1.3 Analytics and the 4Ps

With data becoming the cornerstone of many business operations, marketing analytics specifically are increasingly used throughout organizations. Data on sales may lead to new product development. Data on customer service can lead to reduced customer wait times and improved store service. Data from web traffic can lead to an entirely new website design. Importantly, measuring marketing results determines the budget for future marketing programs. Throughout the marketing organization, data is used to do the following:

Marketing analytics are needed throughout the organization. Most activities in sales and marketing benefit from clear measurement of results and desired outcomes. In this way, being a marketing analyst is among the most critical jobs in marketing today, contributing greatly to company revenue.

Marketing analytics jobs are as diverse as the sources of business data. Many creative marketers are now also using metrics to measure the impact of the advertisements, videos, websites, and other creative materials they design. Often, the marketing manager Mid-level leader of marketing in many organizations. or director of marketing Senior-level leader of marketing in many organizations. (a senior or lead marketer) is responsible for metrics alongside their work in managing actual marketing efforts. Today’s metrics tools often include easy-to-use interfaces that require no special technology skills or mathematical aptitude—just training in how to set up measurement systems and analyze results. Marketing analytics is as much a business strategy discipline as it is a technical or mathematical one. Indeed, many of the same tools that marketers use to perform everyday marketing tasks have metrics dashboards built into the tool, so the person doing the marketing work is also the person measuring the results.

On the other hand, some large organizations have dedicated marketing analytics teams that employ groups of data scientists. These scientists mine complex datasets to discover market trends, predict consumer behavior using sophisticated algorithms, and even design machine learning systems that allow computers to respond to consumer actions automatically. Even smaller organizations can access such services by contracting with vendors that offer complex data science tools and services.

Most marketers today need to understand data. As the volume of data available to companies keeps growing, it’s being used throughout organizations. Jobs involving marketing data are becoming more specialized. While marketers will commonly need to master the concepts in this book, students interested in data science will find many applications for the most sophisticated data analysis and application in marketing-related roles, as well. Everyone in a modern business organization uses data, while, at the same time, the number of highly specialized data jobs involving complex tasks is also growing.

Just as most marketers use data in their jobs, organizations are also becoming more comprehensive in using data across the organization. One challenge that we will discuss in depth in Chapter 2 “Internal Data: Many Sources, One Goal” is the phenomenon of data silos. Traditionally, departments in an organization collected data based on their own needs. A sales team may have logged quarterly sales, while accounts payable tracked outlays on various company expenses. This departmental data remained within the group that created it, creating what we call data silos: data that is collected in isolation, and not shared with other groups, much the way grain is contained in a farm’s silo. This siloed data has been the norm within companies for decades.

Today, as businesses discover the value of data in making business decisions, they are also realizing that the best decisions come about when decision-makers have access to all important data. A marketing team may need to know the cost of store furniture, for instance, when factoring in whether to build a store in an attractive new shopping center. In the past, a marketing team may have recommended expanding to new store locations based only on potential sales in that location. Now, thanks to a deeper understanding on the value of interdisciplinary data collection, they may also factor in costs in a more sophisticated way, to make a more meaningful decision. Another benefit of sharing data across the organization is that it gives employees in different job functions a better understanding of each other’s jobs. Marketing teams come to appreciate the hard work of accounting teams, while finance starts to see marketing for what it is, a driver of revenue, rather than simply another cost. In this way, data offers the secondary benefit of increasing organizations' cooperation. By encouraging workers to collaborate on data, it can also allow fresh perspectives on every department’s work, which can lead to more creative innovation.

Breaking down silos is the term used to describe the process by which siloed, departmental data becomes available across an organization. In large organizations that have been collecting data the traditional way for decades, breaking down these data silos to share data can be a major effort. In this new data-driven economy, small, nimble organizations that have never siloed their data can have an advantage.

Breaking down silos is one thing. What organizations do with all the un-siloed data is how they truly gain competitive advantages.

Another challenge with the complexity of today’s data is “analysis paralysis The phenomenon when companies have so much data that they become overwhelmed by it. .” This is the phenomenon when companies have so much data that they become overwhelmed by it. This is often a result of disorganization. Data collection may be poorly organized, or data may be presented without structuring it properly. For example, a retail store may have years of sales data, amounting to millions of data points. Presenting this data as a simple spreadsheet of millions of lines is not likely to be useful to the typical user. However, organizing the data into a line graph showing seasonal growth in sales makes this complex data useful by making it easy to understand. Sometimes, all that is needed to make data actionable is to organize it for clarity.

At other times, analysis paralysis sets in because of competing interpretations of data. There may be a lot of data but insufficient differences between one dataset and another, making it unclear what course of action to pursue. For instance, let’s say that Store A had 1% more sales in January compared to Store B. In February, Store B is the winner, with 3% more sales than Store A. How should a business interpret these results? Should they close Store A? Expand Store B? Is the difference in sales really significant between the two stores enough to make a decision based solely on this data? Often, companies collect data as part of their operations, then try to use it to make decisions. Such data may not point clearly to decisions, making it difficult to act. It is often this type of unclear data that leads to analysis paralysis.

In addition, sometimes data is not truly analyzed at all before being presented. At such times, companies examine raw data and strive to see patterns in complex information. The first step in analyzing information is to describe it. An analyst may present charts to an executive team saying that sales have grown 10% over the last three quarters. That information is useful in itself. However, it doesn’t tell us what is leading to increased sales. The company would like to know what activities have led to this success so that they can replicate the successful efforts for even more sales growth. This is why it’s important to analyze data for insight. Looking beyond mere description, analysts should look for the “why” behind phenomena. They should look to provide answers rather than simply describing facts.

In this book, you will learn how to collect data with a clear purpose, analyze the data that already exists, and capture insights from many datasets. When data is collected purposefully, analyzed with goals in mind, and presented clearly, analysis paralysis often subsides.