How and why you need to tame predictive analysis?

predictive-analyticsIntelligent assistant market has seen an incredible action in recent weeks. In a bid to take on Amazon’s Alexa, Google announced Google Assistant and associated devices. At Ignite 2016, Microsoft touted to develop a new and improved Cortana, Einstein was launched by Salesforce and Viv was bought by Samsung. From customer service to marketing,home to car and from factory to community these Al-driven enhancements are becoming ubiquitous.

 

One thing in common – all these enhancements deliver results using predictions to help you. Predictive analysis like data science had literally over-hyped the existing scenario and this is the reason why.

 

Siri 1 Allo

 

Allo is the iPhone incarnation of Google’s Assistant. I told Allo my daughter’s name — which it acknowledged — and then asked “who is my daughter?”. Allo had no idea. I then told Siri, and stated to Siri “My daughter is Misha”. To which Siri replied “OK… but I already know that”. The above said example illustrates that Al is intriguing but at the same time it is also pretty spotty.
CMO’s are scared about Einstein in this regard. They thought that Einstein being spotty is sure to and shivers up the spine. The research conducted by Accenture and CSO Insights reveals the following – 59% of global sales executives conclude that they have access to sales tools aplenty which effectively leaves them bombarded with too much disaggregated customer data to be effective. Sales tools are an obstacle to selling say another 55%.

 

Having a tame environment has in fact provided Einstein and Siri a good boost, strikingly different from the rush to hire 100 scientists and deploy predictive analysis “somewhere” in the business.

 

The last mile — predictive analysis in context

 

The Internet of Things will notify the customer support person about a failing internet device. Based on CRM, Einstein will have the best solution and other operational data which involves information about the device and the model’s history. The initiated action will follow-up soon after the person approves the action.

 

The outcomes and the contexts have to be limited whilst also fit patterns of use cases, whether they be support or marketing or advertising or customer engagement.

 

Making a prediction remains to be seen the hardest part than finding scientists to employ and employees have to begin to trust these predictions.

 

With that in mind, here are the 3 big questions of predictive analysis

 

1. What question do we want answered? What is the use case and how does it integrate with current systems and align with business outcomes?

 

2. What can we do when we have that answer? If we knew the answer do we even have the resources, skills, culture, capacity, will, knowledge and system to be able to act upon it?

 

3. How do we influence the actions in the way we desire even when we know that we can act. We’re willing and able, but can we actually produce a result?

 

 

Be First to Comment

Leave a Reply

Your email address will not be published. Required fields are marked *