Is Product Personalisation An Illusion?

Hello, UXDX EMEA. It is a pleasure to be speaking here again. My name is Nadia Udalova and I'm a Head of Product Design at Moonpig Group. It is a bit more than a year ago since I was presenting at UXDX, Netherlands. And I'm so happy to be back.
Today, I would like to challenge everyone listening to me here and ask, do you believe in the true product personalisation for customers? How personalised is the content that some e-commerce shops are offering to us and proposing to purchase? Is it based solely or on your preferences and interests as a user, or is it some kind of an illusion that today's e-commerce platforms are trying to create?

During the next 25 minutes, I'll make you familiar with Moonpig as a business, I'll walk you through some of the things that we've done and I will talk a little bit about our product range. We will look into some data and wrangle about product personalisation, discussing Moonpig's approach to this. And at the end, I would like to leave you with some of the biggest learnings that we concluded too, while trying to create the optimal personalised experience for our customers.

I appreciate those listeners to me today in the United Kingdom, or even Ireland who may be already familiar with the Moonpig brand, for those who haven't heard of us yet, we are all about celebrating those great life moments, the big birthdays, the new arrivals, those surprise engagements and just that becauses. We are helping our users to connect to the people that they care most about. We are one of the biggest online shops that offer personalised and non-editable greeting cards of all sizes from standard to giant and as well as a wide range of flowers and gifts to make a great pair and a great gift with them.

At Moonpig; our mission, our aim is to create those better, more personal connections between people who care about each other and we are doing this by making gifting experience effortless. Imagine choosing your own card, adding the personal picture of your loved one, typing their name and adding any message you want to the card. We're turning our brands into the ultimate gifting companion. This means having the perfect gift range and leveraging all of the data and all technology that we have to be as thoughtful as possible for every gifting occasion throughout the year. But before moving forward, I would like to walk you through some data to prove how hard it can be to select the right gift today and how often we have humans fail with this mission.

There was a survey done by worst gift giver in August, 2019 with almost a thousand adults. And it has revealed some very interesting. Just listen to this, 45% of respondents said that the person who gave them their worst gift, did so more than once in their lives. So, this means more than one celebrating occasion. The average estimated price of a worst gift was about $20. Just now imagine how much money is wasted on the bad gifts. Relatives seem to be the worst gift givers more than anyone else. This is very surprising to myself because I would expect that my family and my close ones would know me the best. Also 16% of respondents said that receiving their worst gift ever worsen than their relationship with a gift-giver. So, all of these data, just indicates that's all people are different in our taste is now our taste and our preferences. And we may need some help in surprising our loved ones. Also, as we will see later in my presentation, it may not always be the fault of the person who is choosing the present when they give something that isn't perceived positively.

So, in order to understand the intention and preferences of our customers better. At Moonpig, we have done quite some extensive research to segment, the type of people who are buying with us in order to fulfill their needs better. We used behavioral segmentation to get a better understanding about those people, not by just who they are, but by what they do, using the insights derived from customers action.

Behavioral segmentation is based on patterns of behavior displayed by as they interact with a company or brand or make a purchasing decision. It allows businesses to divide costumers into several groups accordingly to their knowledge or attitudes towards the or the response towards the product service or brand. So, our objectives here was to identify the customer segments and to understand how to address the particular needs or desires of that specific group. Discover the opportunities to optimise their customer journeys and quantify their potential value to our business. Unfortunately, here during the confidentiality of data, I cannot show you the exact information and data we have come up with here.

We have tons of data and by using the prioritisation, what metrics we have identified the top customer segments that we would be focusing on with all our efforts in the future, as we believe we can we can get the best value out of focusing on those customers. Also, we quickly understood that was all of those people's specialties and personalities, personalization is going to be a big help and will be fundamental to providing the optimal customer shopping experience for our users. It is also super beneficial for businesses to generate more sales and increase customer retention. However, there was also one quite big problem that we came across when trying to build for the best customers experience, in terms of personality.

So, conventional personalsation engines, they take certain information about each shopper such as their age, their location, gender. And then, they run this data through statistical algorithms that have been developed based on the data of thousands of other customers. It runs this data through the data that was produced by shopper actions, like clicking on an item, liking an item, adding the item to the cart. It runs this data through the algorithms and surfaces back items for the customer.

So, for example, if you are 33rd year old woman and you live in London and you're searching for a floral dress to add to your basket, you found it, you clicked it's in your cart right now, this action will make you see and you will get as a result more products shown that were liked or purchased by other British women in their thirties who also bought that dress. So, here's the problem, sequencing algorithms that base recommendation on the prior action of thousands of other customers, they are not personal at all. They are predictions based on statistics, but not an individual trait. So, does this mean that personalisation is indeed an illusion?

