Although data analytics provides valuable customer insights, you may encounter roadblocks. Being aware of these will help you tackle them head-on.

The tech industry is continuously shifting, with new tools and innovations being introduced. And amidst all this, analytics remains the cornerstone of informed decision-making. However, some B2B brands struggle to retrieve actionable insights from raw data.

With new data ushering in, businesses must streamline information and invest in the right technologies. Brands can see a massive difference in their performance efficiency with data analytics.

However, there are some challenges associated with its application. Let’s dive into them.

Slaying the Common Challenges in Data Analytics

Watching out for these hurdles will help refine your approach and derive better business outcomes with data analytics.

Data Literacy

Without a thorough understanding of data, your teams may be unable to make the most of it. Data literacy involves understanding data sources, infrastructure, analytical methods, and the ability to describe scenarios and resulting business outcomes. Improving data literacy by organizing workshops and training sessions can help bridge the gap.

Data literacy isn’t just about complex algorithms but involves knowing where to derive the right data and how to manage it effectively. Brands need to get an idea of the data they are dealing with and then use the right tools to analyze it. When teams are well-versed in data, it helps them take the right actions. The opposite is also true- having no or low data literacy is likely to misinterpret insights, resulting in poor decision-making. You can overcome this gap by organizing training sessions and hands-on workshops. When you encourage a data-literate environment, it will help empower your teams to use tools for targeted initiatives.

Technical Knowledge and Skills

Sometimes, your teams may not be willing to participate in the training programs.Continuous training and upskilling help keep pace with evolving tools and tech. Even powerful analytics tools require some technical knowledge and skills. These tools allow users to correctly interpret data, refine strategies, and make informed decisions.

Data Quality Issues

Data quality is the crux of driving good decisions that promote growth. Poor quality data can take you off the tangent, reducing the capacity of good decision-making. For instance, if you use data that is not updated, it can influence your interaction with them and affect the sales cycle. Data quality has the power to affect the quality of decisions, and as businesses grow, it becomes more so crucial to maintain consistent data quality.

Data Security and Privacy

As your brand expands, so does the data volume and the risks associated with it. Keeping essential data safe is the need of the hour. The consequence of even a minor breach can be severe. These can be avoided by integrating data security measures and following compliance protocols. Such initiatives not only protect data but also your brand reputation.

Data Overload

Businesses generate data at an unprecedented rate- which may seem like a collection of great insights, but it can be overwhelming. The volume of data could become so high that it’s cumbersome to process and analyze. And if you have scattered data, it’s even worse. All this takes smart strategies and skilled personnel. Or else businesses would drown in data, not knowing which ones to pick for performance efficiency.

The turning point here is to source the right information that supports informed decisions. Poor quality or incorrect data will do the exact opposite. Brands can overcome this problem by putting in place data prioritization, a practice that focuses on data that matters. You can identify the best metrics that align with business goals and create systems to monitor these regularly. Brands can also benefit from AI-integrated tools to automate data categorization and provide relevant, real-time insights.

Adopting a structured approach will eliminate the burden of data overload and instead help convert data into a strategic asset.

Integration Issues

Integration glitch is one of the biggest hurdles companies come across. Since data is present in multiple systems, it is present in a fragmented format. Data often resides in silos across various departments or systems- which makes it difficult to merge and analyze effectively. When data is trapped in these silos, it’s almost impossible to understand the performance efficiency of brands.

The problem becomes even more important when organizations fail to establish a unified data strategy. A lack of cross-departmental data sharing can result in missed opportunities and misaligned goals. To overcome integration challenges, brands can invest in modern data structures involving a centralized data warehouse. This helps you unlock the full potential of data and drive actionable insights.

Data access

Ever had the experience that data is scattered all over the place, and you are struggling to find what you need? Perhaps it is stored in different systems, across multiple departments, or in formats. This kind of disorganization can make it difficult to consolidate and analyze data. Not only that- it can make your data vulnerable to unauthorized access. 

So, how do you keep data secure? Brands must focus on centralizing data, making it more streamlined and structured. By doing this, you can minimize data movement and limit access to only authorized personnel.

Cost

Data analytics requires investing in the right tech, people, and infrastructure. But, unless organizations are clear on the benefits they are getting from an analytics effort, IT teams may struggle to justify the cost of implementing the initiative properly.

A strong data analytics platform via a cloud-based architecture can eliminate most upfront capital expenses while reducing maintenance costs. But what’s the real payoff?

When done right, data analytics can derive insights that optimize all processes, from marketing to supply chains to operations. While quantifiable data is crucial, some benefits might be hard to measure directly, so IT teams need to think beyond just line-item numbers. For example, a data project might improve decision-making agility or customer experience, which can lead to long-term gains.

