Preparing for the Digital Value Exchange Economy in B2B. As the B2B sector undergoes its own digital transformation how do manufacturing SMEs integrate the impact it will have on the value proposition of their existing marketing and sales resources?

This thesis investigates how SMEs in the manufacturing sector integrate the impact on the client

relationship of digitally transforming their existing marketing and sales resources.

It examines how digitised client interfaces can contribute to building incremental value in their client relationship strategy through the categorisation and optimisation of data. The Data Value

Efficiency model (DVE) is proposed as an outcome of the research undertaken and contributes

to literature on value creation in the digital transformation of sales and marketing. This model

is supported by a suggested definition of the B2B Data Value Exchange Economy.

Recommendations are made to guide industrial SMEs in categorising and optimising the flow

of collected client data in order to interact with their client base in a timely and resourceful

manner. By adopting a pragmatic, mixed-methods approach applying Grounded Theory

methodology, primary research was undertaken, comprising a longitudinal behavioural analysis

of client facing functions within the selected SME. Additionally, semi-structured interviews

with practitioners, academics and company stakeholders were recorded and subsequently

analysed using nVivo software. The behavioural analysis identifies that a partial digital

transformation of the client relationship management role results in projected efficiency (time,

financial and resource) gains of 44%. The qualitative analysis exposes an evolution of initial

client/supplier engagement through Robotic Process Automation (RPA) and social media

strategies. The research suggests that the digital transformation of sales and marketing functions

in manufacturing SMEs will create value through enhanced websites evolving into value exchange platforms by integrating marketplaces of expertise. The marketplace will match client requirements to supplier expertise through artificial intelligence. As a consequence, the marketplace will formulate the value proposition of the sales and marketing teams in both a timely and efficient manner. The successful implementation of such a platform will be based on SMEs ability (internally or through consultative support) to identify and optimise four types of datasets based on client interactions: automated, designated, generated and co-created datasets It also suggests that SMEs need to integrate innovative digital interfaces with the proposed marketplace platform to enable internal and external communication between client

and supplier, which responds favourably to clients’ expectations of a single point of contact

with the supplier.