The Need for a Marketing Data Strategy
As funnel data consultants, we speak with executives and business owners across many industries. The common link between all of these organizations is that they know that they need a data strategy to stay competitive in their industry. Accordingly, every company needs a marketing data strategy, whether they are small, medium, or large. Without taking advantage of their data, your growth is stagnating. Not having a funnel data strategy is costing you business.
The modern world of digital marketing has sophisticated lead and customer management platforms, visitor tracking, user behavior monitoring, and other emerging technologies. As a result, these systems are generating a massive amount of data. Your business must have a marketing data strategy that identifies how this data can be used to your benefit.
In order to maximize the potential in your marketing and sales funnel, you absolutely must be progressing in data maturity. You have to be consuming massive amounts of data and turning it into actionable funnel optimization tactics. Even so, businesses are still failing to prioritize data. Here are 6 fails to avoid in your marketing data strategy.
It seems pretty self-explanatory, but if you don’t know what your customer wants, your marketing efforts are already way behind. Despite how random purchasing patterns may seem, they follow a pattern clearly visible in the data. Having an awareness of customer demand patterns and how those play into your marketing strategy is critical. Marketing agencies attempt to work with many industry verticals, or even different product/service mixes within the same industry. As a result, this can cause them to lose touch with your company’s demand patterns, dramatically affecting their performance.
There are a variety of ways to determine these patterns. It may be that you can analyze historical lead data. You can look at search impression volumes for keywords that indicate someone is in the market for your product or service. Ultimately, the goal is to pursue the data that lets you see these patterns. Below are some time periods or events where we often see demand patterns. We have also included phrases your marketing agency might say that indicate their awareness is lacking.
|Customer Demand Pattern||Phrases Indicating Lack of Awareness||Reality of Demand Pattern|
|24 hours of the day||Today has started off really strong||Every day starts off strong|
|7 days of the week||Monday and Tuesday indicate a strong start to the week||Monday and Tuesday are the biggest days of the week|
|Days of the month||We may need to pull back on spend this month||Demand decreases towards the end of the month|
|Month’s of the year||Our month-over-month performance looks great||Seasonality always shows an increase between these two months|
|Coming in and out of holidays||The holiday last week affected us more than we thought||The holiday fell on Monday, the biggest day of the week|
|Reaction to negative industry news||I wonder if that news headline is affecting us||Negative industry news shows pattern of up to 30 day decreased demand|
|Reaction to new competition||It seems like leads are down due to increased click cost||Click bids always go up when a big marketing spender enters the space|
|Reaction to loss of competition||I am surprised to see the negative performance given less competition||Initial bad press of closure decreases demand, followed by a return to previous levels|
Marketing Strategy Should Adapt to Customer Demand Patterns
Being aware of demand cycles, proactively or timely, should change how you execute a marketing strategy. Though you can’t always reverse a pattern, there are ways to lessen the impact or take advantage of it by shifting tactics accordingly. This type of nimbleness and data awareness can separate you from the competition.
You may be aware of your customers’ demands, but is your marketing data strategy filled with unrealistic goals?
To demonstrate the harmful effects of unrealistic expectations, we can look to the experience of our CEO, Derek. Once, he joined a company that dropped from 190k to 140k marketing-generated leads year-over-year (26% reduction). This all to place while marketing spending remained the same. However, Sales performance at every step of the funnel was drastically up. Even so, year-over-year performance in sales/revenue remained flat. This went down in history as the most prominent marketing agency and internal team failure in company history. They honored the sales department like kings for maintaining their sales despite the massive lead reduction. Consequently, the agency and several internal team members were fired and replaced.
In the year following the disaster, they onboarded a new marketing agency and gave them a 190k lead goal. Regardless, the marketing spend budget remained the same. Why? The company had realized this success level before and expected the new agency to right the wrongs. They wanted to return to their previous glory days. Long story short, the new agency accepted the challenge without digging into the data, and they failed. Leads did not return to 190k, and company sales results were relatively flat once again.
After the three-year tragedy above, Derek started working with the company the following year. Soon enough, he was able to look into the data. Strangely, the data showed that the drop from 190k leads was evenly spread across all sources. After further research, he found that the company had switched to a new CRM in December of the 190k lead year. The new CRM implemented duplicate management rules that would merge leads for the same prospect. Thus, the new duplication rules had led the leads to drop to 140k due to an incoming leads merge rate of 20%+.
