The problem with Machine Learning
Machine Learning programs, like humans, learn by example in a process called Training.
This typically involves supplying the computer with numerous examples of the things we want it to learn.
And therein lies the first challenge.
If a human was being taught to recognise different shapes, visual senses would be enough to understand the characteristics of the different shapes. For example, we might first consider the number of sides a shape has and then use the angles between sides to categorise it further.
This allows our brain to make generalisations such that, if we are shown a different image of a shape that we have previously seen, we can quickly determine what it is.
Computer systems, by contrast, don’t have this ability. Instead, they need to be told which characteristics they should consider in the learning process. This manual process is called Feature Extraction.
Recognising the characteristics of shapes is a relatively straightforward exercise. But imagine how complex this process becomes for non-trivial problems.
This is where data scientists are required. They are the clever folks responsible for Feature Extraction.
Whilst not insurmountable, this approach has meant that traditional Machine Learning is limited to the dexterity of the data scientist and the quality of Feature Extraction performed.
This approach also means that, regardless of how many examples are provided to the machine, it will eventually hit a ceiling on the level of accuracy it can achieve.
Deep Learning – A more accurate AI
Although aided by increases in computing power and data storage, the same approach to Machine Learning has existed since the 1970s. Meaning that many – if not all – modern AI solutions available today still follow the traditional method.
In the last few years, however, a step-change has revolutionised Machine Learning.
Known as ‘Deep Learning’, this new approach is already outperforming traditional Machine Learning by huge margins.
Deep Learning has introduced the ability for the machine to determine for itself what features are important. Alleviating the burden of Feature Extraction.
Unlike traditional Machine Learning, Deep Learning benefits from extensive amounts of data. Learning from it and becoming more accurate over time.
Cloudapps are the first and only providers of ‘Deep Learning’ powered AI in the CRM sector.
The advantages of Deep Learning AI include:
- Accuracy – Deep Learning significantly outperforms traditional Machine Learning
- Self-learning – this approach continuously learns as your business evolves
- Future-proofed – Deep Learning will continue to see advances where traditional methods will not
- Fast to get started – traditional methods require the processing of vast volumes of data before producing results, Deep Learning does not
Deep Learning drives even greater levels of accuracy if you can feed it with time-sequenced data
Cloudapps – AI you can trust.
Trust comes from reliability.
The Cloudapps AI engine is unique in its ability to generate forecasts that are over 95% accurate.
How is it so accurate?
- It learns from rich behavioural data: More data delivers greater accuracy. We generate a rich data audit trail for every deal based on the sales behaviours applied to it.
- It learns from the deal journey: Timing is everything. The data picture we build is time-sequenced, recording not just which sales behaviour happened but crucially when.
- It uses the latest innovation: Not all AI is as smart. We use the very latest ‘Deep Learning’ approach that significantly outperforms traditional AI.
CRM systems only provide a picture of ‘what’ happened. Cloudapps provides the missing ‘why’. Our Deep Learning AI engine makes use of this insight to deliver unprecedented levels of accuracy.
Accuracy you can trust.
Claim back 60% more selling time with AI guided selling.
Recommending the next best action a rep should perform requires a deep understanding of the impact each action will have on the desired outcome.
Cloudapps have been observing and recording the behaviours performed by sales reps at many blue-chip organisations for the last ten years.
We designed a scientific approach to selling – steeped in these behavioural observations – to provide sales leaders with rich and intuitive insights that help drive their teams towards significant sales targets.
From this experience, we built our Deep Learning algorithm, which is capable of determining the impact any given behaviour will have on any opportunity, taking into account its specific and often unique set of circumstances at each point in time.
Understanding the actions that have already been performed, and the sequence that they have been undertaken in is essential for predicting which actions should happen next and the sequence in which they should be performed in order to maximise the desired outcome.
The recommendations our engine provides are not merely a list of opportune actions that would be beneficial for any generic deal, they are only suggested based on the specific requirements of each case, taking into consideration, for example, the ‘achievability’ of an action at that particular time or the intent of the prospect.
So you know the next step your reps take will always be the right one.
Achieve 95% accuracy with AI-powered sales forecasts.
There are 3 reasons our AI engine is unique in the marketplace.
Firstly, unlike other vendors in the CRM space, our algorithm uses Deep Learning – not just traditional Machine Learning.
Secondly, it has been trained to observe Opportunity related sales behaviours performed by sales reps. A granularity of insight not reached by our competitors.
And lastly, the Cloudapps engine is able to record time-stamped and sequenced data, meaning it’s able to understand not only if changes to Opportunities happened, but when and why.
All this results in the highest level of accuracy available on the market today. Our customers are attaining forecast accuracy of over 95%.