B2B Sales & Marketing Has a Data Problem, And It’s Only Going To Get Worse
Unlike many B2C companies who have been able to use customer and transactional data to transform their sales and marketing efforts, B2B companies are behind….way behind. Not only are the vast majority of B2B companies laggards when it comes to leveraging their data to drive growth, the data they have is hindering their ability to effectively acquire, grow, and retain customers. And the increased availability of AI and machine learning technologies that can help companies optimize sales and marketing performance requires not only better data, but more of it.
Looking at the root causes
There is no easy way to fix the problem because there are several factors that are causing it in the first place.
Data decay: Companies by their very nature are in a constant state of change. They’re adding new employees, losing employees, introducing new products, making acquisitions, the list goes on. The data you have in sitting in your CRM today will be obsolete within several months if not several weeks.
Inaccurate and incomplete records: While much of this can be attributed to data decay, incomplete and inaccurate company or contact records can also frequently be tied back to low quality 3rd party data or simply negligence on the part of the internal resources who are logging data.
Lack of sound data governance: Most sales and marketing teams have little to no data governance and stewardship in place. You could argue that this is the root cause of a lot of the issues around inaccurate and incomplete records
Lack of historical data: Sales and marketing teams are not tracking enough historical data on their interactions with prospects and customers. When and why was an opportunity lost? What steps were completed to convert a prospect to a sales qualified lead or a customer? What interactions took place prior to a customer churning? These and many other data points when rigorously tracked and logged become immensely valuable and provide insights into how to optimize performance. But the problem is most B2B companies don’t have this information, the right mechanisms to track it, and they almost certainly don’t have it in a structured format.
Why it’s going to get worse from here
AI and machine learning technologies are beginning to create tremendous opportunities for sales and marketing teams to improve their ability to acquire, grow, and retain customers. The challenge is that in order to be effective many of these technologies need data. Not only do they need data, but they need a lot of data and the data must be accurate and complete.
This demand for increased volumes of data places an even bigger burden on B2B sales and marketing teams. The companies that have the foresight to make the necessary investments to tackle these data challenges will create a strong competitive advantage.
Building a path to better data
In order to repair the data issues that are hampering B2B sales and marketing teams and put them on a path to leveraging data to drive growth, a lot must happen. It requires a systematic and phased approach and even more importantly a shift in mindset away from viewing data as an administrative burden to that of a valuable tool for growth.
At a high level this path consists of several steps;
1. Get complete organizational buy-in from sales, marketing, and customer support leaders.
2. Develop a blueprint as well as data governance rules and procedures.
3. Re-evaluate 3rd party data providers and internal processes to ensure data quality.
4. Implement the necessary changes to systems used to manage sales, marketing, and customer support to achieve easier tracking of interactions with prospects and customers. (CRM, marketing automation, sales engagement platforms, etc…)
5. Begin tracking events on interactions with prospects and customers.
Before an organization can even begin to evaluate using their sales and marketing data to accelerate growth, it’s imperative that a solid foundation for ensuring data quality is put in place.
Turning a problem into a growth opportunity
B2C companies are unquestionably the leaders when it comes to leveraging their data to drive growth. Much of this can be attributed to the fact that they are typically dealing with larger volumes of customer and transactional data, making the development of advanced analytics models easier. Although not on quite the same scale, there still remains an immense potential for B2B companies to follow a similar path. And while B2B companies have been laggards when it comes to implementing a robust data and advanced analytics strategy those who have already begun to embrace data as a growth mechanism are establishing a competitive advantage.
With enough historical data in place across a strong customer base B2B companies can start using data science to;
• Identify target accounts that have a higher likelihood of purchasing.
• Understand what customers are at risk of churning.
• Deliver greater personalization and relevance through advanced customer segments.
• Predict customer lifetime value.
• Develop data-driven ideal customer profiles.
• Optimize pricing across different solutions and market segments.
• Improve their ability to expand revenue with existing customers by making better cross sell and upsell recommendations.
• More effectively allocate budget and resources to the right accounts and activities.
These insights can help B2B sales and marketing teams; improve the quantity and quality of leads, increase opportunity win rates, realize a higher average customer lifetime value, and reduce churn. And on an even more advanced level this data can be used to guide product development and improve the end user experience.
It’s very apparent that B2B sales and marketing teams are facing a data problem and the demand to fuel AI and machine learning technologies with even more data will further exacerbate this. But the visionary B2B companies who understand data can be their most valuable growth asset and make addressing these issues a priority will not only grow faster they’ll leave their competition behind.