4 ways to optimize B2B data analytics that really work

Many businesses lack a clear strategy for organizing and visualizing data. This is particularly the case with B2B data, where companies generate a vast amount of information every day.

Many B2B enterprises use legacy systems and fragmented storage solutions. Therefore, integrating this data can seem like a daunting task. However, it is essential that businesses recognize that accurate reporting is essential to sales and marketing strategy. For example, it is impossible for businesses to measure the effectiveness of their email or social media marketing campaigns without analytics. Without capturing lead information, tracking response rates, or tracking clicks, businesses cannot measure traffic to their channels. In this article, we cover four practical ways to optimize B2B analytics efforts, so businesses can work towards developing an evidence-led approach to marketing strategy.

Introducing B2B data analytics for sales

Before discussing the practical steps toward B2B data optimization, first we will introduce some of the foundational concepts. Primarily, it is key to define what is meant by ‘optimization’. In this instance, optimization refers to the ongoing process of making a workflow or protocol as effective as possible. Subsequently, optimization is not possible without accurate data. So, in the case of B2B sales and marketing, these data are reliable information about customer behavior. This intelligence allows businesses to enhance campaign performance, improve ROI, and streamline the sales process.

As such, the sales cycle must align with the buyer journey. Understanding the customer’s motivations and preferences are essential for building a long-term relationship and nurturing loyalty. Therefore, the business needs to seek to place the customer at the center of their operations. Every time a customer interacts with a business’s digital presence, they leave a trail of data. This data holds a wealth of valuable information about customer wants, needs, and expectations. In response, companies need to ensure they have appropriate data capture software in place to retrieve this intelligence. From here, they can combine this information with CRM data to gain insights into buyer intent.

Once a business has a picture of buyer intent, they can develop their understanding of the customer journey and begin building buyer personas. Through the careful segmentation of a customer base, companies can develop a customer-centric map of the sales cycle. With this map, businesses can identify key motivations, activities, and conversion points. Furthermore, in B2B sales, the customer journey is likely to be extremely diverse; therefore, businesses need to have a clear idea of what drives different types of clients to convert. With these tools, business can effectively tailor their sales strategy depending on a segment’s individual needs.

The 4 steps of optimization

Once a business has these foundations in place, they can seek to further optimize their process. With a proper data capture framework and a clear strategy for customer segmentation, businesses can further refine their approach to drill down even deeper into behavioral analytics.

1. Data hygiene

Poor data hygiene is a common issue in B2B data analytics. Often, data sheets include duplications, incorrect formatting, or superfluous records. However, with a good business intelligence platform, businesses can clean, integrate, and transform their data. As a result, businesses can implement proper data hygiene strategies that eliminate errors and anomalies. In order to prepare to automate their data cleaning protocols, businesses should carry out the following processes:

• - Deploy text filters to clear unwanted data.

• - Remove duplicate values from fields containing unique information.

• - Integrate data sources to generate custom datasets.

• - Convert ID fields to text fields.

• - Implement time-stamped batch updates for historic trend analysis and monitoring.

2. Full automation

The purpose of a business intelligence platform is to analyze data from numerous sources, extract insights, and present this intelligence in real time, using legible visualizations. The most advanced software products will use cloud storage systems to extract data using scheduled API calls. Using these systems, companies can develop an ordered and systematic approach to insight extraction.

3. Refine visualizations

Legible visualization is essential for analysis and the development of actionable insights. By visualizing data in a dynamic dashboard interface, management can gain a comprehensive overview of business activities. With these visualizations, companies can identify trends, process speeds, red flags, and develop more streamlined business strategies. Through closer analysis, management can seek to answer questions with greater agility, thus creating a more responsive marketing decisions.  

4. More accurate data monitoring

Keeping track of multiple B2B data sources and extensive visualization platforms can be a time consuming process. Therefore, implementing automated alerts is a good way to make sure senior management are on the pulse of new developments. Now, some business intelligence systems can generate text and email alerts when a specific target is met. For example, management may wish to be on the pulse of:

• - When new leads are converted from the MQL stage to opportunities.

• - Fluctuations in website sessions and online behavior.

• - Social media engage metrics, including click-through rates.

Gain the competitive advantage with data analysis

The capture, analysis, and interpretation of unwieldy datasets – otherwise known as big data analytics – is increasingly pivotal to gaining a competitive advantage. Moreover, this is increasingly the case as the volume, velocity, and quality of data available to businesses continues to proliferate. Therefore, it is essential that businesses are aware of how to streamline, refine, and fully optimize their B2B data analysis efforts. Generally, this will require some experimentation; in order to find out which data strategy best suits a particular organization’s needs, they to constantly monitor and evaluate their processes.

Although tackling big data analytics may seem like a daunting process for many enterprises, with the four steps described above, organizations can work towards developing an agile marketing strategy informed by evidence. In summary, businesses need to remember the following principles:

• - Clean and prepare data, to eliminate anomalies and customize analytics

• - Automate extraction to optimize capture processes.

• - Clearly visualize data in a sophisticated analytics dashboard to uncover insights, trends, and bottlenecks as part of a process of continuous improvement.

• - Create alerts to allow management to stay on top of fluctuations, milestones, or necessary actions.

With these steps, organizations can create a fully optimized data handling process that provides them with a meaningful competitive advantage.