Buyer behavior has changed in dramatic ways over the course of the last 16 months. Seller behavior has had to adapt. How buyer behavior has changed and the best ways for sellers to respond has been a topic of much interest to sales and sales enablement leaders. Everyone touts the use of AI to stay ahead of changes in buyer behavior; however, how do we use data to understand what this means for sales enablement?
As we mentioned in our earlier blog post we must rediscover our customers and focus on selling moments that matter. How do we do that? By extracting vital customer details from our sellers. When we look at traditional AI, we know on a personal level what it is like for Amazon or Google to tailor our buying experiences based on our behavior. This approach is not as straight-forward in a B2B world. Sellers in the B2B environment deal with multiple decision makers while pursuing deals that unfold over time. Capturing the most relevant customer information is something that is beyond the realities of our current CRM systems. And the hygiene of CRM customer data is suspect at best.
So what is the alternative? How do we get the information we need to better enable our sellers to better understand our customers? Deal-level data is the key. There are important nuances in buyer behavior that are difficult to glean, richness waiting to be discovered to get a much clearer picture of how our buyers are changing. By tapping into the knowledge of our salespeople and gaining deep insights to the deals they win and lose, we can analyze that information and dramatically narrow the playing field.
If we want to better understand buyer preferences, we can gather very detailed information about the trajectory and buying factors that influence deals. In our research of over 25 buying factors, we’ve discovered that there are five categories of factors that most significantly impact the buying journey.
Clearly this is an over-simplification. Multiple factors exist within each of these categories. However, the richness of these five categories allow sales leaders to truly understand how these factors work to shape buyer preferences. By using machine learning to analyze deal level data, you can get a clear picture of which buying factors are most significant and how they cluster together to form unique buying situations your sellers face.
The graphic below shows an example of the five factors that were most significant for one of our clients, as well as how those factors cluster together to form four unique buying situations. In our research we’ve determined that each client’s sellers face between four and six unique buying situations. These buying situations even differ amongst organizations within the same industry and can even vary across business units within a company.
If we are to update our selling strategies to reflect the new priorities of our buyers, we have to understand two things. First, what are those preferences, and second, what is the best way to sell to respond to those preferences? Above, you see how buying factors align to form unique buying situations. The next step is to determine the best sales strategy for each situation to increase the chances of a win. Again, machine learning to the rescue.
Just as we can use deal level data to tease out buying preferences and buying situations, we can also identify the best and worst strategies for each of those buying situations. In our research, we’ve confirmed that top performing sellers display four sales strategies, not one. The only sellers who stick to one approach regardless of the buying situation are core performers. Agile sellers select amongst the following four sales strategies depending on the buying situation they face.
There are tactics at play within each of these four strategies that can be adjusted, adapted, and changed based on changes in deal dynamics over time. Top performing sellers simplify their approach by zeroing in on which tactics and overall strategies best fit at a given point in time. They have guidelines they follow to help them make the best decisions in the moment to align with buyer preferences and increase the chances of a win. The image below shows the four buying situations from above as well as the most optimal and least optimal sales strategies for each situation.
What’s interesting in this analysis is that the optimal strategy was the most prevalently used in only one of the four buying situations. In the first situation, the optimal strategy was the least used. In this client organization, three of the four sales strategies were at play. We know which strategies to deploy and which to avoid for each buying situation. If we want to equip our sellers to execute to win as many deals as possible, these insights reduce ambiguity and provide very specific guidance that lead to wins.
If you are interested in further information about using machine learning to better understand buyer preferences and seller behavior, watch our webinar “How Machine Learning is Changing the Game of B2B Sales”
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Read the original Accenture Article
Companies can leap ahead of the curve by building upon existing tech platforms. By infusing them with real time data that is currently available about changes in customer preferences and behavior.
More than 80% of CSOs are not confident about the adoption of the various sales technologies they have deployed.
This is why we need a salesforce app. We can continually monitor buying situations and sales strategies to determine what changes are taking place. We could do an initial diagnostic to determine buying factors and sales approaches. Then we could do periodic analysis of salesforce data to identify new patterns in buying and selling behavior.
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