By Seth Redmore, Lexalytics
The old saying “retail is detail” has never been more true. From
what’s in stock to the size of the customer service desk, data science rules
the decision-making process.
While machine learning has been the darling of large chains for
years, increasing accessibility, ROI and AI hype are encouraging uptake among
mid-sized chains looking to maximize operational efficiency alongside
delivering superior customer service. Here are some of the ways AI can be used
to drive growth, efficiency and profit.
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Store Location
Location is king in retail, and the difference between an okay and
a prime location can have a significant impact on your bottom line. AI can help
crunch the data around population trends, shopping habits, travel patterns and
competitor reach, identifying the factors that represent an ideal location for
your store. These factors, and their associated profits or costs, can be used
in decision-making around new store openings — and also for closing down
lower-performing stores.
Layout Optimization
Cookie cutter doesn’t cut it any more. Retail has long been moving
towards the experiential. But retail store design still needs to meet the needs
of consumers — while ensuring efficient processes for staff and suppliers. AI
can tap into local demographics, consumer preferences and operational
efficiency to help develop layouts that deliver the best possible customer
experience, while still providing a consistent brand experience across each
store.
Product Optimization
Effective product display and merchandising is crucial for moving
stock. AI can be used to help optimize product position and space allocation,
finding the sweet spot that drives maximum value. Data around the effectiveness
of shelf displays can also be gathered through customer monitoring: Procter &
Gamble is currently piloting technology that gathers customers’ facial expressions as they encounter products.
Dynamic Pricing
Stock that moves too slowly affects margins, while selling out too
quickly affects brand reputation. Using AI, pricing strategies can be optimized
for variables around seasonality, demand and availability. Dynamic pricing can
be used to encourage movement of perishable or seasonal stock, and can help
handle issues with overstock as well. AI can also be used to predict sales
numbers based on local store demographics and behavior across your other
stores, helping you to optimize your orders in the first place.
Opening Hours And Personnel
Effective staff
allocation is critical to your bottom line. Being understaffed affects both the
customer experience and staff morale, while being overstaffed is inefficient
and costly. According to research from McKinsey, taking a data-driven approach
can save up to 12% in staffing costs. AI can be used to monitor and
predict peak times day-to-day and seasonally, as well as to gauge demand and
preferences around checkout type. Using this data you can adjust your opening
hours, along with staffing levels and allocation. For example, for off-peak
hours or times when people make quick trips, you may rely more heavily on
self-checkout. Notably, Macy’s has recently launched “scan and go”, while Amazon Go has no cashiers at all.
Fulfillment
Chances are that your retail chain also has an online presence.
Traditional fulfillment processes one order at a time, resulting in split
shipments, higher delivery costs and slower shipping. AI can be used to
optimize order fulfillment across all orders, juggling multiple orders at once
in order to ship like orders together, improving efficiency and boosting
margins. Canadian brand Aldo Group has saved millions doing so.
Store Monitoring
Real-time intervention can help ensure a positive customer
experience. Store camera footage is now cheap and easy to store and monitor,
and can be combined with computer vision technology to identify customers in
need of assistance. Staff members can be easily dispatched to assist customers
in the short term, while this data can also be used to help inform future store
improvements.
Voice Of The Customer
Finally, AI-powered natural language processing can also be used
to analyze text-based customer feedback posted on social media or review sites.
This allows you to gain insight into what customers are saying about you — or
your competitors — and to adapt accordingly. Retail giant Kroger uses
information from social listening to create personalized communications and offers.
AI is a powerful tool that allows chain retail companies to use
existing data to predict trends, behavior and opportunities. By optimizing your
store design, practices and related services, you can improve the customer
experience, create more efficient processes and maximize profits.
But you don’t need to be a Walmart, Amazon or Target to benefit
from AI. Medium-sized chains can partner with AI, NLP or computer vision
providers to determine which optimizations will have the greatest impact on
profitability and customer experience, and go from there. Be strategic about getting
started: start small with something like voice of the customer monitoring, and
expand your touch points as resources allow.
With over 20 years of combined
experience in product management, marketing, text analytics and machine
learning, Seth Redmore is currently the CMO of
Lexalytics, the leader in “words-first” machine
learning and artificial intelligence. Prior to
this role, Redmore served as Vice President of Product Management and Vice
President of Marketing at Lexalytics. He has held executive positions at
both hardware and software companies, and was co-founder of Netiverse (acquired
by Cisco Systems). During his tenure at Cisco, Redmore built Cisco’s
first internal text analytics solution for reputation management. He has a
degree in Chemistry from Carnegie Mellon University
