16th Feb, 2022
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- Data is heterogenous, everchanging, and dynamic in nature that resides in different sources and demands heavy computational force to make it meaningful
- The most debilitating roadblock to database success is the Volume, Variety, Velocity, and Veracity of the data being generated at several touchpoints
- Database marketers should orchestrate unstructured data in a meaningful way to fully utilize B2B data assets
Poor database practices are endemic in almost every sector. There are several factors that cause a database to underperform. A fair share of these problems stems from the heterogeneous, everchanging, and dynamic nature of the data. They can become a conduit of strain for sales & marketing teams. The International Data Corporationâs report reveals that the worldâs data will grow by 175 ZB by 2025 â which is a whopping 61% growth rate. Smart enterprises with intelligent database management platforms to discover useful data and sales intelligence will be able to productize their sales cycles and leave their competitors behind in the dust.
The most debilitating roadblock to database success is the volume, variety, velocity, and veracity of the data being generated at several touchpoints. The biggest challenge is ensuring data mobility & extraction and making sense of the unstructured information. Data resides in different sources, navigating & identifying corporate databases becomes a fraught business and demands heavy computational force. These problems stemming from the technology side snowball into bigger problems for marketers. Leveraging the disparate data held in silos for business intelligence becomes the next big challenge. Data collection systems or b2b email list providers collect the same data multiple times which results in data deduplication. In technical terms, this problem is termed as Dark Data. Database marketers should orchestrate unstructured data in a meaningful way to fully utilize B2B data assets. Let us look at these common database-related challenges:
#1 Login based access of conventional subscription-based database models
Database marketers turning to conventional subscription-based database models are more likely to fall behind in the race. These models donât offer a wide range of options for bolstering AI-based engagement with customers. Moreover, these models donât facilitate large-scale data processing for leveraging technology — AI and ML, NLP, etc. It is the main barrier to creating an impactful outcome with demand generation campaigns.
Most subscription-based database models are not built for targeted/ demand generation strategies ABM prospecting. They miss out on critical buying signals including firmographics, and technographic information. Storing and leveraging huge volumes of data while ensuring data cleanliness, accuracy and contractibility turn gruesome with these models. These gaps lead to the failure of sales and marketing automation initiatives and shatter marketing budgets.
Despite enormous volumes of data being generated on a global scale, only a few companies become successful in consolidating it into a B2B database and refining it for sales intelligence. The problem might lie with the conversion process leading to errors or customization issues.
In the contrast, modern-day databases successfully collect data from millions of sources and provide features to study customer data. NextGen Contactable databases sprawl to deliver accurate enterprise, industry, geographic, and contact, data. The contextual information under their hood sheds light on purchase history, current contracts, business objectives, and digital footprints.
#2 Limited options of moderating data consumption & Standard Attribute Tags
Many database models provide limited options for moderating data consumption volumes and costs. Database marketers requiring a smaller dataset end up paying for a much bigger dataset and hence lose efficiencies from the per-contact rate.
Along with this, the lack of standard attribute tags restricts access to meaningful and transparent data storage. Fewer database marketers know the art of spinning algorithms, protocols, and languages to connect data with meaning and relevance. In such cases, they fail to get the right contacts at the right time.
Advanced data attributes enable organizations to store contextual info of unstructured elements. Whereas standard attribute tags create the opposite effect, they inhibit sales intelligence. They lack depth-profiling features required to highlight the attributes of an ideal customer profile or prospects. With these techniques, subscription-based models fail to add value to the data. They lag behind in differentiating value from the sea of data. If an attribute tag isnât present on a database, then the website canât effectively execute and collect the required data specific to campaigns.
Also, because of the lack of the right attribute tags, data errors get thrown into the outreach console. This is the reason that 90% of sales representatives fail to get a clear picture of their target audience. And this is how most sales campaigns are detonated. Therefore, database schema needs to be structured with the right attribute tags for supporting the specific and distinct needs of the outreach programs.
#3 Redundant Data or lack of Net New Data
IT problems or siloed databases cascade into a bigger problem called âLack of Net New Data.â It is actually an information deficit of new customers that lands marketers in a cold-start position. Scarcity of customer information slouches the marketing campaigns and prevents teams from reaching their new customer. This blind spot is a major hurdle that prevents sales & marketing teams from revenue-building opportunities. They fall behind in tapping the bigger proportion of the pie (prospects willing to buy products or services).
Another related problem caused by silos is redundant data. Database teams buy SME data which is redundant and outdated. Teams end up reaching existing customers who have already purchased their products or services. This also results in data deduplication. This inability mismatch makes it difficult for aligning sales and marketing functions and teams miss out on several business benefits.
