Artificial Intelligence Example to Rock Sales Controlling in B2B
Machine learning (ML) and artificial intelligence (AI) hold the key to a tremendous improvement in sales controlling efficiency. A software is intelligent if it can solve problems independently and efficiently.
The degree of intelligence depends on the level of autonomy, the extent of complexity of the problem, and the efficiency of the problem-solving process. In artificial intelligence terms, researchers speak of “strong” versus “weak” AI – “general” and “narrow” AI, respectively.
As Enrique Dans wrote in Forbes, the world’s most valuable companies already agree that their future success crucially depends on artificial intelligence. Unfortunately, sales controllers in medium enterprises so far could not afford the implementation cost of artificial intelligence.
Here we present you, how these two worlds are falling in love with each other using an example of weak AI. We need to define first sales controlling, artificial intelligence and how well they mix.
What is Machine Learning and Weak AI?
There is General Artificial Intelligence (General AI) and Weak or Narrow Artificial Intelligence (Weak AI).
General Artificial Intelligence aims to develop machines that behave “as if they possessed intelligence”. We offer here two traditional definitions of artificial intelligence given by Elaine Rich and John McCarthy. Ms Rich, a renown British researcher in the field, presented in 1983 AI as the enabling technology to make computers do things, that people currently do better, for instance, speech recognition.
Before her, in 1955, John McCarthy declared that AI aims to develop machines that behave “as they possessed” intelligence.
The public usually associates General AI with Terminator or Hal9000: an artificial entity that could eventually replace a human with a combination of computer software and mechatronics. This technology is progressing slowly and will probably not replace a sales controller – at least in the next 50 years.
On the contrary, Machine Learning is a part of Narrow Artificial Intelligence. Narrow AI works with specific problems for which intelligent entities (mainly software) can perform better than humans.
Researchers define narrow artificial intelligence from the problem that the technology is undertaking. Examples of Narrow AI, sometimes also called “weak AI”, are computer games, speech recognition algorithms, image recognition and machine learning.
Machine learning, one exciting example of Weak AI, is the artificial generation of knowledge based on experience. What kind of experience? In our sales controlling case, experience with sales data. Expertise in sales controlling and planning requires specific data mining techniques for ERP and CRM data.
What is the role of Sales Controllers and Business Analysts in B2B?
Since researchers define Weak AI by the problem this technology is solving, we need first to agree on the role Sales Controlling in Business-to-Business plays, its primary responsibilities, and challenges.
Sales Controllers in B2B medium size organisation are usually responsible for the planning, coordination and controlling of sales. Their job is to manage and watch sales processes across their company.
Sales controllers usually work closely together with their B2B sales team to make sure they align sales plans, forecast, and quotas. They routinely confront increasing amounts of sales data; they need to set and measure critical key performance indicators, find and suggest improvements and regularly report to management.
In the last three years, the same amount of reps and sales managers need 8 % more sales controllers; 14 % in the USA.
Of course, not every B2B organisation employs a sales controller, but yes, most of them should have sales controlling activities covered, in some cases by the general management, finance management or the sales manager herself.
Sales Controlling in B2B: From data to wisdom
We often read that “data is the new gold.” However, what value can you extract from that “gold” when you cannot interpret the information it contains? Without your Sales Controller doing the interpretation job, you can measure as many as KPIs as you please, but you will not get any single answer, conclusion, call-to-action or insightful recommendation from your sales data.
Sales controllers are the intelligent entities accountable for extracting wisdom from sales data, and they are facing increasing amounts of it. Under this increased amount of data, their job is still to provide critical insights into any sales operations.
Sales Controllers importance is raising. Why are business analysts in general and sales controllers in particular critical in Business-to-Business? For several reasons. First, in B2B, where sales cycles are long and investing in a customer relationship is expensive, sales controllers ensure that B2B sales teams keep budgets and risks under control. As a consequence, in the last three years, the same amount of key account managers, sales reps and sales managers needed 8 % more sales controllers.
Considering that sales teams in B2B one of the most valuable resources of any organisation, sales controllers can make sure companies invest them where it makes the most sense. Excellent sales controllers are the magnifying glass for sales efficiency.
Also, sales controllers are regularly the middleman between the mined field of sales and the naturally sophisticated finance department. In some cases, surmounting the political and operational differences between these two agencies needs a combination of rare analytical skills and a human touch.
