What’s the difference between data and a packet of ketchup?
Both of them have an expiration date, but only one of them has the date printed on its back. Consume expired ketchup and you’ll be let off with an upset stomach. Consume expired data in your CRM and you’ll lose millions of dollars in revenue and waste countless man-hours on dead leads.
And who’s #1 on your suspect list? A pest that goes by the name of data decay. So, what is data decay and how does it manage to hurt your GTM engine? Here’s everything you need to know.
B2B data decay can be defined as the deterioration of the quality of a database. Data expires with time. The leads in your database may change emails, switch phone numbers, move locations, or switch companies without your knowledge. And since this data is the very life force of your sales and marketing efforts, the price tag on its decay can be quite expensive.
According to Marketing Sherpa, the average monthly B2B contact data decay rate is 2.1%, which translates to an annual rate of 22.5%. Meanwhile, a shocking number of B2B organizations continue to remain ignorant of the cost of data decay. Ignorance is bliss, isn’t it?
To wrap my head around the actual impact of data decay, I spoke to Sales Growth Consultant Dale Yasunaga, Syncari’s Head of Partnerships & Alliances Mary Vue, as well as Syncari’s Head of Revenue Operations Mollie Bodensteiner.
- The Different Manifestations Of Data Decay
- Logical Data Decay
- The Types of Logical Data Decay & The Cost of Each Kind
- The Grand Total Loss in Revenue Due to Data Decay
- What Data Decay Means For Sales And Marketing Teams
- The Remedy to Data Decay
- How Slintel Helps Combat Data Decay
The Different Manifestations Of Data Decay
The 2 main forms of data decay are mechanical data decay and logical data decay.
Mechanical data decay is what happens when there’s a hardware failure in the database system that corrupts your data or erases it altogether. Hard drive failures are the most popular kind of mechanical data decay. Other forms include damaged servers and even cyber-attacks.
Compared to logical data decay, mechanical data decay is easier to prevent. You can keep it at bay by merely backing up your data regularly.
But this is not what we’re going to discuss today. Let’s talk about logical data decay, and how it can hurt your marketing/sales database.
Logical Data Decay
Logical data decay is what happens when the data in your database becomes outdated or incorrect due to the time-sensitive nature of data. Statistics show that Americans move about 11 times in their lifetime, and 32% of respondents in a poll conducted by PhoneArena, reported that they change their phone numbers every year, while 23% reported that they do so more than once a year.
Some of the common forms of logical data decay include:
- Outdated information
- Duplicate entries
- Inaccuracy/lack of specification
- Misinterpreted data
- Lack of new data, and so on.
Of these, the most common one is outdated information. People move, switch jobs, change phone numbers, and alter their names post marriage. That one website they sent their details to three months ago isn’t necessarily the first place they’d alert.
“Your biggest piece of logical data decay is generally people leaving their companies or switching jobs—general attrition. Then come the changes in address, phone numbers, and email addresses.”
— Mollie Bodensteiner, Head of Revenue Operations at Syncari
In other words, the data in any sales database is prone to go obsolete over time, and the odds are against you if you leave your data exposed to logical data decay.
The “cost of quality” or the “1-10-100 rule” states that prevention is better than cure, which in turn, is better than failure. Investing $1 in prevention is better than spending $10 on the cure, which is better than losing $100 on the failure.
And that’s not without reason. Preventing logical data decay is indeed tougher because a lot of time and energy goes into identifying the decayed data and segmenting it from the relevant data before even beginning to update it.
With so many opportunities for your data to go sour, why risk waiting until the next month, quarter, or year to clean up?
The Types of Logical Data Decay & The Cost of Each Kind
#1 Contact Data Decay
According to Dale, the existing database is at its best, relevant for a period no longer than 90 days. This is best complemented by adding new contact data on a daily/weekly basis.
“In my opinion, the contact data is the most important type of data that needs to be updated on a regular basis. It’s the foundation of a lot of sales and marketing efforts. You can have the best product and the best salespeople who can sell ice to the Inuit, but if you don’t have anybody to sell to, who cares?”
“I’ve worked in companies that only update lead data once a year. With how quickly things change in our lives today, lead info gets outdated really fast. So, if it isn’t updated on a regular basis, it’s going to put you to a big detriment.”
