It’s funny, as I began writing this post about predictive analytics, machine learning and artificial intelligence (AI), it made me think of a movie I watched recently on BBC 2.
The movie was the 1968 sci-fi classic ‘2001: A Space Odyssey’ by Stanley Kubrick.
One of the main themes of the movie centres around the spaceship’s main computer – HAL 9000. ‘HAL’, who controls all the ships operations, is a sentient computer which uses AI to perform functions more accurately and efficiently than a human. However, HAL also expresses real emotions, such as fear, which nearly ends in complete disaster for the crew members.
View our graphic to learn more about AI and where machine learning and predictive analytics sit within this field of study.
It’s now 2017 and it would be fair to say we’re a little behind where the movie may have envisioned how AI would be used. Nonetheless, we’re not far off.
In many people’s cases, AI is now a part of their daily lives, even if we don’t know it. We’ve seen huge advancements in autonomous vehicles, personal AI assistants such as Alexa and Siri, as well as predictive recommendations around purchasing, music and tv streaming services.
Personally, it feels like a watershed moment. AI computers have now passed the Turing Test – which means a machine’s ability to exhibit intelligent behaviour is equivalent to, or indistinguishable from, that of a human.
We are also hearing and reading a lot more about AI, machine learning and predictive analytics in the press, on social media and in business journals.
SMB’s and large enterprises are also at a crossroads. With digital transformation a hot topic and more and more customer data being collected and stored, companies with legacy systems containing heterogeneous and disparate data can have a real fear of being left behind by agile, forward-looking companies.
When you hear or read about machine learning, perhaps predictive analytics isn’t the first thing that jumps to mind. Machine Learning means that computers are, more or less, able to learn without being specifically programmed.
How does this work?
The simple answer – Algorithms. Algorithms are the backbone of machine learning and form the basis for prediction or clustering models. In this post, we’re solely focusing on prediction models – we’ll come back clustering in a later post.
Predictive analytics, a subset of machine learning, has been defined as a form of advanced analytics that uses both new and historical data to forecast future activity, behaviour and trends.
TechTarget sums it up perfectly “It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models that place a numerical value, or score, on the likelihood of a particular event happening.”
Some of the top algorithms used in predictions models included:
Check out this in-depth post from Dezyre to learn more about these powerful algorithms.
AI and Machine Learning are having a significant impact in improving business processes and functions within an organisation.
Most companies are in the business of making money – winning new customers and increasing revenues.
Businesses, with large amounts of data, have a real, unique competitive advantage. This data is a priceless strategic asset. By implementing a predictive analytics solution, this data can be tapped into to help simplify organisational processes. This simplification can lead to increased productivity, improved forecasts, executive strategic alignment and so on.
Most importantly, predictive models tell you about your customers. These strategic insights are based on customer’s behaviours, purchasing decisions and buying intentions.
Which leads me back to the question – how can predictive analytics help improve an organisation’s sales performance?
In the last few years, there has been a significant shift in companies towards a customer-first experience.
For sales teams, there’s now more focus on winning and retaining customers through building deeper relationships. This means teams need to be proactive and know when, where and how to initiate contact with their customers.
It’s also fair to say customer needs have grown more sophisticated and motivations have shifted from price to value.
In a recent Salesforce report on the ‘State of Sales’, over half of sales reps reported that relationship building was their favourite activity, however, most are too bogged down by day-to-day tasks to engage with customers as much as they’d like.
The report also states that on average, sales reps spend 64% of their time on non-selling tasks. The data shows that most of an agent’s time is still spent on non-selling tasks such as manually entering data, calendaring, and account maintenance. As expectations increase around customer service, organisations without a customer-first approach can expect a higher rate of customer churn.
It is also true that most sales organisations struggle with forecasting. According to SiriusDecisions research, 79% of sales organisations miss their forecast by more than 10 percent. Predictive analytics plays a crucial role improving an organisation’s forecasts and allows management to prioritise opportunities.
The Salesforce report also stated that predictive intelligence is being used or forecast to be used in the next three years by 86% of high-performing teams. Even more interesting is that high-performing sales teams are 10.5x more likely than underperformers to experience a major positive impact on forecast accuracy when using intelligent capabilities.
Sales manager and agents are constantly under pressure to maintain a healthy pipeline and continually surpass their targets.
In many cases, reaching quarterly targets may seem like a remote possibility, but what if they had the answers to questions that would help them turn things around.
What questions would you ask? Well, as a manager or sales agent wouldn’t it be great to know:
This is where machine learning and predictive analytics really come into their own.
For example, by feeding historical data on previous deals won or lost into predictive analytics models, businesses can begin to predict which current opportunities have the highest possibility of closing successful.
Armed with strategic intelligence, not only can you target top opportunities but also improve the processes and functions of the organisation through increased efficiencies and productivity.
How can predictive analytics be used across the customer lifecycle?
We are now starting to see how AI is influencing the buying decision of B2B and B2C companies who are looking to take advantage of this exciting technology.
As highlighted above, the moment a customer embarks on the journey with a business, it can be enhanced every step of the way with predictive analytics.
From the moment a potential customer interacts with a business – by visiting a website, filling in a form, subscribing, etc. – they can be nurtured in an optimal way that will not only be efficient for the business but also profitable.
Predictive analytics gives sales managers and agents the keys to unlock the hidden insights that will help them win more deals, improve productivity and most importantly, increase revenues for their business.