In this case, the company’s reports after the CRM change counted leads after the leads had been merged. Thus, the drop from 190k to 140k was nothing more than a data definition change. Basically, the company had moved from counting Gross leads to Net Leads. Admissions metrics now had a smaller denominator (net) with the same numerator giving a false impression of improvement. The previous marketing agency failed because they were willing to accept any goal to obtain the business. The company failed because a simple data definition change kept them from investing more in marketing. The devil was in the data details, and that is where this company could progress into a more sound marketing data strategy.
While the marketing agency in the story above failed to challenge unrealistic goals with data strategy, ultimately, it was the initial failure by the company to maintain data integrity that led to the misconception. Similarly, there are two places data integrity problems can come from:
The first of these places can be your business processes and systems. There may be issues in your system that may hinder data integrity. Accordingly, here are some questions you may ask to determine your system’s effectiveness.
- How does your CRM handle duplicate prospect records?
- Do you have a set of automated merging rules?
- Are your leads merged manually in an unpredictable way?
- Do you lose information about the old or new lead when you merge?
- What dictates if a lead is a valid duplicate, a re-inquiry, or a net new lead?
- When running reports on data, are all of these scenarios accounted for?
In light of this, our experience has shown that these practices need work, to ensure that marketing attribution is accurate in all businesses.
To this end, lead management systems and business processes will have to support the goal of providing accurate data that can be used for business intelligence. Overall, preserving lead source attribution data has to be your mission in your marketing data strategy. This will tie to the individual lead which was generated by that source. You need sound processes and procedures to handle subsequent leads for the same individual to track that interaction and all related sourcing data. Consistent definitions should be developed to determine when a lead is a re-inquiry or a net new lead.
Another, more sinister, source of data corruption can be the perceptions and actions of your marketing department or agency.
Let’s be honest, we all want to succeed, and your marketing agency is no different. In most marketing relationships, the company applies accountability pressure in specific areas that the agency or department needs to improve. From this, marketing’s desire to show improvement can lead to confirmation biases about data. What does this mean?
In the book “Thinking Fast and Slow,” author Daniel Kahneman addresses that our human brain is flawed and has a propensity to make errors. One of the areas addressed is confirmation bias. Due to confirmation bias, your agency or department might be tempted to jump to conclusions upon the slightest performance uptick in the data. You all might celebrate data that shows your efforts are working. At the same time, you may ignore data that shows a problem.
Your business must be on the lookout that your department or agency does not fall into confirmation bias. Otherwise, marketing presentations, weekly performance reports, and touch-base calls will be distorted versions of the truth. In reality, your department or agency may exclusively seek out data showing a desired improvement and/or progress trend. This reality highlights the need to embrace data-driven insights to support marketing data strategy.
With this, it has never been more important for companies to set and track key performance indicators (KPIs) for their marketing agency or department. Additionally, these KPIs must support the KPIs of the company as a whole. Marketing cannot live on its own island. To know the real impact of your department or agency, you must measure actual values. Is marketing cost per customer acquisition (not lead) becoming cheaper? As you gain new customers, is the average lifetime value of a customer the same or improving? Is the visit-to-lead conversion improving for paid and organic clicks? Establishing and measuring actual value earning benchmarks upfront is critical to see if your marketing agency or department stays true to your data strategy.
Simply enough, your business may fail because you do not have access to any meaningful data. . You may lack the capability to tap into this rich resource or may not have known that such data exists. However, without this data, a complete marketing data strategy is impossible.
Moreover, as cloud applications take over, businesses continue to spread their operations over more and more systems. Enterprises are choosing only the best when it comes to their technology. They choose applications and platforms with features that serve a consolidated platform approach. Consequently, having a complete view of your data is getting more difficult with an increasingly detached data environment.
You may have tried to solve this problem by pulling data from each system and combining it manually in spreadsheets. This approach is very labor-intensive, error-prone, and typically does not allow for multiple data refreshes on the same day.
Or maybe you have tried to pick one system to push data into from all other systems, but in most cases, this is a square peg in a round hole. This is because the data needs to retain its structure and scope to remain complete and accurate.
To maximize the value of the data, it needs to be consolidated in a common location accessible by the reporting tools for additional analysis, and transformations based on business needs.
