Highly Effective Marketing Analytics

A Practical Guide to Improving Marketing ROI with Analytics

Mu Hu

• Verlag: Business Expert Press
• Jahr: 2019
• ISBN:
  9781951527099
• Thema: Économie
• Thema: Statistiques pour les entreprises et l'économie

Highly Effective Marketing Analytics infuses analytics into marketing to help improve marketing performance and raise analytics IQ for companies that have not yet had much success with marketing analytics.

The book reveals why marketing analytics has not yet kept the promise and clarifies confusions and misunderstanding surrounding marketing analytics. Highly Effective Marketing Analytics is a highly practical and pragmatic how-to book.

The author illustrates step by step many innovative, practical, and cost-effective methodologies to solving the most challenging real-world problems facing marketers in today's highly competitive omnichannel environment.

CHAPTER 1

Why Has Analytics Missed the Mark?

Chapter Overview

Why has analytics missed the mark? This chapter first explores the main reasons responsible for the lackluster performance of marketing analytics and then introduces three prevailing analytics maturity models that not only will help organizations to recognize their analytic maturity state but also provide macrolevel solutions to achieving analytic successes.

This chapter is organized as follows:

• Why Has Analytics Not Yet Lived Up to the Promise?
• Davenport’s Five-Stage Analytics Maturity Model
• Davenport’s DELTA Model
• The Gartner Continuum Model
• Wayne Eckerson’s Analytical Maturity Model
• Challenges and Opportunities Facing Small- to Medium-Sized Companies
• The ALADA Model—Five Pillars of Analytics Success for Small- to Medium-Sized Firms
• Conclusion

Why Has Analytics Not Yet Lived Up to the Promise?

Analytics is the scientific process of transforming data into insight for making better decisions. Since Professor Tom Davenport published his breakthrough book—Competing on Analytics: The New Science of Winning, more leaders see analytics as a new wave of competitive advantage. The application of analytics is becoming commonly accepted in marketing.

Investments in analytics have been increasing steadily. The 2018 chief marketing officer (CMO) Survey conducted by Duke University’s Fuqua School of Business reports that the percentage of marketing budgets companies plan to allocate to analytics over the next three years will increase from 5.8 percent to 17.3 percent, a whopping 198 percent increase.1

However, in the same CMO Survey, top marketers also report that the effect of analytics on companywide performance remains modest, with an average performance score of 4.1 on a seven-point scale, where 1 = not at all effective and 7 = highly effective. It is bothersome that the analytics performance impact had shown little increase over the last five years when it was rated 3.8 on the same scale.2

A 2015 Forbes Insights Report indicates that only 22 percent of marketers have data-driven initiatives achieving significant results. According to ITSMA and Vision Edge Marketing, 74 percent of marketers can’t measure or report how their efforts impact their business.3 These numbers are startling, considering the importance and prevalence of analytics in marketing success and proving return on investment (ROI).

So, why has analytics missed the mark? Below are eight of the most common reasons:

Data Problems

Data make or break a business because that data fuel marketing analytics. Commonly seen issues with data are as follows:

1. Poor data quality. Poor quality data include inconsistent data, missing data, wrong data, duplicate data, and outdated data. Several reasons for poor data quality include:
   • Lack of budget for timely data merge and hygiene. For instance, one customer may have multiple records in the database under different names;
   • Outdated store POS system that is unable to capture key customer information;
   • Human error. For instance, sales rep typed the name wrong into the database;
   • Data value is not consistent across all databases because IT only updates selected databases;
   • External data feeds were not imported into the databases promptly;
   • Data dictionary was created by IT, not by the businesspeople.
2. Scattered and disconnected data. Data are typically owned and maintained in separate systems by separate departments across organizational silos. There is(are) no common variable(s) that can stitch them together. For instance, the CRM database, social, e-commerce, and call center data are stored in different databases, and they disconnect from each other.
3. Inaccessibility to data. Data are not available to all stakeholders. Because data are isolated in different systems and places, marketers cannot access some of the critical pieces of information about customers. A typical example is that e-commerce, call center, and marketing team are three separate business units, and the e-mail marketing team typically does not have access to the CRM database, and vice versa.
4. Insufficient data breadth. Many people say we are living in an era of big data overflow. While companies do seem to have far more data than they can process, the reality is that due to budget constraints and technical difficulties, they do not have enough useful data that can be leveraged for analytics and action. Useful data include both structured data and unstructured data. The structured data are data stored in a relational database such as customer demographics, behaviors, campaign responsiveness, product usage, cross-channel interaction, and so on; the unstructured data are data that aren’t stored in a fixed record length format. Examples include documents, social media feeds, digital pictures and videos, call center interactions, on-site interactions, survey opinions, and so on. Lack of data breadth limits an organization’s ability to gain deeper insights into its customers.
5. Not using external data. There are two reasons why some companies are not taking advantage of external data. First is lack of budget. Companies, especially small- to medium-sized companies, do not have a budget for purchasing external data such as customer demographic, geographic, and attitudinal information. Second, although there are so many data (i.e., economic, job, population, weather, housing, etc.) available free to the public that can be used for research and modeling, some analysts are either not aware of them or do not know where to find them from public domains.
6. Poor data management and governance. Many companies do not have an effective data governance strategy. There are no good QA and QC procedures in place to ensure the integrity of the data. Data dictionary was not created or updated promptly. Data processing procedures were not properly written and archived; knowledge got lost in transitions after key personnel left or because of the change of service providers.

Technology and IT Support Issues

Some commonly seen problems include but are not limited to:

• Outdated and rigid legacy data system. For instance, the outdated store POS system is unable to store some key customer data. Replacing such a system requires a lot of money. In some cases, the database is so old that the database administrator dares not make any changes to tables for fear that any significant changes will trigger a collapse of the entire database system.
• Lack of IT support. IT team does not allocate enough people to support the marketing and analytics team.
• Lack of effective communications. Marketing treats IT as a back-office function. There is no consistent and meaningful communication between IT and marketing, marketing and analytics, and analytics and IT. For instance, marketing and analytics decided to purchase a new analytics software without consulting IT. Later, they found out that additional servers are required to host it, but neither the marketing nor the IT side had the extra budget for purchasing the servers. That analytical software ended up sitting idle for several months until marketing secured the additional funds in the next fiscal year.

Poor Investment Decisions

Poor investment decisions waste your limited marketing dollars, which is one of, if not, the biggest reasons why your analytics ROI was unsatisfying.

• Assuming the wrong approach to tool and software selections. Companies with low analytics IQ tend to choose tools and software not based on their capabilities but rather based on how the vendors claim their tools can solve companies’ primary problems. Often, these companies do not have proper protocols, standards, and procedures in place to compare and evaluate tools from different vendors. Therefore, they cannot objectively compare the pros and cons of each vendor and make the right purchase decisions.
• Buying vanity software and tools that add little value to the business. A good example is the multichannel campaign management system, which could easily cost retailers half a million dollars every year. For instance, many retailers believe that a campaign management system is a mus...

Inhaltsverzeichnis

• Cover
• Halftitle
• Title
• Copyright
• Abstract
• Dedication
• Contents
• Introduction
• 01_Chapter 1
• 02_Chapter 2
• 03_Chapter 3
• 04_Chapter 4
• 05_Chapter 5
• 06_Chapter 6
• 07_Chapter 7
• 08_Chapter 8
• 09_Chapter 9
• 10_Reference
• 11_ATA
• 12_Index
• 13_Adpage