CUSTOMER NEEDS PREDICTION

Creating adaptive ways to anticipate customer requirements

 

Challenge identifier: DPC5-2018

 

Proposed by

Konica Minolta

enables its clients to champion the digital era: as a provider of comprehensive IT services, Konica Minolta delivers consultancy and services to optimise business processes with workflow automation and implements solutions in the field of IT infrastructure and IT security as well as cloud environments. Being a strong partner for the production and industrial printing market, it offers business consulting, state-of-the-art technology and software. Konica Minolta also drives digitalisation of clinical workflows and offers a broad range of next-level diagnostic solutions in the healthcare sector.

The organisation is making anonymised client data available to startups in order to anticipate client needs and provide them with a better service and solutions tailored to their businesses.

 

Background

Answering the needs of customers is the ultimate challenge of every company. In the business to business (B2B) sector, answering these needs with tailored solutions that answer the specific expectations of a client can be a real challenge: between the decision taker, the buyer and the user, needs, expectations and outcomes can vary drastically.

Large companies are multifaceted entities and data are often collected multiple times and for different purposes across the many activities characterising the company business. While it is now clear from many years of managing data that companies benefit from breaching the these data silos, this is rarely fully implemented and, even when data are combined, it is a non-trivial task to analyse such data to extract further business value. Such business value can be derived in numerous ways, but it is particularly interesting to cross-check customers data with the aim of  anticipating future needs. Anticipation means that technological and operational transitions can be much smoother, minimising associated disruption while still maintaining or gaining competitive advantage such as the ability to compile narrowly targeted commercial offers.

 

Description

Konica Minolta operates in a B2B scenario, offering, among others, ICT services to other companies. By analysing data describing companies and their customers, they wish  to create a flexible toolset that can anticipate customers’ needs.

The technological challenge is manyfold. It includes and it is not limited to:

  • Identifying and isolating significant behaviour patterns across customers;
  • Classifying existing services offered by the company;
  • Associating such patterns with a need for something more or new in terms of required services;
  • Defining a list of possible new types of  services satisfying the identified needs.

The quickly evolving ICT market together with the desired goal of anticipation means business information derived from historical data must be superseded by data science. Successful and durable solutions should be highly configurable and

 

Data

Anonymized client data in the following categories :

  • Size, sector, region
  • Konica Minolta sales (products, solutions, amount)
  • Interest score : interactions with Konica Minolta (pre-sales appointments, web interactions)
  • IT/Digital maturity index

More detailed information about the data can be found in our data catalogue

 

Expected outcomes

  • The expected outcome is a flexible toolset that can anticipate customers’ needs, guiding the sales force and the research and development departments to figure out what clients need and want both now and in the future, and suggesting the best way to approach a prospect in order to make a relevant offer.

Expected impacts

  • Better relationship with clients as we can better anticipate their challenges
  • Better sales and marketing decisions
  • Better brand recognition by positioning Konica Minolta as the best all-round partner and solutions provider in the Information Technology Services field

This project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Grant Agreement 732506. Unless otherwise indicated, all materials created by the Data Pitch consortium are licensed under CC-BY 4.0