TechBytes with Andrew McCasker, Chief Technology Officer at PreciseTarget

Tell us about the role and team/technology you handle at Precise Target.

The team is a mix of data scientists and application developers. The data scientists design models and process billions of records to create consumer taste profiles for 220 million U.S. adults. The application developers create the platform that allows our brand and retail partners to target those new and potential customers and analyze results.

How has Retail Tech industry evolved in the last decade or so?

Predictive analytics, AI and machine learning have gone from the fringe to the mainstream of Retail Tech over the last decade. Developing insights into customer behaviour and predicting what those customers want to buy has gone from a dream to reality.

Most disruptive Customer Experience / engagement technology of the decade — (you may also quote more than one tool)

Big data analytics, AI and machine learning have changed the landscape, but the most significant disruption has been caused by the intersection of those technologies and privacy regulation like GDPR and CCPA. The drive for personalization is colliding with the need to protect consumer privacy and enforce consumer rights.

Could you tell us about PreciseTarget’s products (Product Taste Audiences and Brand Taste Audiences)? How do they enhance customer experience?

At PreciseTarget, we wanted to understand not just what consumers buy, but why they buy it. What drives their retail purchase behaviour? We create that insight by identifying a consumer’s taste profile – the brands and products they are likely to purchase. We can use the affinities to group consumers together into Brand and Product Taste Audiences. The benefit to consumers is that they see product ads for brands and products they are likely to purchase. Advertisers can more accurately target consumers with ads that are more likely to interest them.

How do you utilize AI/ Machine Learning at PreciseTarget? Do you have an in-house AI team?

Our in-house data science team utilizes AI to develop models that allow us to understand the products that consumers like and are likely to purchase. We identify consumer tastes: brands they prefer, the kinds of products they like, and retailers where they like to shop. Customers can target consumer audiences in their advertising that align with the products and brands they sell and the ones they want to be selling right now.

As a CTO, how do you deal with data privacy regulations? Do new data laws impact your product development journeys?

We have developed comprehensive data privacy policies that ensure that we operate in compliance with GDPR and CCPA. We enforce a strict “no PII” rule on any data we receive, effectively avoiding many of the challenges that are raised by data privacy regulations.

What lessons can technology leaders and developers learn from what Amazon, Walmart, Tencent and other retail tech platforms offer to the customers–

Measure, analyze and refine, and then repeat. We all have to embrace a philosophy of continuously improving the customer experience – through big data analytics, AI and machine learning – to provide the consumer with the products they want and streamline the user experience to make selecting and purchasing retail products as frictionless as possible.

What kind of technologies do you foresee in the next 2-3 years for the retail tech landscape?

The large online retailers – Amazon, Target, Walmart – have a market advantage that makes it difficult for smaller retailers to compete. With their broad product reach and large transaction volume, their own in-house data scientists can develop models and proprietary technology to enhance the consumer experience and build on that advantage. Smaller online retailers are going to need to work with leading-edge tech companies like PreciseTarget to effectively compete. Further advancements in AI and machine learning will provide the smaller retailers with the leg up that they need.

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