For this reason, the data warehouse is an indispensable resource for your marketing data strategy. Here, data is collected into a single point of truth. This serves to consolidate separate data sources into a single source that is easily accessible. Additionally, the warehouse acts as a base from which you can make your data actionable. You can send your data back to your data stack from the warehouse. This allows your data to be used across your business in various applications while maintaining a central data source. It’s the best of both worlds.
Another point of failure in a data strategy is marketing attribution data accuracy. When this accuracy is not prioritized, a business suffers significant gaps. To make matters worse, organizations are not clear on who is responsible for attribution accuracy. Is it IT, Marketing, or Agency partners?
To resolve this, organizations usually lump tactics into broad categories, which does not allow tactic-level analysis and optimization. This approach limits the scope of your marketing data strategy. For example, organizations may be able to evaluate paid search overall, but cannot evaluate a specific non-branded paid search keyword tactic through to revenue.
This issue exists because marketing attribution data is either not being collected, getting lost in the pipeline between prospects, data, and marketing reports, or both.
This is a classic issue of too many hands in the pot. Landing pages, chat agents, phone numbers, web forms, surveys…the list goes on. The landscape is complex regarding the different ways we capture leads. One person may provision phone numbers, another develops the landing pages, and someone entirely different handles live chat configuration. Your agency handles some but not all of these activities. As a result, there are multiple disparate efforts with nobody policing the accuracy of attribution data.
Before ironFocus, our CEO Derek worked at an organization that spent $600k a year on SEO with a particular agency. It was the largest investment they had ever made in SEO. Hence, they expected significant improvements in organic search leads. The company made its expectations clear, and the pressure on the agency to perform was high! However, a few months into the year, the performance wasn’t there. This led the company’s leadership to question everyone involved with this investment. After some heart-to-heart performance discussions, the agency turned it around and started knocking it out of the park. The company was getting more organic leads than ever. What a success story…or was it?
Unfortunately, the overall aggregate leads across all channels did not improve. It took a while to notice that trend. As a developer turned marketer, Derek traced the issue back to the website’s underlying code. Random pages had been hard coded to submit all leads as Organic Search regardless of where the traffic came from. The reason for the improvement turned out to be a complete fraud! There are many reasons why lousy marketing attribution might show itself. The question is whether or not you have practices in place to audit and catch marketing attribution issues before they unravel your data strategy.
Having sound data is a must for making sound data-driven decisions in your marketing data strategy. To ensure data integrity, start employing end-to-end attribution auditing of each marketing source. Include everything from organic search, to search engine marketing, and even billboard advertising. Then, any issues found causing inaccuracies should be viewed as a priority and fixed. Overall, your business should be able to attribute their entire click/visit to revenue funnel back to the source or sources that influenced that customer journey. Also, it is crucial to maintain data integrity as technologies change and new marketing sources are introduced.
The Need for Proper Marketing Attribution Models
Alongside attribution accuracy, businesses often fail to incorporate appropriate attribution models in marketing data strategy. The prospect’s journey from the first engagement to the time they convert is getting longer. Moreover, at each step of that journey, your business uses a marketing tactic to capture the prospect’s attention. The need to accurately model and understand this journey is crucial.
With this in mind, businesses with insights into their data can find ways to optimize marketing strategy. It is important analyze outcomes against different marketing attribution models to find the one that most accurately fits your marketing strategy. Digital marketing opens the door to multiple interactions with a prospective customer. Companies know which initial marketing effort is tied to customer conversion. However, this is no longer a valid tactic. Other factors need to be looked at throughout the entire customer journey to get a precise picture of marketing efforts influenced them. To do so, you must have fine-grained data collection and business logic created to identify each attribution type. Once you have this, a deeper analysis can be conducted to view metrics based on any of the attribution model dimensions.
