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 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% to 17.3%, a whopping 198% increase.

However, in the same CMO Survey, top marketers also report that “the effect of analytics on company-wide 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.”

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

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

1. Data Problems

Data makes or breaks a business because that data fuels marketing analytics. Commonly seen issues with data are as follows:

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 Poor quality data include inconsistent data, missing data, wrong data, duplicate data, and outdated data.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Outdated store POS system that is unable to capture key customer info;

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.

Scattered and disconnected data. Data is typically owned and maintained in separate systems by separate departments across organizational silos. There is 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.

Inaccessibility to data. Data is not available to all stakeholders. Because data is 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, the email marketing team typically does not have access to the CRM database, and vice versa.

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, etc.; the unstructured data are data that aren’t stored in a fixed record length format. Examples include documents, social media feeds, and digital pictures and videos, call center interactions, on-site interactions, and survey opinions, etc. Lack of data breadth limits an organization’s ability to gain deeper insights into its customers.

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 midsized companies, do not have a budget for purchasing external data such as customer demographic, geographic, attitudinal info. 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.

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. The 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.

2. 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 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.

3. 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 must-have to pull campaigns, but indeed, it is not, especially for companies that do not have many customer records. You may use free tools such as R or the Basic SAS to create an in-house campaign management system that will cost you nothing or only cost several thousand dollars a year. The in-house campaign management system not only can save you up to quarter million dollars a year, it will also significantly improve the work efficiency and shorten the turnaround time of campaign creation by at least a day or two.

Overspending on tools and data not on people. Some companies spend more than 80% of their analytics budget on tools and data, and less than 20% on people and training. They tend to buy tools and technologies that have way more functions than what they need. For instance, data vendors like to pitch two sexy things to marketers: First, data must be comprehensive, which means that to develop a true 360-degree customer view, you must capture every touchpoint and every nuance of a customer crossing the entire customer journey. Second, you must capture data in real-time so you can respond to their behavior promptly. Both sound very attractive and seem to make perfect sense. But the problem is that capturing every touchpoint is highly expensive and arguably unattainable. Data is only as good as you use it. If you don’t use it, you waste your time and money big time.

Letting IT, not the business, lead the software search. IT project managers usually do not quite understand what the business side wants. They tend to choose products based on technical requirements rather than business requirements.

Not getting IT involved when selecting tools. When selecting analytical tools, ignoring IT partners’ opinions or even totally not getting IT involved will cause problems, too. For instance, the analytics team of one of my clients decided to purchase an analytical tool without consulting the IT team; only later, they found out that that the IT side lacks the knowledge to maintain and support that device.

Falling into the trap of “User-Friendly.” Software vendors like to pitch user-friendly features to executives, and that trick always works like a charm. Don’t get me wrong; we all want user-friendly tools. The problem is that more often than not, you get these so-called user-friendly features at the expense of sacrificing much-needed functionalities and flexibilities. Even worse, many so-called user-friendly tools are not so user-friendly at all. Also, a genuinely user-friendly tool does not always guarantee that users will use it. For instance, many companies purchased the expensive Tableau viewer licenses for their executives, hoping that they would pull reports every day by themselves. However, what we’ve found out was that only a small number of executives would do that. Most executives still prefer the marketing or analytics team pulling the reports and presenting them the insights instead of doing the job by themselves.

4. Lack of Analytics Marketers

To compete on analytics, companies desperately need to bring the left-side and the right-side brains together. The convergence of marketing and analytics, which was once nice to have, is now becoming a new trend and business-critical. Analytics marketers are individuals who know analytics and can also speak the language of business. They translate business requirements into terms that analytics and technologists can understand. Conversely, they can also use plain English to show business the value of data, justify investments in analytics, and translate insights gleaned from data into easy-to-understand stories for better decision making. Analytical marketers are critical to improving the competitive advantages of the company. The more analytics marketers a company has, the more likely the company is to adopt an analytically oriented culture and use analytics to make better decisions than the competition. Many companies do not have qualified analytics marketers; they are not yet ready for the era of insight-driven marketing.

5. Lack of Executive Support

Like any other project, support from executives is critical to the success of analytics. Organizations need analytical leaders to set and clarify strategic objectives and ensure appropriate project funding. Analytical leaders help secure resources, provide project governance, create high-level organizational buy-in from all stakeholders, manage risks, and make critical decisions. Leading analytically oriented companies often have a couple of senior executives sponsor analytics initiatives; they raise the awareness and analytics IQ within the organization and create and maintain a culture of excellence.

