Laying the Foundation for Success – Interview with a Customer Success Manager (CSM)

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Jeanba Collet

Jean-Baptiste Collet has joined aifora a year and a half ago, since then he has accompanied various retailers through the onboarding process and project execution. In an interview he told us a bit more about his role as CSM.

Explain what aifora does in two sentences.

At aifora, we enable retailers to optimize prices and inventory management across all channels by forecasting the demand for merchandise at SKU level. For that, we use the vast amounts of data that retailers already collect and add data of influencing external factors that help define future demand more accurately.

What are your tasks as Customer Success Manager (CSM)?

As customer success manager, I build the bridge between our customers (retail professionals) and our data science experts. Both speak a very different language and I act somewhat as a translator. It’s my job to figure out how to turn retail needs into realistic technology in order to create added value for all parties involved. I also accompany clients from the moment the project gets the “go” to ensure their success with our platform through the entire project process.

It’s also very important to build a relationship that is based on transparency, honesty and trust – because only that leads to a truly successful project. Besides the direct customer-related tasks, I support the product development by passing on customer requirements to our developers and regularly test the system as a whole.

What do you like best about your job?

It is difficult for me to choose one task in particular because it’s such a diversified position – which I really enjoy. I get to work with cross-functional teams and with different kinds of retailers. There is no other position like that at aifora – we are right in the middle of the operative business! I really like supporting the customer – answer questions and build a deeper understanding and trust with our platform. I also enjoy analyzing the figures and translating those into actionable insights. Of course, seeing the results improve and seeing users trust the algorithm more and more over time, is the icing on the cake.

You worked previously in retail yourself, tell us about your experiences.

I worked as a department manager and deputy store manager in several stores of a large fashion retail chain for about 15 months. I was responsible for all store operations – from receiving the goods to personnel planning. But I spent most of my time in sales, customer service and visual merchandising.

As I was a career changer, I didn’t know anything about the retail business at all when I started. I had to learn everything from scratch, starting with the folding of shirts. But what struck me very quickly and completely surprised me was that nearly all decisions made in the store were based predominantly on feeling and experience.  Since I came from a completely different industry, I had neither one nor the other. Fortunately, I was lucky enough to work in one store that was managed by someone who based all his decisions on scientific knowledge and numbers which he calculated himself with Excel. The result was clear: no matter where he worked, he always had the best results – so the other stores gradually followed his lead. From this point, I understood that there were still a lot of unexplored opportunities and great potential to boost retail business.

Where do you see the biggest challenges for brick-and-mortar retail?

Clearly in keeping up with digital transformation. Many brick-and-mortar retailers have missed the opportunity to invest in the future of retail and to develop their business at a time when profits were still flowing. As soon as sales of physical stores started to decline, many retailers tried to become more profitable by reducing costs, when they should’ve been investing in advanced technology and adapt to the changing market. As a result, many brick-and-mortar retailers went bankrupt because they couldn’t cope with the new standards that were raised due to rising e-commerce businesses – who could deliver a better customer experience because they relied on data and advanced technology. Nowadays, traditional retailers don’t generate enough profits to grow their business sustainably anymore, or at least don’t consider it a priority. It’s a vicious circle – retailers are not up-to-date but the sector continues to develop and they get left further behind, followed by an endless race of survival. While I don’t believe that brick-and-mortar retail will die, it certainly is changing – and only adaptive retailers will overcome the industry’s transformation.

How do you measure your success?

On a day-to-day basis it is difficult to define actual success. Personally, I measure success by our customer’s learning curve, their gratitude and appreciation and how this influences the overall project result. If the customer is successful, so am I.

What customer experience has stuck with you and why?

One of our customers was acquired by a new investor – and like all new investors – they turned every stone in the company. Obviously, one of these stones was aifora. We had to proof that our product brings added value to the company. We didn’t know under what criteria they would evaluate us. So we became a little nervous when we learned about this. The end of the story: the investors actually advised our users to trust the decisions made by our algorithm more. Obviously, this was the greatest acknowledgement we could possibly receive for our work.

What tips would you give new customers?

Trust the algorithm! New users are often skeptical towards the machine’s decisions. They tend to question the decision making because it is different from what their gut feeling and their experience tells them. The thing is – humans alone don’t have the capabilities to take all influencing factors into account and are therefore biased. Trends and customer expectations are constantly changing, always having the same mindset that was developed solely on past experience can’t improve future outcomes. Only if they take full advantage of data-driven merchandise management, the business can expect better results.

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