Hearing about how our customers use Amperity is one of the best parts of our job. We love to see how they bring customer data to life, and we love to share it so that others can learn from it too.

Elizabeth in five:

• Customer Insights & Analytics Manager at J.Crew, where she’s worked for about 2.5 years

• Has led the charge to socialize and evangelize metrics like customer equity and predictive CLV at J.Crew

• Together with her team, built dashboards using data from AmpIQ which are shared with multiple teams across the organization

• Has been instrumental in the overall QA and validation of customer data and transactional data in AmpIQ

• Got into mixology over the past year, becoming the quarantine bartender of what her family is calling their own speakeasy — a place for drinks and conversation on a Saturday night with no screens allowed!

You started your career at J.Crew on the IT team — how has that shaped what you bring to your current role?

So much of what makes customer data actionable is the work that goes into the technical, behind-the-scenes details. A single data misstep can trickle down into so many other outputs, so taking the time during the set-up process, even if it takes longer to launch, saves headaches down the road. Partnership in some of the less glamorous data work is important to the success of making data-driven decisions, which is the fun part. My experience in IT helps me appreciate the value and time put into data integrity and it definitely helps me understand how important it is to have the right partnerships. I often fancy myself a translator speaking both the technical and business language to get everyone on the same page, which I have found has shaped both my IT and current roles.

How do you define customer equity, and is this something that has been easy to do?

In the best practices guide we created at J.Crew, we define Customer Equity (or Customer Asset Value) as a combined measurement of customer quantity and predicted quality changes in customer spend. We use it to help us understand the value of our customer beyond a benchmark comparison — which is increasing in importance in our changing retail landscape. Finding a metric that could help evaluate our customer base in this qualitative way was easy to identify as a need for our business, but the process of defining it was more challenging! AmpIQ’s tools and [our Customer Success partner] Megan’s expertise really helped us get a handle on how we could align on measurement and best practices.

You’ve mentioned that one of your favorite parts of working with customer data is that you get to use data for “mythbusting” — any unexpected insights or findings you can share?

Yes, the scientist in me loves taking a hypothesis and proving or disproving it with data! The Insights team finds that some logical hypotheses are more influenced by our surroundings or current climate than customer data. It is important to not get carried away by what we see in our immediate surroundings. For example, a ubiquitous brand we see every day in New York City can seem like the new up and coming trend we have to buy into as soon as possible before our competitors, however when we look at our customers across the country, in warmer cities, or in less populated areas — that trend does not translate and doesn't have the same clout we think it does in our vicinity. We apply the same testing to longstanding retail beliefs as well to make sure everything we do is driven by data rather than assumption. For example, the idea that discount seekers are inherently of "lower value" in the long run is common within the retail space. Using a combination of customer value tiers and a discount sensitivity model, we were able to better understand our customers' relationships with promotions and how we could best fit into that.

How does the organization use the information from the dashboards you create? What kind of teams use these and what action do they take from it?

We report on a suite of KPIs that we have identified as best for understanding the J.Crew and J.Crew Factory customer — this includes standard metrics like customers and spend as well as predictive metrics like CLV. Marketing, Stores, Ecomm, Merchandizing and even Finance use the data in our dashboards to set and measure strategies. In particular, our Loyalty team has done a great job of this as they've built out our J.Crew Rewards program over the past three years. It always makes me smile when I hear our leaders referencing customer numbers to inform strategy — from CLV to customer frequency to retention rates.

I think this year really stretched us. Pairing the quantity and the quality of the customers was especially important when stores were closed and we couldn’t simply compare to past performance — there was no benchmark for 2020! Although year-over-year can be used, relative predictive metrics have been very useful this year when understanding recovery in stores or changes in customer behavior online. Forward-looking metrics help us focus on cultivating our best customers, reducing churn, and acquiring valuable new customers in an ever-changing retail environment.

What’s the coolest thing you’ve been able to do with data for J.Crew?

I think some of the coolest work we have been able to do is around connecting customer value to our products. It is simple to identify a bestselling product, but we also measure how a particular best seller drives customer acquisition and customer lifetime value. For example, although a product acquired a lot of new customers, those customers might not be as valuable as customers who were acquired via another product at a higher CLV. I geek out when we can report a top acquisition style that overlaps with a high CLV style. This is especially compelling to the business when we see value coming from our signature products. We continue to find more applications of lifetime value and future revenue as we have been introducing them to the business. These metrics help us to effectively measure both our current customer base as well as acquisition efficiencies at both the channel and product levels.

What is your favorite feature or capability in AmpIQ and what do you use it for?

High Value Tier is one of my favorite capabilities as it has such a broad range of uses and keeps us focused on understanding our best customers. I find it very useful to double-click into an analysis and add the context of our highest value tiers. It is very helpful with understanding our customers and customer behavior to inform strategy — for example, measuring whether high value tier customers were among the first who shopped our stores when we able to re-open this summer.

Thanks Elizabeth! And to our readers, stay tuned for more Customer Spotlight.

Elizabeth mixing it up at her speakeasy