— Dale Yasunaga, Solo Sales Growth Consultant
Sometimes, there’s no way to tell when you’ve got the wrong information. You might end up sending a message to an inactive email address, and end up in a “black hole” inbox—you don’t know whether it has been ignored or whether the address is inactive because there’s no autoresponder that says, “Hey, this individual doesn’t work here anymore.”
From your perspective, your lead just ignored your email when in reality, they could actually have been an interested prospect. Not only is this a loss of revenue, but also a loss of your sales team’s time and energy.
Let’s go ahead and estimate the annual loss in revenue due to contact data decay.
Earlier, we mentioned that the average monthly data decay rate for contact info is 2.1%.
Assume that you have 50,000 companies in your database. Now, let’s compound and calculate the validity of this database one quarter down the line.
- Month 1: 50,000 * (100-2.1)% = 48,950
- Month 2: 48,950 * (100-2.1)% = 47,922
- Month 3: 47,922 * (100-2.1)% = 46,915
Within just a quarter, 6.17% of your database has become obsolete (you can run this calculation on your own database to understand the cost of contact data decay in your system). This translates to almost 12% of your database in the half-yearly quarter, and about 22.5% by the end of the year.
Of the 50,000 companies in this database, 11,241 of them have become obsolete by the end of the year. Let’s assume that at least 15% of those companies convert, each worth the average B2B ACV value of $1080.
Your total loss in revenue due to contact data decay = $1.82 million.
#2 Funding Data Decay
Companies that are funded are typically in the market to buy more technology, so funding info can be critical to you discovering high-intent prospects. 90 days is way too late, you can’t wait that long. Due to the time-sensitive nature of funding data, the best time to get funding information is as and when it’s announced.
“Funding is an important data set. So, I’m going to want real-time funding. I don’t want to know the funding from last year. I don’t want to know the funding from last week. I need to know the funding that was announced this morning.”
— Mary Vue, Head of Partnerships & Alliances at Syncari
- Month 1: 50,000 * (100-1)% = 49,500
- Month 2: 49,500 * (100-1)% = 49,005
- Month 3: 49,005 * (100-1)% = 48,514
Within a quarter, you’ve missed out on 2.97% of opportunities within your database by not staying updated about their funding info, despite its potential to point out your best active buyers. This translates to almost 5.85% of your database in the half-yearly quarter, and about 11.36% by the end of the year.
With 50,000 companies in your database, you’ve lost 5,681 opportunities throughout the year. Since companies that recently received funding are more likely to buy from you, let’s assume that at least 25% of those companies convert, each worth the average B2B ACV value of $1080.
Your total loss in revenue due to funding data decay = $1.53 million.
#3 Firmographic Data Decay
Firmographics refers to attributes such as company size, industries served, total revenue, market share, acquisitions, and other information that can help you profile companies.
Analyzing the performance of an organization is a crucial step in lead generation. However, this information can change over time and what was once a low-intent lead may have become a high-intent lead and vice versa.
That’s why it’s important to stay up to date on the firmographic data of an organization. Granted, its weightage may not be as heavy as contact details or funding information. But any form of data decay has the potential to bite off from your revenue, and so, firmographics data decay cannot be ignored.
- Month 1: 50,000 * (100-0.4)% = 49,805
- Month 2: 49,805 * (100-0.4)% = 49,611
- Month 3: 49,611 * (100-0.4)% = 49,417
Within a quarter, 1.16% of your database has become partly unreliable.
This translates to almost 2.32% of your database in the half-yearly quarter, and about 4.79% by the end of the year.
Of the 50,000 companies in your database, 2,395 of them have become obsolete by the end of the year in terms of firmographic data. Once again, let’s assume that at least 15% of those companies convert, each worth the average B2B ACV value of $1080.
Total potential loss in revenue due to firmogrphaphic data decay = $387 K
#4 Technographic Data Decay
Technographics is the profiling of organizations based on their current software stack, technology usage behavior, and software adoption/rejection.
In essence, technographic data gives you information about the software and tools used by your target accounts. Like firmographics, the decay of technographic data too might play a less important role in costing you revenue. However, this information allows you to infer insights around which accounts are most likely to convert into your customers based on the knowledge of their current technology stack, past technology stack, and software usage, and so, it should be treated with care.
