Black Friday is what happens when the dangerous mixture of anxiety and excitement are blended in a retail test tube. Although we might think that we know what is going to happen, the result is almost never as predictable as we would like it to be; with the risk that it might all blow up always a possibility. Each year in the build-up to the day, both consumers and suppliers deal with the fact that things can go horribly wrong.
For the consumer it is the fear of overspending, of losing out on a deal or of purchasing the wrong item from the wrong seller. For suppliers it is the fear that there won’t be a return on the considerable marketing budget, that the buyers will go elsewhere or that something will fall over, and will cause considerable reputational damage.
There is hardly a supplier who sleeps easily and who is without concern in the weeks leading up to the day of Black Friday. The attempt this year to spread the event over a few weeks might provide some respite but there is no moving away from the day itself. What has undoubtedly further complicated it further this year is COVID-19, and the very obvious impact that the pandemic has had on consumer behaviour. This is been exacerbated by the limited and contradictory information they might have received in that regard.
These factors include:
- The knowledge that most shopping will be moved online to avoid crowds.
- Many potential consumers have lost jobs and might have less disposable income.
- Yet others, who have remained employed have not been able to go on holiday, will have spent little on entertainment, and therefore might have more liquidity in prior years.
What this means is that retailers need to be prepared for all eventualities. According to Senior Cloud and Dev-ops engineer at Synthesis, Jonathan Sidney, there are ways that companies can win at Black Friday.
He has used the 2nd Way of DevOps – Fast and continuous feedback – as a guide:
- Black Friday gets chaotic and operations teams risk being overloaded by the amount of data and metrics that monitoring systems can generate.
Companies need to Isolate and define which metrics relate directly to customer experience and monitor those more carefully.
For example, if customers start complaining about slow response times on certain webpages, the seller needs to be able to isolate what it causing this slow down. It is not good enough to just see that the systems are under stress – knowledge of the cause of that strain and how it affects your end users becomes critical.
- Companies need to already know how the system reacts to unexpected loads.
It is advisable to run smoke tests and load tests of each environment. It is especially easy when using Infrastructure as Code and the cloud to scale up development environments to the same level of production.
Then, using tools such as Bees with Machine Guns and JMeter to test where the system could face bottlenecks. This will allow scale of services intelligently (as well as pre-emptively fix any possible issues).
- Finally, metrics are only useful when they are actually used.
If they are just being used to satisfy some external requirement, then they are not worth anything. Teams need to be able to see the metrics and respond to outages/performance issues based on these metrics.
Enable your teams by giving them access to front-end tools such as Grafana and Kibana and let them respond intelligently and scientifically to the Black Friday load.
There are also simple methods that consumers might consider in order to maximise the day:
- Add items to your cart ahead of time in order to not miss out.
- Sign up early for Newsletters in order to become knowledgeable.
- Shop online, not instore to avoid crowds.
- Stay connected via social media as many retailers and e-tailers give away prizes and additional discounts.
What is clear is that for both consumers and suppliers, Black Friday is a high-risk high reward day. In order to reduce the risk it is critical to be as prepared as possible so that when the excitement and anticipation meet, the result is a happy customer and a successful seller.