Different Techniques in Data-Driven Marketing

Introduction

Data-driven marketing is a collection of strategies and approaches that use large volumes of data to construct successful marketing processes that address particular audiences and consumer segments personally. This approach strengthens customer segmentation and improves every personalization strategy implemented by organizations. This data can offer excellent insights into customer behaviour, and using it can be extremely beneficial to your company. For elite marketers, data is becoming exceedingly relevant, and it has the potential to become the most useful weapon in marketing. Big data analytics is derived directly from consumer experiences, and it is this type of data that can aid in the refinement, enhancement, and improvement of any marketing campaign while supporting marketing ROI optimization.

Customers’ wishes, expectations, and future behaviour are predicted using data-driven marketing. Such knowledge aids in the creation of tailored marketing campaigns and personalization strategy frameworks that maximise ROI (ROI). This blog discusses the different techniques in data-driven marketing. In a rapidly evolving business climate, data-driven marketing has become increasingly digital and technology-enabled, a fascinating evolution. This transformation has aided in expanding the marketing function’s position and reach, from designing and maintaining innovative communication to introducing data-driven and technology-enabled marketing activities that are important to the firm and customers, measurable through marketing performance metrics, and financially accountable. Figure 1 shows the history of marketing to this stage.

Figure.1. Role and scope of Marketing

Evolution of Data-Driven Marketing Techniques and Marketing Performance Metrics

Econometric and operations analysis methods started to look at marketing data at both the household (micro) and aggregate market levels (macro). MRCA and Chicago Tribune household screens, as well as scanner data on goods, labels, and stock holding units, were among them (sku). This occurred during the 1960s and 1970s. A vast number of studies conducted on PIMS data on market share on profitability offered a substantial boost. These early quantitative foundations supported structured marketing performance metrics and systematic marketing ROI optimization approaches that are still relevant today.

Growth of Multivariate Techniques for Customer Segmentation and Predictive Analytics

Marketing analytics moved from econometric to psychometric techniques and from univariate to multivariate techniques in the 1970s and 1980s. Multidimensional scaling (MDS), cluster analysis, conjoint analysis, discriminant analysis, and finally, the LISREL were among the techniques used. These newer approaches have resulted in the publication of hundreds of scientific papers. They needed a variety of information, including surveys and multi-attribute altitude data. These multivariate statistical techniques became essential tools for customer segmentation, positioning strategy development, and predictive modelling in competitive markets.

Although the multivariate revolution lasted more than three decades, biometrics such as pupil dilation and Galvin skin pressure began to appear. Multivariate approaches began to plateau with the emergence of what is known as preference models in the 1990s. Customer lifetime value (CLV) became a common tactic when the marketing discipline concentrated on loyalty schemes and partnership marketing, strengthening relationship marketing and enabling more accurate marketing ROI optimization decisions.

Customer Lifetime Value (CLV) and Marketing ROI Optimization

The amount of a customer’s cumulative cash flows—discounted using the weighted average cost of capital—during his or her entire relationship with the company is the Customer Lifetime Value (CLV). It’s worth noting that we’re using the focal firm’s perspective here rather than a broad view of what a customer’s overall value is to all the firms in a given market. Customer equity is calculated by adding the CLVs to all a company’s clients. CLV remains one of the most powerful predictive analytics techniques in modern data-driven marketing strategies and plays a critical role in personalization strategy and customer segmentation planning.