Assuming the same conditions as the previous calculations, and a 0.4% monthly technographic data decay rate, your total potential loss in revenue due to technographic data decay can be calculated similar to that of firmographic data decay, which comes to $387 K.
The Grand Total Loss in Revenue Due to Data Decay
Calculating the loss in revenue taking all four into account—a company with 50,000 companies in its database, each worth an annual average ACV value of $1080 is subject to a potential average annual loss in revenue of $1 million due to data decay.
This figure is, of course, calculated using the lowest possible variables. The real cost varies depending on the type of company and the target market. In fact, the actual annual figure that companies estimate as loss in revenue due to bad data is $13 million.
What Data Decay Means For Sales And Marketing Teams
“Data decay has a huge impact on sales and marketing teams – you’re missing out on revenue opportunities, you’re missing out on opportunities to improve the customer experience, and you’re also increasing your likelihood of hitting spam and risking your domain.”
— Mollie Bodensteiner, Head of Revenue Operations at Syncari
The Effect of Data Decay on Your Sales Teams
1. Deterioration of the Sales Pipeline
With a huge number of leads with outdated data in your pipeline, your sales team takes a major hit in converting leads. Compared to marketing teams, this lack of opportunities hurts them more as they spend time in one-to-one prospecting and have only a set number of leads in their pipeline.
“Companies in the US usually build their sales reps’ territories based on the data in their CRM. If they assign a territory of 1000 leads with a quota of $1 million, but 20% of those leads are outdated, was it really fair to give their sales reps that territory at that quota?”
— Dale Yasunaga, Solo Sales Growth Consultant
2. Time Wasted in Fixing the Decay
With so many leads in their pipeline with outdated or incorrect data, your sales reps waste a significant amount of their time trying to fix the outdated entries. This takes their focus away from the quota and is not the best use of their energy.
Even with an adequate number of reachable leads, a significant portion of the data in your CRM regarding those prospects might have turned stale, leading to lower conversion rates.
3. Poor Overall Sales Performance & Drop in Revenue
The lack of specificity or the incorrectness in your data wastes your sales team’s time and energy and leads to an overall poor sales performance. This translates to a major drop in revenue. Meanwhile, companies that update their CRM have 23% higher win rates.
The Effect of Data Decay on Your Marketing Teams
1. Risk of Getting Blacklisted
Sending emails based on decayed data can put your domain name at risk and you could end up sending future emails to your recipients’ spam folders. This causes an increase in the email churn rate.
A damaged domain reputation is painfully difficult to fix. And that’s only after you realize how long your domain has been damaged.
2. Impacts on 3D Marketing
Every time a customer changes their address and you’re unaware of it, you run the risk of sending physical products to the wrong address. Or the wrong person might get the wrong product.
However, unlike emails, this is mostly immediately detectable as the package returns back to the sender. But it still makes for a huge waste of time and money that could have been prevented with proper data hygiene.
The Remedy to Data Decay
So far, I’ve been going around town spreading the word about how the sky is falling down. But what’s the solution? One plausible solution to data decay (albeit a difficult one to implement) is a marketing strategy that’s heavily focussed on inbound.
Here’s a visual representation of the yearly progress of the number of contacts in your database when you use inbound marketing (orange line) as opposed to when you don’t (gray line).
“Having a standard guide as to how data decay should be controlled that includes a clear management strategy is a huge piece of how you fix data decay.”
— Mollie Bodensteiner, Head of Revenue Operations, Syncari
However, launching an inbound marketing plan can be demanding. Other strong remedies to battle data decay include:
- Using CRMs that give out last contact reports of inactive customers
- Using automation apps like Neverbounce (to validate emails) and Voicent (to validate phone numbers).
- Developing a well-defined data hygiene strategy.
How Slintel Helps Combat Data Decay
With 30% to 70% of your database going outdated every year, it’s always a good idea to invest in a good data management system. People may move, tech stacks may change, and businesses may merge and get funded—but with Slintel to back you up, your database is always up to date.
Your database is not something you want to risk ignoring. Bad data has the capacity to impact your business goals by a huge margin. With the right strategy in place and the right data management tool to aid you in keeping your database updated, you can always stay at the top of your game.