Sales Forecasting: Science or Instinct?
Recently, we had talked about marketing automation, a digital transformation in the field that involved both artificial intelligence and machine learning. Even Natural Language Processing (NLP) has come a long way, enough to change the content marketing game. But while marketing has shifted to a digital ecosystem, sales as a function remains largely people-centric.
With the advent of e-commerce, it is likely to change in the foreseeable future. Then again, the more complicated the product, the more people-centric a sale becomes. It’s largely due to buyer sophistication, which enabled consumers to become more informed about their need and product utility.
In a way, to survive, sales required the allied support of marketing – now more than ever before. The time for marketing and sales to work in silos are long past, and should such a division persist, experts conclude the eventuality of organizational failure.
But sales as a function isn’t entirely straightforward – there are those that acquire customers, those that nurture them, and others better suited to sales research among many others. Another critical function of an organization’s sales ecosystem is forecasting.
Digital Transformation in Forecasting: Good for Sales?
A good sales forecast is essential to business growth, but it’s historically relied on the human element. Yes, emotions and hunches could make or break an organization’s quarter. However, progressive companies have begun using big data and artificial intelligence to pervade this aspect of sales. At the same time, while several may infer this as a threat to their own jobs, forecasting succeeds only as a combination of both artificial and human intelligence.
How can we do this?
- Honest Pipelines: If you’re in the sales ecosystem, you know what a pipeline is. Too often though, deals remain hidden under the radar or opportunities aren’t pounced on early enough. Ranking how likely sales opportunities are won is an important component to automating your forecast. How does human intelligence factor in? Well, you don’t need an expert in AI to identify hidden opportunities that might lead to forecasting errors. Ownership within the sales department is key here. To over-promise or under-deliver a potential is a detriment to any organization, which is why both data and operations require constant attention. Visibility is the hallmark of both accountability and predictability.
- Commit to the Technology: Big disruptions are bound to become commonplace after a specific period of time. At one point, cloud computing shook the market at its very foundation. Now, organizations can’t imagine life without it. A similar process is in the works with machine learning and AI. These technologies are reshaping how we leverage and learn from data, by really digging into the depths of its importance. It enables us to create hyper-personalized customer experiences as well.In terms of forecasting, AI is destined to transform organizational interaction with data from and for sales. It’s also unbiased and brutally honest; without emotion and definitely unequivocal in its results. What you get from AI is the hard, cold data truth. But for it to learn faster, it needs you, your guidance, and input. This is necessary for AI to amplify your data. Organizations don’t feel attached enough to the technology claiming it to rank as a branch of poor investments, but AI requires patience. It needs time to learn about your business. Let it grow. Coach it. And best of all, remember that it never forgets.
- Justify the Hunch: Potential deals stem from sales executives and their hunches about how a deal is expected to play out. This can go one of two ways – spot on or far off. Human emotions are important in forecasting, but technology enables organizations to process it differently. Instead of merely running on instinct, forecasting must also contain variables that can be logically explained and replicated. Several AI solutions also incorporate the feeling or emotional aspect of a sales executive into its learning or processing methodologies; these are then measured and benchmarked with real values as they occur. It’s complimentary and accelerates the learning process.
- Take a Leap of Faith: A new approach to forecasting might come across as a challenging endeavour, perhaps even unnecessary. It’s human tendency to stay rooted in your ways. But there will come a time when change is necessary, leaving you with a chance at success or market demolition. Learning from mistakes and accepting change is how organizations have become better, at least from history.
How Do You View Forecasting?
Without logic backed by science, forecasting often falls into becoming either overly optimistic or drastically pessimistic. Both scenarios impact company growth. AI enables a certain rigor and discipline to sales forecasting, using nothing more than data and facts to reach a conclusion.
But to put this perspective to an end – a correct prediction is great but being able to explain the logic behind the same is even better. So, how should we treat forecasting? For what it is – a science.
Jay Nair – Chief Operating Officer, Marlabs Inc.
As the COO, Jay has played an important role in accelerating the transformation of Marlabs into a digital services and solutions provider. He spearheaded the Digital360 initiative, which offers a complete suite of digital services across industries. Jay’s broad and varied business experience and skills helped Marlabs incubate NexGen technologies that provide outstanding business value. He also played an important role in transforming the company from a small group of 15 to more than 2,300 employees globally, growing into a $100 million company.