Touting for new business is never as simple as your potential client thinks it is, because the trick on your part is to make it look like everything is completely straightforward, when little could be further from the truth.
Just because an expert might make something look simple, doesn’t mean that it’s easy. There are a myriad of factors to consider when quoting, or even estimating, for a business proposal.
From the simplest job like a car valeting service to manufacturing truck parts, the devil is in the detail.
If you don’t take into account absolutely everything that affects the quote you give to the client, you could end up working for next to nothing or pricing yourself out of the running.
The Complexity Of Interdependencies
Giving a price quotation to a client or potential new customer can be fraught with problems if you don’t fully consider all the interdependencies of whatever service or product you’re pitching.
Let’s take the truck parts example. Your prospect, say Truckparts Inc, wants a quote for 5000 towbars. They intend to fit them to a variety of Ford, Daihatsu and other vehicles.
The fittings aren’t universal, so the mounting holes must be drilled in different places for different units. The Ford also has an extra electrical hook-up for caravan trailers.
If you just quote for 5000 towbars and take a stab at an average guesstimate of manufacturing cost, you could well find that your profit margin is so low that you’re effectively supplying the Ford units for free. Or you quote so high that your prospect flatly refuses your business.
That’s a relatively simple scenario. Imagine now if you’re asked to quote for airplane parts with dozens of interdependencies for safety and regulatory legislation across the air transportation organizations of several different countries.
Now you’re really going to strike out big time if you get it wrong. Say hello to Configure, Price, Quote – known as CPQ Software.
CPQ uses Artificial Intelligence (AI) to scrutinize the hundreds or more interdependencies that interrelate when quoting for complex products or services.
Some investment in time is needed at the outset. AI uses machine learning to draw upon a series of rules-based logical Yes/No binary decisions.
All the potentially impinging factors therefore need to be added as rules into the CPQ software before first using it. But once that exercise is performed one single time, the rules apply for any product or service that you might wish to quote for.
Naturally, a user dashboard allows managers to delete, edit or add rules at any time, as you would expect in any dynamic market sector.
Configuration Engines
The AI draws upon ‘configuration engines’ to sift and collate all the various interdependencies, that’s the purpose of the addition of the initial rules, to fuel up that configuration engine.
In the case of truck parts, for example; if the customer asks for engine brackets for a given type of chassis, the choice of brackets may be limited, because certain engines simply won’t fit into other models’ chassis configurations.
Then, add the complication of whether a trailer facility is required on the chassis. If so, a more powerful engine is obligated, thus narrowing down the possibilities of engine types and potential fitments still further. And so it goes on.
Conversely, if the chassis is readily configurable, the quote provider may encounter the opposite problem, the prospect of ‘combinatorial explosion’, where so many solutions are possible that there are suddenly thousands upon thousands of choices.
This is where the rules-based configuration engine can alleviate the situation – by presenting a series of options with transparent manufacturing stages and clear pricing options.
Of course, a CPQ platform should not be the only weapon in your armory. Any Customer Relationship Management (CRM) system that you choose needs to be compatible with other SaaS cloud platforms, in order to transfer data from CRM to CPQ and also to accounting software seamlessly, without excessive manual entries.
The Importance Of Marketing
Just to make the picture a bit more complex, let’s not forget that you wouldn’t need a CRM, CPQ or a SaaS platform, nor even a business premises if you didn’t perform any marketing activities, because you wouldn’t have any customers in the first place!
It’s crucial that your marketing outreach is accurate and reflects your pricing structure. So if the cost of an engine bracket for a Ford Thumper truck is, say, $10 retail, your CPQ, CRM and any marketing activity needs to show that you’re all in the same ballpark.
A social media post on a Facebook Truck Mechanics’ group proclaiming that your Thumper engine brackets are only $99 a piece is going to put you out of business faster than you can say ‘forget it, buddy!’
Careful Handling And Interpretation Of Data
There’s a final piece to the jigsaw too. So you’ve got your marketing, your CRM, your CPQ software up to date, you’re ready to quote the next lucrative contract that comes in.
You kickstart your AI-driven configuration engine, brush up the rules dashboard, hit a few keys and a quote comes straight onto your desktop.
You email it to your prospect, who returns a polite message within half an hour “Sorry, your competitors are all quoting around half that price…”
How on earth did that happen? Consider this, the data that you fed into the configuration engine, was it correct in the first place? Maybe you made the classic typo error and told the CPQ that the cost of a Ford Thumper engine bracket was $100 instead of $10. So it’s the accuracy of data that’s absolutely crucial; the first thing you should be checking.
Big organizations can be surprisingly lax about verifying these things. They just assume that whoever entered data into the silos has kept it up to date and got it right from day one.
It’s easy if you’re running a Mom & Pop store, you shout upstairs “How much do we sell those boxes of free range eggs for, Mildred?”
“A dollar 99 per dozen, sweetie…” Job done. Try the same exercise with Thumper brackets and a manufacturing company having 10 branches across as many states.
So, you can see how data handling, maintenance, interpretation and accuracy is absolutely crucial. Let’s leave the last word to mobile phone manufacturing giant Ericsson’s head of data enablement, Sonia Boije.
She explains here how her company is making full and effective use of data by their disciplined and innovative techniques. It’s an interesting read.
In conclusion, sales teams and business managers would do well to remember – only when you have all the pieces of the jigsaw can you successfully complete the puzzle.