Decades ago, before the internet, the most successful shopkeepers were those who kept the list of their products, that their best customers loft and they call those customers when they got the new stock. This type of personalisation kept customer satisfaction really high, and it cemented a lifelong loyalty. I was born and raised in a city in Ukraine. And my mom's still many, many years after I have left the country. She is still living there and buying her favorite cottage cheese from the same lady at the local market. This lady, the shopkeeper, calls my mom by her phone every time when she gets the stock of the cottage cheese in, and my mom always runs to her and returns to buying with this lady, because it's simply convenient and it meets all of her needs.

Today as technology evolved and e-commerce was born artificial intelligence promised us to enable a much more sophisticated playbook, not only for providing those real-time recommendations, but also for elevating engagement of the customer and better communicating with them. However, the real truth is that personalisation is still hard and this is why it's so easy to get it wrong.

Here a couple of examples where the other companies haven't done such a great job about this. Unfortunately, many of you have been the victims of the first name fail marketing model, and it probably happened quite a lot to you. Have you ever received those emails that would say their first name instead of your personal name or F name or having weird characters? This is occurring when something, when something goes wrong during the collecting of your data from multiple services or resources. Or cases like I've shown on the right, this is an unfortunate situation for Pinterest when they have accidentally congratulated, hundreds of single women on getting married, and those women been simply browsing for wedding related content. This is a particular example of getting user interactions wrong. We don't want to be there.

Therefore, at Moonpig our approach was to start from a very basic level and take an incremental approach to the personalisation. The idea was to start looking at one particular area and focus on it until we get it right. So, we decided to give our attention to the cross-sell experience and make it better than it is today. We had some specific challenges due to the nature of the business. We are not a typical e-commerce business because for us, it's all about that relationship between the users who are sending each other cards and gift, and not the users themselves who are purchasing the card. So, for us, it's harder to collect and analyse the data. It is also taking us longer to learn about the user. Our average order frequency is only three times a year. So, we are not collecting tons of data as often as other big e-commerce businesses do. Cold start problem is also a real challenge for us. This is about making a recommendation to those customers who are first timers with us, and it's there due to the all the above reasons that are just mentioned.

So, in order to understand the different goals of our customers, we also have done lots of user research to define and outline the core job to be done of our customers and those that are related to it. This helped us to realise how many different variables there are impacting the success of the right gift selection. So, to give you a concrete example, the core job-to-be-done that we have identified was of course, giving a card or a gift to celebrate an occasion with a loved one. And we have found more than 50 related jobs-to-be-done. For example, finding a suitable card or a willingness to be thought positively by the recipient when they get the card. Also finding the right words to write in the card or to save with a gift getting the correct address of the recipient and making sure it arrives to the right place and on the right time and many, many others.

To show you a little bit more detail our Moonpig journey of developing personalised experience. Here's how we started with enhancing our cross sell three and a half years ago. And by the way, a little disclaimer here, everything that I'm going to show in the next couple of slides is only a part of the work that we've done in this time. It's not, it's not everything.

So, three and a half years ago, we had started from a very limiting situation. Our cross-sell experience would look like the following. After a customer would add a card to the basket he would be introduced and invited to the cross-sell step that would be showing 16 products as a proposal for a gift in a static list. Those 16 products would be our best sellers. As I said, there will be shown in the, to the customer in a list of static items. So, for example, if you will be choosing a card for your kid's birthday, we would show you the bestselling 16 gifts that can be going in a pair with this card that you selected. So, good, kind of okay-ish cross-sell experience, but pretty static, isn't it? We want it to improve. So, we looked at proposing a unique pairing using the lift algorithm. What does it mean is that when the customer would be choosing the card, we would show her gifts that were previously bought uniquely with this same card? Some customers don't always know the perfect gift for the recipient. So, we decided to leverage the data from similar customers who previously shopped successfully for the same type of recipient and occasion. During this essentially, it would mean that our customers who are more confident shoppers are helping those customers with less confidence.

So, for example, if someone chooses a traditional card, these cards are usually more likely to be bought with flowers, for example, and what we would do, we would match these cards with couple of nice book hats as a gift on the other hand, humorous. Or those cards about wine drinking, they tend to match a drink gift. So, we would follow this pattern. This was very simple improvement and it worked great for our customers. The first test that we've, that we've done to validate this, we saw a big win = 10% uplift. So, we're just trying to give a little bit more relevant gifts to our customers. We already saw a big increase in metrics.

After this, we wanted to attack our biggest challenge, biggest issue, the cold start problem. So, this is one of the first-time customers comes to our website. So, remember the 16 gifts as a recommendation for after adding a car to your basket that I told you about earlier. So, we have tried expanding the range from 16 gifts to 160 in order to provide a wider, at least a wider selection of gifts, your recommendation to buy was the same card. How we done it? We worked with grouping of cards and missions to ensure that we have 160 products to recommend with each card. So, if you bought a card of congratulating someone, for moving to a new house, you would get a house decor gifts as a recommendation for every card we would have. We would have a mechanism group level recommended and also as we would see your orders as customer coming in we would be checking if we can recommend a better product based on what you have bought before. And we will rely less and less on those mechanism group level recommendations.