Resistance to change

Let’s face it-change can always be difficult. And this is also true for data analytics. Integrating this change pushes teams out of their comfort zones. So, how do you get your team on board for this revolution?

Connect with your team and talk about their resistance to change. Work with them to help through the transition and employ quick decision-making, demonstrating the value of analytics. The objective is not just to encourage the change but to convince how analytics can promote better decisions across the business. And once the teams understand this, the resistance will slowly fade away.

Wrapping up

Data analytics can assimilate valuable customer insights, like customer behavior, purchase history, and more. And integrating this tech doesn’t have to be daunting. Adopting the best strategies, tools, and resources prepares you to combat the challenges. Brands can also leverage high-quality data as opportunities for better decisions, improved performance, and continuous growth.

As you navigate the challenges listed here, remember that these can pave the way for a more data-driven.

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Data is a critical component of a successful ABM strategy. How can you leverage data-driven insights for effective marketing?

Designing a winning ABM strategy begins with market research, understanding your prospects and their buying patterns, and aligning sales and marketing teams to pursue the accounts. In this article, we have compiled resources to help you utilize data-driven insights to enhance the performance of ABM campaigns and achieve the desired outcomes.

Accelerating ABM with data

The success of ABM is paramount, with the tailored messages and personalized customer experience. And without data-driven insights, it can be challenging to hit the mark. The secret to accelerating the performance of ABM programs is to use the right database. A data-driven ABM approach will enable you to understand your best prospects and curate campaigns that resonate with them. An ABM program driven by data helps to launch a fast, effective start and makes it convenient to identify high-performing content.

Gathering and analyzing customer data is an essential ingredient of ABM’s targeted approach. You need current and accurate data to evaluate relevant target accounts, their pain points, preferences, and needs. You can procure relevant information using various routes, such as surveys, customer interactions (emails, cold calls, social media channels), and website analytics. The goal is to find out whether the target accounts resonate with your offerings and how you can tailor campaigns to win their interest.

Optimizing data

In the digital age, when information is abundant, you need to strategically use data to optimize the buyer’s journey. There are three steps to accomplish this:

Prepare the Data

The first step is consolidating information from all corners of your business. Bring all the data in one place and clean them to get a clear overview.

Enrich Data

You can enrich existing data by adding unique data points to get clarity about the customers. The more you know about them, the easier it gets to curate personalized campaigns.

Conduct Market research

Utilize data visualization tools to identify the segments where your strengths lie and where you have scope for growth. It also helps align sales and marketing teams to pursue the target accounts. Without a shared understanding between these departments, discrepancies may arise while utilizing organized data and implementing personalized ABM initiatives.

Data-driven insights for Targeting specific accounts

Precise targeting is the highlighting feature of data-driven ABM. When you identify the pain points and ideal solutions of prospects, you can personalize messages that help build strong customer-brand relationships. Categorize the data into segments, making it easier to identify the accounts you want to target first.

Implementing Predictive analytics

Analyze customer data using relevant information and statistical algorithms to understand the future outcomes of customer behavior.  Predictive analytics can be utilized to identify the ideal customers for your business based on the data collected, including historical buying patterns. It saves time you would rather invest on accounts less likely to respond.

Segment data for ideal profiles

ABM allows account targeting and segmentation to divide potential accounts into groups based on relevant characteristics aligned with the brand’s goals. These are some types of data that can be used for account segmentation and targeting, such as demographic, behavioral, and customer feedback.

• Demographic: gives an overview of the characteristics of the customers within the target accounts, including age, gender, designation, location, etc.
• Behavioral: looks into the buying patterns of the target account segments, their actions, and interactions with a company’s offerings. You can utilize this information to understand their purchase history.
• Feedback from customers: this can be a valuable resource to improve ABM by identifying the key accounts interested in the brand’s offerings and the decision-makers.

Integrate ABM customer base into sales and marketing data

Combine customer information for an ABM campaign with data outsourced from other marketing and sales efforts. This would give you a holistic view of the campaign performance by providing an idea of its impact on the sales and marketing efforts. The integration allows you to understand how the campaign impacts their efforts, thereby allowing informed decision-making. For instance, if a brand is launching an ABM campaign to target a specific group of accounts, they can learn how these prospects interact with other marketing initiatives like email campaigns, webinars, etc.

Wrapping it up

For your ABM strategy to be effective, the target-specific database must be robust. You need to know your ideal prospects at a deeper level. One way to achieve this is through data-driven ABM insights. While incorporating such information, identify the key metrics important to your brand like customer lifetime value, acquisition cost, and retention rate. Then, using a CRM system you can track and analyse them. The data can be utilized to create customer segments based on shared characteristics. You can utilize this data to deliver personalized messages and design customized campaigns. These can be launched on social media channels frequented by the prospects.