In the post-pandemic world, data has emerged as the most dependable information to manage chaos and uncertainty, especially in the B2B sector. However, data needs to pass through the data crunching process before companies can use it to improve sales. Typically, data crunching involves cleaning, reformatting, and structuring raw data. Earlier, data crunching had been a manual process that involved preparing Excel Sheets, maintaining huge customer data, identifying the right customers, calling them, etc. This method was time-consuming, often erroneous, and expensive.
Presently, technology has automated data crunching processes enabling sales executives to derive information more accurately, quickly, and cost-effectively. It includes removing unnecessary information and translating data into the required format so that relevant applications can process it effectively. It is only after the thorough completion of these processes that advanced analysis tools can use data to provide businesses with valuable insights. While almost all the surveys of B2B companies reveal increased productivity and overall business growth with the help of advanced, automated data-powered tools, only 30% of B2B marketers depend on analytics to make informed decisions.
87% of sales executives believe that data is their organization’s most underused asset. Only 38% of sales executives are confident that data can bring them sharper insights. Also, 50% of marketers admit that they don’t have adequate resources to exploit marketing analytics to its full capacity. The reason for this lower level of confidence and dependence on data-driven sales processes is that managing automated data analytics is difficult. While data is indeed improving B2B sales processes, these statistics clearly show some major challenges sales executives face in the data crunching process.
Let’s look at these challenges and their solutions to manage the data crunching process effectively.
● Inadequate Knowledge of How to Use Data
Technology has made access to data easier. But, sales executives are still falling short of how to use this access for the maximum ROIs. For example, you may have accurate data about the number of customers that open your marketing emails, watch your marketing videos, click on your banner ads or fill out your offer forms. But, many times, sales executives cannot interpret this information correctly to make the perfect sales pitch to the potential customers. This ignorance often results in missed sales opportunities.
The collaboration between the sales team and the IT team can solve this issue effectively. With the help of the IT team, sales executives can learn to interpret data fast and effectively.
● Insufficient Skilled Workforce
The use of data analytics indeed helps companies increase their productivity. But, it is difficult and challenging to analyze it. Companies need a skilled workforce to interpret marketing analytics correctly. According to Sriram Narasimhan, Head of Data, Analytics and AI at Cognizant, “As of 2021, LinkedIn is showing more than 29K job opportunities in data engineering as organizations still face a significant shortage with not enough data engineering talent in the market.”
This challenge can be addressed effectively by building integrated multi-disciplinary teams across the companies. Companies can also consider investing in upskilling their present talent with the help of tailor-made courses and various affordable tools.
● Data Overload
It is estimated that every 2 years humans are creating more data than what existed on the Earth for all of civilization. Most of this data is raw and unstructured which makes it even more complex to collect and analyze. This creates difficulty for companies to get actionable insights that fuel their overall business growth. No wonder why 53% of marketers believe that “you can never have too much data on your marketing analytics management.”
The most effective solution to deal with this challenge of data overload is clarity of purpose. Companies need to have solid clarity about what they want to know from the data, and why. This would save them from collecting unwanted data and keep them more focused and productive.
● Low-Quality Data
According to a Forrester study, although 78% of marketers understand the importance of a data-centric marketing strategy, 70% of them acknowledge that their data is poor and inconsistent. An example of such data includes duplicate contact details of a customer. Many times, sales executives send emails to the same customer based on various versions of his/her contact details. Such errors happen as data stored in a structured database is often incomplete, inconsistent, or outdated. Such a small instance of data error costs a fortune to the company when hundreds of such duplicate emails are sent to the customers. Such erroneous data creates difficulties in targeting the right customers and making informed sales decisions.
Companies can leverage many advanced softwares and solutions to better manage the quality of formatted data. However, the real solution to this challenge lies in developing a mindset and a culture across the enterprise that considers data as a valuable asset of the company.
● Inability to Predict Market Trends
One of the most important purposes of data crunching is to predict the ever-changing customer needs and market trends. Even if companies have enough resources for marketing analytics this is a very challenging task as the market is always in a state of flux. If the advanced data-powered tools and skilled teams are not able to predict future trends, it will be difficult to prepare an overall sales strategy that helps increase close more deals.
You can leverage ML and AI-powered tools that automate data crunching so that you can predict market trends seamlessly and prepare an effective sales strategy accordingly. 83% of decision-makers reported that the early use of AI made a significant or moderate difference to their business.
● Heightened Human Biases
Any complicated data analysis would ultimately need human inference. It is nearly impossible for human inferences to be bias-free. At times, decision-makers tend to depend on the data that validates their perceptions. This makes them avoid the data that may be more beneficial to the overall business strategy. Such human biases prevent objective thought processes during data crunching.
Being aware of your own biases and not always acting on them are the most dependable solutions to this challenge. You can take the help of an objective individual to evaluate your perceptions and judgments around data.
Using data analytics to make well-informed business decisions is not a myth. It is a reality with its unique challenges and pitfalls. Companies often underestimate the challenges of data crunching in preparing their data analytics strategy for effective sales. This underestimation often results in poor performance and unnecessary business losses. Knowing these challenges and their possible solutions beforehand is the key to surviving and thriving in this reality.