Shaping the future of marketing decisions with business intelligence and advanced analytics

By Joseph Opoku MENSAH

In fact, as today’s digital economy is rapidly evolving, decision-making based on the available information has become not only a plus, but a necessity for most companies. Marketing, one of the most vibrant functions in contemporary business, is a science of analyzing consumers’ behavior, forecasting their behavior, and developing programs that would meet the current and future goals of the business.

This is where the tools of business intelligence (BI) and analytics, which include descriptive, predictive, and prescriptive methods, come into play. Combined, these tools form a solid foundation through which marketers can transition from simple observation to meaningful action, which is critical in a constantly evolving commercial environment.

While there are slight differences in definitions, business intelligence essentially refers to the process of capturing, storing, processing, and delivering useful information to decision-makers. It is an extensive process by which organizations are able to assess KPIs, market trends, and consumer behavior.

BI has proven to be a valuable tool for marketing professionals, as it facilitates the transition from data to strategies. BI tools provide marketers with a unified data source from CRM, websites, social media platforms, and other sources, enabling them to evaluate the effectiveness of their campaigns.

For instance, BI dashboards enable a company to assess the real-time performance of a product launch, identify effective strategies, and identify areas requiring changes. However, the real value of business intelligence is not in the utilization of the mentioned tools as standalone applications but in their use in combination with other methods of data analysis.

These three types of analytics enhance business intelligence by offering marketers not only past results analysis, but also future forecasts and action recommendations. This integration turns data into a strategic weapon that used to be purely tactical.

Descriptive analytics, as the initial layer, focuses solely on historical data. It is a process of analyzing data on what occurred and how in an attempt to answer the question of what happened. It is useful because, by condensing trends, patterns, and relationships, it offers marketers a snapshot view of campaign and strategy performance.

For instance, a descriptive analysis could show that the open rates for a brand’s email are higher on weekends or that posts containing videos receive more engagement than posts containing only images. These insights are very useful in determining strengths and weaknesses, and they help the marketer build on success and failure. Descriptive analytics is useful for clarity on the past but lacks foresight. This is the role that predictive analytics is about to play on stage.

Predictive analytics uses methods, for example, machine learning, regression analysis, and time series analysis, to forecast the future results.It provides an answer to the question, “What is likely to happen?”  In the context of marketing, predictive analytics allows the business to forecast consumers’ behavior, forecast the demand for their products, and even look out for a new trend. For instance, an online shop may utilize the predictive models to determine which customers are likely to be repeat buyers or which products are likely to be popular during a particular festive season.

It also enables firms to manage their stock effectively, improve the approaches that they use to reach customers, and communicate relevant content to targetconsumers. .However, predicting is not enough.Knowing the expected events is beneficial, but deciding on actions based on these expectations is even more crucial. Prescriptive analytics focuses on asking “What should I do?” instead of just analyzing past events and future predictions.

Prescriptive analytics includes optimization algorithms, decision trees, and simulation models to give specific advice about what to do next. For instance, we can apply prescriptive analytics to ascertain the most effective allocation of advertisement expenses in a food delivery service, as well as the potential advantages and disadvantages of this decision.

Prescriptive models work by mimicking various situations and thus assist marketers in avoiding or overcoming confusing situations depending on the goals set. BI and advanced analytics align in a way that builds a strong ecosystem for marketing decisions. BI sets the stage by integrating and presenting data; descriptive BI situates the past, predictive BI forecasts the future, and prescriptive BI suggests the best course of action.

When combined, they ensure that marketing strategies are not only grounded in data, but also dynamic and future-focused. For instance, a streaming service that operates on a subscription model and aims to increase its usage rate can benefit from this integration. BI tools may show that the level of engagement is lowering among a particular group.

Descriptive might show what material appealed to this group, while prescriptive might predict future genres or formats. Finally, prescriptive analytics could recommend specific campaigns, such as promotions or playlists, to entice these users back. This method also ensures that the platform detects and fixes issues with evidence.

The use of Business Intelligence (BI) and advanced analytics in the field of marketing presents several disadvantages. The value of these tools depends largely on the quality of data used in the analysis. Data can be inconsistent, incomplete, or biased, and such data can produce negative results and hence wrong strategies. Furthermore, the use of complex analytical methods calls for experienced personnel who can understand results and explain them in terms of implementation plans.

Beyond technical factors, there are virtues that significantly contribute to achieving the desired results. Since consumer data remains at the core of marketing efforts, marketers must pay particular attention to transparency and consent in light of regulations such as GDPR and CCPA. The role of data-driven marketing will only increase as more and more changes occur in the business environment. Business intelligence, alongside descriptive, predictive, and prescriptive analytics, provides a framework for dealing with this issue.

These tools do not only help businesses to gain insights into customers and markets but also prepare them to respond to change in a flexible and accurate manner. In the swift and ever-changing global market, this ability to move ahead of the changes in the consumer market is the only way organizations can sustain their competitiveness.

Future marketing must be data-savvy, not just aware of past events but also of future ones. To this end, businesses should embrace BI and advanced analytics as the key to revolutionizing their marketing strategies in a rapidly shifting data-driven world.

The writer is a communications and marketing professional with a strong background in media relations, corporate communications, and event management. He also serves as an adjunct lecturer at the University of Media Art and Communication.

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