they purchased and in what order, it can be deduced which offer will be the best for comparable existing customers. This paves the way for an optimal sales strategy, and previously untouched potentials can be tapped.

3. Next Best Offer: offering the right product at the right moment

Account owners must always know which products a customer is currently using and also what offer was last made to this customer. This is particularly challenging for globally operating players with widely branched corporate structures. By analyzing the global customer structure and purchasing status, it becomes clear what the Next Best Offer (NBO) for a specific company is. Leading data analytics engines link all available data – whether from internal sources such as CRM or external sources that can be additionally connected – thereby providing a global view of existing customers, making it easy to determine the ideal NBO.

Growth in the existing customer segment thanks to modern Data Analytics

With the help of data analytics, existing customer development experiences a noticeable boost. However, this requires suitably powerful software that can link data sources and generate valuable knowledge from the available information with the help of machine learning. Then, potentials become visible, success patterns can be reproduced, and predictive applications like Next Best Offer become possible.

The most important features of a Data Analytics solution for existing customer development:

  1. Completeness: Only the amalgamation of internal and external data on a central platform provides a holistic view of the global customer structure.

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  2. Speed: Modern in-memory database technologies enable complex data analyses at the push of a button.

  3. Superiority: Leading analytics solutions support strategic decision-making through modern machine learning methods. This way, success patterns can be recognized and reproduced in real time.

  4. Simplicity: A good analytics solution allows employees without a data science background to conduct targeted data analyses thanks to its intuitive operation. A significant cost advantage in data-driven sales & marketing.

  5. Openness: Often, data integration is the bottleneck when it comes to lean processes – from market observation to lead engagement. Being able to link all data sources easily and quickly paves the way for agile go-to-market planning and the right timing in sales.

*Nils Niehörster is the Managing Director of Modelyzr GmbH.

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