People talk. People have been finding ways to share information forever but since the invention of social media we do it even more publicly. The big question remains – can you manage word-of-mouth? In this blog we’ll look at how to avoid bad word of mouth for your business and ensure you understand all of your customers, even the shy ones who won’t easily tell you how they feel.If you want to increase new business coming from referrals the first thing that comes to mind is increasing conversion rates of referral mechanisms. There are multiple tips and techniques to achieve that goal,however, we’ll examine a slightly different perspective in this blog post. Before thinking about increasing conversion rates, the easier and more beneficial long-term strategy would be to increase the pool of potential referrers by converting unhappy customers to satisfied ones. One of the biggest challenges with converting unhappy customers is actually finding them.
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Contrary to what your intuition might say, the bigger problem for your business are not the customers who talk negatively publicly – on Facebook or on your Amazon product listing. Just the opposite: for every complaint filed in a form or in person, there are three more that you will never learn about. This is the Customer Complaint Iceberg Theory, created by John Goodman.
He says that no more than 5% of all unhappy customers, or the tip of the iceberg, complain to management or headquarters. About 10-20%, positioned in the middle of the iceberg, complain to a branch, frontline or an agent representative. The remaining 75% are the silent customers who don’t complain, regardless of facing an issue and making it increasingly harder to manage word-of-mouth.
These non-complainers, however, deserve your attention too. The more dissatisfied they are and holding it back, the more likely they are to either not recommend your brand to their network, or even engage in negative word-of-mouth. According to a study, 37% of silent dissatisfied customers are likely to spread negative word-of-mouth, in contrast to only 16% of customers, who filed a complaint.
These are the customers that you need to focus on. If you fix things for them, they are also more likely to become your most passionate promoters, proving you can manage word-of-mouth. In the same study, results showed that the group with the highest repeat purchase and the lowest negative word-of-mouth intention was the one that received a proactive, company-driven recovery.
If you want to Focus on these three customer engagement initiatives follow these three principles: listen, ask, and predict.
You need to be able to listen and personalise your messaging. Actively listen to your customers to understand what their main pain points are, as well as what made them happy interacting with your product or service. If you want to do this in a systematic way and come up with actionable insights, you need an automated text analytics solution, which allows you to leverage different types of text data gathered through your own channels. These channels can be internal such as call centres, company websites, and emails, or external ones – social media, review websites and more.
The unstructured feedback from these channels is an incredibly rich pool of genuine customer feedback. Free text customer feedback contains insights about any aspect of the customer experience – how customers use the product, who they share it with, how they feel about it and much more. To get your hands on this amount of data you would normally need to run a lot of surveys.
By just looking through complaint forms, social media, rating and reviews or other sources at your disposals, you can get very profound insights that you can use to adjust your messaging in referral campaigns – for example, you can say something like “4 out of 5 customers on Amazon recommend our product because of its long battery life”. In the long run, you can identify the biggest drivers of both satisfaction and dissatisfaction and work on improvements, so your customers get happier and happier.
The second principle relates to actively asking how your customers feel without bombarding them with surveys, of course. Best-in-class VOC programs combine all levels of feedback (transactional, relationship, product/service and brand). Use different quantitative methods to get a deeper understanding of your customer base, and serve as a substantial addition to what you already know from actively listening to your customers. If executed properly, surveys allow for effective follow-ups and a personalised approach to customers.
The biggest challenge with asking is that only a limited amount of customers actually respond to survey questions – in B2C industries average response rates usually vary between 5% -15% and rarely go higher than 20%.
That’s why you need the third element, as well.
Tie customer analytics to business impact. The predictive models, like listening, should be an ongoing effort. The asking mode provides for better and tailored personalisation, and it is very useful in particular situations such as the example above. It doesn’t, however, allow your business to cover the entirety of your customer base and tie customer data to actual business impact.
As I mentioned, one of the big industry challenges is knowing how satisfied your customers are – all of them at any moment in time. To solve this challenge without clients we apply a model, called Predictive NPS.
Predictive NPS accurately predicts satisfaction scores for the whole customer base at any moment of time, not just after a key interaction when you send a touchpoint survey. In the model we combine:
- Behavioural data – what products & services customers use, interactions with different touchpoints like a call centre, etc.
- Altitudinal data from customers who have already responded to traditional surveys
The algorithm then identifies which factors in customer behaviour have the highest impact on what the relational NPS of a specific customer would be, allowing you to manage word-of-mouth effectively.
Now that you have sufficiently increased the pool of people willing to recommend you, the next step is to increase the efficiency of the referral program. There are two main challenges with this task. First, It’s hard to identify potential referrers. Second, personalised incentives are the most efficient, but it’s hard to personalise at scale.
While it’s fairly easy to identify which behaviour signals mean a customer is a potential referrer, choosing the right moment to ask for a recommendation is crucial for success. That’s why a lot of bring-a-friend programs depend on the customer filling out an NPS or other satisfaction survey as the first signal to activate a referral. Predictive NPS allows you to skip this step entirely – you already know how satisfaction scores change daily for each and every one of your customers.
Now, imagine an algorithm working on top of the ‘scale-up’ one – let’s call it ‘action-generator’. This algorithm can mine all previous actions you have done to activate promoters, calculate the impact depending on hundreds of customer variables at the same time and can suggest the actions that will have the highest impact on customers you haven’t reached out to yet!
Next Best Action allows you to make sure that every personalised action is bound to have the best success rate and the highest ROI. It can help you be more precise in the:
- type of referral offers you suggest to customers
- type of channel you reach potential referrers through
- and even time of day when the referral nudge is most likely to drive an action
Managing word-of-mouth is not as hard as it may seem. You just need to listen to your customers effectively, ask the right questions and deploy the correct predictive models to make sure people are recommending you.