So in the last post we talked about the first way we can use AI and Data Science to innovate, Automation. And this week we are going to talk about the second way to do so: Customers Satisfaction.
Automation is the process of reducing the operating cost of running a company. But that alone doesn’t allow a company to make money. Which is kind of the main purpose of a company.
In other hand, Customers Satisfaction does exactly that. The more you are able to provide value to your customer and the more they are satisfy of the product or services you offer, the better are you chance that they will come back for more.
So the ability of a company to continuously improve the level of satisfaction of their customers is directly linked to the level of success they will have in the business they are in.
This isn’t as easy as you might think, and there is a lot of danger that can come with this. So let’s talk about that and see how AI and Data Science can enable us to innovate while staying away from those dangers.
What is Customers Satisfaction?
Before moving further, it’s important that we define what we are talking about.
- Brand Experience VS Customers Experience
Customers Satisfaction come from the merge of two key concepts:
- Brand Experience
- Customers Experience
The Brand Experience is the promise that a company makes to their customers on what they can expect from them. Where the Customers Experience is about how you deliver what you promise.
- Danger of improving Customers Satisfaction
In order to improve customers’ satisfaction, we can either improve both the Brand Experience and the Customers’ Experience. That way we change our promise and we deliver on that change.
If you are a company that is just getting started, that could be fine. But If you are an established company that has loyal customers, you are risking to shoot yourself in the foot by doing that. Where you are turning the back to your current customers and look for attracting new one. Which can be very dangerous if done incorrectly, where your loyal customers can feel betray.
The other way is to stick to the vision of the company and maintain the current Brand Experience, or even reinforce it. And focus on improving Customers Experience instead.
But even there, there is a danger that we need to avoid. The danger of abandoning the Brand to make peoples happy.
What I mean by that is you are going to get feedback from your customers and based on those feedbacks, you will know things you can improve. So this is really easy to just follow the feedbacks and improve only based on what customers are complaining about.
The direct risk is to drift away from the promise of the Brand Experience to just please people. Also, there is something to keep in mind. Generally peoples that are satisfied are the one we don’t hear about and the one that aren’t, are the one that we hear the most about.
- 2 Ways to improve Customers Experience
This brings us to the 2 ways you can improve your customers experience:
- Doing things differently
- Doing things better and more efficiently
The first one is what I have just talked about. Where you change what you do in order to satisfy more customers. But you take a major risk of making your old and loyal customer unhappy by the change.
A far easier and better way to go about improving Customers Experience is to do things better and more efficiently. Because you will make your current customers more satisfy and they will be more likely to recommend your product or services more easily. And you will attract new customers that were previously not happy about the complexity of what you offered.
- Why AI & Data Science can help?
AI & Data Science does exactly that. With those methods and technology, we can innovate in order to make our products and services more efficient.
Like we see in the Automation part, AI & Data Science focus on analysing what is already done in a certain way, to then focus on how to do it better and faster.
This schema can be replicated to improve Customer Experience while staying align with the Brand Experience.
I would argue that thanks to that you can even improve your Brand Experience by promising more while staying align with your current vision. Which you will deliver using AI & Data Science.
If until now you weren’t using those technologies and methods, it’s a pretty safe claim to say that you are going to do more and better. As long as you stay align with your past, current and future vision. Everything will probably work out just fine.
Now let’s investigate how we can actually do that.
Using AI & Data Science to improve Customers Experience
When researching to build this blog post, I was looking for other things that can be used here in addition to the one I had already in mind. And from what I found, I would say that there are primarily 3 ways in which you can improve customers’ experience using AI and Data Science.
- Frictions – The actual experiences of the product or service
- Personalisation – Product/Services personalised to the user
- Rewards – What get them back for more and build loyalty
Everything else that I found was actually closely related to one or the other. So let’s dive into what each one means and how to use AI & Data Science to innovate.
- Frictions
First the Frictions. When we are talking about frictions, we are talking about everything that will create resistance or complexity in the product’s use or in the experience of the services render.
Human being are energy preserving creature that want to save their energy for important things. So every time something low-value pop-up and prevent us from getting what we want, it bothers us.
So there are a few ways we can reduce friction and improve the customers’ experience:
- Managing Speed
- Managing Expectation
- Easing the Communication
Let’s start with the Speed parameter. Depending on the situation, both extreme can generate frustration from the customer. Too slow when the person expects to go fast and reach a specific goal. Or too fast when the customers want to take their time to enjoy the moment. Neither of which are good.
Let’s take the first case, when things move too slow and it’s obvious that it is. Like for instance, with call center, where the faster we are able to answer customers’ question the better. Here we can use first Data Science to analyse and find sticking point within the process. To then find the systematic parts that we can remove or improve. Like building a FAQ (Frequently Asked Question). Once it’s done, we can use AI to automate the discretionary parts. In the example of call center, we can build Chat Bots to answer more complex question that the FAQ doesn’t cover. But we still leave room for human intervention when the question cannot be resolved by the Chat Bot. So most likely 95% of the question will be answered in an automated manner and the few that are left unresolved are going to quickly be redirected to a human.
