Jojo Maman Bebe: A growth spurt from testing to machine-driven personalisation

By Charlie Kennedy

May 13, 2019

We love to talk to our customers about their experience with our platform: The customer’s voice is always the guiding force in our product roadmap design, and seeing how brands are using Monetate can be inspiring and instructive for others. Some of our favorite examples are those in which a customer’s approach has evolved over time, as in the case of Jojo Maman Bebe. The brand has been working with Monetate since late 2017 and their journey has taken them from basic A/B testing to ambitious 1-to-1 personalisation use cases. We spoke with Natalie Ong, Online Merchandiser, and Laurin Senior, Ecommerce Website Manager, to learn more about how their use of the platform has grown as they learned more about their customers.

Laurin Senior and Natalie Ong
Laurin Senior and Natalie Ong

Can you tell us a little bit about Jojo Maman Bebe?

We’re a maternity and children’s wear retailer offering new parents, friends and family everything they need from pregnancy to preschool. Jojo launched from a tiny shared office in London back in 1993 and now has almost 100 stores in the UK and 4 stores in the US. 

What were some of the initial experiences that you ran when you started with Monetate? How has your use of the platform developed since then?

Being able to A/B test was the initial focus, and making fast tweaks to content and messaging tweaks on the fly was an added bonus. Now we’ve scaled up to full personalisation.

In the early days we focused on tests that were designed to convert new visitors. Lots of potential customers who have never shopped with us before search for terms like “maternity wear”, so we tried offering difference incentives off the first shop for these visitors —simple but effective. The result? We immediately saw a 9% uplift in new visitor conversion and now all new visitors receive an incentive.

After finding success with that tactic, we started to think about other ways we could capitalise on insights about customer intent based on their search terms: First, we trialled an experience in which visitors who landed from a search for “maternity dresses” would see a link saying “View other similar dresses”. Offering an alternative exit worked really well, so we did the same for the other high search terms.

Another top priority for us is driving traffic to our stores for specific promotions or events or for new store openings, so localised promotions are key. For example, we’ll run experiences where, if a user is within a certain radius of a soon-to-be-open store, we will push a lightbox to collect their email and offer to notify them when the store opens. Since our expansion into the US, it’s been incredibly important to encourage our new customer base to visit us in-store. With the expansive geography of the US, Monetate has been instrumental in ensuring that we’re only promoting in-store if the user is located within an appropriate distance.

Your brand found success early on using A/B testing, so you could have stopped there. What encouraged you to explore Monetate’s artificial intelligence (AI) offerings, Individual Fit Experiences (IFEs) and Majority Fit Experiences (MFEs)?

It largely had to do with our audience. We play in three separate markets— Maternity, baby and kids —but there’s a huge overlap between these segments. It is hard to define who our customer is because maternity customers will likely be thinking ahead and looking at babywear, but they may also have kids already, so they’ll be interested in the childrenswear category too.

In these situations where the segments are quite loosely defined and constantly evolving, both A/B testing and rules-based personalisation can have their limitations. That’s why we’ve begun to explore Individual Fit Experiences.

We set up our first Individual Fit Experience (IFE) in November 2018. We had a Partywear promotion running and used The Engine to decide whether to display the maternity partywear or kids’ partywear content on the main homepage placement. This performed really well, so we activated a similar version in January with the launch of our SS19 collection. We had three content blocks on the homepage (Maternity, Baby and Kids’) for the new season launch. In this test, the three blocks were ordered based on the Monetate engine, showing the most relevant block at the top to each individual customer.  We saw over a 90% uplift in conversion for visitors that interacted with this experience.

We’ve also had success with Majority Fit Experiences. Christmas is our busiest time of the year and with Monetate we wanted to learn and drive revenue in this short timeframe – a great case for trying out Majority Fit Experiences (MFEs). We displayed our key categories on the homepage, but tested a small banner at the top of our homepage to push smaller categories and learn which of these our customers were most interested in.

Out of 4 smaller categories 1 stood out as a clear winner, which we hadn’t expected, so we took this information and gave this category a permanent spot on the homepage. The machine was learning in real time which variant was driving the best results and allocating traffic accordingly. Not only did we see a 20% uplift in conversion, but we also gained useful customer insights that will inform our decision making in future campaigns.

What’s next on the roadmap for Jojo Maman Bebe?

Based on the success of the above, we want our landing pages to be even more targeted. For this to happen, we need to gather richer data to feed into The Engine, which is a focus for this year.

What advice would you give to other customers?

Experiment and test as much as you can as it is the best way to learn what works for you and your customers.


Thank you for sharing your experiences, Natalie and Laurin! We are excited to see where Jojo Maman Bebe goes next with personalisation.

Charlie is a Client Success & Customer Marketing Associate and has been with Monetate since April 2018. On the weekend, he's either playing pool at his local pub or escaping to the countryside.

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