The Hidden Cost of Low-quality Data: Undermining B2B Sales and Marketing Strategies

September 28, 2023

Companies driven by data are 23 times more likely to acquire customers. Data is the crucial factor that differentiates successful and unsuccessful sales and marketing teams. Successful teams harness the power of data to make decisions. 

On the other hand, low-quality data impedes marketing and sales opportunities for customer acquisition. Companies incur a loss of 550 hours per year on average due to low quality prospect data. These lost opportunities could have been avoided by focusing on superior data quality. 

Before we go any further, let’s define low-quality data.

What is Low Quality data?

Low quality business data is inaccurate, outdated or incomplete information. It hinders the company’s strategic decision-making process, and refrains them from drawing clear insights and performing accurate analysis. 

A significant contributor to this issue is relying on acquiring one-time lists for outreach efforts, which often come with inconsistencies and inaccuracies in the database. The consequence of bad data negatively impacts both sales and marketing strategies.

These impacts translate into hidden costs like wastage of resources, improper planning, inaccurate segmentation, poor conversion rates and damage to the company’s reputation. 

From here, we will take a deeper look at the impacts of poor data on sales and marketing operations, and how companies can take certain measures to focus on high quality data. 

Impact of Low Quality Data in Sales and Marketing 

6 areas where low-quality data impacts sales and marketing operations negatively

Inaccurate Segmentation, Targeting and Forecasting

Accessing data with inaccurate or outdated information like a company’s firmographic details, tech stack information, technology usage, causes challenges in identifying the right customer segments and forecasting sales accurately. Without reliable data, the efforts of segmentation, targeting, and customer analysis are compromised, leading to poor decision-making, low potential targets and missed opportunities.

For example, the marketing team of an enterprise IT company is planning an email outreach targeting IT function VPs and directors in the US market. However, the database they are using is inaccurate and outdated. Therefore, when the emails are sent out by the marketing team,  they get lower response rates and  high bounce rates. This leads to possible blacklisting of the company's domain for spamming.

Furthermore, the company might have forecasted a 5% conversion rate, however, in reality it can be significantly short, say 1.5%. Such forecasting leads to low sales productivity, overallocation of resources to ineffective campaigns while underallocating to other potential opportunities, ultimately harming their sales performance and bottom line.

Misguided Strategies

In B2B sectors, sales cycles are longer and the decision processes are complex. Therefore, marketing teams have a huge responsibility on their shoulders to formulate an effective marketing strategy backed by precise data about customer behaviors, preferences, intent, and decision-making processes. In such a case, low-quality data can skew the understanding, leading to inefficient targeting. 

For example, a basic foundation of any marketing strategy is identifying the buyer personas. But, if a company’s data is inaccurate or missing information about these personas, such as their role in the company, their job function, or their influence over purchasing decisions, the marketing team completely misses targeting with incorrect messaging, reducing the effectiveness of the strategy.

Lower Conversion Rates 

Bad data has detrimental effects on customer acquisition. Any form of prospecting efforts like email campaigns and digital advertising rely on high quality data for targeting relevant and interested audiences. 

However, due to inconsistent or inaccurate data, resources are devoted to faulty premises thereby driving up the cost of customer acquisition, lowering ROI. Low-quality data also hinders the process of lead scoring which results in improper nurturing, low sales engagement, incorrect personalization, causing the leads to disengage, thereby poor conversions. 

Impacted Client Relationships and Trust

Marketing team plays a key role here by establishing an open and personalized communication with these clients. When marketing teams use out-of-date data they risk hampering this communication channel with their clients by sending irrelevant communication, missing essential details, or failing to recognize changes within the client's organization.

For example, consider a B2B SaaS company Innovate Inc. which sells an AI-powered HR management system. The marketing team plans a promotional webinar for their latest addition - a recruitment optimization module to Property Pros., their long-term client.

Property Pros. have moved to a new CRM - Salesforce. Every department, including HR, uses specific Salesforce email addresses for interacting with vendors, service providers, and corporate partners.

Jason, the HR Director at Property Pros. and Innovate Inc.’s key point of contact is keen on exploring how AI can bring more efficiency to their recruitment strategy. However, prior to the Salesforce transition, Property Pros.' HR team was using standard corporate email for vendor communications.

Unaware of this migration, Innovate Inc. sends the webinar invitation to the old email addresses of the HR team members. Jason and his team do not receive the email for Webinar invite. The lapse in communication not only denies Innovate Inc. the opportunity to showcase their new addition, but also gives impressions of unawareness about the client updates. 

