Challenges Using Predictive Analytics for Cross-Selling in B2B: A Deep Dive for Experts
Say goodbye to inconsistent data and missed opportunities. With the right data and strategy, your B2B sales team can master cross-selling, driving higher revenue and customer loyalty.
Cross-selling is one of the few areas in which B2B sales strategies cannot operate without predictive analytics. Using past data for the analysis, along with the application of machine learning (ML) techniques, enables sales teams to define customers’ needs and, therefore, opens the door to higher revenues.
However, To achieve the maximum potential of the predictive analytics application in B2B cross-selling, it is important to identify the challenges that the idea of cross-selling involves. This article attempts to decode the process that B2B sales personnel and data analysts have to go through to get the right cross-selling solutions.
1. Technical Hurdles
Cross-selling is one of the best strategies that can be used in B2B to generate large amounts of revenue. However, to get maximum benefits, several issues need to be addressed. Described are the challenges and how they can be addressed to make cross-sellers wiser and more efficient.
1.1 Data
Hurdle
Dirty data insights invariably lead to inconsistent data that is broken, and a lack of data can negatively impact a model. Suppose you have built a house on sand; your cross-sell recommendations will be in the same category as the house: unstable.
Mitigation
A complex challenge: data consolidation and data cleaning from multiple B2B systems like ERP, CRM, MAP, etc., are complex tasks as all these systems are in different formats to be integrated.
1.2 Segmentation
Hurdle
It is important to note that there will always be some issues when classifying prospects or clients based on the size of the company or the industry in B2B. Purchasing decisions are not only initiated by end-users but also require the approval of various other people at the top of the hierarchy.
Mitigation
Unlike conventional demographic data, it is distinguished by the fact that “firmographic” data allows you to consider the organizational and procurement characteristics of a firm, so there are more detailed customer profiles. This enables them to procure cross-sell recommendations that will be of interest to specific buying centers.
1.3 Model Bias
Hurdle
The bias in the recommendation system trained from past sales data can only recommend a specific segment of customers. This can hamper efficient cross-selling to the entire clientele base.
Mitigation
A whole new approach that’s called the ‘explainable AI’ or ‘XAI’ technique. When the thinking of your model is broken down to you, one can uncover assumptions and, thus, eliminate prejudice, which will lead to more trust from the customers.
2. Strategic Considerations
One of the most promising strategies that can be adopted is to increase sales to your current customers, which is also known as cross-selling. But to achieve this potential, organizations must adopt a different approach that transcends the traditional functional structure and traditional tools and techniques.
2.1 Alignment for Impact
● Collaboration is key. The sales strategies that are used to support the structures created by data scientists are only as formidable as the predictive models they are built on. It is also important for communication and understanding of the cross-sell goals to be presented and updated among the sales, marketing, and data science departments to make sure that the model predictions are aligned well with the actual sales strategies.
● Clear Communication Channels: Effective communication channels, where ideas can be exchanged freely, create a constructive atmosphere. This enables the sales teams to give feedback on the effectiveness of models and allows the data scientists to improve the models for suitable sales situations.
2.2 Empowering Your Sales Force
● Addressing Resistance: The transition to data-driven cross-selling is likely to face resistance from the sales departments that are used to traditional approaches. Address these issues and stress the fact that the use of models is simply a help rather than a replacement. Stress the fact that the tools or platforms help to better comprehend the client’s needs and achieve higher win rates.
● User Adoption Strategies: Ensure that, as the leaders or sales managers, you incorporate the use of extensive training sessions for the salespeople. Show them how to use models in practice, including how to apply or interpret them, and how to use the results to uncover new possibilities in the customer base.
2.3 Measuring what matters
● Beyond Basic Metrics: The focus on clicks or leads achieved is not sufficient as it provides limited insight. To achieve B2B cross-selling, it is crucial to monitor KPIs that have a direct impact on your company’s profitability. When it comes to cross-selling opportunities, it might be useful to focus on the average order value, customer lifetime value, and win rates.
By focusing on the above-mentioned strategic factors, sales directors or managers can foster an innovative culture of utilizing cross-selling not only for your organization’s sales force but also for achieving long-term B2B revenue growth.
3. The Winning Formula: Data and Strategy for Cross-Selling Success
It goes without saying that in the current age of B2B relationships, data is the most valuable commodity. However, the key to unlocking that power is the ability to apply data analysis with purpose.
Cross-selling is a key business activity enabled by predictive analytics, as it allows you to foresee the needs of your customers, but it is your tactical or strategic planning that helps turn this vision into a reality. When cross-selling is approached comprehensively, which is both data-driven and strategic, the possibilities for success are endless. This winning formula helps guarantee that you are selling the right products and services at the right time, which in turn helps optimize customers’ lifetime value and advance your business.
