Sales Performance: Big Data, Analytics, and a Culture of Transparency (3/3)

What steps should be taken to optimize the exploitable capabilities of Big Data for strategic performance purposes?

Today, more and more companies, possessing a gigantic amount of data from multiple internal and external sources (e.g., social networks, online transactions, interactions), are wondering what means to adopt to optimize the exploitable capabilities of this database to serve their sales performance.

Furthermore, with the rise of revolutionary new data analysis technologies, such as artificial intelligence, sales professions have drastically evolved. In this context, the salesperson’s challenges are far from over: Matthew King even estimates that 95% of sales professionals will be replaced by lines of code by 2036.
A question arises: how to steer your commercial enterprise towards a “Big Data vision” while motivating your sales force to achieve their performance goals?

A “Big Data Vision” Step by Step

The implementation strategies for Big Data at the heart of organizations are varied and can lead to significant impacts in terms of transformation and organization.
However, across sectors, it is estimated that less than half of a company’s structured data is actively used in decision-making, and less than 1% of unstructured data is analyzed and utilized. Indeed, analysts spend an average of 80% of their time discovering and organizing data. Pure data analysis for profit purposes is therefore largely minimized.

Consequently, developing a comprehensive data processing strategy, accompanying the implementation of tools for data management and analytics, is essential for a company’s analytical capabilities to reach a profitable level. Thus, effective data processing with analysis can fully contribute to strategic decision-making and will inevitably become a source of sales performance.

To this end, Harvard Business Review proposes a two-pronged strategy: “offense” and “defense”.

Data Strategy: Offense versus Defense

Different objectives:

• “Defense” seeks to minimize the risks of performance slowdown. The operations employed comply with regulations, using data analysis to limit fraud. This strategy guarantees the integrity of data from internal and external sources: client data protection is paramount here. This strategy can be defined as operational. At Procter & Gamble, for example, it is characterized by a permanent IT system focused on data management and information security.
• “Offense” seeks to support the company’s objectives such as revenue growth, profitability, and customer satisfaction. Activities that improve customer knowledge (e.g., data analysis and modeling) and integrate customer and market data to support managerial decisions. This strategy involves partnering with other business leaders based on tactical and strategic initiatives.

It goes without saying that optimizing one’s sales strategy involves finding a balance between offense and defense. However, sales professionals operating in a relatively unregulated environment, where intense competition requires more robust consumer data analysis, should focus on a more offensive data strategy. Indeed, sales directors prefer to collaborate with business leaders such as Salesforce and Windows and thus optimize commercial and marketing services rather than relying on data management.

Technological Advancements: What Implications for Sales Professionals?

Sales professionals spend, on average, 14% of their time in professional appointments or handling administrative tasks; 16% managing client accounts; 22% researching sales opportunities; and 12% in training. Thus, only 35% of their time is spent selling . However, with the development of AI and digital technology, the sales profession is transforming. So much so that, according to Gartner, 85% of customer interactions will no longer require human intervention by 2020.

Facing a radical transformation of businesses by Big Data, how can sales professionals benefit from technological advancements instead of suffering from them?

• Leverage more powerful tools: use new analysis techniques developed directly through artificial intelligence (AI) to save time. For example, it is estimated that sales professionals will now spend 70% of their time face-to-face with their clients, instead of 35%, thanks to new technologies that facilitate, among other things, the implementation of the sales process.
• Personalize your coach: in the future, AI will guide sales professionals in their actions thanks to its precise and rapid data analysis capabilities. AI can also serve as a coach for the salesperson during client meetings with information such as: “attention, you are talking a bit too much”.
• Develop your CRM: AI brings transparency to the salesperson’s actions, which will strengthen their client relationships

Conclusion

In this context of cultural, organizational and strategic transition for commercial enterprises, it is reasonable to estimate that a prosperous sales force will thrive, not only within companies that introduce new technologies accompanying the Big Data era, but also those that know how to closely align them with their marketing strategy, performance evaluation, compensation system, and career management.

Other articles that might interest you

• Sales Performance: Big Data, Analytics, and a Culture of Transparency (1/3)
• Sales Performance: Big Data, Analytics, and a Culture of Transparency (2/3)