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Over 50% of all marketing decisions aren't currently influenced by analytics (according to Gartner)

blog data quality marketing analytics Jun 18, 2022
 

 

Marketing Analytics: A dying trend or a misunderstood powerhouse for success?

 

Gartner stated in their The State of Marketing Budgets 2021 report that "data and analytics have failed to live up to marketing expectations." The report goes on to show that nearly half of all marketing decisions are currently made without the influence or analysis of analytics. 

Because poor data quality and unclear recommendations hinder analytics’ usefulness, the Marketing Data and Analytics 2020 Survey highlights it as a top reason analytics is not used in forming marketing decisions.

As a marketer, you’re likely either experiencing this phenomenon yourself or are completely shocked by it.

Specifically, Gartner finds that 54% of marketing decisions are currently influenced by analytics. And while this does seem shocking at first, if you take a moment to consider just how difficult it is to gather clean data and analyze it correctly, this stat becomes a bit more understandable. However, this also coincides with COVID-19 forcing many companies to cut budgets – and marketing budgets were cut (from 11% of revenue to 6.4% of revenue).

With less disposable budget, marketing departments have been forced to really look at what is working for them and what isn’t. As a result, data analytics have fallen below the top three expenditures. But this doesn’t mean data analytics as a whole doesn’t work, just that the willy-nilly, fast-as-lightning analytics that has been trending isn’t working.

 

Poor data quality leads to poor marketing decisions

 

Poor Data Quality Can Cost You Millions

 

While we would like data to always be clean and easily understood, the fact is that it isn’t. Data analytics can provide incredible insights that, when acted upon correctly, can lead to explosive revenue and ROI growth. Unfortunately, more often than not, it isn’t gathered, analyzed, or acted upon correctly. Therefore, it makes sense that executives are fed up with data and deny budgets or attention to it. Acting on poor data can be far more hurtful to an overall marketing strategy than choosing to ignore data.

Instead, Gartner states in their 2020 report that marketing operations and brand strategy have taken over as top areas of spending, pushing analytics below the top three. This is because senior marketing leaders are increasingly disappointed in their analytics investments and in response, are spending less on analytics. But what does this mean for data?

  

Data can be an incredibly powerful tool for brand and revenue growth

 

Though it may seem like it, data hasn’t failed marketers.

According to a SiriusDecisions study, a strong organization realizes nearly 70% more revenue than an average organization purely based on data quality. And if making more money isn’t exciting to you, on average companies are losing 12% of revenue from bad data quality.

What has failed is the fast-paced growth that many marketing departments have tried to implement. We have discussed this before; proper data collection and analysis take time! An operations team that does not know how to do it, is a waste of budget.

So how do we do it right? How can we harness the full strategic potential of quality data? First, we look at what contributes to poor quality data:

 

1. Data entry issues in your CRM

 

If you are not tracking the number of data entry problems in your CRM, then you are setting yourself up for failure. Checking data entry quality issues is a critical first step to ensuring you are gathering as much quality data as possible and eliminating the trash. The more quality data you have coming in to begin with, the faster your data can provide value (we call this speed-to-value).

When analyzing data entry issues, you want to pay close attention to the number of records in a field with empty values you have. Empty values will leave holes in your data causing your CRM to either skip the data set all together or will skew analytic results if processed with the holes as a zero value. Either way, this will result in unreliable analytics - and you don’t want that.

 Number of empty values: 

  • Empty values in fields that should have values signifies that information was either missing or recorded incorrectly. 

A common issue B2B Marketers commonly see is missing “Lead Source” data not being attributed to the lead, contact, or opportunity within their CRM system. With missing attribution data, marketing and sales are flying blindly of where the most profitable customers are coming from, limiting your ROI as you continue to invest in channels that may or may not be working.

 If this is happening frequently, you should look at your collection form, or wherever the data is coming from, to determine why the data is not being captured correctly. Again, this should be monitored and tracked over time to see if your adjustments are solving the issue or making it worse.

 

2. Disconnected data in your CRM

 

Did you know disconnected data can hide growth?

When you have disconnected data, the relationships between objects in your CRM will be disjointed and, well, disconnected. With disconnected data, data will be difficult to process leading to inaccurate analysis, hiding growth, and slower speed-to-value.

 

  • Lead Conversion: For example, it’s common practice for leads to be converted into contacts once a salesperson has qualified a lead. When converting a lead, critical information such as “Lead Source, Title, Department” about the lead is manually put into your contact record. However, entering the data manually leaves a high margin of error resulting in repeated contacts, information entered incorrectly, or data entered in the wrong areas. This process is also very slow.

 

  • Opportunity Creation: Another example of disconnected data occurs when a CRM user decides to create an Opportunity without converting a contact into an Opportunity. By not creating an Opportunity from a Contact Record, you miss out on adequately persisting the “Lead Source” and “Account Information” from the Contact.

 

  • Opportunity Contact Role: One last example is when you have an orphan Opportunity, where there are no contacts associated with an Opportunity. This occurs when CRM Users fail to associate contact roles with the opportunity. The ramifications are that marketing and sales don’t have a holistic view of the buying group within the deals they are closing.