However, in the absence of individual customer level modelling, some methods compute the overall calculation of Customer Equity directly and the total Customer Lifetime Value by dividing Customer Equity by the number of customers in the firm’s database. While this method is based on strong hypotheses, it can be justified for assessing and analysing several firms because individual customer operation data analysis for multiple firms at the same time is usually inaccessible to analysts. However, we must calculate profitability based on marketing costs and sales for each customer, using clearly defined marketing performance metrics and structured reporting dashboards supported by data visualization techniques

Big Data Analytics, Data Visualization, and the Digital Data Tsunami

Since the new millennium’s digital era, there has been an influx in data (cell phones, Twitter, and the World Wide Web). The way we store digital data has changed dramatically. A flash drive will now hold large amounts of data. As marketing communications, retail sales, and customer reviews become more prevalent online, a flood of unstructured data has resulted (text messages). The unprecedented proliferation of social media platforms such as Facebook, Whatsapp, YouTube, and Instagram has added this. Because of text and video messaging, they both produce more unstructured (nonnumerical) data. For the first time, data is seeking techniques rather than techniques seeking data.

The majority of current methods are focused on numbers. As a result, numerical evidence is required for statistical inference and analytical results. Inferential statistics are replaced by non-inferential statistical methods such as Natural Language Processing (NLP), text mining, and sentiment analysis to extract insights from big data analytics. These insights are increasingly presented through data visualization tools that help decision-makers interpret marketing performance metrics more effectively.

Six new emerging fields of study opportunities in digital and data-driven marketing are depicted in Figure 2. However, marketing scholars face several difficulties. These challenges will be related to (a) data curation – ensuring that the data collected is relevant and authentic, (b) data analysis – choosing the right set of techniques to analyse the increasingly complex and diverse data, (c) insights – dealing with multiple perspectives or overcoming biases related to complex data interpretation, and (d) half-life of knowledge – the changing context will present challenges. The effect of social media and text mining analyses on substantive marketing and industry results is investigated in the next five articles.

Fig. 2. Future research in marketing

Future Trends in Data-Driven Marketing, AI, and Advanced Marketing Analytics

The “Influence of New-Age Technology on Marketing: A Research Agenda” gives a good overview of how emerging technologies will affect marketing shortly. The future of data-driven marketing is bright. It will integrate with emerging databases and territories and adjust accordingly. Focusing on policy analysis and social trends such as poverty, emerging economies, sustainability, health, and education would be the real chance. Advanced technologies such as artificial intelligence, deep learning, blockchain, and marketing automation platforms are reshaping predictive analytics, personalization strategy development, and marketing ROI optimization frameworks.

Data-driven marketing will make a real difference by going past the firm and its profit motive. Finally, the next frontiers in Marketing research analytics technology would necessitate multidisciplinary research teams. It will necessitate collaboration among computer scientists, behavioural scientists, quantitative scientists, and policy analysts. The data-driven campaigns would necessitate programmatic analysis outside of the marketing department. Artificial intelligence, deep learning, and blockchain would make it easier to develop new approaches based on heuristics and algorithms while strengthening measurable marketing performance metrics across industries.

Reference

[1]Denish Shah, B.P.S. Murthi, Marketing in a data-driven digital world: Implications for the role and scope of marketing, Journal of Business Research, Volume 125, 2021, Pages 772-779, https://doi.org/10.1016/j.jbusres.2020.06.062.

[2] Kumar, V., Ramani, G. and Bohling, T. (2004), Customer lifetime value approaches and best practice applications. J. Interactive Mark., 18: 60-72. https://doi.org/10.1002/dir.20014.

[3]JagdishSheth, Charles H. Kellstadt, Next frontiers of research in data driven marketing: Will techniques keep up with data tsunami?, Journal of Business Research, Volume 125, 2021, Pages 780-784, https://doi.org/10.1016/j.jbusres.2020.04.050.

[4] Maurice Mulvenna, Marian Norwood & Alex Büchner (1998) Data-Driven Marketing, Electronic Markets, 8:3, 32-35, DOI: 10.1080/10196789800000038.

[5]Paul D. Berger, Nada I. Nasr, Customer lifetime value: Marketing models and applications, Journal of Interactive Marketing, Volume 12, Issue 1, 1998, Pages 17-30, https://doi.org/10.1002/(SICI)1520-6653(199824)12:1<17::AID-DIR3>3.0.CO;2-K.