As sales becomes increasingly digital, the question arises: What tasks can sales managers focus on in the future thanks to modern data analysis software?
Data, data, data. The modern “currency” in companies is also changing the tasks in sales.
It is common for sales managers to know that they should use their customer data effectively due to the competitive pressure emanating from the megatrend of digitalization. But how?
Buzzwords, such as Big Data, are haunting corporate hallways. The effective use of customer data is not trivial – precisely because customer data is not trivial!
That is where modern, digital solutions based on AI can help, enabling sales managers to create comprehensive analyses of customer data and forecasts for probable customer behaviour.
In turn, this leaves more time for strategic sales work so that you can drive the actual core business forward.
Sales managers can therefore concentrate on the following five tasks in the future.
1. Reading and interpreting data.
Digital “helpers”, such as solutions for predictive analytics, support sales in analyzing and evaluating customer data.
Therefore, sales staff can focus on reading this data from relevant sources to gain in-depth insight into what is happening with customers.
Exciting insights into possible sales potential, churn risks or even pricing options emerge.
That enables a future-oriented interpretation of the data: Sales can forecast certain developments and take preventive measures instead of just reacting.
2. Recognizing and evaluating data correlations and drawing conclusions from them
Modern predictive sales analytics solutions make it possible to pick out individual pieces of data from a sea of customer data and establish certain relationships, correlations between them.
A correlation measures the strength of a statistical relationship between two variables.
A positive correlation means that the more variable A, the more variable B, and vice versa.
A negative correlation means: the more variable A, the less variable B or vice versa.
Correlations between data or defined KPIs provide sales with essential indications of where it may make sense to adjust or change the sales strategy.
With digital solutions for data analysis, sales managers can recognize data correlations and subsequently draw certain conclusions from them for future sales planning and sales management.
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3. Making strategic sales decisions.
Sales management and sales planning are core elements of strategic sales. Sales strategy means a long-term, planned design of the sales function within a marketing plan framework.
That involves, for example, the selection and structure of target customers, the definition of the type of customer relationships, a definition of the targeted competitive advantages or the definition of sales channels and the sales process.
Suppose predictive analytics software can establish specific correlations between different data and use them to make forecasts for likely events.
In that case, sales managers can focus on strategic sales tasks. For example, they can use these correlations and probabilities to set specific sales targets, such as a particular product’s revenue goal.
However, they should take into account not to confuse a specific forecast result with a sales target.
That can correspond to the forecast value but does not necessarily have to.
4. Prioritization of actions and customers.
Digital solutions for predicting customer behaviour, such as churn risks or cross- and up-selling potential, give sales managers a deep look into customers’ “inner workings”.
This inside comprises many individual pieces of data that lie dormant in ERP and CRM systems, waiting to be analyzed. Now, of course, customers don’t always behave the same way. Some buy more, others less. Some remain loyal to a company for years, while others quickly leave.
Therefore, sales managers are busy using customer analysis to gain valuable and necessary insights into the company’s most important asset.
The customer value, which answers how much a customer, or a group of customers contributes to value creation, is derived from the customer analysis.
This qualification considers both monetary factors, such as sales, and non-monetary ones, such as significant reference customers or customers who contribute to essential improvements in processes through their complaints.
From customer value determination, sales managers can then prioritize both their customers and corresponding sales actions.
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5. Targeting customers in the customer journey.
Most companies claim that the customer is king. This phrase is quickly formulated and then appears in large letters on the company website’s homepage.
It expresses that the company primarily considers its customers’ wishes and needs. But how do you recognize the desires and needs of customers? What options are there for tracking down the wants, needs and preferences of customers?
The instrument of the customer journey, i.e. the “itinerary” of a customer, helps this. It helps to understand these wishes, needs and preferences by linking all the touchpoints of a (potential) customer with the company.
The customer journey (CJ) begins with the first product-, brand- or company-contact. And the CJ extends to the intended action, for example, the purchase of a product.
Sales can also use the Customer Journey to pick up customers at the right “stop” in their journey. According to the sales funnel model, a customer leaves the sphere of marketing at a particular stage and dives into the sphere of sales – in other words: they are ready to buy. A contact, a lead, becomes a customer.
Some leads still need to be developed further, while others are “ripe” for purchase at an earlier stage. Digital solutions for lead scoring, for example, help sales managers decide which potential customers to focus on and how intensively.
Based on machine learning, lead scoring enables the early targeting of leads with the highest closing chances, thus shortening sales cycles and increasing revenue.