 

 

 

The first step ensuring you have connected data is to go through your marketing and sales flow process making sure that your CRM is reflective of what’s going on in real life as you work a lead into a customer. If you have failure, find out why. Perhaps you have a training issue, a technical issue, or there is an issue with your reporting process.

Regardless of why you have disconnected data, a missing relationship will inhibit you from analyzing your data and gaining any helpful insights. After all, data quality plans are intended to gather and analyze data effectively.

With disconnected data, meaningful relationships won’t be effectively analyzed and could be completely misleading, hurting brand and revenue growth and leading to extremely poor ROI.

 

3. Monitoring your data speed-to-value

 

We started this article by discussing the lack of perceived value in data analytics marketers are facing. So, it makes absolute sense that monitoring the speed-to-value of your data would be a critical component of quality data. But what does this mean? Simple, speed-to-value is how long it takes your team to derive results from a given data set.

Simple to explain, not simple to manage. If your data quality is poor and/or you are experiencing any of the previous issues discussed above, it will take a great deal of time to process data that you can actually gain insights from. Yes, lack of automation of your data processing can slow this process down, but ultimately, if your data quality is poor and you are having data issues, the process will be very slow.

However, other factors can contribute to speed-to-value:

  • The amount of data to process

Understandably, the ability to process ever-larger data sets indicates your speed-to-value is good or improving. If you have multiple processes that don't run efficiently, that indicates a poor speed-to-value. The reason why is very simple, larger data sets analyzed at one time will provide far more comprehensive insight than small bits of data. If your data cleansing processes are working well, then your data will be increasingly cleaner and will be able to be processed quicker and quicker. This directly relates to greater ROIs on your analytics tools as well as helping to drive greater revenue more efficiently.

  • Having a Single Source of Truth

Understandably, the ability to process data within one system gives you the best chance to maximize your speed-to-value. Whereas processing data outside your system of record will likely extend the time it takes to deliver value. In other words, keeping all your data and analytics processes in one place is more convenient and enables much quicker processing speeds, larger amounts of data to be processed at once, and greater speed-to-value.

The reason why is very simple, data analyzed in one system will provide far more comprehensive insight than data processed in silos. If your data cleansing processes are working well, then your data will be increasingly cleaner and will be able to be processed quicker and quicker. This directly relates to greater ROIs on your analytics tools and helps drive revenue more efficiently. 

  • Error rates in data transformation

Data transformation, or aggregation, is the process of taking data that is stored in one format and converting it to an alternate format. Ideally, if your data is clean, this should be a painless process. However, if your data is of poor quality… just think of what it was like to convert Word documents to MAC’s Pages around 2005. Yup, that is the type of frustrating transformation you will find yourself up against. 

An example is if you are relying on aggregating data to simplify your reporting and you have a newly introduced picklist value that isn’t mapped yet. 

Your data transformation tools will struggle and you’ll get inaccurate reporting that will result in a data transformation that will be essentially useless. 

To avoid this, start by detecting any new values that may affect your transformed fields that your simplified reports are using. Then proactively resolve issues by constantly monitoring the number of error rates you experience to see if the process improves over time.

Here's an example of how we use the Salesforce App, Kudoz, to maintain a lookup table of Lead Source values mapped to sales and marketing. Having the mapping table within the source system of record allows for greater transparency and empowers anyone to resolve issues before the month-end reporting scramble proactively.

 

Kudoz, the data analytics app that makes data quality easier

 

Get the power of analytics for your business decisions

 

Kudoz the Opportunity Analyzer App, simplifies data-related activities when using Salesforce by identifying missing contacts, lead sources, and other data issues to improve your speed-to-value, analysis speeds, and the overall analytics insights gained. As we said earlier, we wish gathering and analyzing quality data was simple – unfortunately, it isn’t. But by using Kudoz with your Salesforce CRM, the process can be simplified.

The app will enable you to efficiently:

  • Fix data quality issues quickly and proactively (Kudoz provides data matching and quality tips as well as lead source data opportunity suggestions)
  • Deal with missing values in data as soon as they occur
  • Accelerate monthly and quarterly reporting
  • Unlock growth from your data by creating relationships within your data to fuel downstream systems (for example, on average there are six buyers in a B2B buying group. If we only find that our CRM has three, then there are three people that marketing needs to reach to have a higher opportunity-to-close)
  • Enable marketers to prove their success with revenue and pipeline reporting features and demonstrate the ROI of your marketing efforts
  • Allow your team to claim missing Salesforce revenue from disconnected data

Data analytics remains a powerhouse to grow brand recognition, revenue growth, and increase ROI – when done right. Gartner reports that some marketers and executives are getting frustrated and giving up on analytics, yes. But of those who are giving up, 44% are planning to continue to develop their analytics capabilities over the next couple of years. Those who can gather clean data and master the analytics process will experience greater insights than those who cannot or do not. The key is to remember it is a marathon, not a race, and to keep monitoring your data and processes to maintain steady growth.

 

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