Insights on how retailers are adapting to a changing market from Shoptalk 2019
Increasingly, today’s marketers need to account for every single advertising and marketing dollar you spend. Although it’s a daunting task on its own, this challenge is also an opportunity to emphasize the impact you’re having on your business in a real way.
For years, we have talked about the need for personalization, but is 2019 finally the year we move from talk to actually delivering on the promise of personalization?
Marketing is getting smarter, and the ways we use data, personalization and machine learning were all hot topics in Las Vegas last week for Shoptalk 2019. Here, we recap the larger themes we saw at the event around how brands are using technology and information to drive more relevance for their customers.
Personalization comes down to relevance for the customer
There was a lot of discussion about creating relevance for the customer through personalization, but there was less talk about personalization as its own separate topic. It’s becoming a part of the customer experience, not living outside of it.
- Dick’s Sporting Goods' VP and Head of Data Science, Analytics and CRM, Vimal Kohli, said it best: “Personalization is a business term; customers think about it as relevance.” He also mentioned that retailers used to complain about not having enough data, but now they have too much. They’re using their influx of data to offer better products to customers online—trying to understand if the item the person is currently viewing is even the right one they’re looking for, or if they need to offer an alternative or complementary product.
- Another player in the sporting goods industry, Modell’s, had a lot to say on the topic of personalization as well. Tami Mohney, EVP of Marketing, Ecommerce and Human Resources at Modell’s, said they’re using data to better define where each customer is going, and what they (the customers) want that experience to look like. It’s about “making sure you’re allowing the customer to choose the path they want to take,” she said.
In a recent post, Kara Trivunovic, who leads Epsilon’s email solutions team, talked about this shift from “1:1, a messaging strategy that’s generically personalized with promotional offers to 1:You, a holistic customer experience strategy that’s personalized with the best choice for individuals across all points on interaction.”
We’re starting to see brands understand personalization through the lens of the consumer, who doesn’t see or feel personalization. At its basic level, we’re talking about relevance, does what you’re sharing or showing, connect, inspire and drive someone to take an action?
At the opposite end of the spectrum is where brands like Levi’s are doubling down with custom jeans, rolling out Levi’s FLX technology that allows the brand to shift from finished goods to a blank canvas. “Custom jeans produced just for you gives the power of self-expression in the hands of everyone,” said Marc Rosen, EVP and President of Direct-to-Consumer at Levi’s. The next retail wave is customized, personalized product.
Levi’s EVP and President of Direct-to-Consumer, Marc Rosen, shared their FLX technology, which allows people to customize their Levi’s products as they purchase them, at Shoptalk 2019.
The art and science of customer data
It’s clear that brands and retailers are trying to collect as much data as possible on their current and potential customers. However, an important differentiation is that just having the data is not the end goal. What you do with this data is the biggest challenge. And with a growing number of disparate sources and silos, how do you ensure your data is actionable, accurate and persistent over time.
- Sarah Engel, VP of Marketing and Creative Communications at Lilly Pulitzer, shared her perspective on data collection and aggregation: “Customer data is going to tell you what happened, what’s happening and maybe what is going to happen, but it’s not going to tell you why.” That is the art of the equation; the “why” is the most important component, but it’s far more challenging to compile and understand. Brands need to have processes in place to help capture the “why” and make sense of it in a real, actionable way.
- D2C brands excel at customer data collection and mining it for explicit and implicit insights. “We start with a questionnaire where clients answer more than 25 questions that we translate across 90 different points of data to understand style needs,” said Mike Smith, President and COO at Stitch Fix. Beyond the initial customer onboarding, Stitch Fix is also building a feedback loop into all levels of customer experience, understanding shopping behavior, customer feedback and return data and then leveraging this data to improve both the product and experience.
Why is this important? For Stitch Fix, the brand is the experience and understanding client preferences and how to better curate a more personalized product experienced drives both sales and retention.
Mike Smith, president and COO of Stitch Fix, shares how Stitch Fix uses customer questionnaires to understand their customers across 90 different data points related to their individual style.
Brands have more customer data than ever before, but the reality is that we’re just at the beginning of using it to its full effect. Brands can—and should—consider how their data works with complementary insights to deliver a full view of the customer. “First- and third-party data are a must for accurate identity,” said Ric Elert, President of Conversant, in a recent article on AdExchanger. “For example, if a mother buys items from a beauty site she and her daughter both visit, it’s important to be able to distinguish the mother’s path to purchase from the daughter’s. The insights from live third-party data can provide the additional information needed to do so.”
Everyone is talking about AI, but it’s really machine learning
AI, naturally, is a hot topic in almost any industry right now. As data becomes more central to every marketing plans, accurately using and deploying AI to improve the customer experience is incredibly important.
But in all of this discussion of AI, we’re not talking about real “AI” in that sense at all. We’re talking about machine learning, which is a subset of AI, but the two are not one and the same.
Vidya Jwala, Chief Ecommerce and Supply Chain Officer at Dick’s Sporting Goods was the first to call it out. He was asked a question about AI, and then the interviewer backpedaled because it’s not the correct term for how we (collectively as marketers) actually deploy this concept in our marketing efforts
Machine learning is a branch of AI based on the idea that machines can learn patterns and make recommendations based on data inputs; it can learn and improve from experience without constant supervision from humans. This is how brands can use customer data to deliver relevant, meaningful messages over time, but it’s not the whole universe of AI.
And any deep dive into AI and machine learning gets pretty complex pretty quickly. Swanson Health, an online vitamin and supplements company, recently spoke about the benefits but also challenges of implementing a machine learning strategy. AI “isn’t just ‘turn it on and let it run,’” said Corey Bergstron, Chief Marketing and Merchandising Officer at Swanson, in a recent article with the Wall Street Journal.
It’s a small, but important difference to note, especially as some of the biggest brands at Shoptalk talked about their AI capabilities. It starts to show who actually knows how their data, processes and partners operate.
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