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Louis DiCesari, global head of data, analytics and AI, Levi Strauss & Co

Louis DiCesari, global head of data, analytics and AI, Levi Strauss & Co

How is your organisation using data and analytics to support the corporate vision and purpose?

 

Levi Strauss & Co’s mission is to deliver profits through principles to make an outsized impact on the world. There’s no better way to make an outsized impact than through data and analytics!

 

We’ve embedded data and analytics into a core strategy and AI function with the vision of fuelling the digital transformation of LS&Co and digital disruption in our industry. We touch all areas of the business: assortment, planning, inventory management, merchandising, retail, wholesale, e-commerce, and pricing/promotion. Across these areas, data and analytics are “how we win” - driving digital transformation (data-driven decisions, digitally-enabled processes); operational excellence (agility inventory management, working capital), and financial discipline (revenue, EBIT, margin optimisation, cost savings).

 

We approach our work in accordance with a data and AI code of ethics that emphasises LS&Co’s core values of empathy, originality, integrity and courage.

 

2020 was a year like no other - how did it impact on your planned activities and what unplanned ones did you have to introduce?

 

While the age of Covid-19 has been terrible for all us, it is also an age in which the value of data and AI has never been greater. Despite the challenging retail environment, the data and AI team met targets for our financial year through unprecedented creativity and agility.

 

Three examples helped make inventory decisions while keeping our customers and staff safe.

  • Dynamic promotion optimisation: While our stores were closed, we turned our European web site into a lab, testing all of the promotion strategies we always wanted to try, but couldn’t because of cross-channel complexities. We learned what worked where and we generated better price elasticity data than we ever had before. When stores re-opened, we tested these promotions in an omni-channel setting, then scaled quickly. What started as an online experiment in April 2020 ended as “business as usual” cross-channel practice by October, with AI-optimised Black Friday across 17 countries in Europe. We also exported the core model to China for use in 11/11 sales. As mentioned by our CEO, Chip Bergh in LS&Co’s Q2 and Q3 earnings releases, our May AI-optimised e-commerce event in Europe had a 400+% improvement in revenue, profit and margin compared to the previous year.
  • Ship-from-store margin optimisation: We didn’t just let our stores sit closed, we turned them into micro fulfilment centres, leveraging AI-enabled ship-from-store capabilities to fulfil online orders and move through inventory. Of online demand in May, 30% was fulfilled by stores, contributing to e-commerce growth of 79% that month. The ship-from-store margin optimisation engine (algorithm to be patented) weighs the costs and benefits of sourcing units from distribution centres versus stores by running scenarios to predict outcomes. When products are selling for higher price online than in stores, there is an opportunity to optimise margin (while accounting for greater shipping costs) by relieving stores of underperforming products that might be liquidated at much lower prices later in the season. Shipping from locations closer to the consumer may also reduce delivery times and improve consumer satisfaction.
  • Retail health check tool: To help with war room decision-making, we packaged together various public and internal data into an automated retail health check tool, which predicted with 90+% accuracy when stores would need to close and re-open. We incorporated natural language processing (NLP) on publicly-available data to stay ahead of consumer and competitor trends, related to clothing preferences, shopping preferences and financial health of competitors. This helped us keep everyone safe, plan labour better and move inventory to less impacted areas with better chances of selling. The automation of the tool alone saved six hours of manual data gathering each day.

Looking forward to 2021, what are your expectations for data and analytics within your organisation?

  • Continue to deliver tangible benefits: Revenues, gross margin, cost savings, and EBIT, while showing uplift;
  • Scale the products we’ve developed across the globe while adding new and modular features;
  • Automate and digitise everything we do; spend as much time on tooling as analytics;
  • Invest time to “de-mystify” AI at all areas of the organisation: everything from up-skilling c30 people to become junior data scientists through an eight-week intensive bootcamp to executive training;
  • Promote diversity in everything we do, with the goal to have the most diverse AI team in the industry.

 

Is data for good part of your personal or business agenda for 2021? If so, what form will it take?

 

Sustainability is our key focus area on using “data for good”, in particular:

  • Use of AI-developed lasers in the manufacturing process to reduce chemicals;
  • Improved forecast accuracy that leads to lower waste, excess and obsolete product;
  • Virtualising physical processes, eg, digital showrooms.

