Challenge

About a year ago, I took part in the construction of the promotion plan for a new  restaurant which was an undertaking of my sister — a great cook with a degree in  foodstuffs commodity science. This task was assigned to me as to a graduate in the  ﬁelds of marketing, ﬁnance and management, and, formally speaking, I was a  consultant/advisor. A really solid promotion was needed given that the budget  constraints were quite strong and the recession in the economy deﬁnitely gave no  advantage to entrepreneurs.

The main “input data" was that the restaurant  menu contained food mainly for  families with children and for female companies (very little alcohol, lots of healthy  dishes), and it was positioned as a place for healthy pastime. The expected proportion  of female customers among the adult audience was around 60-70%.

Selection

I felt that there was a need to quickly put to a test some method of guerrilla  marketing, since it is a set of low-budget and therefore affordable instruments. My  strategy was the “word of mouth” targeted at the female part of our audience. At  some moment I came up with a concrete type of how this could be realised. I asked  the team: what if the customers learned about the place from someone who they  have established trust with? The hypothesis was that, if they did, they would be likely  to come around themselves, to try more dishes and to drop in from time to time later.

I analysed different interactions that women have during a week/month’s period and  came to the conclusion that an average woman visits the hairdresser’s with a certain  regularity.  An average hairdresser serves from 6 to 9 people in a workday and has a  wide “chat network”. Why not to use hairdressers as the promotion messengers? — I  asked the team.

The ﬁrst concern was that if we paid local hairdressers to tell about us to their  customer, it would be a risk, as they wouldn’t have enough enthusiasm to recommend  us not having really been at our place. But then it dawned upon me that we could just  invite the local hairdressers to our place for a day — for free. If they liked it — and  we would do everything to ensure it — they would spread the word further to their  regular customers who they chatted with while doing the hair work. That would cost  a lot (which the team understood), but I suggested that we invited just a few  hairdressers for free under colour of an invitation to a “random person”, and then

saw the increase in the customer inﬂow. If the inﬂow was sufﬁcient, we would invite  all the hairdressers who we could cover.

Now, as I have completed the “Design Thinking for Innovation” course, I can say that  the method I suggested back then, was the “Learning Launch” — a pilot or an  experiment carried out as promptly as possible to test an idea in the market.

Application

So we did invite four, and traced the results carefully. In the ﬁrst month, almost  nothing changed. In the second month, a slight increase was observed, but it was  within the possible error value range. On the third month, however, we got +4-5%  customers, which showed that the method chosen had worked well. We got assured in  the efﬁciency of the Learning Launch that we had undertaken.

In two weeks after our three-month experiment, we held a free-meal day for the  hairdressers of our district and a few from other ones. There were around 15 that  time, and it turned out later that their average rate of customer attraction and  attraction period didn’t differ much, so the experiment had been very appropriate and  illustrative.

Insight

The experiment showed me that, in order to reach out to your customer, you can use  the “hack” of knowing them really well — in our case, you should learn what their  needs and interactions are (the need to keep well-groomed and the interaction with  the hairdresser, correspondingly).

Approach

I am happy to say I’m contented with the experiment made; in my future enterprises,  projects and other endeavours I will continue acting promptly to test my hypotheses.  The only thing I am eager to improve is the speed of transition from experiment  termination to the full implementation. Last time it took two weeks to my team and  me, and I hope that in the coming projects this time period will be reduced due to  improved collaboration within the team and smoother communication with the  “messengers”.

