Let’s dig into something we all want to avoid – being left behind. In business, the term churn describes situations when someone quits a certain relationship you had. It’s like you’ve been going to a certain hairdresser for years, and one day decided to start going to a different one. It’s not cheating like some of us think – it’s churning. Another situation is when you have decided to stop going to your hairdresser. Period. You just decided to let your hair grow forever. Then, you are a drop-out, which is a special type of churn. And, even though it does not hurt your hairdresser’s feelings as much, it does hurt her wallet equally in both cases, and early signals can be similar. Let’s dive deeper into four categories of churn, and look at the examples of triggers that your algorithms need to watch out for to be able to prevent churn.
1. Customer churn
Here, by customer, we normally mean consumer, but this can also be generalised to businesses. Typical cases include quitting a streaming service subscription (videos, books, music), switching to a different bank, choosing a different grocery chain, changing a gym (or simply stopping going to the gym) or giving up your favourite fashion brand. For all these businesses it’s equally important to detect your intention of abandoning them early (ultimately, earlier than you have detected it yourself) and do something about it to keep you as a paying customer. Triggers of this type of churn are normally customer complaints, decreased frequency of service usage or simply unfair conditions.
2. Employee churn
Onboarding of a new employee is an investment and after you have invested in someone you want to keep that person close to you. In some spheres, employee attrition is huge. For example, annual employee turnover at McDonalds is almost 44%. Annual employee turnover in hotels is about striking 73%. Automation of repetitive tasks helps these percentages in the long run. For tasks that you want to be executed by humans, you need to take care of these humans. Ask your AI to keep track of certain triggers such as conflicts, company mergers, re-organisations and personal shocks and make sure to act proactively.
3. Drop-outs from education
Universities want the students to finalise their education and get their diploma. It is important for the society. Students want to party. UK universities have 6% drop-out rate on average. London Metropolitan University has 18.6% dropout rate (source). All universities take measures to decrease the drop-out rate and to help the students to get through with their education. And they do have plenty of data – grades, attendance records, group dynamics, team constellations – for the AI to analyse and prevent the drop-outs early by, for example, setting aside extra resources to help the students with their education. Another interesting factor to take into account is the influential users – watch out for them in all the scenarios, because if they decide to churn they will trigger many others.
4. Drop-outs from medical treatments
Patient drop-outs is another costly and unwanted case of churn. A study shows that among patients aged ≥60 years attending the walk-in clinic, over 28% dropped-out from treatment. The most common reason for dropout is “no relief” of symptoms, closely followed by complete relief of symptoms, according to another study. “No progress”-related drop-outs can also be seen among companies offering help with losing weight. Again, for everyone’s best, we need to catch them early and make sure they stick to their treatment/diet for a while to see the progress. And sometimes dropping-out of a healthy diet actually correlates with quitting the Netflix subscription and dropping out of university – maybe the person just dropped-in at a new job?