Reaching out to prospects with the latest information adds a lot of credibility to our interest in their relationship and efforts towards establishing it.

Lower Sales Productivity

Most of the time, low quality data translates into cluttered and disorganized databases. This means sales executives have to spend a disproportionate amount of time sorting, corroborating, and rectifying data. This data is often related to account information such as misaligned ideal customer profiles, erroneous customer data, incorrect contact information, or even misleading consumer insights.

One-time lists can act as a catalyst in this situation, as they are often aggregated from different sources where data accuracy has not been validated or standardized.  Therefore the data is frequently inconsistent and unreliable, making the outreach tedious and time-consuming  for sales executives. .

For example, a sales rep who relies on a one-time list for potential leads may find that the contact information is outdated, job roles have changed, or that leads belong to industries unrelated to the product offering. Consequently, the sales rep would have to put in extra hours researching for the accurate, updated information about the potential target companies. 

A sales representative could have used this time to craft better pitches, close more deals and drive more revenue for the company. 

Lack of Trust in CRM

A company’s CRM effectiveness is as good as its data quality. Good quality data means the sales team is empowered to manage their customer relationships and guide sales operations effortlessly. 

However, inaccurate, inconsistent, and outdated data erodes the trust that sales reps have in their CRM system and the information there, leading to decreased user adoption and undermining the overall potential of CRM to improve sales operations. According to Forbes, 52% of sales leaders indicated that their CRM platform is costing them potential revenue opportunities.

In the long term, this jeopardizes the foundation of the sales process in the company, as sales executives may start relying on their individual, often isolated, methods to track leads and customer interactions, causing inconsistent sales processes and customer experiences.

How can companies overcome poor data quality?

Companies amass lead data from different sources, and acquiring  high quality data requires investment in the right platforms or using the right methods for enrichment. Sales intelligence tools like Enlyft play a critical role in helping companies automate this process to a large extent, including enrichment and updation of latest information. Here are a few ways tools like Enlyft can help - 

Invest in the right Sales Intelligence Solutions

Manually gathering data from different sources takes time. Buying one-lists is risky, as it exposes the company database to inconsistencies and inaccurate information. Automating the data collection process can take the guesswork out of data collection. Tools like Enlyft use AI, proprietary technology, and human researchers to collect data from billions of public documents and combine them with reliable third-party sources. 

Data Verification on auto-mode

As companies collect more data, it becomes pivotal to verify them on an ongoing basis. Hiring a data specialist helps to set some standards, but the process is still labor-intensive and prone to errors. Sales intelligence tools like Enlyft ensure the relevance of the database by continuously undertaking reviews, manual fixes and removing poor data. With the help of AI models, it’s ascertained that the standards for data quality are not compromised. This enables the company to focus on their core strengths and area of business, while leveraging the expertise of data solutions. 

Contact information Accuracy

Accurate database fuels successful business communication, and builds strong customer relationships. Wrong email addresses or incorrect phone numbers result in missed business opportunities and ineffective outreach efforts. Despite its importance, maintaining contact data accuracy is a huge challenge for companies. This is especially true for companies that have international clients. Varying time zones, language barriers, and changing contact details elevate the risk of data discrepancies. Enlyft works as a trusted partner in countering these challenges. Enlyft combines proprietary technology, AI models, vetted data partners and network of user contributions to maintain precise data. The algorithm crawls billions of public documents collecting only the relevant information. With Enlyft, companies can shift their attention from solving data inaccuracies to implementing core business strategies. 

Custom Data Enrichment

Data enrichment is necessary for companies to access relevant account-based insights. Updating the existing data with more relevant information from different sources is an exhaustive process and leads to burnout. Using Enlyft, on the other hand, takes the tediousness out of the process, as it automatically appends the missing information or incomplete data fields in the existing database. The information can be related to the company size, location, Technographic data or intent data. In a nutshell, your business is equipped with necessary account insights in a much shorter span of time when compared to manual methods. 

In conclusion

Low-quality data in a company's database results in poor performance of  sales and marketing efforts. Companies fall into the temptation of building a big database through one-time lists, but it comes with its own set of limitations in the form of data inconsistency, inaccuracy and duplication. Poor quality data has a far-reaching impact on decision-making, customer engagement, and business performance which gives extra weight to companies' need for a shift in their strategy.

For long-term success and growth, businesses must ensure investing in reliable sources for accurate data.  Partnering with specialized marketing and sales intelligence solutions like Enlyft can not only help in collecting and verifying the data, but also help in enriching the data.

By fostering a culture of data excellence, companies can build a foundation for sustained growth and profitability.

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