Part Four: Q&A with Aéropostale’s SVP of Planning & Allocation (Bonus Questions!)


“Emerging technologies driving disruptive innovation, many yielding transformational benefits, must be prioritized for investigation, even though the ‘buzz’ and expectations for them are just on the rise.” – Gartner, Use Cases Harness Emerging Technologies to Deliver Delightfully Disruptive Customer Experiences tweet-image

How are you keeping up with disruption in the industry?

This question resonates with merchants globally—from luxury to fast fashion to off-price—as most are challenged to meet consumer expectations, anticipate future demand, and make the right investments and decisions throughout the merchandising cycle.

Given the circumstances retail is in today, the technology available to help businesses become digitally enabled provider[s] of unified retail commerce is, as Gartner so accurately highlighted, “yielding transformational benefits.”

As an example, retailers are expected to use AI to hone the accuracy and speed of human decision making well beyond current levels by 2020. 

And 2020 is RIGHT around the corner.

As such, we wanted to share some final insights from our recent Q&A with Aéropostale’s SVP of Planning & Allocation, Karen Walter, who shared plenty on how her team is incorporating emerging technologies (like advanced analytics!), as well as:

  • A new approach to the Merchandise, Planning, and Allocation process (Part One)
  • How new technologies are blended with existing business process (Part Two)
  • Why and how Aéropostale adopted analytics in today’s retail environment (Part Three

At the end of the Q&A, we gave retailers a chance to submit their own questions to Karen, which we outlined below! We hope her responses help provide some clarity on any looming questions you may have on Aéropostale's newly AI-enabled merchandising process or strategies you may be considering at the moment.

Also, feel free to access to full webcast Q&A video here for more context! 

1. Can we really forecast demand at style level for the next season to help produce in advance?

Karen: Yes, I believe we can. Using attributes, selling price, fabric, silhouette, etc. and aggregating that information over years of history will give an accurate estimate. 

2. With Distributed Order Management at your store locations, who is getting credit for the sale & how does this affect demand planning. 

Karen: We currently do not do omni-channel. However, I think it is important to capture the demand where the demand occurred.

3. How are you running these analyses? In Excel or through a specific data analysis platform provided by a consulting company? Are you specifically only optimizing data you already have (yours) or also your competitors’? 

Karen: We are optimizing our data only. Celect runs the analysis on their platform, and we incorporate it into our Excel tools. We are not using a consulting company.

4. In today's dynamic environment for store sales (hitting peaks and troughs frequently) - should the OTBs% increase?

Karen: Yes, we are actively keeping more $$ in OTB for chase, testing, and to capture trends that emerge quickly.

5. How would you weight the importance of internal data (past season's performance, product attributes, CRM data, etc.) versus that of external data (trends from social media, latest fashion events, the year's holiday calendar, etc.) for buying and merchandising decisions?

Karen: The calendar shift needs to be done first—it is extremely important to get the volume correct by week.  Then, I weight internal data at 80% and external data at 20%.

6. What were a couple of your biggest struggles before even getting to the point where you could do advanced analytics?

Karen: Understanding where the biggest priorities were to improve the business—the true opportunity areas. Creating a standardized process in the organization so we could efficiently incorporate the new analytics. Getting buy in from the entire cross functional team that we were ready, willing, and able to adopt the new data. 

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Topics: allocation, predictive analytics, merchandise planning, inventory optimization, data, advanced analytics

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