"Alexa, will it rain today." A 50% chance.
"Alexa, play billboard top 10." Ok.
"Alexa, order chocolate hazelnut ice cream." Ordered, with an extra scoop as last time."
Alexa, what will be the revenue next month." - A little shy of a hundred thousand dollars.
No, I won't be talking voice assistants. Instead, through this post, I am opening up a discussion for businesses to employ predictive technologies, with the objective of predicting revenue and growth better.
The question is, How?
But, let us start with the "Why"?
I am writing in the context of B2B marketing and sales teams. But, it could apply to all, including B2C.
Customer acquisition sucks
It is (lot of) hard work to acquire one customer. You just need to be with a sales guy towards the end of the month, to truly understand the effort.
Repeated calling. Then follow ups. Emails. SMS. Meetings. Proposal. Negotiations. Deal !!!
A customer takes effort.
It happens often that marketing and sales teams end up spending a fair amount of time on a deal 'hoping' it would close. Someone (possibly in sales) said, 'Hope is not a good strategy'.
Single digit closure rates for leads are common and accepted as normal. Marketing and sales teams accept and believe that they must wade through enough muck to reach their goals. It is understandable. Unfortunately, it is a big waste. And needs to be called out. Because, waste isn't just in terms of time lost. There are more reasons.
- Sales time is expensive. It is better spent chasing quality deals than just unqualified leads.
- Unworthy leads cost money - directly and indirectly.
- Sales teams become tired and unenthusiastic working through difficult leads. This impacts their enthusiasm and energy when dealing with 'hot leads' thus compromising deal flow.
- False Positives - These leads create ambiguity. This ambiguity impacts good decision-making negatively.
The above reasons are subjective. To make them objective, there is a need to continuously collect data (lots of data), analyse and make inferences. That is where predictive marketing and sales comes in.
Predictive Marketing / Sales to the rescue
Predictive marketing and sales technologies collect data points from numerous variables continuously. These data points are tallied with those from existing customers. Over a period of time, this mapping is able to tell businesses which leads are better placed to close into customers. It isn't 'Yes' and 'No' to begin, but the goal is.
Imagine. A lead comes in. Enters the CRM. The sales team looks it up. The predictive technology layer calls out 'Yes'. Everybody wins.
Imagine the savings in time and effort this could bring in. I can already see my sales friends shed 'tears of joy'. This will also give sales teams more time to deal with 'hot leads' and for hunting.
Collecting high quality data is critical to the success of this predictive technology intervention. An inbound marketing strategy backed by a marketing automation software / CRM stack enables data gathering. Data can be active (demographic, firmographic) and passive (e.g. website behaviour in terms of pages viewed, forms filled).
Reality is very different.
It is only recently that most businesses have taken initiatives in inbound marketing. Legacy data still dominates. And that data is dirty, very dirty.
"Alexa, play some sad songs."
