DATA DECAY is Killing Businesses! Improve your Management Process Now!

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The Challenge

The business world increasingly runs on data. Understanding customer demographics, personal preferences and buying behaviors all help fuel a superior, customized customer experience. Companies know that to remain competitive, they must utilize data and offer customers a highly cultivated encounter that includes recommendations, convenience and content that is all timely and targeted.

Every year, the urgency around data cleanup and enhancement is highlighted by the gradual decay that naturally occurs as organizations make changes and individuals move on to new positions, leave early to start a new career elsewhere or are let go. Some experts estimate that the natural annual decay rate is around 20%, some believe it is even higher = 34%. Everyone agrees that the exact rate of decay is often based on your target audience, industry and job function.  In other words, some industries experience a greater degree of change and attrition, and some job functions come with greater job security, while other experience more excessive change.

In 2020, data decay rules as we know them changed dramatically.  COVID-19 is serving as a catalyst for an exponential decline in data quality. From unemployment and job changes, to an upheaval in how and where employees work no industry sector escaped change.  COVID-19 is the reason data errors grow so significantly, in fact since March 2020, errors doubled in some industry segments.  

The Cost of Bad Data 

While there is a significant global toll on the economy resulting from bad data, the numbers may be more staggering when examined at the individual business level:

Inefficiency: This is the impact of a bad or wrong phone number, the wrong email or wrong demographic information. Harvard Business Review estimates that data professionals spend 50% of their time tracking down and correcting data errors. 

Higher Costs for Resources and Maintenance: Costs are higher to compensate for bad data than it is to maintain a clean database. 

Poor Prospect and Customer Relationships: The loss of new sales opportunities, as well as lost opportunities with existing customers, are a side effect of incorrect data. The total revenue of a company may be negatively impacted by as much as 25%. 

Low Morale: From database managers to B2B sales reps, it is frustrating for employees to be forced to rely on inaccurate information. Database managers become weary of inaccuracies, while sales teams resent practices that deny them commission opportunities. 

A Problem That Only Multiplies: This is just the beginning. Marketing departments make bad decisions based on incomplete or inaccurate data. Customers and leads react negatively to bad data, unsubscribing to emails that do not seem to apply to them or even complaining on social media about a company that seems out of touch with who they are. 

The Importance of Investing in Data Accuracy and Marketing in an Economic Downturn

As the concerns of recession and the lingering impacts of Covid continue to affect global commerce, many company executives are contemplating whether it would be prudent to scale back on marketing efforts and conserve resources. Not only are companies asking whether it makes sense to invest in data clean-up, but many are debating whether to trim broader marketing budgets. 

When the economy dips, the gut reaction by most is to cut costs everywhere possible throughout the organization. Marketing departments often see the first and deepest cuts. That, as history tells us, is a huge mistake.

The advice to continue investing in marketing is backed up by decades of research. Brands that continue to advertise during an economic downturn outpace their competitors. 

As far back as 1927, an advertising executive named Roland Vaile published a report in the Harvard Business Review that detailed the outcomes for companies who advertised during the 1923 recession, and those who ceased investing in marketing. Those who continued advertising experienced 20% growth, while those who cut back finished the recession 7% behind their pre-recession levels. 

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Following the 1980s recession, a study by McGraw-Hill found that of 600 businesses examined, those that advertised from 1980 to 1982 were 256% ahead of those that did not. 

How should companies decide where to invest those precious marketing and advertising dollars? Companies need to evaluate what they are doing and keep investing based on measurable results:

  • Focus on core products and services 
  • Tailor messaging for specific audiences 
  • Learn more about your customer
  • Balance short-term and long-term needs 
  • Do something good to help the local community

At the core of every marketing investment is the data. When resources are scarce, the answer is not to cut marketing investments; the answer is to be sure that the marketing investment is resting on quality data.

