Analytics and AI Opportunities for Store Performance
Some days back a (retail) industry friend reached out to me asking about any service providers of ‘AI Solutions’ for Store Analytics – leading to improved Store Performance. I candidly admitted that I didn’t know any top of mind. But if he could help me with a few specific opportunity statements, I could help dig some more and get a sense of direction. At the same time, I recollected 2 compelling reads that I had come across, on optimizing store performance with the use of analytics and emerging AI tools. In this post, Outside In tries to create a blended version that highlights the Analytics and AI Opportunities for impacting Store Performance… Hope it will be a nice mix…
The Importance of Store ‘Intelligence’
From understanding revenue generated per customer to managing staffing to measuring marketing effectiveness and informing corporate decision-making, store analytics provide retailers the information they need to boost their bottom lines. This ‘intelligence’ is spread across the various functions of a store and an improvement in any (ideally, all) area directly impacts the store performance – and thereby, its feasibility over a long term. How does one go about pursuing this quest for ‘Intelligence’ and following that – align operations? Prima-facie, we will articulate many analytics opportunities that exist in tracking store performance and we will illustrate specific AI opportunities within these analytics opportunities that can help optimize operational efficiencies
1) Know your Store and your Customer
a) Identify each Store’s Power Hours – regular times of peak foot traffic throughout the week
b) Know your Total Sales Potential or shopper opportunity in your store at any time. Calculate conversion rates: Transactions/Shopper Traffic
c) Ensure a positive Customer Shopping Experience by positioning an optimum number of staff to meet customer needs. Measure STAR: Shopper-to-Associate Ratio
d) Evaluate Employee Performance based upon actual conversion results
AI Opportunity – Store Floor Management
Example: John Lewis Shop Floor App
John Lewis has deployed a ‘shop floor app’ for all their staff – which will enable them to give customers real-time shopping options, accurate product information and inventory status
2) Efficient Allocation of Staff
a) Use historic Traffic Data to staff at optimum levels by time and day of week
b) Optimize Staff Allocations based on Shopper Patterns:
Schedule more personnel to peak selling times
Schedule merchandise deliveries, restocking and non-selling tasks during low traffic times
Assign the most qualified/effective associates to times of highest traffic
Set up incentive programs coordinated with increased foot traffic and opportunity
Map sales associates to product mix based upon actual conversion results
AI Opportunity – In-Store AI
The channel that bridges the gap between desktop and in-store is mobile, and therefore in-store technologies should utilize that. This is where mobile apps can really make a difference -an app interface enables numerous in-store benefits
Example: Nordstrom Geo-location-led Services
3) Competent Forecasting
a) Use Traffic Indices to evaluate Shopping Venues (lifestyle center, mall, central city, etc.) for presence and expansion planning
b) Understand Traffic impact of competitive Store Presence and Promotions
c) Forecast Inventory Levels based on foot traffic and conversion rates
d) Use Objective Data to evaluate, internalize and focus on best practices for shopper conversion
AI Opportunity – Sales Forecasting
Machine learning algorithms can be used to predict product performance and identify demand based on factors such as sales history, location, weather, promotions, etc. The algorithms can correlate trends and create a continuous cycle of feedback for constant learning, therefore giving retailers an increasingly clearer forecast of their future sales. In addition, machine learning opens up the opportunity to incorporate data that is traditionally difficult to analyze, and may have historically been discarded -such as customer comments
Example: Blue Yonder Forecasting Solution for Morrisons
They are have helped to improve stock forecasting and replenishment requirements across their 491 stores. Since implementing their machine learning technology last year, BlueYonder have reduced shelf gaps in-store by up to 30%, and have automated over 13 million ordering decisions that occur on a daily basis. You can read the detailed case study here at: https://blueyonder.com/knowledge-center/collateral/morrisons-case-study
4) Focus on Store Output
a) Review traffic and conversion levels of each store, compare with like-to-like stores, and identify opportunities for improvement
b) Compare foot traffic levels to a larger retail segment or region
c) Perform seasonal and holiday assessments – compare sales changes from one period to the next
d) Measure the performance of new vs. established stores over time
e) Track impact of new inventory, product offerings, or merchandise on traffic and conversions
AI Opportunity – Store Location Selection & Construct
According to Retail Touchpoints: “For many years, retail companies have been applying analytics to determine where to add locations, but new technology is taking store optimization to a new level. Looking at historical data such as sales, demographics, distance from competitors, nearby events and more allows retailers to be strategic about where and when to open a new location. AI applications that learn from this data can do more than just create and sort a list of best locations to open a store; they can actually provide retailers with an understanding of why, based on identifying the most important “drivers”/variables that contribute to new store success
Example: Celect Solution for improving Store Throughput
As well as store location selection, retailers can use AI to optimize their store layouts.