6. Scarcity of Analytics Professionals and Skills

The shortage of analytics skills means several things:

First, lack of experienced analytics professionals. An analytics team consists of several types of analytics professionals, such as data scientists, data developers, statisticians, and business analysts. As more organizations embrace AI and machine-learning technologies to achieve competitive advantages, good analytics people, especially those who have deep knowledge in data and statistics and also have excellent SAS/R/Python programming skills, are in high demand. Finding the right analytics talent is challenging for all companies.

Second, lack of business domain knowledge and experience. Most analytics professionals come from math and statistics background and have little or no business domain knowledge. Therefore, some of the “smart insights” they discover have no real business benefits. Some analysts can only find patterns but are unable to form assumptions and hypotheses to determine the root causes further.

Third, lack of innovation in analytical techniques and methodologies. Many organizations keep using approaches and methods that once worked very well and don’t try to improve them or don’t know how to improve them. For instance, in the past ten years, response rates and ROIs of many traditional marketing channels such as direct mail, print catalog, and email have plateaued or even declined year-over-year. One of the reasons, of course, is that marketers’ attention and resources have shifted to mobile and online marketing where marketers see more growth opportunities. However, lack of innovation in analytical techniques and the slowness of adopting new technologies such as machine learning are two major reasons that have created the low effectiveness of marketing initiatives.

Fourth, poor communication skills. Some analysts cannot use easy-to-understand English to effectively communicate the findings, insights, and actionable recommendations to marketers.

7. Company Silos

The company silos present when certain departments or sectors do not wish to share knowledge and information with others in the same company. This lack of information flow results in departmental isolation and territorialism. Company silos can lead to negative customer experience, inefficiency, and duplicate work, and may also contribute to the demise of productive data-driven company culture.

Negative Customer Experience. Negative customer experience is often caused by either operational silo, channel silo, or both. The operational silo happens when the various business units present in an organization aren’t aware of one another’s operations and decisions and act autonomously without getting insights from related business units. For instance, recently, I called the customer service of a carpet cleaning company to set up an appointment because I received a great email offer from them. The call center rep searched the database and couldn’t find the offer code in the database. Her supervisor has the authority to see more data in the system. She was able to locate the coupon code, but she told me that the email was sent out by another franchise whose service territory is out of the scope of my home; therefore, they couldn’t use the coupon code. I was disappointed. Customers do not care about how your franchise system works; they view all franchisees as one in the same brand. At the very least, a disclaimer should be included in the email to remind recipients that the offers are subjective to specific regions.

Duplicate Work. In some large organizations, it is not uncommon for each department to have similar positions performing similar functions. Their jobs are highly overlapped. Departmental silos also cause companies to spend money, which is avoidable. For example, I noticed that the credit card department of one of my clients spent a significant amount of money and hired a consulting firm to build a reporting package for them. What I found out was that the company’s analytics team could have developed that reporting package much quicker if the credit card department had reached out to them for help.

Inconsistent Branding. Today, retailers recognize the importance of omnichannel marketing. An omnichannel strategy focuses on delivering consistent brand presence across channels during a consumer’s buying process and making that buying process a seamless and consistent experience. Channel silos hinder the continuous flow of contextual and historical information between channels, thus resulting in inconsistent brand images and different customer experience across different channels.

8. Not Using the Right Metrics and Key Performance Indicators (KPIs)

Metrics are quantifiable measures that are used to monitor and evaluate financial performance, reveal the truth about performance, and provide an actionable way to achieve overall business strategies and goals. KPIs are a subset of metrics that provide a simple, insightful snapshot of a company’s overall performance, as well as reliable, real-time information for effective decision-making. Continuously tracking the trends of KPIs for an extended period will help highlight any issues that might otherwise go unnoticed and discover hidden opportunities for further growing your business. However, in reality, quite a few companies make decisions without using the right metrics, thus resulting in low ROI of marketing initiatives. For instance, recently, quite a few multichannel retailers repositioned print catalogs as a branding tool to raise brand awareness and drive traffic to other channels. Did all these retailers make the decision based on KPIs such as customer lifetime value and incremental margin? Probably not. A couple of them I knew made that move simply because other retailers did so. 

In part 2, we will be discussing how to fix these problems and improve the ROI of your investments in marketing analytics.

January 6, 2020by admin