The Solution: Good Data Practices 

Defining Good Data

Businesses often don’t realize that they have a data problem or understand the cost and implication of bad data on their files, in part because they don’t have a set of metrics for defining the standards that data must meet. In any data cleanup and enhancement efforts, data must be analyzed with metrics that pursue the following qualities:

  • Accuracy
    • The contact’s information is accurate, including their role and title, address, phone, and email
    • Demographic information on file accurately describes the company and contact
  • Quality
    • The contact’s demographic information matches the target audience that the sales and marketing teams are prioritizing.
  • Existence, Completeness and Relevance
    • The data set must contain the information necessary to serve the sales and marketing teams
    • The data is appropriate to serve business goals
  • Consistency and Timeliness
    • When the data is stored across different locations and systems, the records all show the same values
    • Data is regularly updated to accommodate changing business objectives

There are clear benefits to investing in data cleanup:

  • Increase gross revenue and profit margins.
  • Increase employee trust and eliminate frustration.
  • Improve strategic and operational decision making.
  • Improve collaboration between departments.
  • Improve operational efficiency.
  • Increase customer satisfaction and trust.
  • Strengthen the brand.

Successful business takes this even further by leveraging data with a strategic perspective. Those who most effectively manage their data generate the greatest revenue with the best ROI from it. They save money by using fewer resources with their sales and marketing efforts, and they experience the highest customer satisfaction level. 

Each year there are new marketing channels, making it possible to establish additional points of contact with customers. Businesses also must deal with fragmentation and collect data from different sources, all with unique data structures. The amount of new data sources is constantly growing and at a rapid pace. Between 2015 and 2019, the average number of data points grew by 50%, which makes maintaining quality data an increasingly complicated challenge. 

A survey we conducted this year revealed an alarming result.  Most businesses do not know, nor have they studied, the actual cost of bad data on their business.  Moreover, many businesses do not know the growth potential they will experience when they address bad data.  

Eight Steps Businesses Take to Achieve Good Data Practices

Adopting good data practices is within reach for businesses that want to achieve these types of cost savings and revenue opportunities. Implementing the following steps can deliver measurable results in a short amount of time and with minimal upfront investment:

  1. Acknowledge the scope of the problem, including a cost analysis of bad data’s impact on the organization.
  2. Invest in the necessary resources to identify the sources and reasons for bad data.
  3. Appoint a point person who will be responsible for data quality/accuracy and should have a background in IT or marketing with skills and experience in strategic approaches to solving business challenges. 
  4. Include data quality in strategic priorities for the company. 
  5. Rank data importance to prioritize update and enhancement projects.
  6. Calculate the expected return on investment for improving data quality versus the cost of inaction. 
  7. Identify the key changes required to drive improvement.
  8. Develop an ongoing data quality program. 

Revenue Opportunities With Good Data

There are many ways in which businesses create a competitive edge and open new revenue streams with quality data:

  • Increase Revenue: Reliable data enables the company to improve decision-making, because it is based on up-to-date, accurate information.
  • Save Money: Companies stop wasting money on campaigns that target the wrong people and companies or people who no longer work at a company or act as an influencer in their industry. 
  • Improve Customer Satisfaction: Companies deliver the right messages at the right time and may even equip them to provide specific customer recommendations in terms of products and services and content. 
  • Save Time: Less time is spent remedying data errors and tracking down the right information.
  • Improve ROI: Companies receive a greater return on investment (ROI) for their marketing dollars. 

Business Example 1: Good Data Practices 

Company A produces products sold nationally and maintains a database with 100,000 contacts. Each year, the company sells to about 7,000 clients, with a gross annual sales totaling around $25,000,000. Here is how it breaks down:

Average first-time purchase $1,250.00

Average number of repeat purchases each year 3

Average total annual purchases $3,500.00

Average lifetime value of a client $12,000.00

Company A uses a variety of marketing channels, including printed catalogs, SEO, social media, email campaigns and other digital marketing platforms. They also participate in seven local and national events each year. 

Orders are processed online and by an inbound call center, while bigger sales are pursued by an inside sales team with a minimum of $5,000 per order and a lifetime potential value of $50,000.00.

This example deals solely with their catalogs sales. 

Company A mails seven catalogs per year to 100,000 companies (based on company size, type. and purchasing history). Their main catalog is mailed once per year to all businesses in their database, with “thinner” (less costly) catalogs designed to target smaller segments of the market mailed six times per year. 

