The remarkable truth about Predictive Sales Analytics & Controlling
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Customer Analysis: Is Predictive Sales Analytics Software a Secret Weapon in B2B?
Many B2B companies possess valuable sales data but make little use of it. Most of the relevant information sits in the ERP system and remains untapped in day-to-day operations. At the same time, many sales leaders in the B2B sector still lack either the understanding or the infrastructure needed to leverage this data effectively.
Some ignore the value of their sales data, which lies unused in their ERP systems. Even though sales controllers are tasked with collecting an ever-growing volume of sales and transaction data, sales managers often do not apply it. Data has no value unless it is transformed into action that supports revenue and profitability.
In recent years, Predictive Sales Analytics has become a proven and widely used tool. It is no longer a future concept but an established method helping companies prevent churn, strengthen cross-selling and manage margins more consistently. The foundation for this is not a new IT project but the willingness to use existing ERP data strategically. Data only creates value when it is translated into concrete actions that help sales teams set clearer priorities.
Creating Value from Sales Data with Predictive Sales Analytics Happens in Three Steps
In the first step, companies use their existing ERP sales data and, if available, CRM data. This information is sufficient to identify patterns that are critical for sales opportunities, customer churn risk or the profitability of specific segments. The central question is which commercial challenges can be addressed with the available data. Typical examples include identifying unprofitable customers, detecting declining purchasing activity or uncovering potential in specific product areas.
The second step is to define the relevant key performance indicators. The selection of these KPI depends on the company’s challenges and the questions that need to be answered. Successful sales organisations focus on a small number of precise indicators that are reviewed regularly and adapted to changing market conditions. The right KPI make underlying causes visible and provide direction. They show how active a customer is, how their margins develop over time or which potential exists compared to the actual revenue generated.
In the third step, the sales process is accelerated. This does not mean that more activities are required, but rather that existing activities become more relevant. Modern analytics solutions translate data into concrete recommendations and provide daily guidance for sales teams. As a result, companies respond earlier to risks and opportunities, use their potential more effectively and reduce unproductive effort in sales. The speed of decision making increases and the quality of those decisions improves at the same time.
How can Predictive Sales Analytics help a B2B Sales Organization? Fast and Furious.
The reality of B2B sales has changed significantly. Customers expect well-founded advice, individual pricing and seamless processes. At the same time, the volume of information continues to grow, driven by ERP data, transaction histories, pricing structures and increasingly complex assortments. While personal relationships and experience used to be sufficient, these factors alone are no longer enough. Sales teams need to understand which customers are truly valuable, where untapped potential lies and which activities actually generate results.
Predictive Analytics helps maintain this overview. It reveals which customers may be at risk of churn, which buyer groups contain hidden opportunities and which products carry the highest margin risk. As a result, sales teams can respond more quickly and work with greater focus. Time in inside and outside sales shifts toward areas with higher contribution margins, while activities with low impact are reduced. The outcome is a more structured and effective management of daily sales work, based on the actual data available.
To extract value from ERP sales data, sales leaders ask first the business questions with a higher impact on revenues. They usually depend on each company, its market challenge, and its sales force. For a complete ROI calculation, managers also consider the cost of answering each question.
Sales leaders in – almost any – B2B business can start inquiring the following:
Which customers have higher upselling or cross-selling sales potential?
Which recurrent buyers are at risk of not buying again?
In which sales plans should we focus because they have better chances of closing?
How should I diagnose how well KAMs are performing?
Sales analytics provides a framework for decision-making based on data, using business questions as a guiding line. More questions that sales leaders should consider can be found here.
Choosing the Right KPI: predictive sales analytics on the praxis
Once executives have defined the business questions and the sales data, predictive sales analytics depends on deciding what to measure: the key performance indicators (KPI). The business questions determine the KPIs.
There is, again, no simple formula to identify them. Effective sales leaders reflect on the kind of challenge their organisation is facing and the sales data available. This exercise leads to business questions answerable with the ERP and CRM data on hand.
Sales analytics in general (and predictive sales analytics in particular) emphasise the root causes of any business situation. The situation, in turn, influences what performance indicators one should consider. Some examples of possible business questions and their corresponding KPI:
| Performance Question | Predictive KPI |
| Which customers have higher upselling or cross-selling sales potential? | Unfulfilled Potential – Share-of-wallet |
| Which recurrent buyers are at risk of not buying again? | Churn-Risk / Loyalty Ratio |
| How to link activity-based goals with financial results? | Number of orders, transactions, new accounts per KAM. Penetration Ratio across customer base. |
| Which sales plans have better chances of closing? | Average Size Deal, Lifecycle, and other advanced predictive signals |
| How to diagnose how well KAMs are performing? | Ratios: Activities – Sold Per Account vs potential per KAM |
Choosing the right performance indicator is more of a craft than a science. Besides, effective sales leaders concentrate on a reduced set of KPI to avoid the risk of overloading their teams.
Experienced executives are flexible and do not set KPIs in stone. They regularly review their critical sales metrics. Markets can change in one quarter.
Lastly, KPIs are to modify the sales team’s behaviour: which customers, sales plans and sales actions to focus and prioritise. Changing sales team behaviour is a significant challenge that cannot take for long.
Sales controlling with analytics: steer clear and accelerate.
Sales controlling is effective when it leads to concrete action. Predictive Analytics accelerates decision making by revealing risks and opportunities earlier. When a customer becomes less active, this is immediately visible and can be addressed right away. When potential exists that has not yet been utilised, it is clearly highlighted and can be acted upon by the sales team. Companies improve their win rates as a result and ensure that valuable opportunities are not lost.
For many mid-sized companies, it is particularly important that modern Predictive Analytics solutions do not require extensive IT projects. They can be implemented quickly and often deliver actionable insights within just a few weeks. What truly matters is that sales teams understand that data-driven systems are not a replacement for experience, but an amplifier. They help managers and key account teams use their time more intelligently and set the right priorities.
CALCULATE NOW THE ROI OF QYMATIX PREDICTIVE SALES SOFTWARE
Sales Analytics for Success in Sales Controlling – Conclusion:
The fascinating truth about predictive sales analytics in B2B sales controlling is that although business leaders acknowledge its power, they still haven’t experienced its benefits in full.
Predictive Sales Analytics shows companies which decisions have the greatest impact on revenue, margin and overall stability. The foundation is always the ERP system, which contains all relevant sales information. When organisations define their questions clearly, select suitable performance indicators and identify risks and opportunities at an early stage, a new level of quality emerges in sales management. Decisions become faster and more precise, and sales teams can focus their activities on the customers and products that contribute the most value.
The wholesale industry is operating in an environment that is changing rapidly. Companies that actively use their data act not only faster but also more profitably. If you would like to understand the potential within your ERP data or how Predictive Analytics can be applied in your sales organisation, we are happy to support you.
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Further Read:
Link to Huffington Post – The Selling of Sales Acceleration .
Link to Computerwoche – Was ist was bei Predictive Analytics? (in German).
Link to Informationweek – Gartner Magic Quadrant Advanced Analytics: Fast Growth Continues (in German).