In this new series, we’re sitting down with Yieldifiers across the business to find out more about what they do all day.
Next up: Romain Sestier, Head of Product and Data
What’s your role at Yieldify?
I’m Head of Product and Data, which basically means looking after new product and feature development, as well as the data and analytics team (the two are very closely related).
What did you do before you joined the team here?
I have a background in web analytics – I was previously Head of Professional Services at Content Square and also working in A/B testing at Maxymiser. I also had my own company doing Artificial Intelligence-driven data and analytics work.
The main thing that comes from that experience is that I’ve always been client-facing, which is incredibly useful in the kind of product role I have now. In an industry where the volume of data can be overwhelming, it’s really important to keep an eye trained on where the real value is: the recommendations and actions that it delivers for a client.
Product and Data sound like pretty separate things – tell us a bit more about the two teams
Well, the Product team is mostly Product Managers (like Moses, who writes stuff like this), who work directly with our Engineering team to scope, prioritize and deliver features such as our recent Campaign History Targeting release. They also continually look for new ways of doing things to keep us as market leaders in CJO.
Most of their job is talking to clients, engineers and our sales and services teams to collect as much feedback as possible about use cases. the role of the Product Manager is really to maintain that stream of feedback, summarize it and determine what deliveries will make the biggest impact, taking into account what’s feasible, where there are gaps in the market and how we can provide innovative solutions.
On the other side, there’s the Data team – they do two main things. Firstly, they answer client requests and prepare bespoke analysis and recommendations. Secondly, they work directly with Product to prioritize pieces of insight in order to productize them and make them available to other clients.
For example, we recently worked on custom insights. We’re always running campaigns on client sites and the biggest questions are usually about the kinds of interactions and responses they generate, such as how long it takes between interaction and email submit. We initially generated this insight bespoke from client to client, but the proximity of the Data team to Product meant that we were able to productize it and deliver it at scale.
It’s this kind of tight feedback loop that means that the two teams are actually much closer as functions than in other companies, ultimately meaning that our clients get much more value.
How has this interaction evolved in the last year?
It’s actually been evolving for much longer than that. Yieldify started in the affiliate space, running bespoke work for clients on a CPA basis. This all changed when we built the Yieldify Conversion Platform, which launched last year. This was Yieldify’s transition into a productized offering, meaning that the Product team became pivotal to delivering and optimizing at scale for all of our clients.
Where is it going to go in 2019?
2019 is going to be the year of Yieldify becoming even more tightly integrated with other parts of the e-commerce marketer’s stack. We already have a number of integrations built for the likes of Magento and dotmailer, but next year we’ll be looking at more marketing tools such as CRMs. The latter means that we’ll be able to leverage offline and historical data in real-time on the site, which is hugely powerful for targeting.
In addition to that, you’ll see much more in terms of new formats and new triggers for campaigns. We added features such as scroll and inactivity triggering in the last few months, and we’ve got lots more exciting things in the pipeline to come. One of these is in-page campaigns, which allow us to deliver contextualized content with less intrusion, making the customer journey smoother.
How do you think these companies to what other companies in the space are doing?
Other companies take a very ad hoc approach to dealing with clients, whereas we want to learn as much as possible about our whole client base and re-apply that knowledge universally. We surface industry-leading insights by vertical and have consultants for each one who can rely on benchmarking and the learnings from other campaigns to meet their challenges in different verticals.
I’d add that what’s also different at Yieldify is the tightly-integrated Product and Services teams. This means that building campaigns takes much less time, allowing us to be more reactive as well as spend more time working on improving the overall user experience.
Why do you think our clients need the kind of data analytics support that we deliver?
I think there are two key things here.
Firstly, there’s time. A lot of our clients don’t have the time or resource to process the sheer volumes of data that they generate onsite. It can be intimidating to even deal with the tools to do so, but our team work so tightly with data that it’s easy for them to extract the valuable insights from these volumes.
Secondly, data’s only useful if it’s actionable. This comes back to time and resource again, and is another key reason for marketers to look to outside help.
What do you think are the biggest misconceptions about data and analytics for marketing?
The biggest is that more data means more value. Like we mentioned earlier, you have to ensure that you can analyse that data in order for it to be worth something. In addition to that, it’s worth pointing out that not all data is the right data – you have to be able to process and extract what’s valuable.
Secondly, I think it’s a common misconception that you can import a data-driven culture. It really has to come from the inside – and from the top, at that. A lot of companies think that new tools and services bring a data-driven culture with them, but what I’ve seen is that the most successful clients are the ones where the C-level understands and buys into the idea of data use across the whole company.
What advice would you give to someone who’s trying to create that kind of data-driven culture?
The best thing to do is to start small – one key defined use case is better than trying to do everything at once.
For example, let’s take personalization: it’s good enough to start by differentiating your approach for new visitors and returning visitors. This way, you’ll have enough traffic to measure to statistical significance and prove your concept quicker than if you tried to do something too granular.
How have you seen the appetite for analytics in marketing change over recent years?
It’s definitely increased, but the quality of requirements has increased too. It’s not just about making dashboards and reporting on key metrics anymore!
Instead, it’s about understanding the big questions like ‘who are our customers?’, ‘why do they visit our site?’ and ‘why do they leave?’. You can see this being reflected by the increasing number of UX-focused analytics platforms and tools on the market.
Where do you think it’s going to go next?
Real-time reactivity is really key. Marketers are trying to maintain a close eye on the performance of every stage of their funnel and react to shifts in it. Black Friday is a great example of this, where marketers were trying to understand behavior over a very short period of time in order to win against their competitors.
The other thing we’ll see is machine learning. We’ve seen this in Google Analytics and in some early out-of-the-box machine learning solutions, so it’s already started. The good news is that it doesn’t have to be that complicated – it can be as simple as predicting the users you ‘should’ have so that you can compare that number against what you actually get. The important thing is that you need to be able to react to those insights – machine learning can be an expensive mistake if you can’t.
Overall, I think we’re transitioning from viewing users as transactions to viewing them as long-term relationships. We saw this happen in brick-and-mortar many years ago and now that shift is happening in e-commerce as we view building relationships and delivering value as more and more important.
The ones who will win are the companies who can learn more about their customers than their competitors. This is why Amazon experiments all the time, like it’s done with introducing lots of different elements to its Prime service: being able to experiment fast and continually at low-cost is so important. This is why at Yieldify we focus on being able to work quickly and react to the promotional calendar as well as plan for the long-term.
What do you like best about working at Yieldify?
The culture – even though it’s grown quickly, it has a start-up mindset with lots of opportunities for people who really want to do better and find new solutions to problems.
What’s your proudest achievement since joining?
Closing the feedback loop by building the data team to have a dual role between clients and products.
What was your biggest learning?
My ongoing learning is in the differences between markets (Yieldify has clients in over 10 countries).
Being French and having worked with French clients before, I’ve always seen that every market reacts differently to trends and technologies, but the nuances of this is what’s continually interesting.
It applies as much to marketing cultures as well as consumer behavior. For example, in the US, you have more of a builder culture which looks internally for solutions instead of leveraging partners. In Europe, we’re generally more comfortable with outsourcing.
Want to know more about what Yieldify can do with your data? Apply for a free demo and we’ll tell you more about the analysis, benchmarking and strategies we can apply to your customer journey.