On average, Company A invests $25 per contact, per year producing and mailing catalogs. While the company maintains an ongoing address verification process, they experience 15% data inaccuracy each year. Data inaccuracy includes the wrong person or title, mailing to a person that is no longer there, and mailing to companies that have moved or gone out of business.  

Of this inaccurate or bad data, the majority is either mailed to the wrong person who is not a decision-maker or mailed to someone who no longer works at the company. Ninety percent of these inaccuracies result in the mailing going to waste, resulting in a 13.5% incompletion rate for catalog mailings. 

Company A’s direct loss because of “bad mailing” is 100,000 X 13.5% = 13,500 contacts.  At a cost of $25 per year for mailing, this results in a $337,500 loss.

The estimated loss of business because of mailing to the wrong person or people who are no longer there = 13,500 contacts. The 13,500 contacts at a 7% order rate X $3500/average annual sale = $3,307,500.00.

If Company A invested $10 per bad record to correct and update just 50% of their inaccuracies (lowering it to 7.5%), they can experience the following savings and new business growth potential:

The cost to update and enhance their database $67,500 

(13,500 X 50% X $10/each) 

Savings $167,500 

(6,700 less catalogs go to waste X $25.00 per catalog)

New sales $1,500,800 

(New business opportunities with correct contacts 6700 X 7% X $3200)

Net profit $1,433,300 

($1,500,800 – $67,500.00)

The company will experience a net lifetime value of OVER $5,000,000.00.

Business Example 2: Good Data Practices 

Company B provides data, marketing, content development and lead generation services. 

Even before we attempt to calculate the positive impact quality data has on the company, we can measure the immediate impact quality data has on the company’s bottom line in the example below:

Contacts managed in a large database segmented into 10 vertical markets 1,000,000

Number of clients the company services each year 500

The company’s gross annual sales $12,000,000 

Average one-time purchases $3000

Average annual purchases per client $24,000

Average lifetime value of a client $75,000

The company estimated annual revenue from their database (50% of gross sales)

(data sale, survey, industry analysis, lead generation etc.) $6,000,000

The company’s present bad data and error rate 20%

(200,000)

The average value per record in the database $7.5 

(A total of $6,000,000 / 1,000,000 X 80%)

The company cost of bad data $1,200,000 ($6,000,000 X 20%) 

When Clean Data is the Norm

If, instead, Company B invested $3.00 per error to correct and update just 50% of their bad data (which lowers their error rate to 10%), they can experience considerable savings and increase business growth potential.

The company cost to update 50% of their bad records $300,000

  (200,00 X 50% X $3/record)

The company’s new revenue once 100,000 records were updated $750,000 (100,000 X $7.5)

The company’s net profit $450,000

($750,000 – $300,000)

Estimated lifetime value of a new updated record (20% annual churn) $37.5

($7.5 X 5)

Estimated lifetime net profit because of database update/enhancement $3,450,000 (100,000 X $37.5 – $300,00)

Conclusion 

Companies who want to improve their bottom-line profits and overall probability for success must strive for the highest data quality and accuracy possible.  

More than ever before, companies will be experiencing a costly challenge resulting from a high percent of bad data on their files.  Companies who have processes to deal with natural annual data churns will have to intensify these processes to address bad data.  Companies who do not have on-going data cleanup and enhancement processes in place must prioritize following the Eight Steps to Achieve Good Data Practices listed above.  They may choose to identify internal resources or speed up the process by hiring a 3rd party vendor to help them achieve the results their business demands to survive and flourish.

When this goal is achieved, there is no reason companies will not experience an improved ROI. 
About Blue Valley Marketing: Blue Valley Marketing is a firm focused on delivering high quality New Business Development, Brand Development, and Demographic and Interest Based Lead Generation services. Supporting the companies operating in B2B environments, Technology, Manufacturing, service companies and media companies since 1991, Blue Valley Marketing has accumulated an immeasurable wealth of knowledge and experience in audience development, database enhancements, and most importantly New Business Development by creating qualified actionable leads, while building rapport with millions of executives in a large number of industries.

Last Updated on November 20, 2023 by Ronen Ben-Dror

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