Marketers fail to reach benefits as they ignore investments required to scale the redundant database for finding the âNet New Dataâ of customers. The email campaigns built around such databases slouch unproductively as teams donât get the funnel volume to create the impact.
#4 Inaccurate Database
As per Forbes Insights and KPMG, data quality is the concern of 84% of CEOs. However, data complexity proliferating from heterogenous & growing data sources becomes a damper for B2B data quality. Nearly 42% of brands believe that this inaccurate data is the biggest restraint that holds back the marketing initiatives.
Database marketers planning to leverage the data for marketing purposes should prioritize the quality of data in the database. Accurate, clean, and real-time data helps marketers in reaching the target audience and shortening the path to purchase. Whereas poor quality data lands the emails at the wrong places. Another possibility is multiple emails being sent to the same customer over and again. Database marketers should have a clear view of target accounts i.e., department, level, function, industry, size, location, etc.
#5 Insufficient Direct Dial information
Another caveat is the lack of volume of data which hinders the ability to improve the lead generation efforts. Brands that achieve higher conversions validate the data and find more prospects. In some databases, the data covers only trifling % of the market segment.
In such databases, marketers lack the volume of data to build highly accurate behavior models for their campaigns. Because of this inability, building customer interactions & engaging the target audience becomes daunting. This leads to suboptimal performance of multi-channel campaigns. Sloppy databases built with bad pull lists, look up contacts, track opportunities, incorrect geographic spread, etc., sabotage marketing efforts out of existence. Experts believe that 30% of that deficit is caused because of a lack of database volume.
To drive customer engagement campaigns, marketers need to ensure a constant flow of quality data. Voluminous & good quality data pump lifeblood into marketing campaigns and ensure conversions to fast-forward revenues. A healthy flow of information gives AI-driven product recommendations and supercharges customer loyalty programs.
#6 Database caters to only a small % of the Total Addressable Market (Geography, Industry, Profile)
Outdated data which caters to only a small percentage of the market presents its own set of challenges. This data doesnât just restrain conversion capabilities, it also hurts customer acquisition and retention rates. When businesses donât get the contact data of the addressable market, their reach is limited to a small percentage of the prospects.
Not using the full slate of data or not being able to consolidate clear data sets leads to devastating ROI. A stellar database with a good volume of leads serves as a beacon of revenue generation for teams, it discovers potential leads by mapping customer needs accurately. However, gathering data, leveraging it, and constructing an insightful database is one of the biggest hurdles that companies are facing nowadays.
#7 Database with unstructured data is only a database and not an intelligent database
Database marketers can obtain the greatest returns if they are able to leverage real-time data in the database. Real-time or recent data acts like decision-making firepower. However, most marketers fail to respond to the prospectsâ current needs with agility as the data they are using becomes outdated for marketing purposes. Obsolete data holds back database marketers in making smart decisions.
Additionally, read our blog âWhy Data Is Essential for B2B Telesales (And How to Source It)âto learn how to identify and source high-quality contactable data and its benefits in building an ROI-driven telesales campaign.
Here is an illustration to understand the problem:
For Instance, Customer X wants to buy a targeted SME database. The customer begins his buying journey from Google. The customer contacts the top three results. Customer X compares data offerings across different vendors. Going forward, customer X speaks with the sales associate of Brand X. At this point, Brand X knows that Customer X is a potential customer. However, Brand X doesnât have the idea of touchpoints that Customer X has interacted. With a single profile view, Brand X can shorten the path to purchase and fast-forward revenues by leveraging this information and extending personalized offers to Customer X. Without real-time data enterprises donât get enriched customer views and prioritize customers or segments.
Another relevant problem is the Total Cost of Ownership (TCO) or infrastructural overheads associated with the database. Due to lack of technology integration/ adoption, subscription-based databases require users to continuously spend time â in downloading data and collaborating with the vendor. Marketers squander heavy investments in integrating data from different channels. A lot of resources & human capital are needed to extract data, clean it for insights, and derive strategic marketing intelligence from it. Moreover, moving the data back & forth between different becomes a time & resource-intensive marathon in the long run.
As the b2b database becomes essential to drive sales growth and revenue, challenges galore among enterprises to maintain rock-solid and contactable databases. Businesses face several bottlenecks while sourcing, cleaning, and maintaining their corporate database. However, the biggest challenge remains the volume and variety of data being generated across different touchpoints. Furthermore, the lack of technology integrations and manual expertise within organizations impedes database productivity and brings down the overall ROI.
Read our next blog to find out the best-fit solutions on how to chase down and eliminate these database management challenges.