Growth hacking: Insights on data-driven decision-making from three firms

Highlights

• A data-driven orientation to big data analysis enhances B2B marketing-decision making;

• Hacking Growth approach can help managers to improve the effectiveness of decision-making;

• Big data analytics improve companies’ engineering infrastructure, data orientation, skills and marketing objectives;

• The main analytics adopted in the sample are cognitive computing systems, API and web crawlers;

• The firms analysed reach three marketing objectives: strengthening of CRM; smart supply chain management; co-innovation.

Abstract

Theoretical background

The work explores how Big Data analysis can reshape marketing decision-making in B2B sector. Deriving from Data-Driven Decision-Making (DDDM) approach, the Growth Hacking model is employed to investigate the role of cognitive computing and big data analytics in redefining business processes.

Purpose

The main objectives of the study are: 1) to assess how a data-driven orientation to the use of big data analytics and cognitive computing can reframe marketing decisions in B2B segment; 2) to explore whether the adoption Growth Hacking can be helpful in exploiting the opportunities offered by big data analytics and cognitive computing in B2B marketing.

Methodology

The paper is based on Action Research (AR) methodology that permits researchers to participate actively in the observation of businesses and to examine how decisions are undertaken and managed over time.

Results

The main findings allow identifying the most common strategies and tactics employed in three companies operating in different B2B sectors to exploit the opportunities offered by cognitive computing and big data analytics according to a data-driven marketing approach. Based on the application of the Growth Hacking model, the tools of analytics and the main objectives, outcomes and implications on marketing decision-making are revealed.

Originality

The identification of the main objectives and outcomes produced across the three dimensions of the Growth Hacking model (data analysis, marketing and programming) can help academics and practitioners to understand the main levers to attain marketing goals, such as the enhancement of relationship with customers (CRM), continuous learning and development of new products and potential innovation.

Introduction

Contemporary digitized markets provide organizations with the possibility of collecting and analysing large amounts of data easily and rapidly. However, big data should be managed strategically to optimize the use of analytics in business management and to overcome the risk of turning the advantages offered by ICTs tools into threats. There is the need to understand how big data analytics can reframe traditional marketing decision-making by exploring how data-driven orientation at a strategic level can lead to the redefinition of technologies tools in the different marketing phases to enhance the effectiveness of the process. Then, extant research proposes the adoption of a real orientation to manage big data throughout the entire decision-making cycle: data-driven decision-making (DDDM). Data-driven managers should base business decisions on data-analytic thinking in order to use the data collected as a driving force to prescribe actions, predict complexity and “make” the change. One of the techniques based on big data analytics with the most relevant implications on decision-making is cognitive computing, an integrated set of automated learning technologies that extract data in order to reproduce the functioning of human brain.

The shift toward the espousal of a data mind-set in marketing decision-making that pursues the constant development of innovation and continuous learning translates into the proposition of a new marketing perspective for big data analysis: the Growth Hacking model. The framework combines the main elements of marketing, innovation and big data analysis to introduce a new business attitude that encourages companies to act as never-ending start-ups based on constant learning, synthesis and analytical method and adaptability of competencies.

However, previous research on DDDM seems to be grounded mainly on the analysis of big data in Business-to-consumer (B2C) marketing. The investigation of analytics in Business-to-business (B2B) marketing is related to the exploration of business management concepts as e-marketplace and e-supply chain management. For this reason, the need to adopt a framework for marketing decision-making according to a data-driven approach in B2B marketing strategies can be revealed. Some recent contributions in B2B research call for the examination of how technology reshapes marketing strategies and tactics and of how knowledge orientation can drive effectiveness in information technologies management.

Hence, the study aims at revealing how data-driven decision-making in B2B can foster the use and the effectiveness deriving from the use of big data analytics and cognitive computing in marketing decision-making. Secondly, the application of Growth Hacking model to three different sectors (food, building and transports) strives to detect how the main features, steps and objectives of marketing decision-making are reframed thanks to the use of analytics and cognitive computing.

Due to the necessity of analysing deeply the underlying mechanisms, the mood and the orientation that lead businesses decision-making, an empirical research based on action research (AR) is performed. The technique permits researchers to participate in the decision-making process of a given group and to become active members of the community for a given period to observe how an organization can address a complex issue or manage an emerging phenomenon over time. AR understands decision-making as a cycle: therefore, it fits well with the basic assumption of Growth Hacking model and DDDM that companies should turn data into information that can be transformed into knowledge to develop learning and creativity in a circular process of constant improvement.

The empirical research is based on a multiple case study on a sample of three Italian B2B firms. Companies' behaviour has been observed for one year. Semi-structured interviews have been conducted at the beginning and at the end of the period to evaluate potential changes in the management strategies and in the related outcomes.

Specifically, the research aims at addressing the following research questions:

RQ₁

Can data-driven approach improve the use of big data analytics and cognitive computing in B2B marketing decision-making?

RQ₂

Can Growth Hacking mind-set enable the attainment of marketing objectives in B2B sector?

Based on the main findings obtained, an integrated model that can help to detect the main steps to implement DDDM in B2B marketing thanks to growth hacking framework is proposed. This model identifies the main steps, the main marketing objectives and implications related to the effective adoption of big data analytics and cognitive computing in each dimension of Growth Hacking framework (marketing, programming, data analysis).

The paper is structured as follows. In the first section (theoretical background), the main models proposed in data-driven marketing are explored and compared. Then, after the identification of Growth Hacking model as an adequate framework to reframe B2B marketing decision-making, the empirical research is performed to assess how the three dimensions of the model (technical, creative and analytical) can be applied to different markets (food, building industry and transports). The main findings are debated by revealing the most common marketing objectives and outcomes accomplished through the adoption of Growth Hacking mind-set. Lastly, limitations and implications of the work are discussed and suggestions for future research are provided.