You'd think that with all the technology available to businesses these days that organizations would be able to make accurate quotes to ensure that they didn't price themselves out of the market or quote so cheaply that they would make insufficient profit.
But still, many SMEs and enterprises don't use CPQ software, which could potentially increase their quote success rate whilst ensuring high accuracy of any tenders they put forward. Aside from getting prices too low or high, there's also the danger of sending inaccurate figures, then having to eat humble pie in front of a new customer if something gets left out when considering all necessary factors when pitching for new business.
What is CPQ, exactly?
CPQ stands for Configure Price Quote. A CPQ platform is a SaaS (Software as a Service) technology ecosystem that uses Artificial Intelligence (AI) to ensure that all the interrelationships between relevant factors are carefully considered and cross-checked against each other before any quote is produced for a service or product. You'd think that this would be easy enough to do by using a simple spreadsheet and checklist system - so why would any business need a complex software platform to add up a few figures into a total before a salesperson worked out a final price for the article or service on offer, then send that price to the potential customer?
If a small business was doing something uncomplicated such as car valeting or window cleaning, it's true that a CPQ platform wouldn't be needed. All a window cleaner needs to work out how much to charge a new customer is how long it might take to clean X number of windows, the time to travel to the area, the cost of detergent, sponges, chamois leather and the like, and whether hot water is provided by the customer from their kitchen tap - or does the window cleaner have to carry a tank of water in the back of the van? If material and consumable costs are X per day, and the trader needs to make Y turnover in order to make a fair profit, then he should simply charge the customer (X plus Y) / average number of windows in a day (+ profit margin) = final price per window. It's transparently obvious.
When one problem leads to another
However, the more complex any business process, clearly, the more factors there are to consider when producing a quote for any product or service.
A manufacturer of car spares, for example, might be approached by a car maker to supply many thousands of catalytic converters for a given model of vehicle. The manufacturer might already produce very similar units for the car maker's competitor, or indeed might already supply the same car company with exhaust systems for a different model of the same marque.
When things get complicated, it's because that particular car might be available in petrol, hybrid and diesel models respectively, perhaps in right hand and left-hand drive versions for different parts of the world. Various levels of permitted emissions might be required through different diesel particulate filters, depending upon whether the car is to be sold in the USA, Europe or say, the Far East.
At this level of complexity, there will always be several hundred inter-related factors to consider when pricing up the cost of producing thousands of units to a given specification. Will all the parts for the various specs be compliant across the board? Will different fitments be needed? Will the units need to be shipped to different countries for assembly in various geographical markets?
Decision trees
All these factors above need to be considered in context with their various inter-relationships, because putting one foot wrong might mean quoting inaccurately for new business and finding that the intended customer turns to the quoter's competitor for exhaust parts - maybe because someone in the sales team made a stupid mistake in not accounting for an extra bolt here or there.
CPQ software avoids such hazards by using a decision-tree rules-based architecture, together with machine learning to ascertain the exact costs in question when the list of parts required to make a given unit are considered. At the outset, the AI is programmed with a set of rules. For example, an algorithm might alert it to the fact that muffler A isn't compliant with car model B but can be adapted to fit model C with only the addition of a slightly bigger gasket (D). Trying to manually figure out, even using financial spreadsheets, the accurate costs for adapting muffler A to car model B would take a team of engineers several days. Then the material costs and labor time would be sent to accounts for them to add shipping expenses and export taxation levies etc.
But a CPQ software platform does all this in a matter of seconds, using its AI to consider all these interrelated issues from one easy initial programming stage.
Of course, the benefits aren't limited to manufacturing companies; sectors like insurance, energy and utilities, Medicare, communications technology and law enforcement all use Big Data -all such organizations and many more can benefit from CPQ even if it is only to assess accurate costs; the 'quote' part doesn't necessarily have to be produced.
Once the CPQ model has calculated all costs and processes required, it outputs figures and required project stages onto an easy-to-understand dashboard, so that various teams can collaborate and quickly agree a final price to send to the potential customer as a quote. Once that is done, the CPQ platform then integrates those necessary figures into the company's Customer Relationship Management (CRM) platform, and the final price is emailed to the prospective customer in a template form, which allows them to simply add their electronic signature and the deal is sealed.
There is a marvelous book written in the 1970s by Robert M Pirsig. It is about the philosophy of problem solving within the context of motorcycle mechanics, entitled 'Zen and the Art of Motorcycle Maintenance'. One quote about complex projects states:
"You see, we just have to keep going until we find out what's wrong or find out why we don't know what's wrong..."
If only Mr. Pirsig had CPQ in the 1970s, he'd know that such hard work has been consigned to history. But then perhaps a literary masterpiece would never have been written.
* This is a contributed article and this content does not necessarily represent the views of sciencetimes.com
