At GrowthIntel, the better our product is, the less of it we provide to our clients and the more our clients are pleased. This is very unusual. In the classical economics of supply and demand curves, volume scales with value. If you want more potatoes, you’ll be able to feed yourself for longer but you have to pay more money. If you want more of your staff to be able to use Microsoft Office, you pay more money.
But with GrowthIntel, the better our system is at predicting who will respond positively to our client’s marketing approach, the fewer approaches they must make to generate the same revenue.
GrowthIntel represents a new kind of prediction economy, where value scales with the ‘smartness’ of a machine-learning system. Just as companies put more value on fewer staff with better brains, they will value machines for delivering better information, not just more data.
Of course, most of our clients want to keep growing and growing so they end up using GrowthIntel more and more, investing similar amounts in their marketing approaches and getting improved results. GrowthIntel allows marketers to predict the results of new campaigns, so they frequently have the confidence to invest more and more in marketing and expand their market share.
But all other things being equal, the better GrowthIntel is at predicting sales, the less waste there is in the sales and marketing process, the fewer recommendations the system gives that do not result in a sale, so the more money our clients make.
Initially this apparent inversion in traditional economics caused us some confusion when it came to setting the price of GrowthIntel.
All traditional competitors are ‘database’ or ‘list’ providers. They charge by the number of lines of company records (‘data’) they provide to their clients. The lead scoring companies charge in the same way as the marketing automation companies: they price by the number of ‘lead’ records that are in the client’s CRM or marketing automation system. This implies the value to the client is in the data itself — but we don’t believe it is. Worse, it encourages our competitors to provide a greater number of poor quality company records. This is the worst of all worlds for clients because they end up spending more and more for worse results. We’ve seen so many marketing managers paralysed by uncertainty: they can’t grow the marketing-contributed pipeline pro-actively because of the unpredictable (but usually abysmal) performance of marketing data.
Our clients see value in our product when they make opportunities for their sales teams or garner insights to learn more about their clients or prospects than they did previously. Learning about your prospects or clients lets you serve them better and the ultimate result is a greater number of opportunities.
So we charge by the number of opportunities our clients generate. Every time a client makes an opportunity, they feed this back into our system to improve its predictive power and we charge them a small amount of money. Eventually, most clients opt to pre-pay for a certain number of opportunities per year to give them price stability.
We find this correlates very strongly to the value they generate from the software. We have the number of opportunities generated for our clients on a big screen in the middle of the office. It represents the number of times our system has correctly predicted that an opportunity would be generated and a client has followed the recommendation.
Everyone, from the developers to the sales team, are aligned around making more opportunities for our clients. They can all affect this in one way or another: the developers by making the system better at prediction, the sales team by getting more clients to try the system out, and customer success by making sure our clients are not facing any blockers in using the system.
It’s a great way to visualise the effect we’re having on our clients as a whole in tangible terms. Provided we check that the opportunities generated with GrowthIntel convert to sales at a favourable rate versus those from other sources (this ratio is usually greatly improved), it’s a good proxy for the amount of money they’re making from us.
People looking at buying GrowthIntel, as you can imagine, are keen on this method of pricing. We live and die by the true value we deliver to our clients. It has given us a lot more clarity in every aspect of our business.
I can’t think of too many other examples of companies pricing to match the new prediction economy. If you can think of some, I’d love to hear about them.