Somehow, unfortunately, this wasn't a massive success for us, but this was something very useful for the next step that we undertook. So, the next thing here was that we introduced product carousels. We have moved away from those 160 products in a random list to be making a few groups of gifts in few categories of products. This is what we called Product Carousels.

So, previously our customers found it hard to browse such a long static list of products, but yet we knew that they wanted to have more choice. And this month that we had to group products to make the browsing of gifts easier for them. From the UX perspective, this was a big improvement just to show you how it looked like, on the left we see that old across all experience, just a static list of gifts and on the right, we see the product carousels, you see a couple of products grouped together by the product type. So, this allowed us to add more products to show to the customer and provided variety. So, users got to see a set of two to five carousels. And one of them could be flowers and other could be chocolate, et cetera. This provided a great browsing experience and it was a big win for us. It brought us 10% of uplift. One of the most recent things here was an idea that relied on people giving us information themselves. We are currently working on a mechanism that would help us reading a card test that user would type in into the card and that would help us identify the mission from there. So, for example, if you would be choosing a birthday card and writing on the card something like, 'For my Dear Dad.' we would know that you're buying a card for birthday, for mail and for your dad. And we would turn this test into the recommendations to serve as the best gift for you as a customer. This is still coming in the future. So, it's not currently available yet.

So, based on our three and a half years journey, I want to leave you with our most important lessons that we learned through our experience. Start small and iterate to improve. As I said earlier, take one area and focus on it to get it right, target all your efforts around it and find a way to collect the data and learn don't scatter your attention. You saw how we took a main area to focus on around our cross-sell experience and tried little steps around it. Capture the context and data everywhere where possible in order to learn faster, any little piece of information you can learn about what your customers are doing or are willing to do will be helpful for you. Ask your data scientists for support or hire an agency to help you. This will give you so much more confidence in what you're doing and that you are doing it right.

Next point I want to focus on is to say that you should be looking at what your users find the biggest struggle and focusing your attention over that. So, spend some time learning about the biggest jobs-to-be-done of your customer. Pick something where you can genuinely anticipate your users need better than they can. For us, this was proposing the best gift to go with a card because we knew it was a struggle for them and remember not every step needs to be personalised at the individual level. You can also look at what other customers, what other users are doing in the same situation. Use the wisdom of the crowds to surface the right item back to your customer. So, using the behavioral data from the customers who are buying for the same mission and the occasion to help future customers have the same type of the recipient. For us, this was a big help and a big win. Remember those more experienced customers, they can help those who are the first timers at your shop and who have less experience using the product and selecting the gifts. Also using product meta data to suggest similar products to each other will be a big help. For example, like recommended for you or people who bought these bought also that.

Also, we were not looking at the customer only himself, but also what many people have done in the same situation that helped us surfacing the results to the people and making the best recommendation. And last but not least here, relevance to the users should go over everything else. For us at Moonpig designing personalisation strategy around our customer was the biggest goal. Of course, there'd been some parts where business strategy played into that, but our customer needs were always on the first place. We built something where we could genuinely anticipate our users and their needs better than they can. And we help them on.

So, with all of these great stories, did we always win during this journey? And the answer is, of course not. We have failed quite a bit and hear some of the most tremendous failures of us just to showcase the examples.

We were spotted offering alcohol beverages as a gift to the people who were buying cards to congratulate their loved ones with the start of Ramadan. This was a huge miscalculation for our Muslim audience. For those who might not be aware, Ramadan is a month of the Islamic calendar and this is when people fast between sunrise and sunset. Consuming, no food or water. I'm not even saying that alcohol is prohibited at all. And another example at times, we could end up in a situation like this. So, proposing and wisdom of the Husband Book. To go as a pair, as a gift pair with a five years birthday card for a little girl, I'm not quite sure that they need this type of information at five years yet.

So, to round up everything that I told you right now, I would like to go back to my initial question of this presentation is personalisation and illusion? And today I believe the answer to this question is yes. Personalisation seems to be just an extreme segmentation of data. Good personalization can be done due to collecting mountains of personal information about your customer and due to lack of this data that one-on-one personalization is challenging for a large majority of companies to do well today.

Over time, I think we will get so good at segmentation and how we action around it, but that it may eventually become that one-on-one personalisation that we are all striving. Maybe we will get so good as that offline shopkeeper, who knew his customers so well and could anticipate their needs at any times.

So, just to summarise my main learnings from this presentation to leave you with, and they sound as the following:

1. 1Start small and iterate to improve with that one area to focus your intentions around making the personalisation experience.
2. Focused on what users find the biggest struggle and help them around it.
3. Don't forget to use the wisdom of the crowds to surface the right item back to your customer.
4. Relevance to the users should go over everything else, your users will appreciate this, and
5. Don't be afraid to fail. You have to learn from it just like we've done.

If you will have any questions about my presentation, I will try to answer them during the live panel, with the name, Progressive Personalization: Designing a Better Experience. This will happen on 6th October on the main stage at 2:20 PM.

I will see you all there. Thank you for your attention.