The Fintech industry has not only been challenging the way we view our finances but how the industry uses various marketing tactics. The most successful disruptors in the financial market are known for their exponential growth with very low customer acquisition costs (CAC). While the industry doesn’t neglect traditional marketing and you’ll see quirky posters around public places they make full use of all alternative channels out there.
To understand better where the industry is going we’ll look at what has worked and what hasn’t in the recent past and we’ll evaluate what key principles you can apply to ensure the success of your next strategy for exponential growth at low CAC.
We’ll kick things off with an honorable mention – these last few years we have come to appreciate the importance of agility and the place it holds in the future of our marketing. In fact, Deloitte predicts that agility will be one of the top trends for marketers in the future, saying that it, “…encourages organizations to embrace immediate and novel ways of thinking while helping them restructure in a way that allows their brand to join conversations and moments organically.”
What does that agility look like in the marketing of fintech products over the last year?
- 84% of marketers are now using AI. Many marketing professionals have been favoring AI for its ability to quickly process data at scale and to provide customer support 24/7.
- 52% of marketers used three to four marketing channels. Those that favor multi-touch campaigns tend to report they almost always reach their financial targets.
- Marketers that diversified their acquisition channels yielded much better results regardless of ad disapprovals and algorithm changes.
- Word of mouth became a powerful acquisition channel.
Why did word-of-mouth become so popular as a customer acquisition tool?
Customer referral programs have a lot of benefits for companies and customers:
- Low cost of setup and administration. Most of these programs can be fully digital – a preferred way of communication for customers, especially after the pandemic.
- Unbeatable conversion rate. 74% of consumers cite word of mouth as a major influence over their purchasing decisions, helping to overcome the trust barrier.
- It is extremely efficient. Word of mouth is 5X more effective than paid ads because the referrer is already very familiar with both the product and the person they are referring to.
- Word of mouth is scalable. If we apply the right data analytics techniques. We will cover more on this in How To Get More Referrals With Machine Learning section.
So are there any downsides to word of mouth for customer acquisition?
There are, of course, ways in which even the best word-of-mouth acquisition campaign can go wrong.
- It can be too impersonal – we have all been on the receiving end of a box-standard message from a service that our friends are using. It not only fails to convert us as a customer but leaves us with a negative impression of the brand.
- Wrong time – timing can be extremely tricky to get right even for the best of campaigns. Some messages will be received at the wrong time either because the customer is busy, they’ve already made a decision, or simply don’t have the capacity to think about the product at this time.
- Focusing on a single channel – luckily, this is an avoidable mistake! By offering only one channel of communication you are limiting the options your customers have to reach others. Less is not more in this scenario.
A few ideas to increase the impact of your bring-a-friend program
Here are a couple of ideas to improve customer acquisition through referrals.
- Be specific – consumer psychology tells us that if you ask a specific enough question, most human brains will do their best to answer it. By telling people to recommend a specific product, they will automatically think of the best person who would benefit the most of it.
- Make it easy – make every step of the process as easy as possible. Pre-filled texts, intuitive software and to-the-point descriptions are likely to encourage up to 53% more referrals.
- Incentivize the receiver – It may seem illogical at first, but programs offering benefits for the receiver have considerably higher conversion rates compared to programs which benefit the referrer. Researchers have established that they work in two ways. First, the referring side satisfies their need for social approval and belonging. Second, providing an incentive to act to the receiver overcomes their initial barrier, be it a high price or simply the energy they need to spend to purchase the product they are referred to.
- Chose the right moment. This is a crucial element to any successful refer-a-friend program – if you are asking your customers to make an additional effort, you better be sure your nudge is catching them when they are in the right frame of mind. The easiest way to do this is to connect your program to a specific touchpoint of their journey. For example, it when a customer gives a high score at a satisfaction survey, they get redirected a “refer-a-friend” landing page. The customer has just said that they would recommend you – not giving them the tool to do it immediately is a lost opportunity.
Personalised Customer Acquisition: How To Get More Referrals With Machine Learning
Personalised incentives are the most efficient, but it’s hard to personalise at scale. Choosing the right moments of the customer journey to include a referral nudge is straightforward at first look – after a customer fills out a survey, submits a public review, hits a loyalty milestone, etc. The challenge with these types of signals is that they rely on the customer’s activity, while in reality just a fraction of the customer base take the effort to complete those actions. In our observations, response rates to customer satisfaction surveys rarely go over 20% and review completion rates are even lower. This means that companies are missing out on every 4 out of 5 opportunities to ask for a referral. If they go the other way – ask everybody en mass – they risk increasing friction unnecessarily for the other customers who may be annoyed at the company for some reason.
In our experience, the solution for this problem is employing a data science model that predicts the current frame of mind for all existing customers, like Predictive NPS.
To make this example easy to understand, we’ll focus on just one customer of our client, a sportswear brand. Their customer is called Linda. She is an avid marathon runner who mentors beginners in her local running community. Now, because Linda runs a lot, she frequently orders shoes and other gear online and occasionally visits the physical store to try on new models, as well. She is quite happy with the service she gets at her favourite retailer – both online and in the brick-and-mortar location. But she has never filled a survey after purchase – simply because she believes that surveys are for disgruntled customers only. Using Predictive NPS the retailer has identified her as a potential Promoter and now sends her referral codes. It works very well, because Linda has a strong influence on her local running community and when she sends other runners a discount code and product suggestions, they use it.
We found out that approximately 50% of survey respondents indicated a high Likelihood to Recommend. However, their response rate was only 8%. With Predictive NPS our client was able to identify opportunities to activate referrals as they happen. Even if 10% of newly identified Promoters like Linda send codes to friends and 10% of friends use them, this is still around 4 million of additional revenue for our client.