Prediction Series 2019: Interview with Mike Austin, CEO and Co-Founder, Fresh Relevance

What opportunities and challenges did you and/or Fresh Relevance identify at the start of the 2018 and have things worked as planned?

We are all social creatures and tend to base our actions on the behavior of others when faced with an overwhelming range of options. However, tactics that have received a lot of media attention recently, such as Influencer Marketing, aren’t necessarily the most effective or scalable ones. Therefore, many retailers are still unsure how to leverage the power of social in their marketing to drive conversions and engagement.

Social proof is a strategic tactic for retailers looking to increase sales across the board. It helps make customers feel part of a tribe and creates a sense of urgency to purchase in fear that the product might be gone soon. Tactics such as user-generated content, product ratings and scarcity-related information, like showing trending products or how often an item has been purchased recently, can easily be deployed across the website and emails and are very scalable. Also, retailers already have all the data they need to execute social proof campaigns readily available, e.g. inventory and website traffic data or customer photos and posts on Instagram or Twitter.

Fresh Relevance offers a comprehensive set of digital marketing tools to harness social proof and we see that these tactics really do help generate revenue for retailers. Brands usually see a minimum 3% increase in conversions by including a ‘trending products’ feed on the homepage, and can increase that much further by adding social proof widgets to show scarcity or popularity on products.

As a personalization platform, how does Fresh Relevance build on its expertise to connect cross-channel experiences?

Thanks to cross-channel personalization platforms like Fresh Relevance, marketers are finally able to connect the dots when customers are browsing and purchasing using different channels and devices. By breaking down the silos in which this data tends to live, brands have a more complete view of the customer journey, from start to finish. Marketing performance increases significantly when more customer data is both available and can be acted upon by the marketing team.

We further enhanced our platform this year with a number of new or improved features. Advanced Customer Tagging enables sophisticated online behavioral tracking and targeting to build a live picture of a shopper’s preferences and to use these insights to create highly personalized shopping experiences. We also made it even easier to blend behavioral, transactional and contextual data to target shoppers with dynamic content and slice and dice audiences in various ways. For 2019, we have exciting projects in the pipeline that will help brands achieve their marketing goals even better using AI.

What are your predictions on the “Role of CMOs” and the closing gap between Sales and Marketing functions?

Contextual marketing and personalization will become more and more important. CMOs will need to become increasingly data aware and data driven. Having key customer data available in real-time across digital marketing touchpoints will be a top priority, alongside effective real-time execution across multiple channels.

Do you think AI-as-a-Service and Experience-as-a-Service could be the new destination for MarTech companies?

Experiential retail and AI were two of the biggest buzzwords this year in retail marketing. We saw countless examples of brands using creative takes on concept and pop-up shops to drive foot traffic into their brick-and-mortar locations. This is for good reason, since nearly half (43%) of consumers want to be able to try products such as apparel, furniture and technology in-store before buying them online. Brands that offer complimentary services in their pop-ups (personal stylists, makeovers, etc.), were also more likely to reap the benefits of more loyal customers. We’ll undoubtedly see more experience-as-a-service next year, with companies specifically using more sophisticated geo-location services to provide hyper-personalization.

When discussing AI in retail, marketers often refer to its ability to spot behavioral trends about what consumers are doing in order to build predictive models and provide product recommendations. This can work great for “business as usual,” but it’s very hard to change the behavior of the AI model to meet changing business objectives. For example, how do you instruct your AI that there’s a container load of a particular product arriving, and you need to get it sold? That’s where marketers need to be able to set their own marketing rules and override what the AI is trying to promote.