The digital crystal ball: Predicting the next move forward for sales intelligence

In a letter to French physicist Jean-Baptiste Le Roy, in 1789, Benjamin Franklin wrote (of the new American states): “Our new Constitution is now established and has an appearance that promises permanency; but in this world nothing can be said to be certain, except death and taxes.”

But in a modern context, it would be safe to paraphrase Franklin and update his assertion to “nothing can be said to be certain, except death, taxes and the advancement of artificial intelligence”.

AI allows us such a panoptic view of modern Big Data business analytics, which is often referred to as “business intelligence”, that such BI can now be further quantified into subdivisions.

Sales intelligence, creditworthiness, competitive edge – all these things can be finitely analyzed by technology to assess exactly which companies should be high on any supplier’s hit list.

Any organization offering robotics or automation to any other company might wish to take a look at the way sales intelligence is altering the thrust of marketing outreach; a technique that was unavailable a decade ago.

This is simply because Customer Relationship Management systems (CRMs) and CPQ (Configure, Price & Quote) platforms didn’t have the analytical power that they do today.

Let’s take a quick look at how sales intelligence can be driven and assisted by CPQ to create competitive quotes by technology companies for their existing and prospective customers.

First off, what exactly is sales intelligence? Gartner sums it up nicely: “Sales intelligence is the information that salespeople use to make informed decisions in the selling cycle. It includes the tools, techniques and practices that facilitate data collection, tracking and analysis. Sales intelligence provides data insights into customer prospects and leads.”

So in short, by understanding data-driven insights into prospective and existing customers, sales intelligence can assist sales managers and their teams to create more effective strategies for canvasing prospects and creating quotes.

But ensuring that quotes are accurate and profitable enough to be worth sending, while remaining competitive enough to be attractive, is a finely balanced process that becomes ever more nuanced in a growingly competitive world. That’s where CPQ software comes in.

A simple definition of CPQ software is that it ensures absolutely every relevant variable is considered when sending a quote to a new customer.

Imagine that Robotics firm Acme Robots is approached by a car manufacturer, Cars Inc, to supply robot arms that will weld floor panels onto the chassis of their 4 x 4 SUV models on the production line.

Acme will list all the necessary component parts of the robot arm that they intend to offer the customer. Then they’ll calculate an assembly time for each robot arm, a shipping cost, an average profit markup, then produce a quote of several thousand dollars per unit; they’ll no doubt feed that price into their CRM software and the purchasing team at Cars Inc will receive the quote.

But unbeknown to the engineers at Acme, a new welding technique has been introduced by Cars Inc, which requires a linkage with a higher heat resistance than the one currently supplied.

“No problem”, say Acme, “we’ll just add on the price of the new unit and re-quote”. But there is a further complication because the new heat resistant material is thicker, so it won’t fit into the robot arm’s existing bolt-on spindle.

Now the spindle has to be re-tooled… So continues the ripple effect of all these changes, making the new quotation ever more complex and time consuming to produce.

But if Acme had used a CPQ platform, they would benefit from its predictive AI driven rules-based architecture, that considers every part’s compatibility with all other related parts in the whole.

This is achieved by the CPQ software being set up at the outset with an entry for each component; listing the cost, size, weight, heat resistance and so on – and crucially, within milliseconds, if a part needs to be replaced with a substitute, the CPQ will check that this doesn’t require the adaption or replacement of any other associated parts.

The end result is a quote that is accurate the first time, as opposed to the shabby process of quoting the customer three or four times with amendments.

A price can be produced with the certainty that relevant costs, shipping, labor and taxes are considered. The CPQ software then inputs the quote into the CRM, and sales teams send out those quotes to customers from there.

This is now where improved sales intelligence data has given our company its second advantage. The analytics of revenue potential via sales intelligence procedures has already calculated that Cars Inc are selling their 4 x 4 SUV model at a very low profit margin as a loss-leader, and furthermore that the vehicles are only manufactured for export.

Armed with that information, Acme robotics can fine tune their quote to Cars Inc at a lower profit margin than usual – as in turn, their own loss-leader keeps their primary customer happy.

In the final analysis, knowledge is power, and only by using software that accurately and granularly assesses what the customer needs, allied with a platform that can create hyper-accurate quotes, does any company truly control their go-to-market strategy.

Because sales intelligence platforms can assess a customer’s activities, along with those of their closest competitors, suppliers can produce a quote that will be favorable.

Not least, other factors such as the genesis of machine vision, fluctuation of raw material prices like lithium for batteries, silicon for chips and a myriad of other market factors can enable informed decisions based on real-time insights.

As always, the devil is in the detail, and accurate sales intelligence makes sure those details are nailed down tight.