The ability to make decisions based on predictions is becoming an essential attribute for B2B executives. Predictions come with a host of implications and the ability to assess their impact is perhaps the toughest part of strategy planning. One approach that has been used by executives to inform strategy planning is that of scenario modeling. It has been used both in business and in military exercises for several decades. What matters in today’s B2B climate is having the ability to predict multiple scenarios and more importantly – multiple buying scenarios that will shape the organization’s marketing, sales, product, content, and social strategies.
Predictive analytics and modeling has mostly been about exactly what the name implies – an exercise in using analytics to predict and model different scenarios. There are three profound changes occurring in predictive analytics and modeling that is being driven by the impact of the buyer increasingly self-directing purchase decisions:
- Putting buyers at the center of predictive modeling
- Emphasis on modeling buyer behavior
- Bringing a qualitative interface to the quantitative analytics
Some leading B2B executives are proceeding with a level of qualitative buyer research that allows them to understand current buying scenarios as well as behaviors to create predictive buyer scenario models. Using the nuances of each buyer scenario modeled to create specific as well as variations of marketing, sales, social, product, and content strategies that help them to attain key objectives related to growth. One reason B2B executives are turning to integrating qualitative buyer scenario models into predictive analytics is that it allows them to view real world business challenges at an insightful level. This approach gives them an all important interface to existing analytics as well as guiding what analytics to get predictive about in the future.
A case in point could be that quantitative predictive analytics can help predict the types of IT servers needed, what is the average quantity purchased, quantify search behaviors, and how IT servers are being purchased. Buyer scenario models bring the real world insight that will help to predict under what scenarios IT servers are needed, what problems usually surface that causes the need, buyer behaviors during search and decision-making, and why it is important. Integrating the quantitative and the qualitative allows B2B executives to then predict multiple buyer scenarios that reflect real world problems and also represent real-time growth opportunities.
How Can B2B Leaders Make Predictive Buyer Modeling An Important Part Of Strategy?
Recommended for YouWebcast: Sales and Marketing Alignment: 7 Steps To Implement Effective Sales Enablement
For some B2B organizations, jumping into full blown predictive analytics can be an expensive proposition. One key benefit of qualitative predictive buyer modeling is that it can be done less expensively and it also helps to identify where predictive analytics is needed. Here are a few ways B2B leaders can consider predictive buyer modeling and the use of buyer scenario models:
Input – I was in the business information and intelligence industry for a good portion of my career and one tenet that is still true today is that good output is driven by good input. In this case, good input is represented by qualitative research and interviewing efforts that help to identify important behavioral data and insight elements.
Multiple Scenarios – In today’s business climate, the number of possible buying scenarios continues to increase. And they are touching more parts of the organization than ever before. Building buyer scenario models for strategies related to marketing, sales, content, social, and service can be extremely valuable for a C-Suite team in planning.
End-to End – The emphasis should be on understanding the full spectrum of the End-to-End Buyer Experience. Even in quantitative predictive analytics, this point is often overlooked. Buyer behavior is often shaped not only by pre-sale experiences but by post-sale experiences – with bad post-sales experiences having a detrimental impact on future sales.
Implications Analysis – Predictive buyer modeling should be designed to enable B2B leaders with the ability to understand the implications that different buyer scenario models will have on their business. This should include some “what if” modeling around how buyers may respond to different approaches and strategies.
People Involvement – Predictive buyer modeling should not be for the chosen few. It should involve as many people from affected areas as possible. Buyer scenario models enable teams to look at real world challenges and literally play a game of understanding how strategies and tactics can change the game in the real world itself.
Integrate Analytics – Predictive analytics can indicate areas that can benefit from further illumination. In those cases where further illumination is needed, predictive buyer modeling and buyer scenario models can get to the story behind the numbers. In the reverse, buyer scenario models can introduce a new story and predictive analytics can get to the numbers behind the story.
Recently, I witnessed a group of executives use predictive buyer modeling. What became evident to me was how the process opened up the mind to alternative possibilities. Additionally, by putting the buyer at the center of predictive modeling, assumptions as well as implications were easier to assess because the focus was on how buyers would respond. This type of process sparks the creativity needed to look at real world business challenges and think in new ways to reinvigorate as well as sustain a business.
Predictive buyer modeling and buyer scenario models can show B2B executives a new path towards making customer-centric and buyer-centric planning a reality. Enabling a promising future for how B2B organizations can reinvent strategizing and planning – and when doing so, they do so with the buyer at the center.