What’s Your Ideal B2B Buyer Group Size ?

March 16, 2022, 0 Comments

What’s Your Ideal B2B Buyer Group Size?

And how many buyer personas should you include in it?

Decoding the B2B buying journey is tough. Firstly, it isn’t linear. Then, there is a committee of decision makers or a buyer group contributing to a purchase decision. Combine these with the change in buying dynamics brought about by digital buying behavior and you’ve a real challenge on your hands.

Over the course of the buying cycle, customers are increasingly spending more time assessing potential solutions on digital channels. On an average, any customer spends just 17% of their actual buying time meeting potential solution providers, and 45% of it on independent research. And though this might seem to suggest an increase in difficulty for sellers, it’s an equal struggle on both sides of the market.

So, B2B sellers are more than ever required to engage better with buyers to understand their pain points and aspirations. This means engagement with accounts at a deeper and more personalized level with buyers.

Determine your Buyer Group for seamless Account-Based Engagement

Marketing strategies of most B2B companies are centered around certain approaches that are widely believed to make account engagements succesful. Talk to any marketers and you’ll find them touching upon topics like enriching contact data, prioritizing accounts, personalization and engagement tactics. Very rarely will you find them talking about determining buyer group size or the number of buyer personas.

Often, buyer group sizes and personas are generalized across industries. Say, you are targeting a fixed buyer group of 6 contacts for all your target accounts in the enterprise segment. You are assuming a certain combination of personas that will contribute to a purchase decision for all enterprise accounts across all industries. B2B buying decisions aren’t this simple. 

Different accounts will have their own way of going about things. As an example, what if you are pitching a martech solution to an account where there is no CMO. And there’s just one senior marketing manager with the ability to influence a decision on your solution. If you are targeting only C-level folks in this account, you will lose the key piece of the puzzle. This means you’ll be running around in circles without a deeper look into the account’s data to find out how many contacts and personas to actually target. So, don’t you think buying group sizes and stakeholder personas are critical ingredients for running a successful engagement?

For every buyer’s group puzzle, weave a pattern to solve it

Knowing your buyer group and understanding what the stakeholders are collectively looking to achieve through the potential solution will help you build a clear strategy for seamless account engagement. An added advantage of engaging with all the stakeholders is brand recall. Say, you’re targeting an account with four stakeholders in the buying group. If you successfully engage with all of them, your brand might come up in their conversations. When you convert this account into an opportunity, you’ll get a headstart with all four decision makers familiar with your brand. On the contrary, failing to engage with the entire buying group in the early stages of the buying journey means more convincing at the meeting table.

So, buyer group determination is key in B2B selling. But driving the buying process are unique personas, each of them looking to meet a certain requirement with the solution. The needs of these personas must be successfully addressed to win an account.

However, buying group sizes differ across industries, market segments and other account attributes. And this difference in buying group sizes leads to a change in the number of personas too. It can be practically impossible to find the exact number of contacts in a buyer group and the buyer personas involved in a decision. But can we, at least, find a pattern amongst similar accounts? At BambooBox, we believe that B2B revenue teams need to start looking at their historical data to visualize definite patterns on buyer group sizes, and the personas that drive the buying journey. Read on to know why we say this.

Data everywhere but not an insight to apply

Most B2B organizations have massive amounts of data sitting in their marketing and sales platforms. However, these companies rarely extract insights from their data. At BambooBox, we believe historical data is a goldmine of valuable insights about the buyer. While working with our customers on their pipeline-build motions, we realized the importance of having a deep understanding of the buyer groups and thereby a need to employ a data-driven system to determine buyer group sizes and personas. How did we learn this?

We tried to study the correlation between buying group sizes and deal decisions for several customers. These studies were done on a combined population size of around 20000+ accounts engaged over a period of two years by these customers. These accounts were spread across different market segments, industries, geographies and other account attributes. The ultimate aim of our research was to gain a better understanding of the buyer group sizes of target accounts that resulted in a decision or dormancy. To study these large datasets, we employed Machine Learning models.

Our hypothesis across all these studies: the number of contacts within an account’s buying group varies for different industries, market segments and other account attributes. We wanted to draw clear insights about the contact depth. So, for every study we split the total number of accounts into four market segments – Small, Medium, Large and Super Large (>25k employees). Each set of accounts from our customers studied using Machine Learning models resulted in different graphs representing certain patterns. Below is a representation of one such study which would help us understand the exercise we went about for our customers.

Fig: The number of contacts to engage with to win an account increases as the size of the organization increases.

Some critical data points from this study on how to get a clear decision and eliminate deal dormancy are:

  1. While selling to small and medium businesses, you need to engage at least two contacts.
  2. While targeting a large account, you need to engage a buying group with 5 stakeholders.
  3. For super large accounts, the buying group should have a minimum of 9 stakeholders.
  4. Small business buying groups can have any type of personas.
  5. Both middle and large accounts will have a minimum of two personas.
  6. For super large accounts, you need to engage at least 3 different personas.

From this series of ML modeling, we were able to identify a pattern developing between account attributes and buyer group sizes (and personas). Our customers needed to engage with a larger buyer group and more buyer personas as the account sizes became larger. However, we’re not concluding that there will be an increase in the number of contacts within the buyer group for an increase in organization size. We cannot neglect the various other factors that contribute to a decision. But the clear insight is that there is a change in buyer group sizes and number of personas depending on account attributes. With data science and ML modeling, such critical insights can be harvested from all the data coming into your platforms in streams.

Drive the buyer journey with data-driven buyer group determination

Engaging at the account level means orchestrating the entire buyer group. This, in turn, means a closer look at the personas that you need to effectively engage to help the buying group reach a decision. There are definite combinations of multiple personas steering the buying boat. It is this combination that account based marketers look to perfect for their go to market. By determining the buyer group size and the number of personas to engage you will set yourself up for the kind of personalization that any account-based orchestration demands. This is why B2B revenue teams shouldn’t let their data sit idle. They should make their data work for them. Extract clear insights about the buyer group size and personas by employing better data science and machine learning methods to weave a pattern for ABE success.

Meghana
Data Scientist, BambooBox