Lastly, sales teams in B2B are also facing an increasing amount of sales data. They need support in moving upward in a cognitive system: from data to wisdom. Sales data are just numbers, lacking per se any use or goal. After they come sales information, the first understanding of utilisation and goals that this sales data can achieve.
Third comes sales knowledge: the ability and expertise learned from experience. And last, in the pinnacle of the cognitive scale comes wisdom: an exclusively human skill to differentiate between right and wrong. Mrs Chandra and Hareendran explained this in detail in their 2014 fascinating book about artificial intelligence.
Therefore, sales controllers play a critical role in a B2B organization. From them, excellent communication skills and numerical understanding is expected. Moreover, the path from sales data to sales wisdom requires a good blend of experience and analytical skills. Here is exactly where artificial intelligence and machine learning can help.
Machine learning application example: how do Sales Controllers profit from it?
Sales teams in B2B need more support in understanding, analysing and communicating sales data.
As we wrote, the same amount of sales reps and managers needs every year more sales analysts and controllers. We conducted this research in Germany, although similar trends would not be surprising in other OECD countries. For example, in the USA, the number and salary of business analysts are increasing at double the speed, compared with different traditional roles in sales.
However, at the same time, sales controllers are confronting an increasing number of data and an ever-increasing necessity to improve sales efficiency.
Sales controlling has become much a much more complex profession, particularly in business-to-business organisations. A good controller must calculate and analyse several times per day an increased number of output, input, and advanced sales Key Performance Indicators (KPI).
Manually performing this task is an error-prone and time-consuming process, where a business analyst or sales controller only can learn by mistake, summing experience, “flying hours” and relying on her intuition.
Sales controllers are responsible for planning and controlling the sales process. Like an automatic pilot or a self-driving car technology, they can now delegate part of the analytical method to a weak artificial intelligence system. Dedicated machine learning models learn from the mathematical activity and make no mistake.
Sales controlling has become much a much more complex profession, particularly in B2B organisations.
Like any other automated systems, artificial intelligence systems can add real value when working together with sales controllers, not by replacing them, as Matt Beane from the MIT (Massachusetts Institute of Technology) explained in 2016. Dedicated Machine Learning algorithms for sales controlling can significantly improve the efficiency of the information gathering process, but cannot (yet) replace the wisdom building of a sales controller.
Therefore, artificial intelligence will provide immense benefits in sales controlling. To start taking advantage of them, focus on the authentic opportunities within your sales controlling challenges.
HOW CAN AI WORK FOR OUR B2B SALES ORGANIZATION?
Artificial Intelligence Example to Rock Sales Controlling in B2B – Conclusion:
Machine learning, an example of weak artificial intelligence, creates a fantastic opportunity for improvement in B2B sales controlling and business analytics.
Due to a reduction in implementation costs and the increasing amount of sales data, artificial intelligence has become a viable and attractive option for sales planning and controlling in B2B.
We presented in this article the differences between general artificial intelligence and narrow or weak artificial intelligence.
Taking machine learning as one possible example of weak artificial intelligence, we described how sales controllers could complement their toolkit with it.
Companies profit the most when man and machine work together. We can offer you reassurance: there was the man first, then came the computer. Now we live in a time of intelligent learning machines. Take advantage of the opportunities offered by this new technology, but keep your sales controller.
I WANT AI FOR B2B SALES
Also interesting about artificial intelligence
Artificial intelligence in B2B sales. New challenges – new opportunities.
Sales Management in times of Artificial Intelligence – Five tips to redefine B2B Sales
Dans, E. (2016) “Right Now, Artificial Intelligence Is The Only Thing That Matters: Look Around You.” Posted on Forbes on 13.07.2106
Beane, M. (2016) “Robots add real value when working with humans, not replacing them”. Posted on techcrunch on 29.05.2016
statistik.arbeitsagentur.de | Social insurance and marginally employed for the work concerned the Classification of Occupations (KldB 2010)
Chandra, V and Hareendran, A. (2014) Artificial Intelligence And Machine Learning. PHI Learning.
McCarthy, John; Minsky, Marvin; Rochester, Nathan; Shannon, Claude (1955). “A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence”. Archived from the original on 26 August 2007. Retrieved 30 August 2007.
McCarthy, John (12 November 2007). “What Is Artificial Intelligence?”
Rich, Elaine (1983). Artificial Intelligence. McGraw-Hill. ISBN 0-07-052261-8.