The 6 key principles for selling a Big Data product
Big Data is in the middle of the hype… Everybody talks (a lot) about that, yet only a few end-up creating a Big Data product and even fewer of them get to sell it to potential customers.
This post encompases the 6 key principles you should be following to prove the value your Big Data product can add to your beloved customers:
“A demo is worth 1000 words – A playable alpha is worth 1000 demos”
Stop talking and start showing! Stop the we could –packaged in boring I-start-yawning power point slides- and show the we do. Move beyond the scope of the demo creating a minimum viable product and invite your customers-to-be to play with… It will let your users to turn into co-designers of your product, which is a very powerful idea to sell –“you are telling me, that you are not going to purchase the product you helped to shape?” Beware of the smoke test trap! A potential customer might accept a feature to be buggy if the proper expectation has been set, but you don’t want to take her through a set of teasers that are just place holders for functionality to come… the deploy first, code later paradigm might work for conventional web environments, but when it comes to something as abstract as big-data, you really need to offer more.
“My best guess”
You want your Big Data product to be perceived as a hard-headed scientific evidence based highly accurate data product. No matter if your analytics just deliver insights about the raw data, you use a procedure to infer intelligence or you offer predictive capabilities you will face the accuracy question… be prepared to answer it: you might use terms like statistical significance or confidence interval, but make sure you can explain it to an audience with very vague acquired in the primary school statistical knowledge and more important, make sure you know what you are talking about in case a good statistician is around. Still you will need to convince with one output of your tool which is credible, and I mean credible and consistent regarding to a known fact or empiric experience your audience is familiar with.
For example: Let’s say you created a big data product to forecast the demand of a particular product in a particular area. For that you measure the social media activity –frequency, recency, favorable sentiments-, correlated with the TV advertising for this product –GRPs, etc- and the increase of the cost-per-click in Google AdWords for the group of 10 keywords related to this particular product. If your customer does not see any increase in this product’s sales you can be statistically right, but you are not going to prove the value of your product.
“Don’t let the sleeping dogs lie”
Many of the things that are possible now have been so often considered to be impossible –in terms of time-to-results, financial feasibility or level of specialization required- that passed to the catalogue of questions that remain unaddressed and parked for the business decision making. These dogs are not gone away… they might be sleeping, but they are still there! Questions like “Can we decide the pricing strategy for our next product based on how the social media community reacted to the last 3 product launches –own and from the competition-” seemed to be utopian a few years ago, but now are part of the insights big-data can bring to your business. Obviously, if your customer has been making decisions for many years without having this kind of insights, they might have forgotten how valuable they are or how much they need them!
“Magicians do not exist”
If you have watched the master piece of the French director Sylvain Chomet “L’illusionist” you surely remember the note the magician left for the young lady. Your customers-to-be know it as well… so make sure you can explain your “magic” in a Mickey Mouse language: how the data are gathered, combined, processed… how the analytics are produced… how do you get your outcomes…etc. It’s not required to hold a machine learning class, yet the better your customer understands it the more prone to see the value your big data product provides
“Take my breath away”
Exactly! Breathtaking!! Get everybody wowed, open-mouthed… forget that you are selling a data product, you are selling an experience! Move them from the “Well I’m actually curious” to the “let me play with – can I get a demo login?”
What we are talking about here is the so called joy of use. Which can only be achieved by your obsesive attention for the detail and your strive to make your product Apple-easy and Google-fast
“My story, your metrics”
The most popular blog entry of Stephen Wolfram was a story, the story of his life told with charts and statistics instead of words. Take a few metrics and fill them with life –as Karajan would say-.Your story speaks to your customers’ emotions, whereas the metrics keep their right brain hemisphere satisfied… so why not combining both? Just keep in mind, that the story, if properly told, is going to remain in your customers’ head, whereas the metrics are most likely to be blured as the time goes by
“Show me the money”
Big Data is increasingly perceived as Big Revenues, yet creating a rigorous business case might be time costly. There’s almost nothing there you can leverage for, as Big Data is still in its infancy. Instead of creating a really exhaustive business case, just take a concrete problem and explain the great benefit you can add with your Big Data product. Just a few examples: “If you had just a dollar to invest in, where would you put it?”, “If you had to close a store, which one would it be?”, “If you had to focus only on three products…”, “If you had select the best place of the city to advertise…”
Does this stuff here sound familiar to you? Well… It should 🙂
