Execs from Build-A-Bear, David Yurman, James Avery, maurices, The North West Company, YM Inc., and More Discuss Data & Analytics Strategy

Logic TeamBlog

From getting buy-in to educating the organization, tech leaders address change management and why data & analytics (AI/ML) are critical to powering retail success

Data and analytics should be driving business decisions and taking a leadership position inside every retail business, no matter the vertical or specialty. But getting organizational buy-in and establishing well-defined rules around how to use the data takes time.

Logic Retail Leaders Forum

Technology leaders from seven different retailers gathered for Logic’s latest Retail Leaders Forum to share where data and analytics sit within their organizations and how to gain more support for the practice internally. Logic’s Graeme McVie, Global Managing Director, Data moderated the discussion including:

  • Harsha Bellur, EVP/CIO, James Avery
  • Megan Douglas, CIO/CDO, maurices
  • Christian Fortucci, CTO, David Yurman
  • Jack Freedman, COO, YM Inc.
  • Dara Meath, Chief Technology Officer, Build-A-Bear
  • Nico Sabogal, VP, Strategy, Planning, and Analytics, The North West Company

The virtual forum allowed leaders to talk through the challenges of getting internal departments aligned around data and analytics, governing the use of insights, and where to outsource. Here are key takeaways from the conversation:

1. Educate the organization

With so much buzz and talk around AI, it’s not surprising that business teams can have trouble distilling what really matters. Many IT leaders said they need to devote time to explain the differences in AI to their colleagues as well as how it can impact data and analytics. One participant said some peers in their organization are surprised to learn they’ve been using machine learning in their roles for nearly eight years. “ML is not AI to them. Generative AI and ChatGPT has brought AI to the mainstream and somehow people didn’t realize that in many technology platforms, ML has been in a lot of simulations and statistical models.”

Another participant said their team holds weekly global meetings, which associates can attend to learn about AI, and they deliver quarterly definitions of what the technology is and what the advantages are. “Furthermore, you need to educate the board. You need to educate the board or the technology will go nowhere.”

Data and Analytics for Business GrowthTraining teams on data and analytics platforms also never ends. Another leader said some of their merchants and planners learned how to use a new automation and data tool, but rather than leveraging the value of the dashboards and letting the tool do the heavy lifting on analytics, they’re downloading data and analyzing it manually in Excel spreadsheets. “They’re reverting to what makes them comfortable.” Panelists stressed the importance of ongoing training and change management to help team members get the most out of new analytics tech.

2. Gain trust and build a centralized view of data

Except for a few very large organizations with deep pockets, most retailers have been relatively slow to adopt AI in a significant way. Three of the forum participants said that, while the C-suite is enthusiastic about the future of AI, it can be difficult to secure funding for AI initiatives with so many other competing needs. “It can be hard to gain traction for an AI project, especially when you’re competing against the option of opening four more stores,” explained one leader.

While the participants see the value of adopting a big-bang, holistic AI strategy, they said tech leaders were more likely to gain executive buy-in by starting with a limited number of well-chosen use cases that can quickly provide measurable ROI. “Being able to demonstrate the value even in a small area can really open doors,” said one executive.

3. Choose areas with a proven track record of AI/ML success

It would be easy to become caught up in all the AI hype and try to jump start some initiatives around Gen AI, but that could lead you to some false starts. One panel member discussed their enthusiasm for GenAI and copilot tools, but after a few months they were finding that there was not as much value as they had expected.

Other panel members talked about not wanting to be at the bleeding edge and instead wanting to use tried and tested AI/ML capabilities around demand forecasting, inventory allocation and replenishment. Another participant agreed, mentioning their success with price, promotion and markdown optimization implementations.

4. Start with small wins and tests to earn buy-in

If members of a retailer’s organization are hesitant to leap into advanced analytics, the participants agreed that implementing a crawl, walk, run approach can put reluctant team members at ease. IT teams can carve out a use case to test new tools side-by-side with a legacy system, highlighting a small win and the benefits of an advanced analytics or optimization solution.

“We had to do simulations prior to deploying a toolset, showing A/B tests side by side with an older replenishment system,” a retail leader said.

“Our business teams saw the clear benefits of an optimized fulfillment solution and they were surprised by the opportunities ahead. They couldn’t believe how much the company could improve with inventory.”

There’s great value in accomplishing small wins, another panelist said. Each small win leads to the company scaling up. “It’s great if a company can just take the leap, but that comes with a significant amount of time, resources, and even risk. You need to have a risk-reward conversation with leadership and see where they’re comfortable.”

5. Strike a balance between in-house and partner solutions

While many of the forum attendees have a goal to have in-house data and analytics teams, the majority still outsource to service providers or buy packaged software. “We’ve invested to go in-house on business intelligence and reporting, but when it comes to advanced analytics, we’ve farmed out to companies that help us drive custom analytics such as assortment optimization, pricing, and fulfillment,” said one retail executive.

A futuristic display showcasing how Artificial Intelligence revolutionizes the retail industryAnother leader said a lot can be learned from business partners, and they budgeted for internal teams to learn from those solutions partners. “We were at 80% outsourcing to vendor partners and 20% relying on in-house talent, and we’ve now re-leveraged that to 20% vendor partners and that only took about 18 months. I’m going to invest in them and put a lot of money into talent. Vendor partners also see your business in a different way, know about the market, and how the data is performing against the market, so there’s a balance that works.”

Multiple attendees echoed the importance of hiring “full stack data engineers,” who can do the engineering part but also possess an analytical mind. “They’re touching the data, they’re building a pipeline so they understand how the information is sourced, and then they bring a critical eye to how the data can improve the business,” a participant said. “These engineers are out there, they’re not unicorns.”

6. Future agendas will focus on personalization

Looking 12 to 24 months out, delivering more personalization to customers is top of mind among attendees. The panelists emphasized that personalization comes in many shapes and sizes. One leader said personalizing digital experiences should be on every company’s radar, adding that IT teams should be looking at how to personalize hero images on a landing page, tailoring online assortments to a visitor’s purchase history, and delivering customized search recommendations, and browsing information. Other panelists talked about the importance of personalizing marketing offers along multiple dimensions.

“Whoever gets personalization right will win, but it’s very difficult to do so,” another participant added.

In the months ahead, one attendee mentioned that retailers should keep a close eye on AI — and not solely because of its potential. The retail leader said there’s a slight fear that AI could fragment the business again. The company spent a lot of time consolidating operations. If each business unit plays with AI differently or in silos, it can bring up new data governance issues.

To learn more about the importance of developing a holistic data and analytics strategy, read our latest blog on how to explain generative AI to the board.
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