Should you believe what you hear from customer references? Are they representative users of an offering, or are they a select group who are experiencing extraordinary results?
Think of B2B customer reference representation as an iceberg. 10% of a vendor’s customers are “visible” to prospects through traditional customer referencing programs, but 90% are hidden beneath the surface — their results are unknown to the prospective customer.
Is this 10% a fair representation of the average customer? If you are an average prospect, are you more statistically likely to experience results similar to the visible 10%, or results somewhere in the distribution of the other 90%?
Let’s look at a B2C example: the polarization of Yelp reviews. Look at a restaurant’s or bar’s reviews. Notice a trend? How many 3 star reviews are there that say, “meh – it was pretty good, nothing to write home about, but solid”? Compare that with the number of “AMAZING. BEST EVER!” reviews and the “WORST PLACE EVER” reviews. You are likely to see a lot more responses at the extremes than in the middle. Why is this? You are only motivated to go through the effort of posting a yelp review if you are significantly emotionally moved to share your experience.
Does this mean your experience is likely to be either extremely positive or extremely negative? No. Your experience is more likely to lie somewhere between the extremes. There is a selection sample bias in the visible reviews, and in this case, the vendor (Uber in the example below) cannot control which ones are shown.
Now let’s look at B2B references. First, we need to approximate what the distribution of results/outcomes would be for a prospective customer. In most businesses, there is evidence to show customer results follow a statistically normal distribution (bell curve).
Now, we also know that, for a variety of reasons, only 10% of a B2B vendor’s customers are willing to be a named reference. Additionally, the other information we know is that the “visible” references are carefully culled and approved by the vendor (e.g. an unhappy customer might technically be ‘willing’ to be a reference but they would never be selected). So – when you see a reference on the website – what group of customers is that reference likely to represent? Most likely it’s the superlative performers. But if you are an average prospect, where are you likely to end up? Likely as an average customer, in the 90% with the rest of us.
This incomplete information creates uncertainty, and uncertainty with prospects causes sales cycles to stagnate. Savvy prospects in today’s world want a realistic picture of their expected value, grounded in reality and backed up with substantiated proof. It’s OK to acknowledge and be honest that not every customer will be in the top 10% of results. As long as you can give a prospect a read on their expected results and an accurate sense of variation and risk, the prospect can at least make the decision properly and in a timely manner.
So, show your prospect statistical evidence about financial and operational metrics across your customer base, and don’t worry about perfection. Provide believable and targeted customer evidence to your sales team, and prospects will be more comfortable moving to the next stage in the sales conversation.