Predictive analytics company Celect recently used their machine learning technology to boost a $5B fashion retailers’ in-store revenue. By mapping buying preferences and patterns, the retailer was able to gain immediate insight into “high-potential departments and categories at specific stores”, and modify which products were most likely to sell in which locations. Overall, they saw a 7% increase in in-store revenue
5) Effective Marketing
a) Measure the changes in traffic & conversions following marketing campaigns (advertising, promotions, sales, special events, social media, etc.).
b) Evaluate whether marketing created “buying” traffic or “browsing” traffic – Tie store traffic increases/declines to marketing efforts
c) Measure YOY traffic/conversion changes of annual marketing events
AI Opportunity – Promotion Impact Analysis
Example: Oliver Wyman Solution for reducing Promotion Errors and improving ROI
Oliver Wyman Labs recently used their machine learning model to help a large European retailer track the impact of their print promotions. The retailer in question runs up to 60,000 promotions a year, so wanted to get a better understanding of the ROI –for example -‘what impact will a 10 day promotion of shampoo have on the next 6 months of sales of that product?’, or ‘how much discount is needed to drive sales in beans compared to cereal?’. OW Labs explains: “Even a small increase in predictability would drive a huge increase in sales volume or save tens of thousands in wasted discounts.”
The retailer already had a team of six or seven forecasters, who were inputting data by hand and predicting results at a 30-35% error rate. The machine learning model instantly beat this -with an error rate of 24% (that continues to improve over time). OW Labs report that the retailer has implemented these algorithms into their systems for country-wide data, and is now looking to go deeper so that predictions can be made on a store-by-store basis
They say: “Beyond that, the next wave of innovation will center around customization—offering personal promotions to specific customers for specific products at a specific time. Industrializing the understanding of promotions with artificial intelligence is the first step in making this a reality, as each decision to make a specific promotion will need to be undertaken a million times a day
Consumer Think
In a survey commissioned by Adweek, 1,000 consumers were asked how they felt about artificial intelligence in stores. 74% said they had had positive experiences and 57% said that they would happily exchange messages with a online chatbots. However, 65% do not want to see in-store staff replaced by robots –so the line is drawn when discussing the prospect of machines replacing humans
In Conclusion
To use a quote from IBM, interactions between customers and brands have fundamentally changed. This transformation is entering the retail store itself now, as consumers increasingly seek enhanced mobile experiences with in-store navigation, special offers and personalization
AI opens up the opportunity to actually predict the purchasing behaviour or needs of in-store customers. This means sales staff can have an idea of what a customer is looking to buy before they even ask for help. By empowering staff to use this information, customer service and staff morale could be greatly enhanced. If you balance data-based logic with human interaction and emotional intelligence, you have a ‘super powered associate’
References and Sources
1) How Retail Executives Connect Traffic to Cash Flow – Paper by Shopper Trak
2) Bijou Commerce – AI Opportunities in Retail
3) John Lewis Shop Floor App: https://www.johnlewis.com/customer-services/shopping-with-us/our-apps
4) Morrisons Case Study with Blue Yonder: https://blueyonder.com/knowledge-center/collateral/morrisons-case-study