What has been your path to power?

 

In the words of Winston Churchill, “never let a good crisis go to waste.” I started work just before 9/11; I worked in financial services in New York City during the financial crisis; and now I can add retail in the age of Covid-19 to the list of crises I’ve faced. As I reflect on 2020, I think each of those crises were instrumental on my “path to power.”

 

In my first job, I was the last person hired into a boutique consulting firm before a 9/11-related hiring freeze. When traditional business started to dry up, I learned to take on anything and everything - working on random projects including forecasting sales of train car windows or hydraulic hoses, in addition to some of the biggest consumer brands. Lesson 1: Squeeze value out of everything.

 

During the financial crisis, I was working in a marketing analytics role when budgets were slashed. When we had no money to purchase data, conduct surveys or use vendors, we had to be creative in using and generating our own data. This forced me and others to take a better look at the tangled “bowl of spaghetti” that was our own data. Starting to rely on our own data, spaghetti strand by spaghetti strand, was the dawn of the “big data” initiative I would co-create just a few years later. Lesson 2: Necessity breeds invention.

 

Now I am 15 months into my role at Levi Strauss & Co, which had to close most of its stores during the Covid-19 outbreak just six months into my tenure. Roadmaps became directional, but we didn’t abandon them entirely. We figured out new and different ways to get to our ultimate goals, which were value delivery and capability building for company. Lesson 3: Embrace the art of the pivot - resilience and flexibility are not mutually exclusive.

 

What is the proudest achievement of your career to date?

 

My answer is always the same - building high-performing, diverse teams who exceed expectations. But this year added new dimensions to it. During the worst of the Covid-19 trauma, I realised that there was no group of people I’d rather be in a (virtual) room with than the team I have. One, because they’re generally great, kind, empathetic, resilient people who maintained energy and balance despite working around the clock for a period of time. Two, because together as a global team, we cobbled together all kinds of random past experiences - from disaster response modelling to epidemiological training to NLP experience - to produce analysis, tools and product that none of us could have done individually and in record time.

 

During the pandemic, a few of my former colleagues with solid jobs in far less affected industries reached out about opportunities on my team. At a time when I was feeling so uncertain, their confidence in me and my ability to manage through crisis was a morale boost and badge of honour. (And, yes, they joined Levi’s! A couple people have been crazy enough to work for me three times now.)

 

Tell us about a career goal or a purpose for your organisation that you are pursuing?

 

One goal I have is to make as many people data professionals as possible! At a previous company, I trained 100 people throughout the world to be data scientists. I’m starting on this at Levi’s as well, up-skilling 30 analysts – proficient in advanced Excel coding or SQL - to become junior data scientists and join the data and AI community. This has a triple benefit of being a great employee appreciation and retention tool, a great sourcing tool, and further helping to integrate a new capability into a 167-year-old company.

How closely aligned to the business are data and analytics both within your own organisation and at an industry level? What helps to bring the two closer together?

 

At LS&Co, data and analytics are closely aligned to the business with a strong commercial focus. Some of the techniques we use to achieve this are:

  • C-level engagement and commitment;
  • Close partnership with the strategy function, including dotted-line reporting;
  • Joint planning of the roadmap with quarterly reviews with the commercial leaders;
  • Collaborative approach to initiatives, requiring commercial scope of “size of the prize,” a business product owner, and implementation and measurement plans plan prior to work starting;
  • Shared value delivery targets with the analytics/AI team owning the uplift for the desired metric.

In my view, data and analytics functions best operate as a cross-functional strategic enabler to drive desired outcomes.

 

What is your view on how to develop a data culture in an organisation, building out data literacy and creating a data-first mindset?

  1. Start with the “why?” - the commercial value.
  2. Ruthlessly prioritise - Use a simple 2x2 matrix of value and speed to value. Don’t get too hung up on data quality or governance - start where the data is “good enough.”
  3. Embrace agile processes to deliver value quickl, with a clear “before and after” in business and financial language.
  4. Communicate the story with a business champion to all levels of the organisation from interns to the C-suite.
  5. Rinse and repeat, incrementally improving data, building new features, tackling new problems, and up-skilling others from within the organisation.
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