But now, how to improve the experience of something that goes too fast. By basically making it easier for the customer to perform the intended task in the same amount of time. Something might be too fast because simply the skill level or the complexity involve are too important to be done in the given time. So using AI to complete or replace part of the required skill, or by simplify of the overall process, you can ease the experience of your user so they get things done in the required amount of time.
The other answer is you are not giving enough time to your user to do what they need to do. This is something you can monitor and analyse using Data Science, in order to find the right sweet spot where that people enjoy without spending an excessive amount of time on it.
But in some cases the speed at which a service should be performed is not very clear. So what we can do in this instance, is using Data Science to analyse the correlation between speed and user satisfaction. Where for each customer you change the speed of your processes and assess the result.
The example that come in mind is with video games. If your video game is based on reflex and reactivity from the user. Where your game needs the user to react as fast as possible depending on its level. The challenge here is if the pace is too fast, the game will become too difficult and the user will lose interest. But if it’s too slow, the user will get bored and also lose interest. Here, managing the right pace of the game throughout the entire journey of playing it is extremely important. This is something that AI and Data Science can help to do.
Let’s move on with the Expectations. When your users are using your product or experiencing your services, they constantly have some expectation of what should happen. Companies that are able to develop great customers’ experience usually are able to identify those expectations at any given time of the users’ journey, to then exeed those expectations.
The ability of a company to always go beyond the customers’ expectations is directly related to its level of success.
But something to keep in mind is the expectations that I am talking about here are very small. Every time your user performs an action, a movement, a decision, … they have some expectation of what is going to happen next. Those expectations are based on their experience of what they have already used somewhere else, or in the past using older version of your product or services.
So using Data Science, you can analyse people’s expectation and monitor them over time. To then build AI models in order to predict those expectations and act upon those predictions.
Some very practical example is on a website. Instead of having a basic display that shows the same things for all you you user. You can analyse there current behaviors, and prediction what most likely they are going to do next time. So when they come back, you show them what they want to see in a way that they don’t need to go, and look for it.
Amazon is a champion on this topic. With their efficient recommendation system, they grab your attention on things you didn’t even know you wanted. But that you end up buying anyway.
Finally, on the Communication. We already talk a bit on it with the chats bots which are a way to improve communication. But something that really is trending, to become the next way of communicating in the near future is the Voice.
Vocal Commands are an easy en efficient way to reduce frictions. It’s easier to speak than it is to type.
Even though the technology wasn’t really efficient in the last decade, with the help of Data Science and the progress that has been made in AI, things have now changed. The rise of vocal assistant is an example of that.
But more than that, as a society, we are currently trending in the direction of the sound and audio, and progressively step away from image and videos, which had a lot of attention during the past decade.
Peoples use more and more audiobook. Because it’s more efficient and you can be more productive by doing so. Like listening to one while doing your weekly running session.
So the point here is we have a population that is now ready to transition toward that, and the technology allows us to do it. This is an opportunity that we need to take advantage of.
- Personalisation
AI & Data Science allow you to understand your customers better than they understand themselves. By building customers profiles and comparing customer between each other, you can build recommendation system that will allow you to build a highly personalised services for your customers.
Or in the case of a product, you can have a product that evolves throughout its lifetime, to learn more about it’ user and adapt to their behaviors in order to improve the overall customer experiences of the product.
This is not a secret, we like to feel unique and special. And the main issue of automation is that it tends to put everyone in the same bucket. But with the empowerment we have now with AI & Data Science, we can have the best of both world and even more.
Because even before, due to the number of customers, companies were forced to at least build categorisation to group peoples in order to personalise the product or services. The ideas of having a 100% personalised customers experience to each customer was just out of the question back then.
But now we can. By building user profiles and then adapt the services to each individual user, we can totally personalised the customers’ experience and therefore their overall satisfaction.
- Rewards
Now about the Rewards. The idea is to create addiction in the customers by giving them positive rewards for performing the behaviors we wish them to have and negative rewards when they perform the opposite behaviors.
The rewards should aim to build loyalty and the will of the customers to come back for more.
Using A/B Testing, and with the help of Data Science, you can study the different types of rewards and their impact on a different group of customers.
Then you can go further using AI to build a user profiles and automatically fit the best reward system for each customer. That way you’ll have a specific well fitted reward system for each individual customers instead of having one for all customer or group of customers.
This will allow you to build a very personalise customers services, which in itself improve the customers’ experience and therefore their satisfaction.
This technique can also been used to manage the customers’ expectations efficiently, as explained in the Frictions part.
One of my favorite ways of building a good reward system is Gamification. Where the idea is to transform your product or services partially or entirely into a game.
This in human nature to like games. Because games are simpler version of life. They have less complexity and often better aspirations which make us want to play them since we were a child.
Conclusion
The rise of AI open new possibility for business to innovate with the aim of improving customer satisfaction.
This is done by aligning the Brand experience (The promise you give) with the actual Customers Experiences (You products or services).
Then we improve the Customers Experiences on by reducing any Frictions in the process of using the products or services, which we want to be smooth and effortless. By also Personalising them to make our customers feel unique and special. And finally, by Rewarding them for their good behaviors and “Punishing” them for their bad behaviors.
So that’s it for today, I hope this gives you some better insight on how to improve your customer’s satisfaction using AI and Data Science.
See you NEXT TIME 😉