Data Fail 6: Fail to Leverage Proper Metrics and Milestones
Another way your business can fail in having a sound marketing data strategy is by not using proper metrics and milestones. Think of this example. Would you rather generate 100 leads that cost $2,000 (Campaign 1) or 70 leads which cost $2,100 (Campaign 2)? The cost per lead would be $20 and $30, respectively.
|Metric||Campaign 1||Campaign 2|
|Cost Per Lead||$20||$30|
Seems simple enough. You would likely choose the first if you were optimizing around lead volume and overall cost per lead. Now, what if you were able to see that the leads from the first source converted at 5% and had a lifetime value of $800? This would put your total revenue at $4,000. Contrast that against the leads from the second source. These converted at 10% and had a lifetime value of $1,100. This puts your total revenue at $7,700.
|Metric||Campaign 1||Campaign 2|
|Converted Leads||5 (5%)||7 (10%)|
|Lifetime Lead Value||$800||$1100|
The $2,000 marketing investment would create $2,000 in margin, whereas the $2,100 marketing investment would create $5,600 in margin. Knowing that might sway you to believe the second strategy would be more beneficial for your business.
|Metric||Campaign 1||Campaign 2|
Focusing Marketing Spend on the Wrong Metrics
Now, look at a similar scenario. Considering the previous example, let’s make the Lifetime Revenue Value (LVT) of each converted lead equal. If your marketing agency targets CPA bidding around leads, your marketing spend will favor Campaign 1. However, you would rather spend $2100 on Campaign 2 and get 7 conversions versus 5.
It’s important to realize that such scenarios are endless. For instance, we could look at a paid search non-branded keyword tactic versus another metric. Given that this funnel could take 90+ days to mature, how would you or your agency catch this or the multitude of other optimization opportunities?
All of this is to see that the proper metrics are needed for an effective marketing data strategy. The reality is that most businesses do not have full-funnel visibility, and these metrics cannot be measured. This results in non-optimal budgeting. You may be tempted to keep it simple and only look at leads. But as seen above, only focusing on leads can result in a massive loss in potential revenue. The reality for many marketing agencies and companies we work with is that marketing spending is primarily optimized around leads.
Sure, most companies have goals focused on lead conversions or sales. These sales lead into the profitability goals of the company. With this, most companies would claim they are working with their marketing agency to ensure that efforts support these goals. However, we would argue that this optimization doesn’t happen at a level that can make a significant impact.
At the point of web form submission (lead creation), Google Analytics and subsequent goal conversions are likely to be triggered. The process then would be considered complete. However, deeper metrics beyond that lead often exist only in the CRM. Getting to a useful level of detail requires a mature marketing data strategy and associated business processes that accurately capture the required data points.
So how do you solve this problem for your marketing data strategy? You must advance data maturity to ensure that down funnel metrics can be traced to the click/visit. We recommend doing a Google Analytics Client ID integration with your CRM. This will allow you to feed metrics like “Appointments” or “Starts” into Google Analytics with their corresponding value. This unleashes the ability to do target CPA bidding around metrics that drive value (like Campaign 2 above). This data can also help you optimize SEO tactics around keywords that are driving actual value and not just leads. At the very least, you should have reports available that show every step of the marketing and sales funnel. These reports should include marketing “cost per” metrics and revenue for each granular marketing tactic.
Beyond marketing performance analysis, there is also massive potential in the sales process for performance analysis. The sales process data can be invaluable for evaluating the team and individual sales representative performance. For instance, the metrics that are below average will put a spotlight on problems or training issues. This allows your sales teams to efficiently solve specific issues that are limiting your sales potential. Sales data should be tied back to marketing sources so that you can evaluate metrics like contact rate as another performance indicator. The critical sales metrics can also help you distinguish between marketing or sales performance issues to optimize growth investment strategies. Improving sales funnel performance also improves overall marketing cost per acquisition.
Maybe you have realized that you are failing in one or multiple of the areas above with your marketing data strategy. Or maybe, there is a different data issue you are facing. Either way, a solid grasp of your marketing data and analytics is the foundation for any good marketing data strategy. Every company should review its data strategy and maturity and put strategic plans in place to improve them. Ask your marketing agency, “What data could we provide to help you be more successful?”. Before starting a relationship, ask, “How will you be using data to communicate success or failures in marketing?”. Create that common data foundation and stay laser-focused on down funnel metrics that drive actual value for the company.
If your business needs help creating a data strategy for marketing, we can help. This process requires a multidisciplinary combination of knowledge. After creating your data strategy, it now needs to come to life. Software products are prevalent, however, businesses rarely find success by using them. Confirmation bias, politics, cultures, and egos often prevent good data strategies from flourishing. Disjointed and inefficient data processes stumble forward, hoping for success. Or simply, limited resources do not cover all of the required areas of expertise. A solid data science partner can help you build your solution. Get in touch with a team with expertise in marketing, digital advertising, sales processes, software platforms, data engineering, and data analytics.