SalesTechStar Interview with Fleur Sohtz, CRO and GM of Americas at Noogata

Fleur Sohtz, CRO and GM of Americas at Noogata chats about the benefits of no-code platforms in digital retail:

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Welcome to this SalesTechStar chat, Fleur, tell us about yourself and what inspired you to join Noogata?

Data for ordinary people! Throughout my career, I’ve worked in industries steeped in data and analytics, and worked closely with people in data-driven roles – and it’s given me a front-row seat to seeing just how transformational it can be to any business. When I met Noogata, I was immediately awestruck by how they put AI into the hands of ordinary business users to make informative decisions. People like me! It is incredibly exciting.

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Upon joining Noogata, I quickly recognized its potential to transform the way companies can manage, access, analyze and leverage data. In my role, I am excited to build both a data analyst and data consumer community, to drive new partnerships, and to accelerate  Noogata’s goals of getting “no-code AI” building blocks into the hands of as many people as possible – whether they be in sales, marketing, ecommerce or operational roles. 

How are you seeing better data analytics capabilities enhance business performance for retailers today? How do you feel trends regarding this will evolve in future?

The breadth of insights that retailers can unlock with data has a huge potential to alter the way they manage their businesses, thanks to its ability to gain unique and immediate insights on their customers, competitors, and products. The next evolution in retail will be a continual shift towards an omnichannel approach, where retailers can leverage their own proprietary data as well as third-party data sources to merge insights about online and in-store shopping. This will allow retailers to make more informed decisions on product availability and inventory, store locations, consumer purchasing trends, and much more. 

I’m also excited to see the rise of predictive analytics, and how retailers can leverage the mountains of data that are available today – from corporate data, third-party market data, social media data, and other disparate information – to identify and anticipate opportunities or threats in the future. As data management, analytics and AI technologies continue to evolve, predictive analytics will allow enterprises to act even more nimbly and effectively, ensuring they can serve customers better, as well as increase revenues and profits.

Can you talk about a few of the most successful ways in which global retailers are using technologies such as this to enhance the overall buying experience?

No-code AI technologies such as Noogata, while relatively new to the retail industry, are rapidly becoming integral for global retailers to enhance the buyer experience, and offer products that meet their direct needs. With access to consumer data such as search terms, brands can derive insights on consumer buyer intent when searching for specific products, and determine better ways to enhance visibility of their offerings. This involves providing insights on which search terms drive the most traffic, how to optimize product descriptions to improve search rankings and click-through, and surfacing recommendations on how and where to advertise. 

How can eCommerce innovators meet the needs of businesses today, especially retailers/e-tailers as trends change and customer needs evolve? What features/capabilities are more crucial now than before?

Ecommerce has experienced colossal changes during the pandemic, with major shifts in consumer buying behavior – which may have otherwise occurred over years – happening in the span of a few months. As such, retailers should be tap into these capabilities to continue to innovate and grow. 

1) Deeper insights from direct-to-consumer (D2C) channels

Consumer-focused brands are consistently searching for more detailed insights into buyer behavior and consumer preferences. In the DTC realm, retailers will have a fountain of information – from ecommerce marketplaces, D2C channels, advertising platforms and bricks-and-mortar distribution networks – to build profiles, provide personalized offers, forecast demand, model price sensitivity and guide more targeted marketing campaigns. I anticipate we will see new opportunities to grow this D2C dataset and combine it with data collected from other sales channels, such as social media platforms (Twitter, Reddit, product review sites and blogs,) owned apps, etc, to unearth unique insights on customer trends, preferences and behaviors. 

2) Evolving omnichannel strategies

The importance of traditional in-store remains highly relevant. Location-based analytics can  analyze omnichannel data and detect a strong correlation between physical store footprint and online sales from the same area. Through on-shelf visibility and launching neighborhood marketing campaigns, customers have been able to build a strong physical presence and strengthen brand awareness in a given location. 

3) From generic ‘big data strategies’ to targeted use-case driven analytics

Historically, many retailers pursued “big data” strategies – in which they collected and stored data without forethought about why and how it could be used. These days, a more effective approach to data collection is to have clarity from the onset on the business goals and requirements. For example, is the retailer looking to expand into a new location? If so, what kind of location-based data should it collect? This kind of approach ensures technical resources are directed in a way that focuses on business outcomes and solutions. 

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In what ways do you feel AI and ML will play a role in the evolution of buying/selling and ecommerce?

Retailers collect large quantities of data from consumers browsing and shopping on their site and in stores, as well as data from engaging through the retailers’ own apps and social media channels. However, many still rely on data science teams to analyze these large data sets across various channels, which is an often complex, expensive, and time-consuming process. AI and machine learning empowers any retail professional to extract actionable, data-driven insights in minutes and receive recommendations tailored directly to their business challenges. As AI continues to become more sophisticated, it will continue to better help users uncover opportunities and trends, which in turn will help optimize a retailer’s marketing and sales strategies. 

Some last thoughts, takeaways, predictions before we wrap up!

In the next few years, technology will continue to reshape how retailers can access consumer data, derive insights and make informed business decisions. 

A few final takeaways and predictions: 

Organizations are becoming more data-driven in their approach to solving business problems and building consumer brands. In doing so, they will increase reliance on technology to successfully navigate the growing platform economy (i.e. companies like Amazon that offer frameworks for users to build their own companies and platforms), establish closer relationships with customers through DTC channels, solidify their brick and mortar presence, and capitalize on “viral” media trends.  

Advanced analytics will ultimately be the go-to industry solution for retailers and consumer goods companies to successfully navigate all distribution options spanning eCommerce platforms, D2C, and physical outlets, and marketing channels. As this technology continues to evolve, so will brands’ ability to better identify potential market opportunities, unearth consumer behaviors and trends, streamline operations, and ultimately improve the buyer’s experience. 

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